With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper propo...With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.展开更多
The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published s...The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published separate guidelines for NSCLC and small cell lung cancer(SCLC)for the first time to improve clinical usability.展开更多
As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those tar...As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those targeting low latency and high reliability,time synchronization is critical,especially in vehicular networks.However,due to the inherent mobility of vehicular environments,consistently exchanging synchronization packets with a fixed base station or access point is challenging.This issue is further exacerbated in signal shadowed areas such as urban canyons,tunnels,or large-scale indoor hallswhere other technologies,such as global navigation satellite system(GNSS),are unavailable.One-way synchronization techniques offer a feasible approach under such transient connectivity conditions.One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances.In this paper,we propose a WLAN-based multi-stage clock synchronization scheme(WMC)tailored for vehicular networks.The proposed method comprises an initial hard update stage to rapidly achieve synchronization,followed by a high-precision stable stage based on Maximum Likelihood Estimation(MLE).By implementing the scheme directly at the network driver,we address key limitations of hard update mechanisms.Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50μs accuracy,and subsequently transitions to a refined MLE-based synchronization stage,achieving stable accuracy at approximately 30μs.The windowed moving average stabilized(reaching 90%of the baseline)in approximately 35 s,which corresponds to just 5.1%of the baseline time accuracy.Finally,the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility(SUMO).The results demonstrate that more accurate conditions for position estimation can be supported,with an improvement about 38.5%in the mean error.展开更多
Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with inc...Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with incidence and mortality rates continuing to rise(2).The Chinese Society of Clinical Oncology(CSCO)first introduced its guidelines in 2017,and since then,they have been updated annually to incorporate the latest clinical research findings,drug availability,and expert consensus(3-8).This article presents the key updates in the 2025 edition compared to the 2024 version.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring ...Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.展开更多
Medical image segmentation is a powerful and evolving technology in medical diagnosis.In fact,it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the c...Medical image segmentation is a powerful and evolving technology in medical diagnosis.In fact,it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus(COVID-19).Various techniques have been utilized for COVID-19 image segmentation,including Multilevel Thresholding(MLT)-based meta-heuristics,which are considered crucial in addressing this issue.However,despite their importance,meta-heuristics have significant limitations.Specifically,the imbalance between exploration and exploitation,as well as premature convergence,can cause the optimization process to become stuck in local optima,resulting in unsatisfactory segmentation results.In this paper,an enhanced War Strategy Chimp Optimization Algorithm(WSChOA)is proposed to address MLT problems.Two strategies are incorporated into the traditional Chimp Optimization Algorithm.Golden update mechanism that provides diversity in the population.Additionally,the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima.The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index(FSIM),Structural Similarity Index(SSIM),Peak signal-to-Noise Ratio(PSNR),Standard deviation(STD),Freidman Test(FT),and Wilcoxon Sign Rank Test(WSRT).The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy,indicating that it is a powerful tool for image segmentation.展开更多
To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic upd...To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.展开更多
In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different...In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.展开更多
A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction pro...A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction problem with large geometric deformation and material failure and solve the fluid-structure interaction problem of Newtonian fluid.In the coupled framework,the NOSB-PD theory describes the deformation and fracture of the solid material structure.ULPH is applied to describe the flow of Newtonian fluids due to its advantages in computational accuracy.The framework utilizes the advantages of NOSB-PD theory for solving discontinuous problems and ULPH theory for solving fluid problems,with good computational stability and robustness.A fluidstructure coupling algorithm using pressure as the transmission medium is established to deal with the fluidstructure interface.The dynamic model of solid structure and the PD-ULPH fluid-structure interaction model involving large deformation are verified by numerical simulations.The results agree with the analytical solution,the available experimental data,and other numerical results.Thus,the accuracy and effectiveness of the proposed method in solving the fluid-structure interaction problem are demonstrated.The fluid-structure interactionmodel based on ULPH and NOSB-PD established in this paper provides a new idea for the numerical solution of fluidstructure interaction and a promising approach for engineering design and experimental prediction.展开更多
Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat t...Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat transfer,climate,and fluid mixing in industrial processes.This work aims to use the Updated Lagrangian Particle Hydrodynamics(ULPH)theory to address natural convection problems.The Navier-Stokes equation is discretized using second-order nonlocal differential operators,allowing a direct solution of the Laplace operator for temperature in the energy equation.Various numerical simulations,including cases such as natural convection in square cavities and two concentric cylinders,were conducted to validate the reliability of the model.The results demonstrate that the proposed model exhibits excellent accuracy and performance,providing a promising and effective numerical approach for natural convection problems.展开更多
Julie:What are you looking at,Sam?Sam:Oh,hi,Julie.I'm looking at Fairview City's weekly snowfall update.Julie:But it's only Monday.Sam:I know.The update is for last week's snowfall.Julie:I see.It's...Julie:What are you looking at,Sam?Sam:Oh,hi,Julie.I'm looking at Fairview City's weekly snowfall update.Julie:But it's only Monday.Sam:I know.The update is for last week's snowfall.Julie:I see.It'sforthesecond weekofthis month,then.Sam:That's right.The datesare from December 8 to December 14.展开更多
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame i...To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame is first created by group of blocks （GOB） alternation. Motion information is then duplicated in both the descriptions and a process called low quality macroblock update is designed to redundantly encode textures in each frame using standard bit stream syntax. In this way, the output bit streams are standard compliant and better trade-offs between redundancy and single charmel reconstruction distortion are achieved. The proposed method has much better performance than the well-known MD transform coding （MDTC） method both in terms of redundancy rate distortion, and in the packet loss scenario.展开更多
Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extrac...Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.展开更多
Land subsidence significantly impacts the accuracy of the National Elevation Datum in China.In order to solve this issue,a dynamic and economical way was proposed to update the National Elevation Datum with the assist...Land subsidence significantly impacts the accuracy of the National Elevation Datum in China.In order to solve this issue,a dynamic and economical way was proposed to update the National Elevation Datum with the assistance of InSAR in the North China Plain,which served as the research area.Moreover,the GNSS result was used to correct the InSAR result for the vertical deformation field,which has a relatively unified deformation reference.By integrating the vertical deformation field with the national elevation control point,an analysis and evaluation of changes in the National Elevation Datum were conducted.In addition,a regional remeasurement scheme was formulated to achieve dynamic updates and mainte-nance of the National Elevation Datum on a regional scale.Through data acquisition and processing,we successfully improved reliability within the main subsidence areas for future use.As a result,updating the elevation values utilize a regional update method,and a dynamic and economical technical process to update the National Elevation Datum is shown in the study.展开更多
Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequen...Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).展开更多
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol...During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.展开更多
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm...Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.展开更多
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
基金supported by the National Natural Science Foundation of China(Nos.52164001,52064006,52004072 and 52364004)the Science and Technology Support Project of Guizhou(Nos.[2020]4Y044,[2021]N404 and[2021]N511)+1 种基金the Guizhou Provincial Science and Technology Foundation(No.GCC[2022]005-1),Talents of Guizhou University(No.201901)the Special Research Funds of Guizhou University(Nos.201903,202011,and 202012).
文摘With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.
文摘The Chinese Society of Clinical Oncology Non-small Cell Lung Cancer(CSCO NSCLC)guidelines were first published in 2016,ranking among the earliest-released guidelines within the CSCO series.In 2020 the CSCO published separate guidelines for NSCLC and small cell lung cancer(SCLC)for the first time to improve clinical usability.
基金supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(MOTIE)(No.20224B10300090)supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ITRC(Information Technology Research Center)support program(IITP-2025-RS-2021-II211835)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation).
文摘As vehicular networks become increasingly pervasive,enhancing connectivity and reliability has emerged as a critical objective.Among the enabling technologies for advanced wireless communication,particularly those targeting low latency and high reliability,time synchronization is critical,especially in vehicular networks.However,due to the inherent mobility of vehicular environments,consistently exchanging synchronization packets with a fixed base station or access point is challenging.This issue is further exacerbated in signal shadowed areas such as urban canyons,tunnels,or large-scale indoor hallswhere other technologies,such as global navigation satellite system(GNSS),are unavailable.One-way synchronization techniques offer a feasible approach under such transient connectivity conditions.One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances.In this paper,we propose a WLAN-based multi-stage clock synchronization scheme(WMC)tailored for vehicular networks.The proposed method comprises an initial hard update stage to rapidly achieve synchronization,followed by a high-precision stable stage based on Maximum Likelihood Estimation(MLE).By implementing the scheme directly at the network driver,we address key limitations of hard update mechanisms.Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50μs accuracy,and subsequently transitions to a refined MLE-based synchronization stage,achieving stable accuracy at approximately 30μs.The windowed moving average stabilized(reaching 90%of the baseline)in approximately 35 s,which corresponds to just 5.1%of the baseline time accuracy.Finally,the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility(SUMO).The results demonstrate that more accurate conditions for position estimation can be supported,with an improvement about 38.5%in the mean error.
基金supported by the National Natural Science Foundation of China(No.82373415)Beijing Xisike Clinical Oncology Research Foundation(No.Ytongshu2021/ms-0003)。
文摘Colorectal cancer(CRC)is the most frequently diagnosed malignancy of the digestive system and the second leading cause of cancer-related deaths worldwide(1).In China,CRC ranks as the second most common cancer with incidence and mortality rates continuing to rise(2).The Chinese Society of Clinical Oncology(CSCO)first introduced its guidelines in 2017,and since then,they have been updated annually to incorporate the latest clinical research findings,drug availability,and expert consensus(3-8).This article presents the key updates in the 2025 edition compared to the 2024 version.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金co-supported by the National Natural Science Foundation of China(Nos.U223321251875014)+1 种基金the Beijing Natural Science Foundation,China(No.L221008)the China Scholarship Council(No.202106020001).
文摘Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.
文摘Medical image segmentation is a powerful and evolving technology in medical diagnosis.In fact,it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus(COVID-19).Various techniques have been utilized for COVID-19 image segmentation,including Multilevel Thresholding(MLT)-based meta-heuristics,which are considered crucial in addressing this issue.However,despite their importance,meta-heuristics have significant limitations.Specifically,the imbalance between exploration and exploitation,as well as premature convergence,can cause the optimization process to become stuck in local optima,resulting in unsatisfactory segmentation results.In this paper,an enhanced War Strategy Chimp Optimization Algorithm(WSChOA)is proposed to address MLT problems.Two strategies are incorporated into the traditional Chimp Optimization Algorithm.Golden update mechanism that provides diversity in the population.Additionally,the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima.The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index(FSIM),Structural Similarity Index(SSIM),Peak signal-to-Noise Ratio(PSNR),Standard deviation(STD),Freidman Test(FT),and Wilcoxon Sign Rank Test(WSRT).The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy,indicating that it is a powerful tool for image segmentation.
文摘To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.
基金Project supported by the Doctoral Foundation Project of Guizhou University(Grant No.(2019)49)the National Natural Science Foundation of China(Grant No.71961003)the Science and Technology Program of Guizhou Province(Grant No.7223)。
文摘In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.
基金open foundation of the Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanicsthe Open Foundation of Hubei Key Laboratory of Engineering Structural Analysis and Safety Assessment.
文摘A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction problem with large geometric deformation and material failure and solve the fluid-structure interaction problem of Newtonian fluid.In the coupled framework,the NOSB-PD theory describes the deformation and fracture of the solid material structure.ULPH is applied to describe the flow of Newtonian fluids due to its advantages in computational accuracy.The framework utilizes the advantages of NOSB-PD theory for solving discontinuous problems and ULPH theory for solving fluid problems,with good computational stability and robustness.A fluidstructure coupling algorithm using pressure as the transmission medium is established to deal with the fluidstructure interface.The dynamic model of solid structure and the PD-ULPH fluid-structure interaction model involving large deformation are verified by numerical simulations.The results agree with the analytical solution,the available experimental data,and other numerical results.Thus,the accuracy and effectiveness of the proposed method in solving the fluid-structure interaction problem are demonstrated.The fluid-structure interactionmodel based on ULPH and NOSB-PD established in this paper provides a new idea for the numerical solution of fluidstructure interaction and a promising approach for engineering design and experimental prediction.
基金support from the National Natural Science Foundations of China(Nos.11972267 and 11802214)the Open Foundation of the Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics and the Open Foundation of Hubei Key Laboratory of Engineering Structural Analysis and Safety Assessment.
文摘Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat transfer,climate,and fluid mixing in industrial processes.This work aims to use the Updated Lagrangian Particle Hydrodynamics(ULPH)theory to address natural convection problems.The Navier-Stokes equation is discretized using second-order nonlocal differential operators,allowing a direct solution of the Laplace operator for temperature in the energy equation.Various numerical simulations,including cases such as natural convection in square cavities and two concentric cylinders,were conducted to validate the reliability of the model.The results demonstrate that the proposed model exhibits excellent accuracy and performance,providing a promising and effective numerical approach for natural convection problems.
文摘Julie:What are you looking at,Sam?Sam:Oh,hi,Julie.I'm looking at Fairview City's weekly snowfall update.Julie:But it's only Monday.Sam:I know.The update is for last week's snowfall.Julie:I see.It'sforthesecond weekofthis month,then.Sam:That's right.The datesare from December 8 to December 14.
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
文摘To combat packet loss and realize robust video transmission over Intemet and wireless networks, a new multiple description （MD） video coding method is proposed. In the method, two descriptions for each video frame is first created by group of blocks （GOB） alternation. Motion information is then duplicated in both the descriptions and a process called low quality macroblock update is designed to redundantly encode textures in each frame using standard bit stream syntax. In this way, the output bit streams are standard compliant and better trade-offs between redundancy and single charmel reconstruction distortion are achieved. The proposed method has much better performance than the well-known MD transform coding （MDTC） method both in terms of redundancy rate distortion, and in the packet loss scenario.
基金the financial support from Natural Science Foundation of Gansu Province(Nos.22JR5RA217,22JR5RA216)Lanzhou Science and Technology Program(No.2022-2-111)+1 种基金Lanzhou University of Arts and Sciences School Innovation Fund Project(No.XJ2022000103)Lanzhou College of Arts and Sciences 2023 Talent Cultivation Quality Improvement Project(No.2023-ZL-jxzz-03)。
文摘Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.
基金supported by the Scientific and Technological Innovation Project of SHASG(SCK2022-01)National Key Research and Development Program of China(2016YFC0803109)。
文摘Land subsidence significantly impacts the accuracy of the National Elevation Datum in China.In order to solve this issue,a dynamic and economical way was proposed to update the National Elevation Datum with the assistance of InSAR in the North China Plain,which served as the research area.Moreover,the GNSS result was used to correct the InSAR result for the vertical deformation field,which has a relatively unified deformation reference.By integrating the vertical deformation field with the national elevation control point,an analysis and evaluation of changes in the National Elevation Datum were conducted.In addition,a regional remeasurement scheme was formulated to achieve dynamic updates and mainte-nance of the National Elevation Datum on a regional scale.Through data acquisition and processing,we successfully improved reliability within the main subsidence areas for future use.As a result,updating the elevation values utilize a regional update method,and a dynamic and economical technical process to update the National Elevation Datum is shown in the study.
基金upported by the Major Project of Guangzhou National Laboratory(GZNL2024A01002)National Key Plan for Scientific Research and Development of China(2022YFC2303802)+1 种基金National Natural Science Foundation of China(32170651&32370700)Hunan Provincial Natural Science Foundation of China(2024JJ2015).
文摘Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).
基金Supported by the National Natural Science Foundation of China(U24B2031)National Key Research and Development Project(2018YFA0702504)"14th Five-Year Plan"Science and Technology Project of CNOOC(KJGG2022-0201)。
文摘During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.
文摘Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.
