With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown ma...With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.展开更多
Electrochemical carbon dioxide reduction reaction(CO_(2)RR)produces valuable chemicals by consuming gaseous CO_(2)as well as protons from the electrolyte.Protons,produced by water dissociation in alkaline electrolyte,...Electrochemical carbon dioxide reduction reaction(CO_(2)RR)produces valuable chemicals by consuming gaseous CO_(2)as well as protons from the electrolyte.Protons,produced by water dissociation in alkaline electrolyte,are critical for the reaction kinetics which involves multiple proton coupled electron transfer steps.Herein,we demonstrate that the two key steps(CO_(2)-^(*)COOH and^(*)CO-^(*)COH)efficiency can be precisely tuned by introducing proper amount of water dissociation center,i.e.,Fe single atoms,locally surrounding the Cu catalysts.In alkaline electrolyte,the Faradaic efficiency(FE)of multi-carbon(C^(2+))products exhibited a volcano type plot depending on the density of water dissociation center.A maximum FE for C^(2+)products of 73.2%could be reached on Cu nanoparticles supported on N-doped Carbon nanofibers with moderate Fe single atom sites,at a current density of 300 mA cm^(–2).Experimental and theoretical calculation results reveal that the Fe sites facilitate water dissociation kinetics,and the locally generated protons contribute significantly to the CO_(2)activation and^(*)CO protonation process.On the one hand,in-situ attenuated total reflection surface-enhanced infrared absorption spectroscopy(in-situ ATR-SEIRAS)clearly shows that the^(*)COOH intermediate can be observed at a lower potential.This phenomenon fully demonstrates that the optimized local water dissociation kinetics has a unique advantage in guiding the hydrogenation reaction pathway of CO₂molecules and can effectively reduce the reaction energy barrier.On the other hand,abundant^(*)CO and^(*)COH intermediates create favorable conditions for the asymmetric^(*)CO-^(*)COH coupling,significantly increasing the selectivity of the reaction for C^(2+)products and providing strong support for the efficient conversion of related reactions to the target products.This work provides a promising strategy for the design of a dual sites catalyst to achieve high FE of C^(2+)products through the optimized local water dissociation kinetics.展开更多
To address the issue of extreme thermal-induced arching in CRTS II slab tracks due to joint damage,an optimized joint repair model was proposed.First,the formula for calculating the safe temperature rise of the track ...To address the issue of extreme thermal-induced arching in CRTS II slab tracks due to joint damage,an optimized joint repair model was proposed.First,the formula for calculating the safe temperature rise of the track was derived based on the principle of stationary potential energy.Considering interlayer evolution and structural crack propagation,an optimized joint repair model for the track was established and validated.Subsequently,the impact of joint repair on track damage and arch stability under extreme temperatures was studied,and a comprehensive evaluation of the feasibility of joint repair and the evolution of damage after repair was conducted.The results show that after the joint repair,the temperature rise of the initial damage of the track structure can be increased by 11℃.Under the most unfavorable heating load with a superimposed temperature gradient,the maximum stiffness degradation index SDEG in the track structure is reduced by about 81.16%following joint repair.The joint repair process could effectively reduce the deformation of the slab arching under high temperatures,resulting in a reduction of 93.96%in upward arching deformation.After repair,with the damage to interfacing shear strength,the track arch increases by 2.616 mm.展开更多
The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistan...The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistant and optimized)to a quantum probe coupled to a bosonic environment. To demonstrate the precision of cutoff frequency estimation, we theoretically derive the quantum Fisher information(QFI) and quantum signal-to-noise ratio(QSNR) across sub-Ohmic,Ohmic, and super-Ohmic environments, and investigate their behaviors through numerical examples. The results indicate that, compared to the equidistant π-pulse sequence, the optimized π-pulse sequence significantly shortens the time to reach maximum QFI while enhancing the precision of cutoff frequency estimation, particularly in deep sub-Ohmic and deep super-Ohmic environments.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the...In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.展开更多
Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear an...Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.展开更多
Magnesium alloy,as a new material for vascular stents,possesses excellent mechanical properties,biocompatibility,and biodegradability.However,the mechanical properties of magnesium alloy stents exhibit relatively infe...Magnesium alloy,as a new material for vascular stents,possesses excellent mechanical properties,biocompatibility,and biodegradability.However,the mechanical properties of magnesium alloy stents exhibit relatively inferior performance compared to traditional metal stents with identical structural characteristics.Therefore,improving their mechanical properties is a key issue in the development of biodegradable magnesium alloy stents.In this study,three new stent structures(i.e.,stent A,stent B,and stent C)were designed based on the typical structure of biodegradable stents.The changes made included altering the angle and arrangement of the support rings to create a support ring structure with alternating large and small angles,as well as modifying the position and shape of the link.Using finite element analysis,the compressive performance,expansion performance,bending flexibility performance,damage to blood vessels,and hemodynamic changes of the stent were used as evaluation indexes.The results of these comprehensive evaluations were utilized as the primary criteria for selecting the most suitable stent design.The results demonstrated that compared to the traditional stent,stents A,B,and C exhibited improvements in radial stiffness of 16.9%,15.1%,and 37.8%,respectively;reductions in bending stiffness of 27.3%,7.6%,and 38.1%,respectively;decreases in dog-boning rate of 5.1%,93.9%,and 31.3%,respectively;as well as declines in the low wall shear stress region by 50.1%,43.8%,and 36.2%,respectively.In comparison to traditional stents,a reduction in radial recoiling was observed for stents A and C,with decreases of 9.3% and 7.4%,respectively.Although there was a slight increase in vessel damage for stents A,B,and C compared to traditional stents,this difference was not significant to have an impact.The changes in intravascular blood flow rate were essentially the same after implantation of the four stents.A comparison of the four stents revealed that stents A and C exhibited superior overall mechanical properties and they have greater potential for clinical application.This study provides a reference for designing clinical stent structures.展开更多
This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid appr...This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid approach for oil spill detection.The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization(BHHO)and Principal Component Analysis(PCA)for improved feature extraction and selection from input SAR imagery.Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images.The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet.A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features.It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm.Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set.Results indicate that the highest oil spill detection accuracy of 99.2%has been achieved using the logistic regression classification algorithm,with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques.The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model.The significance of our study lies in its unique integration of deep learning with optimized feature selection,unlike other published studies,to enhance oil spill detection accuracy.展开更多
Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controll...Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).展开更多
In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But...In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.展开更多
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h...Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.展开更多
The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.Thi...The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.展开更多
The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical research.Subjective self-reporting i...The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical research.Subjective self-reporting is the foundation of conventional pain assessment methods,which may be unreliable.Deep learning is a promising alternative to resolve this limitation through automated pain classification.This paper proposes an ensemble deep-learning framework for pain assessment.The framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)signals.We integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)models.We then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s robustness.To improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature selection.This enables us to reduce the features’dimensionality without sacrificing the classification’s accuracy.With improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning classifiers.In our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment performance.However,due to differences in validation protocols,comparisons with previous studies are still limited.Combining deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification performance.The evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.展开更多
The complexity of living environment system demands higher requirements for the sensitivity and selectivity of the probe.Therefore,it is of great importance to develop a universal strategy for highperformance probe op...The complexity of living environment system demands higher requirements for the sensitivity and selectivity of the probe.Therefore,it is of great importance to develop a universal strategy for highperformance probe optimization.Herein,we propose a novel“Enrichment-enhanced Detection”strategy and use carbon dots-dopamine detection system as a representative model to evaluate its feasibility.The composite probe carbon dots (CDs)-encapsulated in glycol-chitosan (GC)(i.e.,CDs@GC) was obtained by simply mixing GC and CDs through noncovalent interactions,including electrostatic interactions and hydrogen bonding.Dopamine (DA) could be detected through internal filter effect (IFE)-induced quenching of CDs.In the case of CDs@GC,noncovalent interactions (electrostatic interactions) between GC and the formed quinone (oxide of DA) could selectively extract and enrich the local concentration of DA,thus effectively improving the sensitivity and selectivity of the sensing system.The nanosensor had a low detection limit of 3.7 nmol/L,which was a 12-fold sensitivity improvement compared to the bare CDs probes with similar fluorescent profiles,proving the feasibility of the“Enrichment-enhanced Detection”strategy.Further,to examine this theory in real case,we designed a highly portable sensing platform to realize visual determination of DA.Overall,our work introduces a new strategy for accurately detecting DA and provides valuable insights for the universal design and optimization of superior nanoprobes.展开更多
This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven d...This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates ...Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.展开更多
This paper introduces dynamic mode decomposition(DMD)as a novel approach to model the breakage kinetics of particulate systems.DMD provides a data-driven framework to identify a best-fit linear dynamics model from a s...This paper introduces dynamic mode decomposition(DMD)as a novel approach to model the breakage kinetics of particulate systems.DMD provides a data-driven framework to identify a best-fit linear dynamics model from a sequence of system measurement snapshots,bypassing the nontrivial task of determining appropriate mathemat-ical forms for the breakage kernel functions.A key innovation of our method is the instilling of physics-informed constraints into the DMD eigenmodes and eigenvalues,ensuring they adhere to the physical structure of particle breakage processes even under sparse measurement data.The integration of eigen-constraints is computationally aided by a zeroth-order global optimizer for solving the nonlinear,nonconvex optimization problem that elicits system dynamics from data.Our method is evaluated against the state-of-the-art optimized DMD algorithm using both generated data and real-world data of a batch grinding mill,showcasing over an order of magnitude lower prediction errors in data reconstruction and forecasting.展开更多
基金supported in part by the National Key Research and Development Program of China(No.2022YFB4500800)the National Science Foundation of China(No.42071431).
文摘With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.
文摘Electrochemical carbon dioxide reduction reaction(CO_(2)RR)produces valuable chemicals by consuming gaseous CO_(2)as well as protons from the electrolyte.Protons,produced by water dissociation in alkaline electrolyte,are critical for the reaction kinetics which involves multiple proton coupled electron transfer steps.Herein,we demonstrate that the two key steps(CO_(2)-^(*)COOH and^(*)CO-^(*)COH)efficiency can be precisely tuned by introducing proper amount of water dissociation center,i.e.,Fe single atoms,locally surrounding the Cu catalysts.In alkaline electrolyte,the Faradaic efficiency(FE)of multi-carbon(C^(2+))products exhibited a volcano type plot depending on the density of water dissociation center.A maximum FE for C^(2+)products of 73.2%could be reached on Cu nanoparticles supported on N-doped Carbon nanofibers with moderate Fe single atom sites,at a current density of 300 mA cm^(–2).Experimental and theoretical calculation results reveal that the Fe sites facilitate water dissociation kinetics,and the locally generated protons contribute significantly to the CO_(2)activation and^(*)CO protonation process.On the one hand,in-situ attenuated total reflection surface-enhanced infrared absorption spectroscopy(in-situ ATR-SEIRAS)clearly shows that the^(*)COOH intermediate can be observed at a lower potential.This phenomenon fully demonstrates that the optimized local water dissociation kinetics has a unique advantage in guiding the hydrogenation reaction pathway of CO₂molecules and can effectively reduce the reaction energy barrier.On the other hand,abundant^(*)CO and^(*)COH intermediates create favorable conditions for the asymmetric^(*)CO-^(*)COH coupling,significantly increasing the selectivity of the reaction for C^(2+)products and providing strong support for the efficient conversion of related reactions to the target products.This work provides a promising strategy for the design of a dual sites catalyst to achieve high FE of C^(2+)products through the optimized local water dissociation kinetics.
基金Project(K2022G038)supported by the Science Technology Research and Development Program of China State Railway Group Co.,LtdProject(52178405)supported by the National Natural Science Foundation of China。
文摘To address the issue of extreme thermal-induced arching in CRTS II slab tracks due to joint damage,an optimized joint repair model was proposed.First,the formula for calculating the safe temperature rise of the track was derived based on the principle of stationary potential energy.Considering interlayer evolution and structural crack propagation,an optimized joint repair model for the track was established and validated.Subsequently,the impact of joint repair on track damage and arch stability under extreme temperatures was studied,and a comprehensive evaluation of the feasibility of joint repair and the evolution of damage after repair was conducted.The results show that after the joint repair,the temperature rise of the initial damage of the track structure can be increased by 11℃.Under the most unfavorable heating load with a superimposed temperature gradient,the maximum stiffness degradation index SDEG in the track structure is reduced by about 81.16%following joint repair.The joint repair process could effectively reduce the deformation of the slab arching under high temperatures,resulting in a reduction of 93.96%in upward arching deformation.After repair,with the damage to interfacing shear strength,the track arch increases by 2.616 mm.
基金Project supported by the National Natural Science Foundation of China (Grant No. 62403150)the Innovation Project of Guangxi Graduate Education (Grant No. YCSW2024129)the Guangxi Science and Technology Base and Talent Project (Grant No. Guike AD23026208)。
文摘The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistant and optimized)to a quantum probe coupled to a bosonic environment. To demonstrate the precision of cutoff frequency estimation, we theoretically derive the quantum Fisher information(QFI) and quantum signal-to-noise ratio(QSNR) across sub-Ohmic,Ohmic, and super-Ohmic environments, and investigate their behaviors through numerical examples. The results indicate that, compared to the equidistant π-pulse sequence, the optimized π-pulse sequence significantly shortens the time to reach maximum QFI while enhancing the precision of cutoff frequency estimation, particularly in deep sub-Ohmic and deep super-Ohmic environments.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
文摘In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.
基金supported by the National Natural Science Foundation of China(52350410465)the General Projects of Guangdong Natural Science Research Projects(2023A1515011520).
文摘Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272250 and 12372310)China Postdoctoral Science Foundation(Grant No.2020M680913)+1 种基金Shanxi Scholarship Council of China(Grant No.2022-081)Shanxi Postgraduate Innovation Project and Shanxi Huajin Orthopaedic Public Foundation.
文摘Magnesium alloy,as a new material for vascular stents,possesses excellent mechanical properties,biocompatibility,and biodegradability.However,the mechanical properties of magnesium alloy stents exhibit relatively inferior performance compared to traditional metal stents with identical structural characteristics.Therefore,improving their mechanical properties is a key issue in the development of biodegradable magnesium alloy stents.In this study,three new stent structures(i.e.,stent A,stent B,and stent C)were designed based on the typical structure of biodegradable stents.The changes made included altering the angle and arrangement of the support rings to create a support ring structure with alternating large and small angles,as well as modifying the position and shape of the link.Using finite element analysis,the compressive performance,expansion performance,bending flexibility performance,damage to blood vessels,and hemodynamic changes of the stent were used as evaluation indexes.The results of these comprehensive evaluations were utilized as the primary criteria for selecting the most suitable stent design.The results demonstrated that compared to the traditional stent,stents A,B,and C exhibited improvements in radial stiffness of 16.9%,15.1%,and 37.8%,respectively;reductions in bending stiffness of 27.3%,7.6%,and 38.1%,respectively;decreases in dog-boning rate of 5.1%,93.9%,and 31.3%,respectively;as well as declines in the low wall shear stress region by 50.1%,43.8%,and 36.2%,respectively.In comparison to traditional stents,a reduction in radial recoiling was observed for stents A and C,with decreases of 9.3% and 7.4%,respectively.Although there was a slight increase in vessel damage for stents A,B,and C compared to traditional stents,this difference was not significant to have an impact.The changes in intravascular blood flow rate were essentially the same after implantation of the four stents.A comparison of the four stents revealed that stents A and C exhibited superior overall mechanical properties and they have greater potential for clinical application.This study provides a reference for designing clinical stent structures.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0456.
文摘This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid approach for oil spill detection.The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization(BHHO)and Principal Component Analysis(PCA)for improved feature extraction and selection from input SAR imagery.Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images.The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet.A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features.It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm.Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set.Results indicate that the highest oil spill detection accuracy of 99.2%has been achieved using the logistic regression classification algorithm,with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques.The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model.The significance of our study lies in its unique integration of deep learning with optimized feature selection,unlike other published studies,to enhance oil spill detection accuracy.
基金supported in part by the Scientific Research Fund of National Natural Science Foundation of China(Grant No.62372168)the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ30266)+2 种基金the Research Project on teaching reform in Hunan province(No.HNJG-2022-0791)the Hunan University of Science and Technology(No.2022-44-8)the National Social Science Funds of China(19BZX044).
文摘Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).
文摘In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.
基金supported by the Research Incentive Grant 23200 of Zayed University,United Arab Emirates.
文摘Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.
文摘The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality,stability,and dynamic environmental variations.This paper presents a novel sparrow search algorithm(SSA)-tuned proportional-integral(PI)controller for grid-connected photovoltaic(PV)systems,designed to optimize dynamic perfor-mance,energy extraction,and power quality.Key contributions include the development of a systematic SSA-based optimization frame-work for real-time PI parameter tuning,ensuring precise voltage and current regulation,improved maximum power point tracking(MPPT)efficiency,and minimized total harmonic distortion(THD).The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations,demonstrating its superior performance across key metrics:a 39.47%faster response time compared to PSO,a 12.06%increase in peak active power relative to P&O,and a 52.38%reduction in THD,ensuring compliance with IEEE grid standards.Moreover,the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiancefluc-tuations,rapid response time,and robust grid integration under varying conditions,making it highly suitable for real-time smart grid applications.This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios,while also setting the foundation for future research into multi-objective optimization,experimental valida-tion,and hybrid renewable energy systems.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-02-02341).
文摘The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical research.Subjective self-reporting is the foundation of conventional pain assessment methods,which may be unreliable.Deep learning is a promising alternative to resolve this limitation through automated pain classification.This paper proposes an ensemble deep-learning framework for pain assessment.The framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)signals.We integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)models.We then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s robustness.To improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature selection.This enables us to reduce the features’dimensionality without sacrificing the classification’s accuracy.With improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning classifiers.In our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment performance.However,due to differences in validation protocols,comparisons with previous studies are still limited.Combining deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification performance.The evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.
基金the financial support from the National Natural Science Foundation of China(No.21904007)the Fundamental Research Funds for the Central Universities(China,No.2412022QD008)+1 种基金the Jilin Provincial Department of Education(China),the Key Laboratory of Nanobiosensing and Nanobioanalysis at Universities of Jilin Province(China)the Analysis and Testing Center of Northeast Normal University(China)。
文摘The complexity of living environment system demands higher requirements for the sensitivity and selectivity of the probe.Therefore,it is of great importance to develop a universal strategy for highperformance probe optimization.Herein,we propose a novel“Enrichment-enhanced Detection”strategy and use carbon dots-dopamine detection system as a representative model to evaluate its feasibility.The composite probe carbon dots (CDs)-encapsulated in glycol-chitosan (GC)(i.e.,CDs@GC) was obtained by simply mixing GC and CDs through noncovalent interactions,including electrostatic interactions and hydrogen bonding.Dopamine (DA) could be detected through internal filter effect (IFE)-induced quenching of CDs.In the case of CDs@GC,noncovalent interactions (electrostatic interactions) between GC and the formed quinone (oxide of DA) could selectively extract and enrich the local concentration of DA,thus effectively improving the sensitivity and selectivity of the sensing system.The nanosensor had a low detection limit of 3.7 nmol/L,which was a 12-fold sensitivity improvement compared to the bare CDs probes with similar fluorescent profiles,proving the feasibility of the“Enrichment-enhanced Detection”strategy.Further,to examine this theory in real case,we designed a highly portable sensing platform to realize visual determination of DA.Overall,our work introduces a new strategy for accurately detecting DA and provides valuable insights for the universal design and optimization of superior nanoprobes.
基金supported by China Postdoctoral Science Foundation(Project ID:2024M762602)the National Natural Science Foundation of China under Grant No.62306232Natural Science Basic Research Program of Shaanxi Province under Grant No.2023-JC-QN-0662.
文摘This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
基金the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number"NBUFFMRA-2025-2461-09"。
文摘Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.
基金supported by the Ramanujan Fellowship from the Science and Engineering Research Board,Government of India(Grant No.RJF/2022/000115).
文摘This paper introduces dynamic mode decomposition(DMD)as a novel approach to model the breakage kinetics of particulate systems.DMD provides a data-driven framework to identify a best-fit linear dynamics model from a sequence of system measurement snapshots,bypassing the nontrivial task of determining appropriate mathemat-ical forms for the breakage kernel functions.A key innovation of our method is the instilling of physics-informed constraints into the DMD eigenmodes and eigenvalues,ensuring they adhere to the physical structure of particle breakage processes even under sparse measurement data.The integration of eigen-constraints is computationally aided by a zeroth-order global optimizer for solving the nonlinear,nonconvex optimization problem that elicits system dynamics from data.Our method is evaluated against the state-of-the-art optimized DMD algorithm using both generated data and real-world data of a batch grinding mill,showcasing over an order of magnitude lower prediction errors in data reconstruction and forecasting.
