Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.Thi...Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.This study presents a Bayesian optimization(BO)-based multi-objective framework inte-grated with explainable machine learning(ML)to efficiently explore and optimize the high-dimensional design space of biodegradable Mg alloys.Using ultimate tensile strength(UTS),elongation(EL)and cor-rosion potential(E_(corr))as objective properties,the framework balances these conflicting objectives and identifies optimal solutions.A novel biodegradable Mg alloy(Mg-4.6Zn-0.3Y-0.2Mn-0.1Nd-0.1Gd,wt.%)was successfully designed,demonstrating a UTS of 320 MPa,EL of 22%and E_(corr) of−1.60 V(tested in 37℃ simulated body fluid).Compared to JDBM,the UTS has increased by 13 MPa,the EL has improved by 6.1%,and the E_(corr) has risen by 0.02 V.The experimental results presented close agreement with predicted values,validating the proposed framework.The Shapley Additive Explanation method was em-ployed to interpret the ML models,revealing extrusion temperature and Zn content as key parameters driving the optimization design.The strategy provided in this study is universal and offers a potential approach for addressing high-dimensional multi-objective optimization challenges in material develop-ment.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved pr...This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.展开更多
The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this ...The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).展开更多
In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordinat...In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.展开更多
BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte...BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.展开更多
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challeng...Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML...Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.展开更多
Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requiremen...Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).展开更多
The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi...The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.展开更多
基金financially supported by the National Natu-ral Science Foundation of China(No.52301133)the China Post-doctoral Science Foundation(No.2023M730276)+1 种基金the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.YESS20210415)the Graduate Innovation Pro-gram of Chongqing University of Science and Technology(No.YKJCX2320218).
文摘Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.This study presents a Bayesian optimization(BO)-based multi-objective framework inte-grated with explainable machine learning(ML)to efficiently explore and optimize the high-dimensional design space of biodegradable Mg alloys.Using ultimate tensile strength(UTS),elongation(EL)and cor-rosion potential(E_(corr))as objective properties,the framework balances these conflicting objectives and identifies optimal solutions.A novel biodegradable Mg alloy(Mg-4.6Zn-0.3Y-0.2Mn-0.1Nd-0.1Gd,wt.%)was successfully designed,demonstrating a UTS of 320 MPa,EL of 22%and E_(corr) of−1.60 V(tested in 37℃ simulated body fluid).Compared to JDBM,the UTS has increased by 13 MPa,the EL has improved by 6.1%,and the E_(corr) has risen by 0.02 V.The experimental results presented close agreement with predicted values,validating the proposed framework.The Shapley Additive Explanation method was em-ployed to interpret the ML models,revealing extrusion temperature and Zn content as key parameters driving the optimization design.The strategy provided in this study is universal and offers a potential approach for addressing high-dimensional multi-objective optimization challenges in material develop-ment.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金partially supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2022YFE0114200)the National Natural Science Foundation of China(U20A6004).
文摘This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
基金support from the National Natural Science Foundation of China(22108052).
文摘The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).
基金Supported by National Natural Science Foundation of China(Grant Nos.U22A20246,52372382)Hefei Municipal Natural Science Foundation(Grant No.2022008)+1 种基金the Open Fund of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2023-06)S&T Program of Hebei(Grant No.225676162GH).
文摘In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.
基金Supported by Chongqing Medical Scientific Research Project(Joint Project of Chongqing Health Commission and Science and Technology Bureau),No.2023MSXM060.
文摘BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.
基金the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
文摘Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.
基金supported by the National Key Research and Development Program of China under No. 2019YFB1803200。
文摘Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).
基金the Shanghai Pujiang Program (No.22PJD030),the National Natural Science Foundation of China (Nos.61603244 and 71904116)the National Natural Science Foundation of China-Shandong Joint Fund (No.U2006228)。
文摘The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
