Helsmoortel-Van der Aa综合征(HVDAS)是一种罕见的神经发育障碍性疾病,主要由活性依赖性神经保护蛋白(ADNP)基因突变引起,是最常见的孤独症谱系障碍(ASD)单基因病因之一。本文分析2024年就诊于海口市妇幼保健院的1例患儿,男,3岁10个月...Helsmoortel-Van der Aa综合征(HVDAS)是一种罕见的神经发育障碍性疾病,主要由活性依赖性神经保护蛋白(ADNP)基因突变引起,是最常见的孤独症谱系障碍(ASD)单基因病因之一。本文分析2024年就诊于海口市妇幼保健院的1例患儿,男,3岁10个月,因“运动、语言发育迟缓2年余,社交障碍1年余”入院,检查发现患儿存在孤独症谱系障碍、全面发育迟缓、特殊面容、矮小、脑积水等临床表现,基因检测发现患儿携带ADNP基因杂合突变{NM_001282531.3(ADNP):c.2189(exon6)delG[p.(Arg730Glnfs3)]},其父母及妹妹均未携带该突变。总结临床资料并结合文献复习,ADNP基因在染色质重塑和神经发育障碍中发挥重要作用,ADNP基因突变可累及多系统。此例HVDAS具有典型临床表现,并拓宽了脑积水的表型谱,为此类基因变异的遗传咨询以及临床医师的早期识别提供了参考依据。展开更多
NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998...NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998. The accuracy of SST retrieved from daytime split window NLSST algorithm and nighttime triple window NLSST algorithm for NOAA 14 AVHRR data was investigated in this study. A matchup dataset of drifting buoys and NOAA 14 satellite measurements in the global ocean was generated to validate these operational split window and triple window algorithms. For NOAA 14 in 1998, we had 14095 and 22643 satellite and buoy matchups that matched within 25 km and 4 hours for daytime and nighttime, respectively. The satellite derived SST had a bias of less than 0.1℃ and standard deviation of about 0.5℃. This study also showed that the NLSST algorithm provided the same order of SST accuracy in different time of the year and under a wide range of satellite zenith angle and water vapor represented by the channel 4 and 5 brightness temperature difference. Therefore, NLSST algorithms are usually independent of season, geographic location, or atmospheric moisture content. Comparison between the low resolution AVHRR GAC data accuracy and high resolution Local Area Coverage (LAC) data accuracy is also discussed.展开更多
SaaS software that provides services through cloud platform has been more widely used nowadays.However,when SaaS software is running,it will suffer from performance fault due to factors such as the software structural...SaaS software that provides services through cloud platform has been more widely used nowadays.However,when SaaS software is running,it will suffer from performance fault due to factors such as the software structural design or complex environments.It is a major challenge that how to diagnose software quickly and accurately when the performance fault occurs.For this challenge,we propose a novel performance fault diagnosis method for SaaS software based on GBDT(Gradient Boosting Decision Tree)algorithm.In particular,we leverage the monitoring mean to obtain the performance log and warning log when the SaaS software system runs,and establish the performance fault type set and determine performance log feature.We also perform performance fault type annotation for the performance log combined with the analysis result of the warning log.Moreover,we deal with the incomplete performance log and the type non-equalization problem by using the mean filling for the same type and combination of SMOTE(Synthetic Minority Oversampling Technique)and undersampling methods.Finally,we conduct an empirical study combined with the disaster reduction system deployed on the cloud platform,and it demonstrates that the proposed method has high efficiency and accuracy for the performance diagnosis when SaaS software system runs.展开更多
Helsmoortel-Van der Aa综合征(HVDAS),是一种罕见的神经发育障碍疾病,主要由功能活性依赖神经保护蛋白(ADNP)基因突变引起。该文回顾性分析了就诊于济宁医学院附属医院的1例HVDAS患儿,系1岁3个月男性幼儿,其存在运动、语言、智力发育...Helsmoortel-Van der Aa综合征(HVDAS),是一种罕见的神经发育障碍疾病,主要由功能活性依赖神经保护蛋白(ADNP)基因突变引起。该文回顾性分析了就诊于济宁医学院附属医院的1例HVDAS患儿,系1岁3个月男性幼儿,其存在运动、语言、智力发育障碍及孤独症样刻板行为,有行为问题,外貌畸形,视力异常,反复呼吸道感染等临床表现,基因检测发现患儿携带ADNP基因杂合突变c.2157C>A(p.Tyr719Ter),其父母均未携带该突变。因此,提示临床工作者要加强对该疾病的认识,做到早发现、早治疗,以积极干预,从而提高患儿的生活质量。展开更多
本文报道1例ADNP基因新发杂合变异Helsmoortel-Van der Aa综合征病例。该患儿为1岁6月男童,存在有特殊面容、智力发育低下、运动、语言发育迟缓、小手等临床表现,基因分析显示患儿ADNP基因有一个新发变异位点C.460_461insAA(p.P154Qfs*...本文报道1例ADNP基因新发杂合变异Helsmoortel-Van der Aa综合征病例。该患儿为1岁6月男童,存在有特殊面容、智力发育低下、运动、语言发育迟缓、小手等临床表现,基因分析显示患儿ADNP基因有一个新发变异位点C.460_461insAA(p.P154Qfs*7),为移码突变。Helsmoortel-Van der Aa综合征可累及多系统,基因检测有助于诊断。展开更多
We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter ...We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.展开更多
A two-dimensional genetic algorithm of wavelet coefficient is presented by using the ENO wavelet transform and the decomposed characterization of the two-dimensional Haar wavelet. And simulated by the ENO interpolatio...A two-dimensional genetic algorithm of wavelet coefficient is presented by using the ENO wavelet transform and the decomposed characterization of the two-dimensional Haar wavelet. And simulated by the ENO interpolation the article shows the affectivity and the superiority of this algorithm.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters t...Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.展开更多
文摘Helsmoortel-Van der Aa综合征(HVDAS)是一种罕见的神经发育障碍性疾病,主要由活性依赖性神经保护蛋白(ADNP)基因突变引起,是最常见的孤独症谱系障碍(ASD)单基因病因之一。本文分析2024年就诊于海口市妇幼保健院的1例患儿,男,3岁10个月,因“运动、语言发育迟缓2年余,社交障碍1年余”入院,检查发现患儿存在孤独症谱系障碍、全面发育迟缓、特殊面容、矮小、脑积水等临床表现,基因检测发现患儿携带ADNP基因杂合突变{NM_001282531.3(ADNP):c.2189(exon6)delG[p.(Arg730Glnfs3)]},其父母及妹妹均未携带该突变。总结临床资料并结合文献复习,ADNP基因在染色质重塑和神经发育障碍中发挥重要作用,ADNP基因突变可累及多系统。此例HVDAS具有典型临床表现,并拓宽了脑积水的表型谱,为此类基因变异的遗传咨询以及临床医师的早期识别提供了参考依据。
文摘NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998. The accuracy of SST retrieved from daytime split window NLSST algorithm and nighttime triple window NLSST algorithm for NOAA 14 AVHRR data was investigated in this study. A matchup dataset of drifting buoys and NOAA 14 satellite measurements in the global ocean was generated to validate these operational split window and triple window algorithms. For NOAA 14 in 1998, we had 14095 and 22643 satellite and buoy matchups that matched within 25 km and 4 hours for daytime and nighttime, respectively. The satellite derived SST had a bias of less than 0.1℃ and standard deviation of about 0.5℃. This study also showed that the NLSST algorithm provided the same order of SST accuracy in different time of the year and under a wide range of satellite zenith angle and water vapor represented by the channel 4 and 5 brightness temperature difference. Therefore, NLSST algorithms are usually independent of season, geographic location, or atmospheric moisture content. Comparison between the low resolution AVHRR GAC data accuracy and high resolution Local Area Coverage (LAC) data accuracy is also discussed.
基金This work is supported in part by the National Science Foundation of China(61672392,61373038)in part by the National Key Research and Development Program of China(No.2016YFC1202204).
文摘SaaS software that provides services through cloud platform has been more widely used nowadays.However,when SaaS software is running,it will suffer from performance fault due to factors such as the software structural design or complex environments.It is a major challenge that how to diagnose software quickly and accurately when the performance fault occurs.For this challenge,we propose a novel performance fault diagnosis method for SaaS software based on GBDT(Gradient Boosting Decision Tree)algorithm.In particular,we leverage the monitoring mean to obtain the performance log and warning log when the SaaS software system runs,and establish the performance fault type set and determine performance log feature.We also perform performance fault type annotation for the performance log combined with the analysis result of the warning log.Moreover,we deal with the incomplete performance log and the type non-equalization problem by using the mean filling for the same type and combination of SMOTE(Synthetic Minority Oversampling Technique)and undersampling methods.Finally,we conduct an empirical study combined with the disaster reduction system deployed on the cloud platform,and it demonstrates that the proposed method has high efficiency and accuracy for the performance diagnosis when SaaS software system runs.
文摘Helsmoortel-Van der Aa综合征(HVDAS),是一种罕见的神经发育障碍疾病,主要由功能活性依赖神经保护蛋白(ADNP)基因突变引起。该文回顾性分析了就诊于济宁医学院附属医院的1例HVDAS患儿,系1岁3个月男性幼儿,其存在运动、语言、智力发育障碍及孤独症样刻板行为,有行为问题,外貌畸形,视力异常,反复呼吸道感染等临床表现,基因检测发现患儿携带ADNP基因杂合突变c.2157C>A(p.Tyr719Ter),其父母均未携带该突变。因此,提示临床工作者要加强对该疾病的认识,做到早发现、早治疗,以积极干预,从而提高患儿的生活质量。
文摘本文报道1例ADNP基因新发杂合变异Helsmoortel-Van der Aa综合征病例。该患儿为1岁6月男童,存在有特殊面容、智力发育低下、运动、语言发育迟缓、小手等临床表现,基因分析显示患儿ADNP基因有一个新发变异位点C.460_461insAA(p.P154Qfs*7),为移码突变。Helsmoortel-Van der Aa综合征可累及多系统,基因检测有助于诊断。
文摘We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.
基金the National Natural Science Committee and Chinese Engineering Physics Institute Foundation(10576013)the National Nature Science Foundation of Henan Province of China(0611053200)+1 种基金the Natural Science Foundation for the Education Department of Henan Province of China(2006110001)the Nature Science Foundation of Henan Institute of Science and Technology(2006055)
文摘A two-dimensional genetic algorithm of wavelet coefficient is presented by using the ENO wavelet transform and the decomposed characterization of the two-dimensional Haar wavelet. And simulated by the ENO interpolation the article shows the affectivity and the superiority of this algorithm.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
基金The project is funded by the Ministry of Higher Education Malaysia,under the Fundamental Research Grant Scheme(FRGS Grant No.FRGS/1/2017/TK07/SEGI/02/1).
文摘Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.
