Spaceborne optomechanical systems face the dual challenges of extreme thermal disturbances and millikelvin-level temperature control precision during orbital operations,demanding robust control strategies.To address t...Spaceborne optomechanical systems face the dual challenges of extreme thermal disturbances and millikelvin-level temperature control precision during orbital operations,demanding robust control strategies.To address the performance limitations of conventional fixed-parameter active disturbance rejection control(ADRC)under complex operating conditions,this work proposes a Qlearning-enhanced adaptive ADRC framework.A thermal-transfer model incorporating multisource disturbances(solar radiation,structural conduction,and contact thermal resistance)is established,coupled with a reinforcement learning-driven parameter optimization mechanism.The ε-greedy policy dynamically adjusts observer bandwidth(ω_(o)∈[0.01,0.2])and controller bandwidth(ω_(c)∈[0.01,0.1])to enable real-time estimation and compensation of total disturbances.Simulation results demonstrate significant improvements over fixed-parameter ADRC and a self-tuning internal model control proportional-integral(SIMC-PI)controller:31.3% and 15.4% reduction in settling time during setpoint responses,respectively;21.8% lower integral absolute error(IAE)than the fixed-parameter ADRC during setpoint step responses;12.7% and 52.5% enhancement in control precision over conventional fixed-parameter and SIMC-PI controllers,respectively,under±10 K periodic and step thermal disturbances.Monte Carlo robustness tests reveal smaller fluctuation ranges of IAE,settling time,and overshoot under±5% parameter perturbations.This methodology establishes a new paradigm for millikelvin-level thermal control in space optical payloads.展开更多
This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relativ...This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.展开更多
The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubi...The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubiquitous connectivity.However,existing research reveals significant gaps in dynamic resource allocation,joint optimization,and equitable service provisioning under varying channel conditions,limiting practical deployment of these technologies.This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning(FAIRDQL)framework for joint resource management and phase configuration in HAPS-RIS systems.Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control,priority-based user scheduling,and dynamic learning mechanisms.The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments.Key findings demonstrate substantial improvements:9.15 dB SINR gain,12.5 bps/Hz capacity,78%power efficiency,and 0.82 fairness index.The framework achieves rapid 40-episode convergence with consistent delay performance.These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications,enabling practical HAPS-RIS deployments in rural connectivity,emergency communications,and urban networks.展开更多
To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this p...To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
A reversed-phase high-performance liquid chromatography(HPLC)method was developed for the direct determination of docosahexaenoic acid(DHA)in sturgeon caviar extract.The assay employed n-hexane extraction combined wit...A reversed-phase high-performance liquid chromatography(HPLC)method was developed for the direct determination of docosahexaenoic acid(DHA)in sturgeon caviar extract.The assay employed n-hexane extraction combined with gradient elution(ZORBAX SB-C18 column),with data collected using a diode array detector.The content was calculated by external standard method and validated against the national standard(GB 5009.168-2016).The study also measured DPPH free radical scavenging capacity and moisture retention rate across different DHA concentration groups.The results demonstrate that the proposed method exhibits excellent linearity(r=0.9997),with recovery rates ranging from 92.1% to 101.1% and relative standard deviations(RSD)of 2.23% to 3.92%.Compared to the national standard method,the relative deviation was 0.67% to 1.68%.At specific test concentrations,the high-DHA group shows significantly higher moisture retention(100.48%),hygroscopicity(100.85%),and DPPH scavenging efficiency(57.46%)than the low-DHA group(10.33%,11.76%,and 3.71%).The RP-HPLC method developed in this study simplifies DHA detection procedures with simple reagents and reliable results,making it suitable for rapid qualitative identification and quantitative analysis of target components in caviar extract quality control.The DPPH experiment further reveals the correlation between DHA content and antioxidant efficacy in sturgeon caviar extracts,providing scientific evidence for developing functional cosmetics.展开更多
The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to r...The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.展开更多
Objective Stroke is the third leading cause of death worldwide,with the highest incidence in Asia,particularly in China,where smoking remains a major risk factor.The smoking prevalence in China is similar to that in A...Objective Stroke is the third leading cause of death worldwide,with the highest incidence in Asia,particularly in China,where smoking remains a major risk factor.The smoking prevalence in China is similar to that in Asia.Whether the risk estimates for smoking-related stroke in China and all Asian countries are still unknown which is worth evaluating.Thus,this study aims to compare the Relative Risk(RR)of smoking-attributed stroke among the Chinese and Asian populations.Methods A literature search was conducted from the inception to September 10,2022.Studies meeting the criteria were included.The articles were screened,and related information was extracted.Pooled RRs stratified by smoking status and sex were analyzed,including subgroup analyses for China,other Asian countries,and Asia overall.Finally,publication bias and sensitivity analyses were conducted.Results Thirty-seven articles on the Chinese population and 15 on other Asian populations were included,with a mean Newcastle-Ottawa scale(NOS)score of 7.25.About ever smokers,there had no statistical difference existed in both sexes and females between China and other Asian countries,while the RR of males in other Asian countries[2.31(1.38,3.86)]was higher than that in China[1.21(1.15,1.26)];further subgroup analysis indicated that other Asian countries had higher RR[3.76(3.02,4.67)]in the morbidity subgroup.The RRs of both sexes,males and females,between China and the whole of Asia were not statistically different.As for current and former smokers,no meaningful statistical difference was observed in the pooled RRs of both sexes,males and females,in China,other Asian countries,and all of Asia.Conclusion The RR of males ever smokers in China was smaller than that in other Asian countries due to the few articles of morbidity subgroup,but had no statistical difference with the whole of Asia;other groups of ever smokers,current smokers,and former smokers were not statistically significant with other Asian countries or the whole of Asia.展开更多
This study extends the self-propelled particle(SPP)model by incorporating a limited vision cone and local density sensing.The results reveal that clusters can simultaneously exhibit velocity polarization and spatial c...This study extends the self-propelled particle(SPP)model by incorporating a limited vision cone and local density sensing.The results reveal that clusters can simultaneously exhibit velocity polarization and spatial cohesion within specific ranges of vision angle and density threshold.The dependence of the dynamical features,including the order parameter and density variation,on the threshold and visual cone is investigated.Furthermore,a critical threshold is identified,which governs the transition between ordered and disordered states and is closely linked to density fluctuations and noise intensity.The clustering results show that the model is explained by the chasing mechanism responsible for cluster formation,density,and shape.These results may stimulate practical applications in swarm maneuvering.展开更多
The escalating global issues of water scarcity and pollution emphasize the critical need for the rapid development of efficient and eco-friendly water treatment technologies.Photoelectrocatalytic technology has emerge...The escalating global issues of water scarcity and pollution emphasize the critical need for the rapid development of efficient and eco-friendly water treatment technologies.Photoelectrocatalytic technology has emerged as a promising solution for effectively degrading refractory organic pollutants in water under light conditions.This review delves into the advancements made in the field,focusing on strategies to enhance the generation of active species by modulating the micro-interface of the photoanode.Strategies,such as morphological control,element doping,introduction of surface oxygen vacancies,and construction of heterostructures,significantly improve the separation efficiency of photogenerated charges and the generation of active species,thereby boosting the efficiency of photoelectrocatalytic performance.Furthermore,the review explores the potential applications of photoelectrocatalytic technology in organic pollutant degradation in solutions.It also outlines the current challenges and future development directions.Despite its remarkable laboratory success,practical implementation of photoelectrocatalytic technology encounters obstacles related to stability,cost-effectiveness,and operational efficiency.Future investigations need to focus on optimizing the performance of photoelectrocatalytic materials and exploring strategies for upscaling their application in real water treatment scenarios.展开更多
Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-relate...Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-related diseases like Alzheimer’s disease,osteoporosis,low immunity,glucose and lipid metabolism disorders,and cardiovascular diseases.With the continuous increase of the elderly population,the pressure on the medical industry is increasing.To lower the burden on the medical industry and increase the average age of the elderly,it is vital to explore effective anti-aging materials.Ginseng Radix et Rhizoma(Renshen),as a traditional and precious Chinese medicinal herb,is known as the“king of all herbs”.It is famous for its effects of“tonifying Qi,restoring pulse”(helping with the generation of Qi(the fundamental,vital energy that continuously flows within the body)and the circulation of blood)and strengthening the body,nourishing the spleen and lungs,generating fluids and nourishing blood,calming the mind and improving intelligence.Recently,its anti-aging effect has received increasing attention from modern scientific research.This study summarizes the pharmacological effects of the main active ingredients of Renshen(ginsenosides,polysaccharides,etc.)on resisting aging,including preventing neuroaging,suppressing skin aging,mitigating ovarian aging,inhibiting osteoporosis and arthritis,enhancing the immune system of the elderly,protecting the cardiovascular system,resisting aging-induced fatigue and exerting the anti-tumor effects.Through network pharmacology and molecular docking,the anti-aging active ingredients of Renshen were screened,and the key targets and pathways of anti-aging active ingredients in Renshen were determined.Using network pharmacology,totally 106 drug targets and 3,479 disease targets were screened,and 79 common targets between aging and Renshen were identified.Three core targets were identified in the PPI network,including TNF,AKT1,and IL-1β.Molecular docking was used to obtain further verification.This study emphasizes the potential of Renshen as a source of anti-aging activity,which can be developed into a novel drug for the treatment of age-related diseases.展开更多
With the rapid growth of cloud computing,the number of data centers(DCs)continuously increases,leading to a high-energy consumption dilemma.Cooling,apart from IT equipment,represents the largest energy consumption in ...With the rapid growth of cloud computing,the number of data centers(DCs)continuously increases,leading to a high-energy consumption dilemma.Cooling,apart from IT equipment,represents the largest energy consumption in DCs.Passive design(PD)and active design(AD)are two important approaches in architectural design to reduce energy consumption.However,for DC cooling,few studies have summarized AD,and there are almost no studies on PD.Based on existing international research(2005-2024),this paper summarizes the current state of cooling strategies for DCs.PD encompasses floors,ceilings,and layout and zoning of racks.Additionally,other passive strategies not yet studied in DCs are critically examined.AD includes air,liquid,free,and two-phase cooling.This paper systematically compares the performance of different AD technologies on various KPIs,including energy,economic,and environmental indicators.This paper also explores the application of different cooling design strategies through best-practice examples and presents advanced algorithms for energy management in operational DCs.This study reveals that free cooling is widely employed,with Artificial Neural Networks emerging as the most popular algorithm for managing cooling energy.Finally,this paper suggests four future directions for reducing cooling energy in DCs,with a focus on the development of passive strategies.This paper provides an overview and guide to DC energy-consumption issues,emphasizes the importance of implementing passive and active design strategies to reduce DC cooling energy consumption,and provides directions and references for future energy-efficient DC designs.展开更多
In this study,the mechanism and characteristics of the responseαparticles and the damage caused by them in CMOS active pixel(APS)sensors were investigated.A detection and compensation algorithm for dead pixels caused...In this study,the mechanism and characteristics of the responseαparticles and the damage caused by them in CMOS active pixel(APS)sensors were investigated.A detection and compensation algorithm for dead pixels caused byαparticle ionizing radiation was proposed,and the effects of dead-pixel compensation algorithms were compared and analyzed under different parameter conditions.The experimental results show thatαparticle response signal has highest accuracy at 9 dB gain,with an obvious“target-ring”distribution.With increasing cumulative dose,the CMOS APS pedestal tends to saturation while dead pixels continue increasing.Though some pixel damage recovers through natural annealing,the dead-to-noise ratio increases with irradiation time,reaching 32.54%after 72 h.A hierarchical clustering dead-pixel detection method is proposed,categorizing pixels into two types:those within and outside the response event.A classification compensation strategy combining mean and majority filtering is proposed.This compensation algorithm can address dead-pixel interference without affectingαparticle radiation response data.When iterated multiple times and with integration time exceeding 6.31 ms,the number of dead pixels can be effectively reduced.展开更多
Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN mode...Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.展开更多
Background:Prostate cancer is a common malignancy,with many men on active surveillance for localized,low-risk disease also experiencing lower urinary tract symptoms(LUTS)from benign prostatic hyperplasia(BPH).Water Va...Background:Prostate cancer is a common malignancy,with many men on active surveillance for localized,low-risk disease also experiencing lower urinary tract symptoms(LUTS)from benign prostatic hyperplasia(BPH).Water Vapor Thermal Therapy(WVTT)is a minimally invasive BPH treatment,but its safety and efficacy in this setting are unclear.Case Description:We report three men with localized PCa on active surveillance who underwent WVTT for LUTS.Conclusions:WVTT appears safe and potentially effective in treating LUTS,especially in those with lower-risk disease and smaller prostate volumes.Further research is needed to confirm safety,efficacy,and optimal patient selection.展开更多
基金The National Key R&D Program of China(No.2022YFB3902902)the National Natural Science Foundation of China(No.52276003).
文摘Spaceborne optomechanical systems face the dual challenges of extreme thermal disturbances and millikelvin-level temperature control precision during orbital operations,demanding robust control strategies.To address the performance limitations of conventional fixed-parameter active disturbance rejection control(ADRC)under complex operating conditions,this work proposes a Qlearning-enhanced adaptive ADRC framework.A thermal-transfer model incorporating multisource disturbances(solar radiation,structural conduction,and contact thermal resistance)is established,coupled with a reinforcement learning-driven parameter optimization mechanism.The ε-greedy policy dynamically adjusts observer bandwidth(ω_(o)∈[0.01,0.2])and controller bandwidth(ω_(c)∈[0.01,0.1])to enable real-time estimation and compensation of total disturbances.Simulation results demonstrate significant improvements over fixed-parameter ADRC and a self-tuning internal model control proportional-integral(SIMC-PI)controller:31.3% and 15.4% reduction in settling time during setpoint responses,respectively;21.8% lower integral absolute error(IAE)than the fixed-parameter ADRC during setpoint step responses;12.7% and 52.5% enhancement in control precision over conventional fixed-parameter and SIMC-PI controllers,respectively,under±10 K periodic and step thermal disturbances.Monte Carlo robustness tests reveal smaller fluctuation ranges of IAE,settling time,and overshoot under±5% parameter perturbations.This methodology establishes a new paradigm for millikelvin-level thermal control in space optical payloads.
文摘This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project,number PNURSP2025R757Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubiquitous connectivity.However,existing research reveals significant gaps in dynamic resource allocation,joint optimization,and equitable service provisioning under varying channel conditions,limiting practical deployment of these technologies.This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning(FAIRDQL)framework for joint resource management and phase configuration in HAPS-RIS systems.Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control,priority-based user scheduling,and dynamic learning mechanisms.The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments.Key findings demonstrate substantial improvements:9.15 dB SINR gain,12.5 bps/Hz capacity,78%power efficiency,and 0.82 fairness index.The framework achieves rapid 40-episode convergence with consistent delay performance.These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications,enabling practical HAPS-RIS deployments in rural connectivity,emergency communications,and urban networks.
基金supported by the National Natural Science Foundation of China(Grant No.52374156).
文摘To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
文摘A reversed-phase high-performance liquid chromatography(HPLC)method was developed for the direct determination of docosahexaenoic acid(DHA)in sturgeon caviar extract.The assay employed n-hexane extraction combined with gradient elution(ZORBAX SB-C18 column),with data collected using a diode array detector.The content was calculated by external standard method and validated against the national standard(GB 5009.168-2016).The study also measured DPPH free radical scavenging capacity and moisture retention rate across different DHA concentration groups.The results demonstrate that the proposed method exhibits excellent linearity(r=0.9997),with recovery rates ranging from 92.1% to 101.1% and relative standard deviations(RSD)of 2.23% to 3.92%.Compared to the national standard method,the relative deviation was 0.67% to 1.68%.At specific test concentrations,the high-DHA group shows significantly higher moisture retention(100.48%),hygroscopicity(100.85%),and DPPH scavenging efficiency(57.46%)than the low-DHA group(10.33%,11.76%,and 3.71%).The RP-HPLC method developed in this study simplifies DHA detection procedures with simple reagents and reliable results,making it suitable for rapid qualitative identification and quantitative analysis of target components in caviar extract quality control.The DPPH experiment further reveals the correlation between DHA content and antioxidant efficacy in sturgeon caviar extracts,providing scientific evidence for developing functional cosmetics.
基金The Central Government Guides Local Foundation for Science and Technology Development(Grant No.YDZJSX2024B004).
文摘The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.
基金funded by the State Key Laboratory Special Fund(2060204)Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(2023-I2M-2-001)Strengthen Capacity of Study and Application on the Burden of Disease in Health Care Systems in China:Establishment and Development of Chinese Burden of Disease Research and Dissemination Center(15-208)supported by the China Medical Board(CMB)。
文摘Objective Stroke is the third leading cause of death worldwide,with the highest incidence in Asia,particularly in China,where smoking remains a major risk factor.The smoking prevalence in China is similar to that in Asia.Whether the risk estimates for smoking-related stroke in China and all Asian countries are still unknown which is worth evaluating.Thus,this study aims to compare the Relative Risk(RR)of smoking-attributed stroke among the Chinese and Asian populations.Methods A literature search was conducted from the inception to September 10,2022.Studies meeting the criteria were included.The articles were screened,and related information was extracted.Pooled RRs stratified by smoking status and sex were analyzed,including subgroup analyses for China,other Asian countries,and Asia overall.Finally,publication bias and sensitivity analyses were conducted.Results Thirty-seven articles on the Chinese population and 15 on other Asian populations were included,with a mean Newcastle-Ottawa scale(NOS)score of 7.25.About ever smokers,there had no statistical difference existed in both sexes and females between China and other Asian countries,while the RR of males in other Asian countries[2.31(1.38,3.86)]was higher than that in China[1.21(1.15,1.26)];further subgroup analysis indicated that other Asian countries had higher RR[3.76(3.02,4.67)]in the morbidity subgroup.The RRs of both sexes,males and females,between China and the whole of Asia were not statistically different.As for current and former smokers,no meaningful statistical difference was observed in the pooled RRs of both sexes,males and females,in China,other Asian countries,and all of Asia.Conclusion The RR of males ever smokers in China was smaller than that in other Asian countries due to the few articles of morbidity subgroup,but had no statistical difference with the whole of Asia;other groups of ever smokers,current smokers,and former smokers were not statistically significant with other Asian countries or the whole of Asia.
基金Project supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX240139)funded by the Youth Independent Innovation Fund of PLA Army Engineering University(Grant No.KYJBJKQTZQ23006)。
文摘This study extends the self-propelled particle(SPP)model by incorporating a limited vision cone and local density sensing.The results reveal that clusters can simultaneously exhibit velocity polarization and spatial cohesion within specific ranges of vision angle and density threshold.The dependence of the dynamical features,including the order parameter and density variation,on the threshold and visual cone is investigated.Furthermore,a critical threshold is identified,which governs the transition between ordered and disordered states and is closely linked to density fluctuations and noise intensity.The clustering results show that the model is explained by the chasing mechanism responsible for cluster formation,density,and shape.These results may stimulate practical applications in swarm maneuvering.
基金financially supported by the National Natural Science Foundation of China (No.52100076)the Fundamental Research Funds for the Central Universities (No.2023MS064)。
文摘The escalating global issues of water scarcity and pollution emphasize the critical need for the rapid development of efficient and eco-friendly water treatment technologies.Photoelectrocatalytic technology has emerged as a promising solution for effectively degrading refractory organic pollutants in water under light conditions.This review delves into the advancements made in the field,focusing on strategies to enhance the generation of active species by modulating the micro-interface of the photoanode.Strategies,such as morphological control,element doping,introduction of surface oxygen vacancies,and construction of heterostructures,significantly improve the separation efficiency of photogenerated charges and the generation of active species,thereby boosting the efficiency of photoelectrocatalytic performance.Furthermore,the review explores the potential applications of photoelectrocatalytic technology in organic pollutant degradation in solutions.It also outlines the current challenges and future development directions.Despite its remarkable laboratory success,practical implementation of photoelectrocatalytic technology encounters obstacles related to stability,cost-effectiveness,and operational efficiency.Future investigations need to focus on optimizing the performance of photoelectrocatalytic materials and exploring strategies for upscaling their application in real water treatment scenarios.
基金supported by the Jilin Science and Technology Development Talent Special Project,Nos.20240601086RC,23JQ08(all to ZH)YDZJ202502CXJD077+1 种基金JLARS-2025-0802-09YDZJ202501ZYTS706.
文摘Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-related diseases like Alzheimer’s disease,osteoporosis,low immunity,glucose and lipid metabolism disorders,and cardiovascular diseases.With the continuous increase of the elderly population,the pressure on the medical industry is increasing.To lower the burden on the medical industry and increase the average age of the elderly,it is vital to explore effective anti-aging materials.Ginseng Radix et Rhizoma(Renshen),as a traditional and precious Chinese medicinal herb,is known as the“king of all herbs”.It is famous for its effects of“tonifying Qi,restoring pulse”(helping with the generation of Qi(the fundamental,vital energy that continuously flows within the body)and the circulation of blood)and strengthening the body,nourishing the spleen and lungs,generating fluids and nourishing blood,calming the mind and improving intelligence.Recently,its anti-aging effect has received increasing attention from modern scientific research.This study summarizes the pharmacological effects of the main active ingredients of Renshen(ginsenosides,polysaccharides,etc.)on resisting aging,including preventing neuroaging,suppressing skin aging,mitigating ovarian aging,inhibiting osteoporosis and arthritis,enhancing the immune system of the elderly,protecting the cardiovascular system,resisting aging-induced fatigue and exerting the anti-tumor effects.Through network pharmacology and molecular docking,the anti-aging active ingredients of Renshen were screened,and the key targets and pathways of anti-aging active ingredients in Renshen were determined.Using network pharmacology,totally 106 drug targets and 3,479 disease targets were screened,and 79 common targets between aging and Renshen were identified.Three core targets were identified in the PPI network,including TNF,AKT1,and IL-1β.Molecular docking was used to obtain further verification.This study emphasizes the potential of Renshen as a source of anti-aging activity,which can be developed into a novel drug for the treatment of age-related diseases.
文摘With the rapid growth of cloud computing,the number of data centers(DCs)continuously increases,leading to a high-energy consumption dilemma.Cooling,apart from IT equipment,represents the largest energy consumption in DCs.Passive design(PD)and active design(AD)are two important approaches in architectural design to reduce energy consumption.However,for DC cooling,few studies have summarized AD,and there are almost no studies on PD.Based on existing international research(2005-2024),this paper summarizes the current state of cooling strategies for DCs.PD encompasses floors,ceilings,and layout and zoning of racks.Additionally,other passive strategies not yet studied in DCs are critically examined.AD includes air,liquid,free,and two-phase cooling.This paper systematically compares the performance of different AD technologies on various KPIs,including energy,economic,and environmental indicators.This paper also explores the application of different cooling design strategies through best-practice examples and presents advanced algorithms for energy management in operational DCs.This study reveals that free cooling is widely employed,with Artificial Neural Networks emerging as the most popular algorithm for managing cooling energy.Finally,this paper suggests four future directions for reducing cooling energy in DCs,with a focus on the development of passive strategies.This paper provides an overview and guide to DC energy-consumption issues,emphasizes the importance of implementing passive and active design strategies to reduce DC cooling energy consumption,and provides directions and references for future energy-efficient DC designs.
基金supported by the National Natural Science Foundation of China(No.11905102)Hunan Provincial Postgraduate Research and Innovation Project(No.QL20230234)。
文摘In this study,the mechanism and characteristics of the responseαparticles and the damage caused by them in CMOS active pixel(APS)sensors were investigated.A detection and compensation algorithm for dead pixels caused byαparticle ionizing radiation was proposed,and the effects of dead-pixel compensation algorithms were compared and analyzed under different parameter conditions.The experimental results show thatαparticle response signal has highest accuracy at 9 dB gain,with an obvious“target-ring”distribution.With increasing cumulative dose,the CMOS APS pedestal tends to saturation while dead pixels continue increasing.Though some pixel damage recovers through natural annealing,the dead-to-noise ratio increases with irradiation time,reaching 32.54%after 72 h.A hierarchical clustering dead-pixel detection method is proposed,categorizing pixels into two types:those within and outside the response event.A classification compensation strategy combining mean and majority filtering is proposed.This compensation algorithm can address dead-pixel interference without affectingαparticle radiation response data.When iterated multiple times and with integration time exceeding 6.31 ms,the number of dead pixels can be effectively reduced.
基金supported by the National Science and Technology Major Project(2022ZD0119901)the National Natural Science Foundation of China under Grant(U2141234,62463004 and U24A20260)+1 种基金the Hainan Province Science and Technology Special Fund(ZDYF2024GXJS003)the Scientific Research Fund of Hainan University(KYQD(ZR)23025).
文摘Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.
文摘Background:Prostate cancer is a common malignancy,with many men on active surveillance for localized,low-risk disease also experiencing lower urinary tract symptoms(LUTS)from benign prostatic hyperplasia(BPH).Water Vapor Thermal Therapy(WVTT)is a minimally invasive BPH treatment,but its safety and efficacy in this setting are unclear.Case Description:We report three men with localized PCa on active surveillance who underwent WVTT for LUTS.Conclusions:WVTT appears safe and potentially effective in treating LUTS,especially in those with lower-risk disease and smaller prostate volumes.Further research is needed to confirm safety,efficacy,and optimal patient selection.
