横波可控震源振动器平板作为页岩气勘探中的关键部件,其疲劳寿命直接影响可控震源的使用寿命和勘探精度。然而,传统的振动器平板疲劳寿命优化方法未考虑平板与平板齿间焊接残余应力的影响,导致平板结构在抗疲劳优化设计方面效果不佳。为...横波可控震源振动器平板作为页岩气勘探中的关键部件,其疲劳寿命直接影响可控震源的使用寿命和勘探精度。然而,传统的振动器平板疲劳寿命优化方法未考虑平板与平板齿间焊接残余应力的影响,导致平板结构在抗疲劳优化设计方面效果不佳。为此,使用局部灵敏度法对平板疲劳寿命进行敏感性分析,确定了焊接残余应力为影响疲劳寿命的关键因素。随后,建立了平板的各向最大焊接残余应力与焊接速度和焊接层间温度之间的数学模型,并以各向最大焊接残余应力为约束,以疲劳寿命为优化目标,建立相应的优化模型。最后,利用NSGA-Ⅱ(nondominated sorting genetic algorithm-Ⅱ,非支配排序遗传算法-Ⅱ)获取Pareto解集,并结合熵权法和TOPSIS(technique for order preference by similarity to ideal solution,逼近理想解排序)法确定最佳优化方案:焊接速度为10.23 mm/s,焊接层间温度为105℃。结果表明,优化后平板的疲劳寿命为10.23年,相比优化前提高了17.72%。研究结果可为横波可控震源振动器平板的疲劳寿命优化提供科学有效的理论方法和工程指导。展开更多
Gastric cancer(GC)and gastroesophageal junction cancer(GEJC)represent a significant burden globally,with complications such as overt bleeding(OB)further exacerbating patient outcomes.A recent study by Yao et al evalua...Gastric cancer(GC)and gastroesophageal junction cancer(GEJC)represent a significant burden globally,with complications such as overt bleeding(OB)further exacerbating patient outcomes.A recent study by Yao et al evaluated the effectiveness and safety of systematic treatment in GC/GEJC patients presenting with OB.Using propensity score matching,the study balanced the comparison groups to investigate overall survival and treatment-related adverse events.The study's findings emphasize that systematic therapy can be safe and effective and contribute to the ongoing debate about the management of advanced GC/GEJC with OB,highlighting the complexities of treatment decisions in these high-risk patients.展开更多
We apply methods of algebraic integral geometry to prove a special case of the Gaussian kinematic formula of Adler-Taylor.The idea,suggested already by Adler and Taylor,is to view the GKF as the limit of spherical kin...We apply methods of algebraic integral geometry to prove a special case of the Gaussian kinematic formula of Adler-Taylor.The idea,suggested already by Adler and Taylor,is to view the GKF as the limit of spherical kinematic formulas for spheres of large dimension N and curvature1/N.展开更多
Background:Changes in lower limb joint coordination have been shown to increase localized stress on knee joint soft tissue—a known precursor of osteoarthritis.While 50%of individuals who undergo anterior cruciate lig...Background:Changes in lower limb joint coordination have been shown to increase localized stress on knee joint soft tissue—a known precursor of osteoarthritis.While 50%of individuals who undergo anterior cruciate ligament reconstruction(ACLR)develop radiographic osteoarthritis,it is unclear how underlying joint coordination during gait changes post-ACLR.The purpose of this study was twofold:to determine differences in lower limb coordination patterns during gait in ACLR individuals 2,4,and 6 months post-ACLR and to compare the coordination profiles of the ACLR participants at each timepoint post-ACLR to uninjured matched controls.Methods:We conducted a longitudinal assessment to quantify lower limb coordination at 3 timepoints post-ACLR and compared the ACLR coordination profiles to uninjured controls.Thirty-four ACLR(age=21.43±4.24 years,mean±SD;70.59%female)and 34 controls(age=21.42±3.43 years;70.59%female)participated.The ACLR group completed 3 overground gait assessments(2,4,and 6 months post-ACLR),and the controls completed one assessment,at which lower limb kinematics were collected.Cross-recurrence quantification analysis was used to characterize sagittal and frontal plane ankle-knee,ankle-hip,and knee-hip coordination dynamics.Comprehensive general linear mixed models were constructed to compare between-limb and within-limb coordination outcomes over time post-ACLR and a between-group comparison across timepoints.Results:The ACLR limb demonstrated a more"stuck"sagittal plane knee-hip coordination profile(greater trapping time(TT);p=0.004)compared bilaterally.Between groups,the ACLR participants exhibited a more predictable ankle-knee coordination pattern(percent determinism(%DET);p<0.05),stronger coupling between joints(meanline(MNLine))across all segments(p<0.05),and greater knee-hip TT(more"stuck";p<0.05)compared to the controls at each timepoint in the sagittal plane.Stronger frontal plane knee-hip joint coupling(MNLine)persisted across timepoints within the ACLR group compared to the controls(p<0.05).Conclusion:The results indicate ACLR individuals exhibit a distinct and rigid coordination pattern during gait compared to controls within6-month post-ACLR,which may have long-term implications for knee-joint health.展开更多
The application of industrial solid wastes as environmentally functional materials for air pollutants control has gained much attention in recent years due to its potential to reduce air pollution in a cost-effective ...The application of industrial solid wastes as environmentally functional materials for air pollutants control has gained much attention in recent years due to its potential to reduce air pollution in a cost-effective manner.In this review,we investigate the development of industrialwaste-based functional materials for various gas pollutant removal and consider the relevant reaction mechanism according to different types of industrial solid waste.We see a recent effort towards achieving high-performance environmental functional materials via chemical or physical modification,in which the active components,pore size,and phase structure can be altered.The review will discuss the potential of using industrial solid wastes,these modified materials,or synthesized materials from raw waste precursors for the removal of air pollutants,including SO_(2),NO_(x),Hg^(0),H_(2)S,VOCs,and CO_(2).The challenges still need to be addressed to realize this potential and the prospects for future research fully.The suggestions for future directions include determining the optimal composition of these materials,calculating the real reaction rate and turnover frequency,developing effective treatment methods,and establishing chemical component databases of raw industrial solid waste for catalysts/adsorbent preparation.展开更多
Agentic AI represents a significant advancement in artificial intelligence,enabling proactive agents that can set goals,make decisions,and adapt to changing situations.However,the performance of these systems is heavi...Agentic AI represents a significant advancement in artificial intelligence,enabling proactive agents that can set goals,make decisions,and adapt to changing situations.However,the performance of these systems is heavily dependent on the quality and relevance of the data they process.This research highlights the critical risk posed by faulty,insecure,or contextually inappropriate input data in modern Agentic AI systems.To address this challenge,this study proposes the Autonomous Data Integrity Layer(ADIL).This flexible architecture integrates best practices from security engineering and data science to ensure that Agentic AI systems operate with clean,validated,and contextually relevant data.By focusing on data integrity,ADIL enhances the reliability,accountability,and effectiveness of Agentic AI systems,leading to more trustworthy and robust intelligent agents.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
This study investigated the validity and sensitivity of a custom-made shoelace tensile testing system.The aim was to analyze the distribution pattern of shoelace tension in different positions and under different tigh...This study investigated the validity and sensitivity of a custom-made shoelace tensile testing system.The aim was to analyze the distribution pattern of shoelace tension in different positions and under different tightness levels during running.Mechanical tests were conducted using 16 weights,and various statistical analyses,including linear regression,Bland-Altman plots,coefficient of variation,and intraclass correlation coefficient,were performed to assess the system’s validity.Fifteen male amateur runners participated in the study,and three conditions(loose,comfortable,and tight)were measured during an upright stance.The system utilized VICON motion systems,a Kistler force plate,and a Photoelectric gate speed measurement system.Results showed a linear relationship between voltage and load at the three sensors(R2≥0.9997).Bland-Altman plots demonstrated 95%prediction intervals within±1.96SD from zero for all sensors.The average coefficient of variation for each sensor was less than 0.38%.Intraclass correlation coefficient values were larger than 0.999(p<0.0001)for each sensor.The peak tension of the front shoelace was greater than that of the front and middle when the shoelace was loose and tight.The rear shoelace had the highest tension force.The study also found that shoelace tension varied throughout the gait cycle during running.Overall,this research provides a novel and validated method for measuring shoelace tensile stress,which has implications for developing automatic shoelace fastening systems.展开更多
文摘横波可控震源振动器平板作为页岩气勘探中的关键部件,其疲劳寿命直接影响可控震源的使用寿命和勘探精度。然而,传统的振动器平板疲劳寿命优化方法未考虑平板与平板齿间焊接残余应力的影响,导致平板结构在抗疲劳优化设计方面效果不佳。为此,使用局部灵敏度法对平板疲劳寿命进行敏感性分析,确定了焊接残余应力为影响疲劳寿命的关键因素。随后,建立了平板的各向最大焊接残余应力与焊接速度和焊接层间温度之间的数学模型,并以各向最大焊接残余应力为约束,以疲劳寿命为优化目标,建立相应的优化模型。最后,利用NSGA-Ⅱ(nondominated sorting genetic algorithm-Ⅱ,非支配排序遗传算法-Ⅱ)获取Pareto解集,并结合熵权法和TOPSIS(technique for order preference by similarity to ideal solution,逼近理想解排序)法确定最佳优化方案:焊接速度为10.23 mm/s,焊接层间温度为105℃。结果表明,优化后平板的疲劳寿命为10.23年,相比优化前提高了17.72%。研究结果可为横波可控震源振动器平板的疲劳寿命优化提供科学有效的理论方法和工程指导。
文摘Gastric cancer(GC)and gastroesophageal junction cancer(GEJC)represent a significant burden globally,with complications such as overt bleeding(OB)further exacerbating patient outcomes.A recent study by Yao et al evaluated the effectiveness and safety of systematic treatment in GC/GEJC patients presenting with OB.Using propensity score matching,the study balanced the comparison groups to investigate overall survival and treatment-related adverse events.The study's findings emphasize that systematic therapy can be safe and effective and contribute to the ongoing debate about the management of advanced GC/GEJC with OB,highlighting the complexities of treatment decisions in these high-risk patients.
文摘We apply methods of algebraic integral geometry to prove a special case of the Gaussian kinematic formula of Adler-Taylor.The idea,suggested already by Adler and Taylor,is to view the GKF as the limit of spherical kinematic formulas for spheres of large dimension N and curvature1/N.
基金supported by the Arthritis Foundation(principal investigator:BP)the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health(P30-AR072580)。
文摘Background:Changes in lower limb joint coordination have been shown to increase localized stress on knee joint soft tissue—a known precursor of osteoarthritis.While 50%of individuals who undergo anterior cruciate ligament reconstruction(ACLR)develop radiographic osteoarthritis,it is unclear how underlying joint coordination during gait changes post-ACLR.The purpose of this study was twofold:to determine differences in lower limb coordination patterns during gait in ACLR individuals 2,4,and 6 months post-ACLR and to compare the coordination profiles of the ACLR participants at each timepoint post-ACLR to uninjured matched controls.Methods:We conducted a longitudinal assessment to quantify lower limb coordination at 3 timepoints post-ACLR and compared the ACLR coordination profiles to uninjured controls.Thirty-four ACLR(age=21.43±4.24 years,mean±SD;70.59%female)and 34 controls(age=21.42±3.43 years;70.59%female)participated.The ACLR group completed 3 overground gait assessments(2,4,and 6 months post-ACLR),and the controls completed one assessment,at which lower limb kinematics were collected.Cross-recurrence quantification analysis was used to characterize sagittal and frontal plane ankle-knee,ankle-hip,and knee-hip coordination dynamics.Comprehensive general linear mixed models were constructed to compare between-limb and within-limb coordination outcomes over time post-ACLR and a between-group comparison across timepoints.Results:The ACLR limb demonstrated a more"stuck"sagittal plane knee-hip coordination profile(greater trapping time(TT);p=0.004)compared bilaterally.Between groups,the ACLR participants exhibited a more predictable ankle-knee coordination pattern(percent determinism(%DET);p<0.05),stronger coupling between joints(meanline(MNLine))across all segments(p<0.05),and greater knee-hip TT(more"stuck";p<0.05)compared to the controls at each timepoint in the sagittal plane.Stronger frontal plane knee-hip joint coupling(MNLine)persisted across timepoints within the ACLR group compared to the controls(p<0.05).Conclusion:The results indicate ACLR individuals exhibit a distinct and rigid coordination pattern during gait compared to controls within6-month post-ACLR,which may have long-term implications for knee-joint health.
基金supported by National Natural Science Foundation of China(Grant No.52270106 and 22266021)Yunnan Major Scientific and Technological Projects(grant No.202202AG050005)Yunnan Fundamental Research Projects(grant No.202201AT070116).
文摘The application of industrial solid wastes as environmentally functional materials for air pollutants control has gained much attention in recent years due to its potential to reduce air pollution in a cost-effective manner.In this review,we investigate the development of industrialwaste-based functional materials for various gas pollutant removal and consider the relevant reaction mechanism according to different types of industrial solid waste.We see a recent effort towards achieving high-performance environmental functional materials via chemical or physical modification,in which the active components,pore size,and phase structure can be altered.The review will discuss the potential of using industrial solid wastes,these modified materials,or synthesized materials from raw waste precursors for the removal of air pollutants,including SO_(2),NO_(x),Hg^(0),H_(2)S,VOCs,and CO_(2).The challenges still need to be addressed to realize this potential and the prospects for future research fully.The suggestions for future directions include determining the optimal composition of these materials,calculating the real reaction rate and turnover frequency,developing effective treatment methods,and establishing chemical component databases of raw industrial solid waste for catalysts/adsorbent preparation.
文摘Agentic AI represents a significant advancement in artificial intelligence,enabling proactive agents that can set goals,make decisions,and adapt to changing situations.However,the performance of these systems is heavily dependent on the quality and relevance of the data they process.This research highlights the critical risk posed by faulty,insecure,or contextually inappropriate input data in modern Agentic AI systems.To address this challenge,this study proposes the Autonomous Data Integrity Layer(ADIL).This flexible architecture integrates best practices from security engineering and data science to ensure that Agentic AI systems operate with clean,validated,and contextually relevant data.By focusing on data integrity,ADIL enhances the reliability,accountability,and effectiveness of Agentic AI systems,leading to more trustworthy and robust intelligent agents.
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This study investigated the validity and sensitivity of a custom-made shoelace tensile testing system.The aim was to analyze the distribution pattern of shoelace tension in different positions and under different tightness levels during running.Mechanical tests were conducted using 16 weights,and various statistical analyses,including linear regression,Bland-Altman plots,coefficient of variation,and intraclass correlation coefficient,were performed to assess the system’s validity.Fifteen male amateur runners participated in the study,and three conditions(loose,comfortable,and tight)were measured during an upright stance.The system utilized VICON motion systems,a Kistler force plate,and a Photoelectric gate speed measurement system.Results showed a linear relationship between voltage and load at the three sensors(R2≥0.9997).Bland-Altman plots demonstrated 95%prediction intervals within±1.96SD from zero for all sensors.The average coefficient of variation for each sensor was less than 0.38%.Intraclass correlation coefficient values were larger than 0.999(p<0.0001)for each sensor.The peak tension of the front shoelace was greater than that of the front and middle when the shoelace was loose and tight.The rear shoelace had the highest tension force.The study also found that shoelace tension varied throughout the gait cycle during running.Overall,this research provides a novel and validated method for measuring shoelace tensile stress,which has implications for developing automatic shoelace fastening systems.
