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
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
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
“双碳”目标背景下,为实现综合能源系统(integrated energy systems,IES)多能耦合利用和低碳化,文中提出含光热模块的先进绝热压缩空气(advanced adiabatic compressed air energy storage,AA-CAES)储能电站和电转气(power to gas,P2G...“双碳”目标背景下,为实现综合能源系统(integrated energy systems,IES)多能耦合利用和低碳化,文中提出含光热模块的先进绝热压缩空气(advanced adiabatic compressed air energy storage,AA-CAES)储能电站和电转气(power to gas,P2G)与储液式碳捕集(carbon capture system,CCS)协同运行的IES低碳优化调度模型。论文建立光热模块与AA-CAES电站耦合模型并将其引入至含P2G-CCS的IES中;提出风-光-碳捕集电厂联合供能碳捕集设备运行策略及碳交易模型,以净碳排放量、综合成本最小化为目标函数构建IES低碳优化调度模型。通过算例对比,验证了含光热模块AA-CAES储能电站与P2G-CCS协同运行能够进一步降低总成本,减少碳排放。展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and t...In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.展开更多
Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW sea...Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW seams of AA6061-T6 plates were made us-ing different FSW parameters.The structure of the FSW seams was investigated using X-ray diffraction(XRD),scanning electron mi-croscope(SEM)and non destructive testing(NDT)techniques and their hardness was also measured.The dominated phase in the AA6061-T6 alloy and the FSW seams was theα-Al.The FSW seam had lower content of the secondary phases than the AA6061-T6 al-loy.The hardness of the FSW seams was decreased by about 30%compared to the AA6061-T6 alloy.The temperature distributions in the weld seams were also studied experimentally and numerically modeled and the results were in a good agreement.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。
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.
文摘“双碳”目标背景下,为实现综合能源系统(integrated energy systems,IES)多能耦合利用和低碳化,文中提出含光热模块的先进绝热压缩空气(advanced adiabatic compressed air energy storage,AA-CAES)储能电站和电转气(power to gas,P2G)与储液式碳捕集(carbon capture system,CCS)协同运行的IES低碳优化调度模型。论文建立光热模块与AA-CAES电站耦合模型并将其引入至含P2G-CCS的IES中;提出风-光-碳捕集电厂联合供能碳捕集设备运行策略及碳交易模型,以净碳排放量、综合成本最小化为目标函数构建IES低碳优化调度模型。通过算例对比,验证了含光热模块AA-CAES储能电站与P2G-CCS协同运行能够进一步降低总成本,减少碳排放。
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金Supported by the Natural Science Foundation of Chongqing(General Program,NO.CSTB2022NSCQ-MSX0884)Discipline Teaching Special Project of Yangtze Normal University(csxkjx14)。
文摘In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.
文摘Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW seams of AA6061-T6 plates were made us-ing different FSW parameters.The structure of the FSW seams was investigated using X-ray diffraction(XRD),scanning electron mi-croscope(SEM)and non destructive testing(NDT)techniques and their hardness was also measured.The dominated phase in the AA6061-T6 alloy and the FSW seams was theα-Al.The FSW seam had lower content of the secondary phases than the AA6061-T6 al-loy.The hardness of the FSW seams was decreased by about 30%compared to the AA6061-T6 alloy.The temperature distributions in the weld seams were also studied experimentally and numerically modeled and the results were in a good agreement.
