Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most...Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.展开更多
BACKGROUND The early acquisition of skills required to perform hemostasis during endoscopy may be hindered by the lack of tools that allow assessments of the operator’s viewpoint.Understanding the operator’s viewpoi...BACKGROUND The early acquisition of skills required to perform hemostasis during endoscopy may be hindered by the lack of tools that allow assessments of the operator’s viewpoint.Understanding the operator’s viewpoint may facilitate the skills.AIM To evaluate the effects of a training system using operator gaze patterns during gastric endoscopic submucosal dissection(ESD)on hemostasis.METHODS An eye-tracking system was developed to record the operator’s viewpoints during gastric ESD,displaying the viewpoint as a circle.In phase 1,videos of three trainees’viewpoints were recorded.After reviewing these,trainees were recorded again in phase 2.The videos from both phases were retrospectively reviewed,and short clips were created to evaluate the hemostasis skills.Outcome measures included the time to recognize the bleeding point,the time to complete hemostasis,and the number of coagulation attempts.RESULTS Eight cases treated with ESD were reviewed,and 10 video clips of hemostasis were created.The time required to recognize the bleeding point during phase 2 was significantly shorter than that during phase 1(8.3±4.1 seconds vs 23.1±19.2 seconds;P=0.049).The time required to complete hemostasis during phase 1 and that during phase 2 were not significantly different(15.4±6.8 seconds vs 31.9±21.7 seconds;P=0.056).Significantly fewer coagulation attempts were performed during phase 2(1.8±0.7 vs 3.2±1.0;P=0.004).CONCLUSION Short-term training did not reduce hemostasis completion time but significantly improved bleeding point recognition and reduced coagulation attempts.Learning from the operator’s viewpoint can facilitate acquiring hemostasis skills during ESD.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
OBJECTIVE:To systematically investigate the clinical effectiveness and safety of traditional Chinese herbs(TCHs)as an alternative to conventional medicine(CM)in children with cough variant asthma(CVA).METHODS:Randomiz...OBJECTIVE:To systematically investigate the clinical effectiveness and safety of traditional Chinese herbs(TCHs)as an alternative to conventional medicine(CM)in children with cough variant asthma(CVA).METHODS:Randomized controlled trial(RCT)studies that were published from their inceptions to March 31,2020,were identified from the electronic databases of China National Knowledge Infrastructure,Wangfang,Pub Med,and Cochrane Central Library.The primary outcome of the review was the total effective rate(TER),and the secondary outcomes were immunoglobulin E(Ig E),peak expiratory flow(PEF),adverse drug reactions,and relapse rates of interventions.RESULTS:For the Meta-analysis,13 studies involving 992 children with CVA were included.In terms of TER and Ig E,the experimental interventions of TCH,when compared with the control interventions of CM,on pediatric CVA were found to be significantly effective(P<0.0001),whereas for spirometry,PEF was not significantly improved in the TCH group(P=0.48).The incident rates of adverse drug reaction and relapse were found to be significantly lower in the TCH group than those in the CM group(P=0.02 and P<0.0001,respectively).CONCLUSION:Compared with CM therapy,the effects of TCH therapy on pediatric CVA were significantly beneficial in terms of TER and Ig E,but not for PEF,and the methodological quality of included studies was poor.Therefore,the results should be interpreted with caution.More randomized controlled trials with rigorous experimental methodologies are required for objectivity in the future.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
Free-space optical(FSO)communication is of supreme importance for designing next-generation networks.Over the past decades,the radio frequency(RF)spectrum has been the main topic of interest for wireless technology.Th...Free-space optical(FSO)communication is of supreme importance for designing next-generation networks.Over the past decades,the radio frequency(RF)spectrum has been the main topic of interest for wireless technology.The RF spectrum is becoming denser and more employed,making its availability tough for additional channels.Optical communication,exploited for messages or indications in historical times,is now becoming famous and useful in combination with error-correcting codes(ECC)to mitigate the effects of fading caused by atmospheric turbulence.A free-space communication system(FSCS)in which the hybrid technology is based on FSO and RF.FSCS is a capable solution to overcome the downsides of current schemes and enhance the overall link reliability and availability.The proposed FSCS with regular low-density parity-check(LDPC)for coding techniques is deliberated and evaluated in terms of signal-to-noise ratio(SNR)in this paper.The extrinsic information transfer(EXIT)methodology is an incredible technique employed to investigate the sum-product decoding algorithm of LDPC codes and optimize the EXIT chart by applying curve fitting.In this research work,we also analyze the behavior of the EXIT chart of regular/irregular LDPC for the FSCS.We also investigate the error performance of LDPC code for the proposed FSCS.展开更多
This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to...This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.展开更多
The ensemble of Information and Communication Technology(ICT)and Artificial Intelligence(AI)has catalysed many developments and innovations in the automotive industry.6G networks emerge as a promising technology for r...The ensemble of Information and Communication Technology(ICT)and Artificial Intelligence(AI)has catalysed many developments and innovations in the automotive industry.6G networks emerge as a promising technology for realising Intelligent Transport Systems(ITS),which benefits the drivers and society.As the network is highly heterogeneous and robust,the physical layer security and node reliability of the vehicles hold paramount significance.This work presents a novel methodology that integrates the prowess of computer vision techniques and the Lightweight Super Learning Ensemble(LSLE)of Machine Learning(ML)algorithms to predict the presence of intruders in the network.Furthermore,our work utilizes a Deep Convolutional Neural Network(DCNN)to detect obstacles by identifying the Region of Interest(ROI)in the images.As the network utilizes mm-waves with shorter wavelengths,Intelligent Reflecting Surfaces(IRS)are employed to redirect signals to legitimate nodes,thereby mitigating the malicious activity of intruders.The experimental simulation shows that the proposed LSLE outperforms the state-of-the-art techniques in terms of accuracy,False Positive Rate(FPR),Recall,F1-Score,and Precision.A consistent performance improvement with an average FPR of 85.08%and accuracy of 92.01%is achieved by the model.Thus,in the future,detecting moving obstacles and real-time network traffic monitoring can be included to achieve more realistic results.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)remains a significant public health concern in South Korea even though the incidence rates are declining.While medical travel for cancer treatment is common,its patterns and inf...BACKGROUND Hepatocellular carcinoma(HCC)remains a significant public health concern in South Korea even though the incidence rates are declining.While medical travel for cancer treatment is common,its patterns and influencing factors for patients with HCC are unknown.AIM To assess medical travel patterns and determinants and their policy implications among patients with newly diagnosed HCC in South Korea.METHODS This retrospective cohort study used the National Health Insurance Service database to identify patients with newly diagnosed HCC from 2013 to 2021.Medical travel was defined as receiving initial treatment outside one’s residential region.Patient characteristics and regional trends were analyzed,and factors influencing medical travel were identified using logistic regression analysis.RESULTS Among 64808 patients 52.4%received treatment in the capital.This proportion increased to 67.4%when including the surrounding metropolitan area.Medical travel was significantly more common among younger and wealthier patients.Patients with greater comorbidity burden or liver cirrhosis were less likely to travel.While geographic distance influenced travel patterns,high-volume academic centers in the capital attracted patients nationwide regardless of proximity.CONCLUSION This nationwide study highlighted the centralization of HCC care in the capital.This observation indicates that regional cancer hubs should be strengthened and promoted for equitable healthcare access.展开更多
The authors regret that the affiliation b and c are wrong.Affiliation b should be changed to“School of Civil and Environmental Engineering,Harbin Institute of Technology,Shenzhen,China;Department of Data Analysis and...The authors regret that the affiliation b and c are wrong.Affiliation b should be changed to“School of Civil and Environmental Engineering,Harbin Institute of Technology,Shenzhen,China;Department of Data Analysis and Mathematical Modelling,Ghent University,Belgium”.And affiliation c should be changed to“State Key Laboratory of Urban Water Resource and Environment(SKLUWRE),School of Environment,Harbin Institute of Technology,China”.展开更多
Thin-walled parts have been widely employed as critical components in high-performance equipment due to the high specific strength and light weight.However,owing to their relatively weak rigidity and poor damping prop...Thin-walled parts have been widely employed as critical components in high-performance equipment due to the high specific strength and light weight.However,owing to their relatively weak rigidity and poor damping properties,chatter vibration is likely to occur during the milling process,which severely deteriorates surface quality and decreases machining productivity.Therefore,chatter suppression is essential for improving the dynamic machinability of thin-walled structures and has attracted extensive attention over the past few decades.This paper reviews the current state of the art in research concerning chatter suppression during the milling of thin-walled workpieces.In consideration of the dynamic characteristics of this process,the challenges in design and application of chatter attenuation methods are highlighted.Moreover,various chatter suppression techniques,involving passive,active,and semi-active methods,are comprehensively discussed in terms of basic concepts,working mechanism,optimal design,and application.Finally,future research opportunities in chatter mitigation technology for thin-wall milling are recommended.展开更多
Spherical bubble oscillations are widely used to model cavitation phenomena in biomedical and naval hydrodynamic systems.During collapse,a sudden increase in surrounding pressure initiates the collapse of a cavitation...Spherical bubble oscillations are widely used to model cavitation phenomena in biomedical and naval hydrodynamic systems.During collapse,a sudden increase in surrounding pressure initiates the collapse of a cavitation bubble,followed by a rebound driven by the high internal gas pressure.While the ideal gas equation of state(EOS)is commonly used to describe the internal pressure and temperature of the bubble,it is limited in its capacity to capture molecular-level effects under highly compressed conditions.In the present study,we employ non-ideal EOS for the gas(the van der Waals EOS and its volume-limited case)to investigate bubble oscillations with a focus on energy redistribution.Bubble oscillation is modeled in two phases:collapse,described by the Keller−Miksis formulation,and rebound,where peak shock pressure is estimated using similitude-based relations.To assess the role of EOS in energy redistribution,we introduce a framework that quantifies energy components in the bubble−liquid system while conserving total energy,tailored to each EOS.Using this framework,we evaluate energy concentration,acoustic radiation,and shock propagation and statistically analyze their dependence on both the driving pressure and the EOS of gas.We statistically derive scaling relations of key bubble dynamics quantities,energy concentration and radiation,and shock pressure using the driving pressure ratio.This work provides a generalizable framework and set of scaling relations for predicting bubble dynamics and energy transfer,with potential applications in evaluating the impacts of cavitation phenomena in complex practical systems.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certifie...This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.展开更多
Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
BACKGROUND Liver cirrhosis is the late stage of hepatic fibrosis and is characterized by portal hypertension that can clinically lead to decompensation in the form of ascites,esophageal/gastric varices or encephalopat...BACKGROUND Liver cirrhosis is the late stage of hepatic fibrosis and is characterized by portal hypertension that can clinically lead to decompensation in the form of ascites,esophageal/gastric varices or encephalopathy.The most common sequelae associated with liver cirrhosis are neurologic and neuropsychiatric impairments labeled as hepatic encephalopathy(HE).Well established triggers for HE include infection,gastrointestinal bleeding,constipation,and medications.Alterations to the gut microbiome is one of the leading ammonia producers in the body,and therefore may make patients more susceptible to HE.AIM To investigate the relationship between the use of proton pump inhibitors(PPIs)and HE in patients with cirrhosis.METHODS This is a single center,retrospective analysis.Patients were included in the study with an admitting diagnosis of HE.The degree of HE was determined from subjective and objective portions of hospital admission notes using the West Haven Criteria.The primary outcome of the study was to evaluate the grade of HE in PPI users versus non-users at admission to the hospital and throughout their hospital course.Secondary outcomes included rate of infection,gastrointestinal bleeding within the last 12 mo,mean ammonia level,and model for end-stage liver disease scores at admission.RESULTS The HE grade at admission using the West Haven Criteria was 2.3 in the PPI group compared to 1.7 in the PPI nonuser group(P=0.001).The average length of hospital stay in PPI group was 8.3 d compared to 6.5 d in PPI nonusers(P=0.046).Twenty-seven(31.8%)patients in the PPI user group required an Intensive Care Unit admission during their hospital course compared to 6 in the PPI nonuser group(16.7%)(P=0.138).Finally,10(11.8%)patients in the PPI group expired during their hospital stay compared to 1 in the PPI nonuser group(2.8%)(P=0.220).CONCLUSION Chronic PPI use in cirrhotic patients is associated with significantly higher average West Haven Criteria for HE compared to patients that do not use PPIs.展开更多
BACKGROUND The undifferentiated-type(UDT)component profoundly affects the clinical course of early gastric cancers(EGCs).However,an accurate preoperative diagnosis of the histological types is unsatisfactory.To date,f...BACKGROUND The undifferentiated-type(UDT)component profoundly affects the clinical course of early gastric cancers(EGCs).However,an accurate preoperative diagnosis of the histological types is unsatisfactory.To date,few studies have investigated whether the UDT component within mixed-histological-type(MT)EGCs can be recognized preoperatively.AIM To clarify the histopathological characteristics of the endoscopically-resected MT EGCs for investigating whether the UDT component could be recognized preoperatively.METHODS This was a single-center retrospective study.First,we attempted to clarify the histopathological characteristics of the endoscopically-resected MT EGCs with emphasis on the UDT component.Histopathological examination investigated each lesion’s UDT component:(1)Whole mucosal layer occupation of the UDT component;(2)UDT component exposure to the surface of the mucosa;and(3)existence of a clear border between the differentiated-type and UDT components.Then,preoperative endoscopic images with magnifying endoscopy with narrowband imaging(ME-NBI)were examined to identify whether the endoscopic UDT component finding was recognizable within the area where it was present in the histopathological examination.The preoperative biopsy results and comparative relationships between endoscopic and histopathological findings were also examined.RESULTS In the histopathological examination,the whole mucosal layer occupation of the UDT component and exposure of the UDT component to the mucosal surface were observed in 67.3%(33/49)and 79.6%(39/49)of samples,respectively.A clear distinction of the border between the differentiated-type and UDT components could not be drawn in 65.3%(32/49)of MT lesions.In the endoscopic examination,the preoperative endoscopic images showed that only 24.5%(12/49)of MT EGCs revealed the UDT component within the area where it was present histopathologically.Histopathological UDT predominance was the single significant factor associated with the presence of the endoscopic UDT component finding(61.5%vs 11.1%,P=0.0009).Only 26.5%(13/49)of the lesions were diagnosed from the pretreatment biopsy as having a UDT component.Combined results of the pretreatment biopsy and ME-NBI showed the preoperative presence of the UDT component in 40.8%(20/49)of MT EGCs.CONCLUSION Recognition of a UDT component within MT EGCs is difficult even when pretreatment biopsy and ME-NBI are combined.Endoscopic resection plays a significant role in both treatment and diagnosis.展开更多
AIM:To investigate the diverse characteristics of different pathological gradings of gastric adenocarcinoma(GA)using tumor-related genes.METHODS:GA tissues in different pathological gradings and normal tissues were su...AIM:To investigate the diverse characteristics of different pathological gradings of gastric adenocarcinoma(GA)using tumor-related genes.METHODS:GA tissues in different pathological gradings and normal tissues were subjected to tissue arrays.Expressions of 15 major tumor-related genes were detected by RNA in situ hybridization along with 3'terminal digoxin-labeled anti-sense single strandedoligonucleotide and locked nucleic acid modifying probe within the tissue array.The data obtained were processed by support vector machines by four different feature selection methods to discover the respective critical gene/gene subsets contributing to the GA activities of different pathological gradings.RESULTS:In comparison of poorly differentiated GA with normal tissues,tumor-related gene TP53 plays a key role,although other six tumor-related genes could also achieve the Area Under Curve(AUC)of the receiver operating characteristic independently by more than 80%.Comparing the well differentiated GA with normal tissues,we found that 11 tumor-related genes could independently obtain the AUC by more than 80%,but only the gene subsets,TP53,RB and PTEN,play a key role.Only the gene subsets,Bcl10,UVRAG,APC,Beclin1,NM23,PTEN and RB could distinguish between the poorly differentiated and well differentiated GA.None of a single gene could obtain a valid distinction.CONCLUSION:Different from the traditional point of view,the well differentiated cancer tissues have more alterations of important tumor-related genes than the poorly differentiated cancer tissues.展开更多
As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physi...As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.展开更多
文摘Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.
基金Supported by the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research,No.23K11902.
文摘BACKGROUND The early acquisition of skills required to perform hemostasis during endoscopy may be hindered by the lack of tools that allow assessments of the operator’s viewpoint.Understanding the operator’s viewpoint may facilitate the skills.AIM To evaluate the effects of a training system using operator gaze patterns during gastric endoscopic submucosal dissection(ESD)on hemostasis.METHODS An eye-tracking system was developed to record the operator’s viewpoints during gastric ESD,displaying the viewpoint as a circle.In phase 1,videos of three trainees’viewpoints were recorded.After reviewing these,trainees were recorded again in phase 2.The videos from both phases were retrospectively reviewed,and short clips were created to evaluate the hemostasis skills.Outcome measures included the time to recognize the bleeding point,the time to complete hemostasis,and the number of coagulation attempts.RESULTS Eight cases treated with ESD were reviewed,and 10 video clips of hemostasis were created.The time required to recognize the bleeding point during phase 2 was significantly shorter than that during phase 1(8.3±4.1 seconds vs 23.1±19.2 seconds;P=0.049).The time required to complete hemostasis during phase 1 and that during phase 2 were not significantly different(15.4±6.8 seconds vs 31.9±21.7 seconds;P=0.056).Significantly fewer coagulation attempts were performed during phase 2(1.8±0.7 vs 3.2±1.0;P=0.004).CONCLUSION Short-term training did not reduce hemostasis completion time but significantly improved bleeding point recognition and reduced coagulation attempts.Learning from the operator’s viewpoint can facilitate acquiring hemostasis skills during ESD.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
文摘OBJECTIVE:To systematically investigate the clinical effectiveness and safety of traditional Chinese herbs(TCHs)as an alternative to conventional medicine(CM)in children with cough variant asthma(CVA).METHODS:Randomized controlled trial(RCT)studies that were published from their inceptions to March 31,2020,were identified from the electronic databases of China National Knowledge Infrastructure,Wangfang,Pub Med,and Cochrane Central Library.The primary outcome of the review was the total effective rate(TER),and the secondary outcomes were immunoglobulin E(Ig E),peak expiratory flow(PEF),adverse drug reactions,and relapse rates of interventions.RESULTS:For the Meta-analysis,13 studies involving 992 children with CVA were included.In terms of TER and Ig E,the experimental interventions of TCH,when compared with the control interventions of CM,on pediatric CVA were found to be significantly effective(P<0.0001),whereas for spirometry,PEF was not significantly improved in the TCH group(P=0.48).The incident rates of adverse drug reaction and relapse were found to be significantly lower in the TCH group than those in the CM group(P=0.02 and P<0.0001,respectively).CONCLUSION:Compared with CM therapy,the effects of TCH therapy on pediatric CVA were significantly beneficial in terms of TER and Ig E,but not for PEF,and the methodological quality of included studies was poor.Therefore,the results should be interpreted with caution.More randomized controlled trials with rigorous experimental methodologies are required for objectivity in the future.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
文摘Free-space optical(FSO)communication is of supreme importance for designing next-generation networks.Over the past decades,the radio frequency(RF)spectrum has been the main topic of interest for wireless technology.The RF spectrum is becoming denser and more employed,making its availability tough for additional channels.Optical communication,exploited for messages or indications in historical times,is now becoming famous and useful in combination with error-correcting codes(ECC)to mitigate the effects of fading caused by atmospheric turbulence.A free-space communication system(FSCS)in which the hybrid technology is based on FSO and RF.FSCS is a capable solution to overcome the downsides of current schemes and enhance the overall link reliability and availability.The proposed FSCS with regular low-density parity-check(LDPC)for coding techniques is deliberated and evaluated in terms of signal-to-noise ratio(SNR)in this paper.The extrinsic information transfer(EXIT)methodology is an incredible technique employed to investigate the sum-product decoding algorithm of LDPC codes and optimize the EXIT chart by applying curve fitting.In this research work,we also analyze the behavior of the EXIT chart of regular/irregular LDPC for the FSCS.We also investigate the error performance of LDPC code for the proposed FSCS.
文摘This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.
基金supported by Ongoing Research Funding program,(ORF-2025-582),King Saud University,Riyadh,Saudi Arabia。
文摘The ensemble of Information and Communication Technology(ICT)and Artificial Intelligence(AI)has catalysed many developments and innovations in the automotive industry.6G networks emerge as a promising technology for realising Intelligent Transport Systems(ITS),which benefits the drivers and society.As the network is highly heterogeneous and robust,the physical layer security and node reliability of the vehicles hold paramount significance.This work presents a novel methodology that integrates the prowess of computer vision techniques and the Lightweight Super Learning Ensemble(LSLE)of Machine Learning(ML)algorithms to predict the presence of intruders in the network.Furthermore,our work utilizes a Deep Convolutional Neural Network(DCNN)to detect obstacles by identifying the Region of Interest(ROI)in the images.As the network utilizes mm-waves with shorter wavelengths,Intelligent Reflecting Surfaces(IRS)are employed to redirect signals to legitimate nodes,thereby mitigating the malicious activity of intruders.The experimental simulation shows that the proposed LSLE outperforms the state-of-the-art techniques in terms of accuracy,False Positive Rate(FPR),Recall,F1-Score,and Precision.A consistent performance improvement with an average FPR of 85.08%and accuracy of 92.01%is achieved by the model.Thus,in the future,detecting moving obstacles and real-time network traffic monitoring can be included to achieve more realistic results.
基金Supported by Dong-A University Research Fund,No.20230598.
文摘BACKGROUND Hepatocellular carcinoma(HCC)remains a significant public health concern in South Korea even though the incidence rates are declining.While medical travel for cancer treatment is common,its patterns and influencing factors for patients with HCC are unknown.AIM To assess medical travel patterns and determinants and their policy implications among patients with newly diagnosed HCC in South Korea.METHODS This retrospective cohort study used the National Health Insurance Service database to identify patients with newly diagnosed HCC from 2013 to 2021.Medical travel was defined as receiving initial treatment outside one’s residential region.Patient characteristics and regional trends were analyzed,and factors influencing medical travel were identified using logistic regression analysis.RESULTS Among 64808 patients 52.4%received treatment in the capital.This proportion increased to 67.4%when including the surrounding metropolitan area.Medical travel was significantly more common among younger and wealthier patients.Patients with greater comorbidity burden or liver cirrhosis were less likely to travel.While geographic distance influenced travel patterns,high-volume academic centers in the capital attracted patients nationwide regardless of proximity.CONCLUSION This nationwide study highlighted the centralization of HCC care in the capital.This observation indicates that regional cancer hubs should be strengthened and promoted for equitable healthcare access.
文摘The authors regret that the affiliation b and c are wrong.Affiliation b should be changed to“School of Civil and Environmental Engineering,Harbin Institute of Technology,Shenzhen,China;Department of Data Analysis and Mathematical Modelling,Ghent University,Belgium”.And affiliation c should be changed to“State Key Laboratory of Urban Water Resource and Environment(SKLUWRE),School of Environment,Harbin Institute of Technology,China”.
基金supported by the National Natural Science Foundation of China(Grant No.U22A20202)。
文摘Thin-walled parts have been widely employed as critical components in high-performance equipment due to the high specific strength and light weight.However,owing to their relatively weak rigidity and poor damping properties,chatter vibration is likely to occur during the milling process,which severely deteriorates surface quality and decreases machining productivity.Therefore,chatter suppression is essential for improving the dynamic machinability of thin-walled structures and has attracted extensive attention over the past few decades.This paper reviews the current state of the art in research concerning chatter suppression during the milling of thin-walled workpieces.In consideration of the dynamic characteristics of this process,the challenges in design and application of chatter attenuation methods are highlighted.Moreover,various chatter suppression techniques,involving passive,active,and semi-active methods,are comprehensively discussed in terms of basic concepts,working mechanism,optimal design,and application.Finally,future research opportunities in chatter mitigation technology for thin-wall milling are recommended.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2022-00155966Artificial Intelligence Convergence Innovation Human Resources Development(EwhaWomans University)).
文摘Spherical bubble oscillations are widely used to model cavitation phenomena in biomedical and naval hydrodynamic systems.During collapse,a sudden increase in surrounding pressure initiates the collapse of a cavitation bubble,followed by a rebound driven by the high internal gas pressure.While the ideal gas equation of state(EOS)is commonly used to describe the internal pressure and temperature of the bubble,it is limited in its capacity to capture molecular-level effects under highly compressed conditions.In the present study,we employ non-ideal EOS for the gas(the van der Waals EOS and its volume-limited case)to investigate bubble oscillations with a focus on energy redistribution.Bubble oscillation is modeled in two phases:collapse,described by the Keller−Miksis formulation,and rebound,where peak shock pressure is estimated using similitude-based relations.To assess the role of EOS in energy redistribution,we introduce a framework that quantifies energy components in the bubble−liquid system while conserving total energy,tailored to each EOS.Using this framework,we evaluate energy concentration,acoustic radiation,and shock propagation and statistically analyze their dependence on both the driving pressure and the EOS of gas.We statistically derive scaling relations of key bubble dynamics quantities,energy concentration and radiation,and shock pressure using the driving pressure ratio.This work provides a generalizable framework and set of scaling relations for predicting bubble dynamics and energy transfer,with potential applications in evaluating the impacts of cavitation phenomena in complex practical systems.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金supported in part by the National Science and Technology Major Project(2022ZD0119902)the National Natural Science Foundation of China(52471372,623B2018,62203015,62233001)+4 种基金the Liaoning Revitalization Leading Talents Program(XLYC2402054)the Key Basic Research of Dalian(2023JJ11CG008)the Fundamental Research Funds for the Central Universities(3132023508)the Collaborative Research Fund of Hong Kong Research Grants Council(C1013-24G)the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University(2023YBPY005).
文摘This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
文摘BACKGROUND Liver cirrhosis is the late stage of hepatic fibrosis and is characterized by portal hypertension that can clinically lead to decompensation in the form of ascites,esophageal/gastric varices or encephalopathy.The most common sequelae associated with liver cirrhosis are neurologic and neuropsychiatric impairments labeled as hepatic encephalopathy(HE).Well established triggers for HE include infection,gastrointestinal bleeding,constipation,and medications.Alterations to the gut microbiome is one of the leading ammonia producers in the body,and therefore may make patients more susceptible to HE.AIM To investigate the relationship between the use of proton pump inhibitors(PPIs)and HE in patients with cirrhosis.METHODS This is a single center,retrospective analysis.Patients were included in the study with an admitting diagnosis of HE.The degree of HE was determined from subjective and objective portions of hospital admission notes using the West Haven Criteria.The primary outcome of the study was to evaluate the grade of HE in PPI users versus non-users at admission to the hospital and throughout their hospital course.Secondary outcomes included rate of infection,gastrointestinal bleeding within the last 12 mo,mean ammonia level,and model for end-stage liver disease scores at admission.RESULTS The HE grade at admission using the West Haven Criteria was 2.3 in the PPI group compared to 1.7 in the PPI nonuser group(P=0.001).The average length of hospital stay in PPI group was 8.3 d compared to 6.5 d in PPI nonusers(P=0.046).Twenty-seven(31.8%)patients in the PPI user group required an Intensive Care Unit admission during their hospital course compared to 6 in the PPI nonuser group(16.7%)(P=0.138).Finally,10(11.8%)patients in the PPI group expired during their hospital stay compared to 1 in the PPI nonuser group(2.8%)(P=0.220).CONCLUSION Chronic PPI use in cirrhotic patients is associated with significantly higher average West Haven Criteria for HE compared to patients that do not use PPIs.
文摘BACKGROUND The undifferentiated-type(UDT)component profoundly affects the clinical course of early gastric cancers(EGCs).However,an accurate preoperative diagnosis of the histological types is unsatisfactory.To date,few studies have investigated whether the UDT component within mixed-histological-type(MT)EGCs can be recognized preoperatively.AIM To clarify the histopathological characteristics of the endoscopically-resected MT EGCs for investigating whether the UDT component could be recognized preoperatively.METHODS This was a single-center retrospective study.First,we attempted to clarify the histopathological characteristics of the endoscopically-resected MT EGCs with emphasis on the UDT component.Histopathological examination investigated each lesion’s UDT component:(1)Whole mucosal layer occupation of the UDT component;(2)UDT component exposure to the surface of the mucosa;and(3)existence of a clear border between the differentiated-type and UDT components.Then,preoperative endoscopic images with magnifying endoscopy with narrowband imaging(ME-NBI)were examined to identify whether the endoscopic UDT component finding was recognizable within the area where it was present in the histopathological examination.The preoperative biopsy results and comparative relationships between endoscopic and histopathological findings were also examined.RESULTS In the histopathological examination,the whole mucosal layer occupation of the UDT component and exposure of the UDT component to the mucosal surface were observed in 67.3%(33/49)and 79.6%(39/49)of samples,respectively.A clear distinction of the border between the differentiated-type and UDT components could not be drawn in 65.3%(32/49)of MT lesions.In the endoscopic examination,the preoperative endoscopic images showed that only 24.5%(12/49)of MT EGCs revealed the UDT component within the area where it was present histopathologically.Histopathological UDT predominance was the single significant factor associated with the presence of the endoscopic UDT component finding(61.5%vs 11.1%,P=0.0009).Only 26.5%(13/49)of the lesions were diagnosed from the pretreatment biopsy as having a UDT component.Combined results of the pretreatment biopsy and ME-NBI showed the preoperative presence of the UDT component in 40.8%(20/49)of MT EGCs.CONCLUSION Recognition of a UDT component within MT EGCs is difficult even when pretreatment biopsy and ME-NBI are combined.Endoscopic resection plays a significant role in both treatment and diagnosis.
基金Supported by Xiamen Health Bureau,No.3502z20089009Xiamen Science and Technology Bureau,No.3502Z20074023Youth Fund of Fujian Health Department,No.2008-1-52,Fujian Province,China
文摘AIM:To investigate the diverse characteristics of different pathological gradings of gastric adenocarcinoma(GA)using tumor-related genes.METHODS:GA tissues in different pathological gradings and normal tissues were subjected to tissue arrays.Expressions of 15 major tumor-related genes were detected by RNA in situ hybridization along with 3'terminal digoxin-labeled anti-sense single strandedoligonucleotide and locked nucleic acid modifying probe within the tissue array.The data obtained were processed by support vector machines by four different feature selection methods to discover the respective critical gene/gene subsets contributing to the GA activities of different pathological gradings.RESULTS:In comparison of poorly differentiated GA with normal tissues,tumor-related gene TP53 plays a key role,although other six tumor-related genes could also achieve the Area Under Curve(AUC)of the receiver operating characteristic independently by more than 80%.Comparing the well differentiated GA with normal tissues,we found that 11 tumor-related genes could independently obtain the AUC by more than 80%,but only the gene subsets,TP53,RB and PTEN,play a key role.Only the gene subsets,Bcl10,UVRAG,APC,Beclin1,NM23,PTEN and RB could distinguish between the poorly differentiated and well differentiated GA.None of a single gene could obtain a valid distinction.CONCLUSION:Different from the traditional point of view,the well differentiated cancer tissues have more alterations of important tumor-related genes than the poorly differentiated cancer tissues.
基金co-supported by the Aeronautical Science Foundation of China(No.2020Z023053002)the National Natural Science Foundation of China(No.61305133。
文摘As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.
