This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sam...This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.展开更多
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization...Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.展开更多
With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels beca...With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate （BER）. To solve this problem, carrier waveform inter- displacement （CWlD） modulation is proposed. It has been proved that CWlD modulation is an effective method to decrease BER. The linear frequency modulation （LFM） carrier-waves are used in CWlD modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization （IPSO） is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excellent performance in solving complicated optimization problems. The multi-objective and multi- peak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved perfor- mance and effectiveness of the optimization method.展开更多
As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of ...As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.展开更多
In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on t...In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.展开更多
A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly foc...A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.展开更多
Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online d...Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles.展开更多
Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more pr...Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol.Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node.All routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange operation.Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs.The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation.This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network lifespan.This paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)algorithm.CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process.LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to 100.To evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming packets.The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.展开更多
This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is ...This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost,which includes the operator cost,user charging cost,and power grid loss.An immune particle swarm optimization algorithm(IPSOA)is proposed in this paper to obtain the optimal coupling strategy.The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems.It also balances the interests of users,operators,and the power grid.Compared with other schemes,the grid loss cost is reduced by 11.1%and 17.8%,and the total social cost decreases by 9.96%and 3.22%.展开更多
INTRODUCTION An effective coordination of immune and non-immune cells is essential for generating optimal regional immunity to combat tumorigenesis and infection at barrier tissues such as lung.Regional immune structu...INTRODUCTION An effective coordination of immune and non-immune cells is essential for generating optimal regional immunity to combat tumorigenesis and infection at barrier tissues such as lung.Regional immune structures such as inducible bronchus-associated lymphoid tissue(iBALT)and tertiary lymphoid structure(TLS)play essential roles in modulating lung local immune responses.While the identification of iBALTs or TLS is generally dependent on conventional histology,it remains poorly understood how immune cells are spatiotemporally coordinated in the lung at single-cell resolution to effectively eliminate malignant cells and invading pathogens.Recently studies have revealed the presence of dendritic cell(DC)-T immunity hubs in human lung with close association with tumor immunotherapy response[1],antiviral immunity[2],and inflammation resolution[3].展开更多
Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the impleme...Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This pa- per aims to propose a novel method to deal with the relative importance ratings （RIRs） of CRs problem considering customers＇ diversified requirements and unknown information on customers＇ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer＇s assessment structure is proposed according to the basic idea of grey relational analysis （GRA）, and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers＇ personalized and diversified requirements. Furthermore, an im- mune particle swarm optimization （IPSO） algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method＇s potential application in deter- mining the RIRs of CRs.展开更多
Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering....Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.展开更多
Introduction:Measles,mumps,and rubella remain significant global health threats despite being vaccine-preventable diseases.The World Health Organization aims to achieve regional elimination of measles and rubella by 2...Introduction:Measles,mumps,and rubella remain significant global health threats despite being vaccine-preventable diseases.The World Health Organization aims to achieve regional elimination of measles and rubella by 2030,yet substantial disparities in vaccination coverage and disease incidence persist across regions.We analyzed global vaccination and disease data to provide evidence for optimizing immunization strategies.Methods:The study analyzed World Health Organization data on measles,mumps,and rubella from 2014–2023.Our analysis included vaccine types,recommended vaccination schedules,coverage rates,supplemental immunization activities,and disease incidence.We employed descriptive epidemiological methods for data synthesis and analysis.Results:All countries implemented≥1 measlescontaining vaccine dose,with 190(97.9%)countries using a≥2-dose schedule.Global 2nd dose of measlescontaining vaccine coverage increased from 59%to 74%during the study period.High-income regions maintained>90%coverage,while the African Region reported the lowest coverage(70%for the 1st dose and 49%for the 2nd dose of measles-containing vaccine).Supplemental immunization activities helped address coverage gaps but required integration with routine immunization systems.Rubella vaccine was implemented in 90.2%of countries,while mumps vaccine adoption remained lower at 63.9%.The African Region experienced high incidence rates for both measles(551.8 per million)and rubella(21.9 per million).The COVID-19 pandemic disrupted vaccination coverage(3%–5%decline globally),with the African Region experiencing a post-pandemic U-shaped resurgence in cases.China’s transition to the measles,mumps,and rubella vaccine has reduced mumps incidence to below 100 cases per million by 2020.Conclusions:While global control of measles,mumps,and rubella has progressed,inequities in vaccination coverage and pandemic-related disruptions threaten elimination goals.Strengthening routine immunization systems is critical.Achieving the World Health Organization’s 2030 targets will require sustained investment in health systems and implementation of equity-focused innovations.展开更多
基金supported in part by National Natural Science Foundation of China(Nos.61563009 and 61065010)Doctoral Fund of Ministry of Education of China(No.20125201110003)
文摘This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.
基金appreciation to King Saud University for funding this research through the Researchers Supporting Program number(RSPD2024R918),King Saud University,Riyadh,Saudi Arabia.
文摘Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.
基金supported by the National Natural Science Foundation of China(61172070,61111130122)the Innovative Research Team of Shaanxi Province(2013KCT-04)the Specialized Research Fund for the Doctoral Program of Higher Education(20126118110008)
文摘With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channels because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate （BER）. To solve this problem, carrier waveform inter- displacement （CWlD） modulation is proposed. It has been proved that CWlD modulation is an effective method to decrease BER. The linear frequency modulation （LFM） carrier-waves are used in CWlD modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization （IPSO） is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excellent performance in solving complicated optimization problems. The multi-objective and multi- peak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved perfor- mance and effectiveness of the optimization method.
基金Key R&D Program of Tianjin,China(No.20YFYSGX00060).
文摘As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.
基金Project(60973132)supported by the National Natural Science Foundation of ChinaProject(2010B050400005)supported by the Science and Research Program of Guangdong Province,China
文摘In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.
基金supported by National Natural Science Foundationof China (No. 60802061)Natural Science Research Item of the Education Department of Henan Province (No. 2008B510001)Innovation Scientists and Technicians Troop Construction Projects of Henan Province (No. 084100510012)
文摘A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.
基金the financial support from the Fundamental Research Funds for the Central universities of China (No. 2009KH07)
文摘Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles.
文摘Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol.Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node.All routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange operation.Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs.The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation.This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network lifespan.This paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)algorithm.CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process.LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to 100.To evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming packets.The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.
基金supported by the Major Science and Technology Projects in Gansu Province(2023ZDGA005).
文摘This paper presents an optimization model for the location and capacity of electric vehicle(EV)charging stations.The model takes the multiple factors of the“vehicle-station-grid”system into account.Then,ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost,which includes the operator cost,user charging cost,and power grid loss.An immune particle swarm optimization algorithm(IPSOA)is proposed in this paper to obtain the optimal coupling strategy.The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems.It also balances the interests of users,operators,and the power grid.Compared with other schemes,the grid loss cost is reduced by 11.1%and 17.8%,and the total social cost decreases by 9.96%and 3.22%.
基金supported by Grants from the National Key R&D Program of China(2023YFA1801400)National Natural Science Foundation of China(92374115 and 82388201).
文摘INTRODUCTION An effective coordination of immune and non-immune cells is essential for generating optimal regional immunity to combat tumorigenesis and infection at barrier tissues such as lung.Regional immune structures such as inducible bronchus-associated lymphoid tissue(iBALT)and tertiary lymphoid structure(TLS)play essential roles in modulating lung local immune responses.While the identification of iBALTs or TLS is generally dependent on conventional histology,it remains poorly understood how immune cells are spatiotemporally coordinated in the lung at single-cell resolution to effectively eliminate malignant cells and invading pathogens.Recently studies have revealed the presence of dendritic cell(DC)-T immunity hubs in human lung with close association with tumor immunotherapy response[1],antiviral immunity[2],and inflammation resolution[3].
基金supported by the Fundamental Research Funds for the Central Universities(K5051399035BDY251412+1 种基金JB150601)the Soft Science Project of Shaanxi Province(2013KRZ25)
文摘Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This pa- per aims to propose a novel method to deal with the relative importance ratings （RIRs） of CRs problem considering customers＇ diversified requirements and unknown information on customers＇ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer＇s assessment structure is proposed according to the basic idea of grey relational analysis （GRA）, and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers＇ personalized and diversified requirements. Furthermore, an im- mune particle swarm optimization （IPSO） algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method＇s potential application in deter- mining the RIRs of CRs.
基金Technology Foundation of Guizhou Province,China(No.QianKeHeJZi[2015]2064)Scientific Research Foundation for Advanced Talents in Guizhou Institue of Technology and Science,China(No.XJGC20150106)Joint Foundation of Guizhou Province,China(No.QianKeHeLHZi[2015]7105)
文摘Masked data are the system failure data when exact component causing system failure might be unknown.In this paper,the mathematical description of general masked data was presented in software reliability engineering.Furthermore,a general maskedbased additive non-homogeneous Poisson process(NHPP) model was considered to analyze component reliability.However,the problem of masked-based additive model lies in the difficulty of estimating parameters.The maximum likelihood estimation procedure was derived to estimate parameters.Finally,a numerical example was given to illustrate the applicability of proposed model,and the immune particle swarm optimization(IPSO) algorithm was used in maximize log-likelihood function.
基金Supported by the operation of the public health emergency response mechanism of the Chinese Center for Disease Control and Prevention(102393220020010000017-251705)the World Health Organization development for manual of measles elimination.
文摘Introduction:Measles,mumps,and rubella remain significant global health threats despite being vaccine-preventable diseases.The World Health Organization aims to achieve regional elimination of measles and rubella by 2030,yet substantial disparities in vaccination coverage and disease incidence persist across regions.We analyzed global vaccination and disease data to provide evidence for optimizing immunization strategies.Methods:The study analyzed World Health Organization data on measles,mumps,and rubella from 2014–2023.Our analysis included vaccine types,recommended vaccination schedules,coverage rates,supplemental immunization activities,and disease incidence.We employed descriptive epidemiological methods for data synthesis and analysis.Results:All countries implemented≥1 measlescontaining vaccine dose,with 190(97.9%)countries using a≥2-dose schedule.Global 2nd dose of measlescontaining vaccine coverage increased from 59%to 74%during the study period.High-income regions maintained>90%coverage,while the African Region reported the lowest coverage(70%for the 1st dose and 49%for the 2nd dose of measles-containing vaccine).Supplemental immunization activities helped address coverage gaps but required integration with routine immunization systems.Rubella vaccine was implemented in 90.2%of countries,while mumps vaccine adoption remained lower at 63.9%.The African Region experienced high incidence rates for both measles(551.8 per million)and rubella(21.9 per million).The COVID-19 pandemic disrupted vaccination coverage(3%–5%decline globally),with the African Region experiencing a post-pandemic U-shaped resurgence in cases.China’s transition to the measles,mumps,and rubella vaccine has reduced mumps incidence to below 100 cases per million by 2020.Conclusions:While global control of measles,mumps,and rubella has progressed,inequities in vaccination coverage and pandemic-related disruptions threaten elimination goals.Strengthening routine immunization systems is critical.Achieving the World Health Organization’s 2030 targets will require sustained investment in health systems and implementation of equity-focused innovations.
