The impact of the difference between Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB) in breast radiotherapy is not clearly due to different uses and further research is required to explain this effect. The ...The impact of the difference between Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB) in breast radiotherapy is not clearly due to different uses and further research is required to explain this effect. The aim of this study is to investigate the contribution of calculation differences between AAA and AXB to the integral radiation dose (ID) on critical organs. Seven field intensity modulated radiotherapy (IMRT) plans were generated using with AAA and AXB algorithms for twenty patients with early stage left breast cancer after breast conserving surgery. Volumetric and dosimetric differences, as well as, the Dmean, V5, V20 doses of the left and right-sided lung, the Dmean, V10, V20, V30 doses of heart and the Dmean, V5, V10 doses of the contralateral breast were investigated. The mean dose (Dmean), V5, V20 doses of the left-sided lung, the Dmean, V5, V10 doses of right-sided lung, the Dmean, V10, V20, V30 doses of heart and the Dmean, V5, V10 doses of the contralateral breast were found to be significantly higher with AAA. In this research integral dose was also higher in the AAA recalculated plan and the AXB plan with the average dose as follows left lung 2%, heart 2%, contralateral breast 8%, contralateral lung 4% respectively. Our study revealed that the calculation differences between Acuros XB (AXB) and Anisotropic Analytical Algorithm (AAA) in breast radiotherapy caused serious differences on the stored integral doses on critical organs. In addition, AXB plans showed significantly dosimetric improvements in multiple dosimetric parameters.展开更多
The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,th...The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,the advancement of automated pharmaceutical continuous manufacturing in China is relatively slow due to some key challenges including the lack of knowledge on the related technologies and shortage of qualified personnels.In this review,emphasis is given to two of the crucial technologies in automated pharmaceutical continuous manufacturing,i.e.,process analytical technology(PAT)and self-optimizing algorithm.Research work published in recent 5 years employing advanced PAT tools and self-optimization algorithms is introduced,which represents the great progress that has been made in automated pharmaceutical continuous manufacturing.展开更多
The idea of AC = BD was applied to solve the nonlinear differential equations. Suppose that Au = 0 is a given equation to he solved and Dv = 0 is an equation to be easily solved. If the transformation u = Cv is obtain...The idea of AC = BD was applied to solve the nonlinear differential equations. Suppose that Au = 0 is a given equation to he solved and Dv = 0 is an equation to be easily solved. If the transformation u = Cv is obtained so that v satisfies Dv = 0, then the solutions for Au = 0 can be found. In order to illustrate this approach, several examples about the transformation C are given.展开更多
The optimal selection of schemes of water transportation projects is a process of choosing a relatively optimal scheme from a number of schemes of water transportation programming and management projects, which is of ...The optimal selection of schemes of water transportation projects is a process of choosing a relatively optimal scheme from a number of schemes of water transportation programming and management projects, which is of importance in both theory and practice in water resource systems engineering. In order to achieve consistency and eliminate the dimensions of fuzzy qualitative and fuzzy quantitative evaluation indexes, to determine the weights of the indexes objectively, and to increase the differences among the comprehensive evaluation index values of water transportation project schemes, a projection pursuit method, named FPRM-PP for short, was developed in this work for selecting the optimal water transportation project scheme based on the fuzzy preference relation matrix. The research results show that FPRM-PP is intuitive and practical, the correction range of the fuzzy rained is both stable and accurate; preference relation matrix A it produces is relatively small, and the result obtherefore FPRM-PP can be widely used in the optimal selection of different multi-factor decision-making schemes.展开更多
In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems...In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems such as the bending, free vibration and buckling of nonhomogeneous long cylinders, it is difficult to obtain their solutions by the initial parameter algorithm on computer. In this paper, the substructure computational algorithm for the exact analytic method is presented through the bending of non-homogeneous long cylindrical shell. This substructure algorithm can he applied to solve the problems which can not he calculated by the initial parameter algorithm on computer. Finally, the problems can he reduced to solving a low order system of algehraic equations like the initial parameter algorithm Numerical examples are given and compared with the initial para-algorithm at the end of the paper, which confirms the correctness of the substructure computational algorithm.展开更多
The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massiv...The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.展开更多
A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brai...A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brain’s structure.This article discusses a strategy for halting the progression of brain tumor.A precise and accurate analytical model of brain tumors is the foundation of this strategy.It is based on an algorithm known as kill chain interior point(KCIP),which is the result of a merger of kill chain and interior point algorithms,as well as a precise and accurate analytical model of brain tumors.The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor.Based on the motion of swarm robots,which are considered a subset of artificial intelligence,this article proposes a new notion of this kind of behavior,which may be used in various situations.The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types.According to the findings,it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors.It is hoped that this study will help researchers better understand the behavior of brain tumors,so as to develop a new drug that is effective in eliminating the tumor cells.展开更多
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp...Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.展开更多
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensiona...The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensional space and one-dimensional time.With broad applications spanning fluid dynamics,shallow water waves,plasma physics,and condensed matter physics,the investigation of its solutions holds significant importance.Traditional analytical methods face limitations due to their dependence on bilinear forms.To overcome this constraint,this letter proposes a novel multi-modal neurosymbolic reasoning intelligent algorithm(MMNRIA)that achieves 100%accurate solutions for nonlinear partial differential equations without requiring bilinear transformations.By synergistically integrating neural networks with symbolic computation,this approach establishes a new paradigm for universal analytical solutions of nonlinear partial differential equations.As a practical demonstration,we successfully derive several exact analytical solutions for the(3+1)-dimensional BLMP equation using MMNRIA.These solutions provide a powerful theoretical framework for studying intricate wave phenomena governed by nonlinearity and dispersion effects in three-dimensional physical space.展开更多
针对传统鱼糕货架期短、运输条件苛刻、食用方法单一等问题,本研究通过配方优化与真空冷冻干燥技术开发新型鱼糕产品,旨在延长其保质期、提升营养均衡性并拓展即食化、多场景应用潜力,同时为水产制品的工业化加工提供工艺参考。以鲢鱼...针对传统鱼糕货架期短、运输条件苛刻、食用方法单一等问题,本研究通过配方优化与真空冷冻干燥技术开发新型鱼糕产品,旨在延长其保质期、提升营养均衡性并拓展即食化、多场景应用潜力,同时为水产制品的工业化加工提供工艺参考。以鲢鱼为主要原料,基于层次分析-熵权法构建综合评分模型,选择猪肥肉、鸡肉、玉米淀粉和蛋清添加量进行单因素实验,并在单因素实验基础上通过遗传算法结合Box-Behnken响应面法对鱼糕冻干配方进行优化。通过扫描电子显微镜(scanning electron microscopy,SEM)分析微观结构,测定蛋白质、脂肪、水分、灰分等理化指标,并基于Arrhenius方程预测货架期。确定了鱼糕冻干最优配方为:相对碎鱼肉用量,猪肥肉添加量10%(质量分数),鸡肉添加量20%,玉米淀粉添加量11%,蛋清添加量8%,综合评分达0.87±0.34。微观结构显示孔隙分布均匀,真空冷冻干燥处理前后关键理化指标无显著变化。基于Arrhenius方程的货架期模型预测25℃贮藏期为77 d,较鲜切鱼糕(4~7 d)延长11倍。本研究得到了色泽均匀、口感酥脆、货架期长以及营养均衡的鱼糕冻干制品,为鱼糕制品常温储运与即食化应用提供借鉴。展开更多
Flexible sensing array integrated with multiple sensors is an attractive approach for flight parameter detection.However,the poor resolution of flexible sensors and time-consuming neural network processes mitigate the...Flexible sensing array integrated with multiple sensors is an attractive approach for flight parameter detection.However,the poor resolution of flexible sensors and time-consuming neural network processes mitigate their accuracy and adaptability in predicting flight parameters.Here we present an ultra-thin flexible sensing patch with a new configuration,comprising a differential pressure sensor array and a vector flow velocity sensor.The capacitive differential pressure sensor array is fabricated by a multilayer polyimide bonding technique,reaching a resolution of 0.14 Pa.To solve flight parameters with the flexible sensing patch,we develop an analytical pressure-velocity fusion algorithm,enabling fast response and high accuracy in flight parameter detection.The average errors in calculating the angle of attack,angle of sideslip,and airspeed are 0.22°,0.35°,and 0.73 m s^(-1),respectively.The high-resolution flexible sensors and novel analytical pressure-velocity fusion algorithm pave the way for flexible sensing patch-based air data sensing techniques.展开更多
In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile sta...In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.展开更多
文摘The impact of the difference between Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB) in breast radiotherapy is not clearly due to different uses and further research is required to explain this effect. The aim of this study is to investigate the contribution of calculation differences between AAA and AXB to the integral radiation dose (ID) on critical organs. Seven field intensity modulated radiotherapy (IMRT) plans were generated using with AAA and AXB algorithms for twenty patients with early stage left breast cancer after breast conserving surgery. Volumetric and dosimetric differences, as well as, the Dmean, V5, V20 doses of the left and right-sided lung, the Dmean, V10, V20, V30 doses of heart and the Dmean, V5, V10 doses of the contralateral breast were investigated. The mean dose (Dmean), V5, V20 doses of the left-sided lung, the Dmean, V5, V10 doses of right-sided lung, the Dmean, V10, V20, V30 doses of heart and the Dmean, V5, V10 doses of the contralateral breast were found to be significantly higher with AAA. In this research integral dose was also higher in the AAA recalculated plan and the AXB plan with the average dose as follows left lung 2%, heart 2%, contralateral breast 8%, contralateral lung 4% respectively. Our study revealed that the calculation differences between Acuros XB (AXB) and Anisotropic Analytical Algorithm (AAA) in breast radiotherapy caused serious differences on the stored integral doses on critical organs. In addition, AXB plans showed significantly dosimetric improvements in multiple dosimetric parameters.
基金supported by the National Natural Science Foundation of China(Nos.21808059,21878088,and 21476077)Key Project of the Shanghai Science and Technology Committee(No.18DZ1112703)。
文摘The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,the advancement of automated pharmaceutical continuous manufacturing in China is relatively slow due to some key challenges including the lack of knowledge on the related technologies and shortage of qualified personnels.In this review,emphasis is given to two of the crucial technologies in automated pharmaceutical continuous manufacturing,i.e.,process analytical technology(PAT)and self-optimizing algorithm.Research work published in recent 5 years employing advanced PAT tools and self-optimization algorithms is introduced,which represents the great progress that has been made in automated pharmaceutical continuous manufacturing.
文摘The idea of AC = BD was applied to solve the nonlinear differential equations. Suppose that Au = 0 is a given equation to he solved and Dv = 0 is an equation to be easily solved. If the transformation u = Cv is obtained so that v satisfies Dv = 0, then the solutions for Au = 0 can be found. In order to illustrate this approach, several examples about the transformation C are given.
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （Nos. 50579009, 70425001 ） the National 10th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02-02）the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [ 2002 ] 350）.
文摘The optimal selection of schemes of water transportation projects is a process of choosing a relatively optimal scheme from a number of schemes of water transportation programming and management projects, which is of importance in both theory and practice in water resource systems engineering. In order to achieve consistency and eliminate the dimensions of fuzzy qualitative and fuzzy quantitative evaluation indexes, to determine the weights of the indexes objectively, and to increase the differences among the comprehensive evaluation index values of water transportation project schemes, a projection pursuit method, named FPRM-PP for short, was developed in this work for selecting the optimal water transportation project scheme based on the fuzzy preference relation matrix. The research results show that FPRM-PP is intuitive and practical, the correction range of the fuzzy rained is both stable and accurate; preference relation matrix A it produces is relatively small, and the result obtherefore FPRM-PP can be widely used in the optimal selection of different multi-factor decision-making schemes.
文摘In[1], the exact analytic method for the solution of differential equation with variable coefficients was suggested and an analytic expression of solution was given by initial parameter algorithm. But to some problems such as the bending, free vibration and buckling of nonhomogeneous long cylinders, it is difficult to obtain their solutions by the initial parameter algorithm on computer. In this paper, the substructure computational algorithm for the exact analytic method is presented through the bending of non-homogeneous long cylindrical shell. This substructure algorithm can he applied to solve the problems which can not he calculated by the initial parameter algorithm on computer. Finally, the problems can he reduced to solving a low order system of algehraic equations like the initial parameter algorithm Numerical examples are given and compared with the initial para-algorithm at the end of the paper, which confirms the correctness of the substructure computational algorithm.
文摘The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.
文摘A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brain’s structure.This article discusses a strategy for halting the progression of brain tumor.A precise and accurate analytical model of brain tumors is the foundation of this strategy.It is based on an algorithm known as kill chain interior point(KCIP),which is the result of a merger of kill chain and interior point algorithms,as well as a precise and accurate analytical model of brain tumors.The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor.Based on the motion of swarm robots,which are considered a subset of artificial intelligence,this article proposes a new notion of this kind of behavior,which may be used in various situations.The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types.According to the findings,it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors.It is hoped that this study will help researchers better understand the behavior of brain tumors,so as to develop a new drug that is effective in eliminating the tumor cells.
文摘Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
基金supported by the National Natural Science Foundation of China(Grant No.62303289)Tianyuan Fund for Mathematics of the National Natural Science Foundation of China(Grant No.12426105)+3 种基金the Scientific and Technological Innovation Programs(STIP)of Higher Education Institutions in Shanxi(Grant No.2024L022)Fundamental Research Program of Shanxi Province(Grant Nos.202403021222001 and 202203021222003)the“Wen Ying Young Scholars”Talent Project of Shanxi University(Grant Nos.138541088,138541090,and 138541127)Funded by Open Foundation of Hubei Key Laboratory of Applied Mathematics(Hubei University)(Grant No.HBAM202401).
文摘The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensional space and one-dimensional time.With broad applications spanning fluid dynamics,shallow water waves,plasma physics,and condensed matter physics,the investigation of its solutions holds significant importance.Traditional analytical methods face limitations due to their dependence on bilinear forms.To overcome this constraint,this letter proposes a novel multi-modal neurosymbolic reasoning intelligent algorithm(MMNRIA)that achieves 100%accurate solutions for nonlinear partial differential equations without requiring bilinear transformations.By synergistically integrating neural networks with symbolic computation,this approach establishes a new paradigm for universal analytical solutions of nonlinear partial differential equations.As a practical demonstration,we successfully derive several exact analytical solutions for the(3+1)-dimensional BLMP equation using MMNRIA.These solutions provide a powerful theoretical framework for studying intricate wave phenomena governed by nonlinearity and dispersion effects in three-dimensional physical space.
文摘针对传统鱼糕货架期短、运输条件苛刻、食用方法单一等问题,本研究通过配方优化与真空冷冻干燥技术开发新型鱼糕产品,旨在延长其保质期、提升营养均衡性并拓展即食化、多场景应用潜力,同时为水产制品的工业化加工提供工艺参考。以鲢鱼为主要原料,基于层次分析-熵权法构建综合评分模型,选择猪肥肉、鸡肉、玉米淀粉和蛋清添加量进行单因素实验,并在单因素实验基础上通过遗传算法结合Box-Behnken响应面法对鱼糕冻干配方进行优化。通过扫描电子显微镜(scanning electron microscopy,SEM)分析微观结构,测定蛋白质、脂肪、水分、灰分等理化指标,并基于Arrhenius方程预测货架期。确定了鱼糕冻干最优配方为:相对碎鱼肉用量,猪肥肉添加量10%(质量分数),鸡肉添加量20%,玉米淀粉添加量11%,蛋清添加量8%,综合评分达0.87±0.34。微观结构显示孔隙分布均匀,真空冷冻干燥处理前后关键理化指标无显著变化。基于Arrhenius方程的货架期模型预测25℃贮藏期为77 d,较鲜切鱼糕(4~7 d)延长11倍。本研究得到了色泽均匀、口感酥脆、货架期长以及营养均衡的鱼糕冻干制品,为鱼糕制品常温储运与即食化应用提供借鉴。
文摘目的对比分析非均整(FFF)模式下Acuros XB(AXB)算法与各向异性解析算法(AAA)在宫颈癌容积旋转调强放射治疗(VMAT)中的剂量学差异,探讨其临床适用性。方法选取15例宫颈癌术后患者,年龄46~75岁,中位年龄62岁;病理类型为13例鳞状细胞癌,2例腺癌。在Eclipse16.1计划系统设计VMAT-FFF计划,分别应用AXB算法与AAA计算剂量,对比靶区剂量、适形度(CI)、均匀性(HI)、危及器官(OAR)受量及正常组织低剂量暴露(V_(1)~V_(40))。结果AXB算法与AAA的靶区D_(max)、D_(mean)、D_(2%)、D_(50%)差异有统计学意义[(5377.07±21.84)cGy vs(5322.22±23.91)cGy、(5086.07±5.22)cGy vs(5077.49±7.34)cGy、(5191.01±10.47)cGy vs(5169.13±14.30)cGy、(5087.33±5.62)cGy vs(5079.59±7.67)cGy。P<0.05],AXB算法所得CI(0.9200±0.0034 vs 0.9172±0.0022。P<0.05)及OAR的V_(50)预测值显著高于AAA[膀胱:(22.63±7.33)%vs(22.11±7.05)%;直肠:(24.33±5.55)%vs(23.24±5.39)%。P<0.05],但HI较差(0.0450±0.0029 vs 0.0409±0.0034。P<0.001)。AAA计算的正常组织V_(1)和V_(5)较AXB算法显著更高[(73.70±7.02)%vs(72.37±7.06)%、(53.47±6.68)%vs(53.27±6.71)%。P<0.05],其余V_(10)~V_(40)差异无统计学意义(P>0.05)。结论AXB算法更适用于需精准保护OAR的宫颈癌VMAT-FFF计划,而AAA在靶区HI上更具优势。临床应根据治疗需求优化算法选择。
基金supported financially by the National Natural Science Foundation of China(T2121003 received by X.D.,and U23A20638 received by Y.J.)the National Key Research and Development Program of China(2023YFB3208000 and 2023YFB3208001 received by Y.J.).
文摘Flexible sensing array integrated with multiple sensors is an attractive approach for flight parameter detection.However,the poor resolution of flexible sensors and time-consuming neural network processes mitigate their accuracy and adaptability in predicting flight parameters.Here we present an ultra-thin flexible sensing patch with a new configuration,comprising a differential pressure sensor array and a vector flow velocity sensor.The capacitive differential pressure sensor array is fabricated by a multilayer polyimide bonding technique,reaching a resolution of 0.14 Pa.To solve flight parameters with the flexible sensing patch,we develop an analytical pressure-velocity fusion algorithm,enabling fast response and high accuracy in flight parameter detection.The average errors in calculating the angle of attack,angle of sideslip,and airspeed are 0.22°,0.35°,and 0.73 m s^(-1),respectively.The high-resolution flexible sensors and novel analytical pressure-velocity fusion algorithm pave the way for flexible sensing patch-based air data sensing techniques.
基金supported by Natural Science Foundation of China(61372125)973 project(2013CB329104)+1 种基金the National High-Tech R&D Program(863 Program 2015AA01A705)the open research fund of National Mobile Communications Research Laboratory,Southeast University(2013D01)
文摘In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.
