Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Deep eutectic solvents for separation and purification applications in critical metal metallurgy:Recent advances and perspectives

Solvent extraction,a separation and purification technology,is crucial in critical metal metallurgy.Organic solvents commonly used in solvent extraction exhibit disadvantages,such as high volatility,high toxicity,and flammability,causing a spectrum of hazards to human health and environmental safety.Neoteric solvents have been recognized as potential alternatives to these harmful organic solvents.In the past two decades,several neoteric solvents have been proposed,including ionic liquids(ILs)and deep eutectic solvents(DESs).DESs have gradually become the focus of green solvents owing to several advantages,namely,low toxicity,degradability,and low cost.In this critical review,their classification,formation mechanisms,preparation methods,characterization technologies,and special physicochemical properties based on the most recent advancements in research have been systematically described.Subsequently,the major separation and purification applications of DESs in critical metal metallurgy were comprehensively summarized.Finally,future opportunities and challenges of DESs were explored in the current research area.In conclusion,this review provides valuable insights for improving our overall understanding of DESs,and it holds important potential for expanding separation and purification applications in critical metal metallurgy.

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

基于DeepLab v3+的涂鸦式图像分割算法

在现有的基于深度学习的交互式图像分割算法的研究中,主要以点击以及边界框的交互方式为主。本文在Deep GrabCut算法的基础上,选择DeepLab v3+作为模型的架构,并提出了“米”字形采样策略,经过大量的训练,最终生成的模型能够很好地适应涂鸦的交互方式。在分割精度上比原方法提升了5%以上,并有效地简化了用户交互要求,拓展了基于深度学习的交互式图像分割技术在涂鸦交互方式上的研究。

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery

Anastomotic leakage(AL)is a significant complication following rectal cancer surgery,adversely affecting both quality of life and oncological outcomes.Recent advancements in artificial intelligence(AI),particularly machine learning and deep learning,offer promising avenues for predicting and preventing AL.These technologies can analyze extensive clinical datasets to identify preoperative and perioperative risk factors such as malnutrition,body composition,and radiological features.AI-based models have demonstrated superior predictive power compared to traditional statistical methods,potentially guiding clinical decisionmaking and improving patient outcomes.Additionally,AI can provide surgeons with intraoperative feedback on blood supply and anatomical dissection planes,minimizing the risk of intraoperative complications and reducing the likelihood of AL development.

基于FCC-Deeplabv3+的城市地下管道缺陷语义分割方法

城市地下管道图像缺陷具有种类多、背景复杂、噪声多、缺陷尺度变化大等特点,导致目前城市地下管道缺陷分割算法精度不够高.本研究提出了一种基于Deeplabv3+的改进分割模型FCC-Deeplabv3+,并将该模型首次应用到城市地下管道缺陷分割.结合十字交叉注意力机制,使模型在预测时获取更丰富的上下文信息;提出了改进的解码器上采样策略,引入多尺度信息,减少中间层信息的丢失;使用基于增强的对比学习策略监督模型,提升了模型分割能力.此外,针对目前城市地下管道缺陷分割领域没有公开数据集的情况,基于Sewer-ML公开数据集,进行数据标注工作,构建了包含900张用于缺陷分割任务的数据集.通过实验验证了提出的缺陷分割模型的有效性及实时性,对比原始Deeplabv3+模型,mIoU提升了3.73%,mPA也提升了1.67%,并且相比其他基于深度学习的语义分割算法,也具有一定优势.

Physically Constrained Adaptive Deep Learning for Ocean Vertical-Mixing Parameterization

Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.

A Deep Learning Estimation Method for Temperature-Induced Girder End Displacements of Suspension Bridges

To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R2 increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R2.Compared to ANN,with an MSE of 4.6371 and an R2 of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R2 of 13.23%,demonstrating a significant enhancement in predictive performance.

Deep ResNet Strategy for the Classification of Wind Shear Intensity Near Airport Runway

Intense wind shear(I-WS)near airport runways presents a critical challenge to aviation safety,necessi-tating accurate and timely classification to mitigate risks during takeoff and landing.This study proposes the application of advanced Residual Network(ResNet)architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear(NI-WS)events using Doppler Light Detection and Ranging(LiDAR)data from Hong Kong International Airport(HKIA).Unlike conventional models such as feedforward neural networks(FNNs),convolutional neural networks(CNNs),and recurrent neural networks(RNNs),ResNet provides a distinct advantage in addressing key challenges such as capturing intricate WS dynamics,mitigating vanishing gradient issues in deep architectures,and effectively handling class imbalance when combined with Synthetic Minority Oversampling Technique(SMOTE).The analysis results revealed that ResNet34 outperforms other models with a Balanced Accuracy of 0.7106,Probability of Detection of 0.8271,False Alarm Rate of 0.328,F1-score of 0.7413,Matthews Correlation Coefficient of 0.433,and Geometric Mean of 0.701,demonstrating its effectiveness in classifying I-WS events.The findings of this study not only establish ResNet as a valuable tool in the domain of WS classification but also provide a reliable framework for enhancing operational safety at airports.

Achieving further refinement of grain structure and improvement of mechanical properties in Al-12Si-4Cu-2Ni-1Mg alloy by Al-Ti-C-B master alloy addition and deep cryogenic treatment

Near-eutectic Al-Si alloys are widely used in automotive manufacturing due to their superior wear resistance and high temperature performance.Because of high Si content,the grain refinement of near-eutectic Al-Si alloy has been a problem for many years.In this study,the effect of deep cryogenic treatment(DCT)on the microstructure and mechanical properties of Al-12Si-4Cu-2Ni-Mg alloy with addition of Al-Ti-C-B master alloy was fully investigated.Results show that the average grain size of the alloy is greatly reduced from 0.92 mm to 0.50 mm,and the eutectic Si and Al7Cu4Ni precipitates are spheroidized and refined in Al-12Si-4Cu-2Ni-Mg after DCT for 24 h and aging treatment.Thereby these changes of microstructures result in a significant increment of about 22.5%in elongation and a slight enhancement of about 6.8%in tensile strength.Moreover,the refinement of microstructure also significantly improves the fatigue life of the alloy.

Estimated Ultimate Recovery and Productivity of Deep Shale Gas Horizontal Wells

Pressure control in deep shale gas horizontal wells can reduce the stress sensitivity of hydraulic fractures and improve the estimated ultimate recovery(EUR).In this study,a hydraulic fracture stress sensitivity model is proposed to characterize the effect of pressure drop rate on fracture permeability.Furthermore,a production prediction model is introduced accounting for a non-uniform hydraulic fracture conductivity distribution.The results reveal that increasing the fracture conductivity leads to a rapid daily production increase in the early stages.However,above 0.50 D·cm,a further increase in the fracture conductivity has a limited effect on shale gas production growth.The initial production is lower under pressure-controlled conditions than that under pressure-release.For extended pressure control durations,the cumulative production initially increases and then decreases.For a fracture conductivity of 0.10 D·cm,the increase in production output under controlled-pressure conditions is~35%.For representative deep shale gas wells(Southern Sichuan,China),if the pressure drop rate under controlled-pressure conditions is reduced from 0.19 to 0.04 MPa/d,the EUR increase for 5 years of pressure-controlled production is 41.0 million,with an increase percentage of~29%.

How Do Deep Learning Forecasting Models Perform for Surface Variables in the South China Sea Compared to Operational Oceanography Forecasting Systems?

It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.

Enhancement of bending toughness for Fe-based amorphous nanocrystalline alloy with deep cryogenic-cycling treatment

The effects of deep cryogenic-cycling treatment(DCT)on the mechanical properties,soft magnetic properties,and atomic scale structure of the Fe_(73.5)Si_(13.5)B_(9)Nb_(3)Cu_(1)amorphous nanocrystalline alloy were investigated.The DCT samples were obtained by subjecting the as-annealed samples to a thermal cycling process between the temperature of the supercooled liquid zone and the temperature of liquid nitrogen.Through flat plate bending testing,hardness measurements,and nanoindentation experiment,it is found that the bending toughness of the DCT samples is improved and the soft magnetic properties are also slightly enhanced.These are attributed to the rejuvenation behavior of the DCT samples,which demonstrate a higher enthalpy of relaxation.Therefore,DCT is an effective method to enhance the bending toughness of Fe-based amorphous nanocrystalline alloys without degrading the soft magnetic properties.

Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

Combining deep reinforcement learning with heuristics to solve the traveling salesman problem

Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.

Optimizing crop yields while minimizing environmental impact through deep placement of nitrogen fertilizer

Nitrogen(N)serves as an essential nutrient for yield formation across diverse crop types.However,agricultural production encounters numerous challenges,notably high N fertilizer rates coupled with low N use efficiency and serious environmental pollution.Deep placement of nitrogen fertilizer(DPNF)is an agronomic measure that shows promise in addressing these issues.This review aims to offer a comprehensive understanding of DPNF,beginning with a succinct overview of its development and methodologies for implementation.Subsequently,the optimal fertilization depth and influencing factors for different crops are analyzed and discussed.Additionally,it investigates the regulation and mechanism underlying the DPNF on crop development,yield,N use efficiency and greenhouse gas emissions.Finally,the review delineates the limitations and challenges of this technology and provides suggestions for its improvement and application.This review provides valuable insight and reference for the promotion and adoption of DPNF in agricultural practice.

Deep reinforcement learning based integrated evasion and impact hierarchical intelligent policy of exo-atmospheric vehicles

Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.

基于YOLOv5和改进DeeplabV3+的青藏高原植被提取算法

青藏高原的植被覆盖度是生态研究和环境监测的重要指标。传统的植被覆盖度检测方法在地形简单且植被分布集中的区域效果较好,但在复杂地形下由于成本高、调查范围受限、耗时长等问题,导致植被提取精度受限。近年来,计算机视觉和深度学习技术的飞速发展为青藏高原复杂地形下的植被精准提取开辟了新的可能性。本研究提出一种结合YOLOv5和改进DeeplabV3+的双阶段植被提取算法。算法引入基于YOLOv5的植被目标检测模型,以减少背景对第二阶段植被分割任务的干扰;设计新型的DeeplabV3+语义分割模型,以实现精准的植被分割提取。改进的模型引入了轻量级主干网络MobileNetV2、优化了ASPP模块膨胀卷积参数,并集成EMA和CloAttention注意力机制。在青藏高原无人机航拍数据集上的实验结果显示,本算法在交并比(IoU)和像素准确率(PA)上分别达到了90.40%和96.32%,显著超过现有技术,且大幅降低了模型参数。本算法在多种环境条件下均展示了高精度的植被提取能力,可以为青藏高原植被覆盖度的快速、精准测定提供有效的技术支持。