A relatively high aerodynamic drag is an important factor that hinders the further acceleration of high-speed trains.Using the shear stress transport(SST)k-ωturbulence model,the effect of various vortex generator typ...A relatively high aerodynamic drag is an important factor that hinders the further acceleration of high-speed trains.Using the shear stress transport(SST)k-ωturbulence model,the effect of various vortex generator types on the aerodynamic characteristics of an ICE2(Inter-city Electricity)train has been investigated.The results indi-cate that the vortex generators with wider triangle,trapezoid,and micro-ramp arranged on the surface of the tail car can significantly change the distribution of surface pressure and affect the vorticity intensity in the wake.This alteration effectively reduces the resistance of the tail car.Meanwhile,the micro-ramp vortex generator with its convergent structure at the rear exhibits enhancedflow-guiding capabilities,resulting in a 15.4%reduction in the drag of the tail car.展开更多
In the quantum Monte Carlo(QMC)method,the pseudo-random number generator(PRNG)plays a crucial role in determining the computation time.However,the hidden structure of the PRNG may lead to serious issues such as the br...In the quantum Monte Carlo(QMC)method,the pseudo-random number generator(PRNG)plays a crucial role in determining the computation time.However,the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process.Here,we systematically analyze the performance of different PRNGs on the widely used QMC method known as the stochastic series expansion(SSE)algorithm.To quantitatively compare them,we introduce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms.After testing several representative observables of the Heisenberg model in one and two dimensions,we recommend the linear congruential generator as the best choice of PRNG.Our work not only helps improve the performance of the SSE method but also sheds light on the other Markov-chain-based numerical algorithms.展开更多
Thermoelectric generators(TEGs)play a critical role in collecting renewable energy fromthe sun and deep space to generate clean electricity.With their environmentally friendly,reliable,and noise-free operation,TEGs of...Thermoelectric generators(TEGs)play a critical role in collecting renewable energy fromthe sun and deep space to generate clean electricity.With their environmentally friendly,reliable,and noise-free operation,TEGs offer diverse applications,including areas with limited power infrastructure,microelectronic devices,and wearable technology.The review thoroughly analyses TEG system configurations,performance,and applications driven by solar and/or radiative cooling,covering non-concentrating,concentrating,radiative cooling-driven,and dual-mode TEGs.Materials for solar absorbers and radiative coolers,simulation techniques,energy storage management,and thermal management strategies are explored.The integration of TEGs with combined heat and power systems is identified as a promising application.Additionally,TEGs hold potential as charging sources for electronic devices.This comprehensive review provides valuable insights into this energy collection approach,facilitating improved efficiency,reduced costs,and expanded applications.It also highlights current limitations and knowledge gaps,emphasizing the importance of further research and development in unlocking the full potential of TEGs for a sustainable and efficient energy future.展开更多
A Solid Oxide Fuel Cell(SOFC)is an electrochemical device that converts the chemical energy of a substance into electrical energy through an oxidation-reduction mechanism.The electrochemical reaction of a solid oxide ...A Solid Oxide Fuel Cell(SOFC)is an electrochemical device that converts the chemical energy of a substance into electrical energy through an oxidation-reduction mechanism.The electrochemical reaction of a solid oxide fuel cell(SOFC)generates heat,and this heat can be recovered and put to use in a waste heat recovery system.In addition to preheating the fuel and oxidant,producing steam for industrial use,and heating and cooling enclosed rooms,this waste heat can be used for many more productive uses.The large waste heat produced by SOFCs is a worry that must be managed if they are to be adopted as a viable option in the power generation business.In light of these findings,a novel approach to SOFC waste heat recovery is proposed.The SOFC is combined with a“Thermoelectric Generator and an Alkali Metal Thermoelectric Converter(TG-AMTC)”to transform the excess heat generated by both the SOFC and the TG-AMTC.The proposed TG-AMTC is evaluated using a number of performance indicators including power density,operating temperature,heat recovery rate,exergetic efficiency,energy efficiency,and recovery time.The experimental results state that TG-AMTC has provided an exergetic efficiency,energetic efficiency,and recovery time of 97%,98%,and 23%,respectively.The study proves that the proposed TG-AMTC for SOFC is an efficient method of recovering waste heat.展开更多
Thermoelectric generators(TEGs)are considered promising devices for waste heat recovery from various systems.The Seebeck effect can be utilized to generate power using the residual heat emitted by the filter dryer rec...Thermoelectric generators(TEGs)are considered promising devices for waste heat recovery from various systems.The Seebeck effect can be utilized to generate power using the residual heat emitted by the filter dryer receiver(FDR)of an air conditioning(A/C)system,which would otherwise go to waste.The study aims to build a set of thermoelectric generators(TEG)to collect the waste heat of the FDR and generate low-power electricity.A novel electrical circuit with two transformers is designed and fabricated to produce a more stable voltage for operation and charging.The thermoelectric generator(TEGs)was installed on the FDR of the A/C unit.The test showed that climate conditions have a significant impact on the output power generated from the system.The results showed that the peak voltage recorded in the current study is 5.2 V per day(wet,cold,and wind weather)with an output power of 0.2 W.These values are acceptable for powering the load and charging a single battery with 3.5 V as the voltage increases battery 0.1 V/20 min charge.A case study of operating the emergency signs in a building was considered.The current heat recovery system is deemed to be easily installed and can be connected to a network of TEGs to produce more power.展开更多
Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a nove...Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.展开更多
Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworth...Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.展开更多
The supercritical CO_(2)(sCO_(2))power cycle could improve efficiencies for a wide range of thermal power plants.The sCO_(2)turbine generator plays an important role in the sCO_(2)power cycle by directly converting th...The supercritical CO_(2)(sCO_(2))power cycle could improve efficiencies for a wide range of thermal power plants.The sCO_(2)turbine generator plays an important role in the sCO_(2)power cycle by directly converting thermal energy into mechanical work and electric power.The operation of the generator encounters challenges,including high temperature,high pressure,high rotational speed,and other engineering problems,such as leakage.Experimental studies of sCO_(2)turbines are insufficient because of the significant difficulties in turbine manufacturing and system construction.Unlike most experimental investigations that primarily focus on 100 kW‐or MW‐scale power generation systems,we consider,for the first time,a small‐scale power generator using sCO_(2).A partial admission axial turbine was designed and manufactured with a rated rotational speed of 40,000 rpm,and a CO_(2)transcritical power cycle test loop was constructed to validate the performance of our manufactured generator.A resistant gas was proposed in the constructed turbine expander to solve the leakage issue.Both dynamic and steady performances were investigated.The results indicated that a peak electric power of 11.55 kW was achieved at 29,369 rpm.The maximum total efficiency of the turbo‐generator was 58.98%,which was affected by both the turbine rotational speed and pressure ratio,according to the proposed performance map.展开更多
This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID ...This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.展开更多
The utilization of vortex generators to increase heat transfer from cylinders installed inside a duct is investigated.In particular,a channel containing eight cylinders with volumetric heat sources is considered for d...The utilization of vortex generators to increase heat transfer from cylinders installed inside a duct is investigated.In particular,a channel containing eight cylinders with volumetric heat sources is considered for different values of the Reynolds number.The effective possibility to use vortex generators with different sizes to increase heat transfer and,consequently,reduce the surface temperature of the cylinders is examined.Also,the amount of pressure drop inside the channel due to the presence of vortex generators is considered and compared with the cases without vortex generators.The results show that although the addition of generators increases the pressure drop,it strongly contributes to increase the heat transfer coefficient inside the duct(up to 80–90%).展开更多
Our community currently deals with issues such as rising electricity costs,pollution,and global warming.Scientists work to improve energy harvesting-based power generators in order to reduce their impacts.The Seebeck ...Our community currently deals with issues such as rising electricity costs,pollution,and global warming.Scientists work to improve energy harvesting-based power generators in order to reduce their impacts.The Seebeck effect has been used to illustrate the capacity of thermoelectric generators(TEGs)to directly convert thermal energy to electrical energy.They are also ecologically beneficial since they do not include chemical products,function quietly because they lack mechanical structures and/or moving components,and may be built using different fabrication technologies such as three-dimentional(3D)printing,silicon technology,and screen printing,etc.TEGs are also position-independent and have a long operational lifetime.TEGs can be integrated into bulk and flexible devices.This review gives further investigation of TEGs,beginning with a full discussion of their operating principle,kinds,materials utilized,figure of merit,and improvement approaches,which include various thermoelectric material arrangements and utilised technologies.This paper also discusses the use of TEGs in a variety of disciplines such as automobile and biomedical.展开更多
Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limit...Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models,such as state-of-the-art machine learning(ML)models.To address these challenges,this study proposes a data augmentation framework that uses generative adversarial networks(GANs),a recent advance in generative artificial intelligence(AI),to improve the accuracy of landslide displacement prediction.The framework provides effective data augmentation to enhance limited datasets.A recurrent GAN model,RGAN-LS,is proposed,specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data.A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data.Then,the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory(LSTM)networks and particle swarm optimization-support vector machine(PSO-SVM)models for landslide displacement prediction tasks.Results on two landslides in the Three Gorges Reservoir(TGR)region show a significant improvement in LSTM model prediction performance when trained on augmented data.For instance,in the case of the Baishuihe landslide,the average root mean square error(RMSE)increases by 16.11%,and the mean absolute error(MAE)by 17.59%.More importantly,the model’s responsiveness during mutational stages is enhanced for early warning purposes.However,the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM.Further analysis indicates that an optimal synthetic-to-real data ratio(50%on the illustration cases)maximizes the improvements.This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results.By using the powerful generative AI approach,RGAN-LS can generate high-fidelity synthetic landslide data.This is critical for improving the performance of advanced ML models in predicting landslide displacement,particularly when there are limited training data.Additionally,this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.展开更多
Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss o...Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.展开更多
As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved general...As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.展开更多
In traditional electricity generation plants,large powerful synchronous,induction,and direct current generators were used.With the proliferation of microgrids focused on electricity generation from renewable energy so...In traditional electricity generation plants,large powerful synchronous,induction,and direct current generators were used.With the proliferation of microgrids focused on electricity generation from renewable energy sources in today’s power grids,studies have been conducted on different types of generators.Instead of the traditional generator architecture,generators with brushless structures,particularly those utilizing magnets for excitation,have found broad applications.Fluxswitching generators(FSGs)are innovative types owing to their robust structure,active stator design,and high power density capabilities.However,designs have typically relied on rare-earth element magnets.Rare-earth magnets possess negative characteristics such as price uncertainty,the potential risk of scarcity in the future,and limited geographical production,leading to research on FSGs that do not depend on rare-earth magnets.This study comprehensively examines FSGs that do not use rare-earth element magnets.The study delves into the usage areas,operational mechanisms,structural diversities,and counterparts in the literature of these generators.展开更多
Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD)has been extensively researched due to its simplicity and security.For practical security of an LLO CVQKD system,there are two main ...Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD)has been extensively researched due to its simplicity and security.For practical security of an LLO CVQKD system,there are two main attack modes referred to as reference pulse attack and polarization attack presently.However,there is currently no general defense strategy against such attacks,and the security of the system needs further investigation.Here,we employ a deep learning framework called generative adversarial networks(GANs)to detect both attacks.We first analyze the data in different cases,derive a feature vector as input to a GAN model,and then show the training and testing process of the GAN model for attack classification.The proposed model has two parts,a discriminator and a generator,both of which employ a convolutional neural network(CNN)to improve accuracy.Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance.It only establishes a detection model by monitoring features of the pulse without adding additional devices.展开更多
In this paper, we study the second-order nonlinear differential systems of Liénard-type x˙=1a(x)[ h(y)−F(x) ], y˙=−a(x)g(x). Necessary and sufficient conditions to ensure that all nontrivial solutions are oscil...In this paper, we study the second-order nonlinear differential systems of Liénard-type x˙=1a(x)[ h(y)−F(x) ], y˙=−a(x)g(x). Necessary and sufficient conditions to ensure that all nontrivial solutions are oscillatory are established by using a new nonlinear integral inequality. Our results substantially extend and improve previous results known in the literature.展开更多
基金supported by the National Natural Science Foundation of China(12372049)Sichuan Science and Technology Program(2023JDRC0062)+1 种基金Science and Technology Program of China National Accreditation Service for Conformity Assessment(2022CNAS15)the Independent Project of State Key Laboratory of Rail Transit Vehicle System(2023TPL-T06).
文摘A relatively high aerodynamic drag is an important factor that hinders the further acceleration of high-speed trains.Using the shear stress transport(SST)k-ωturbulence model,the effect of various vortex generator types on the aerodynamic characteristics of an ICE2(Inter-city Electricity)train has been investigated.The results indi-cate that the vortex generators with wider triangle,trapezoid,and micro-ramp arranged on the surface of the tail car can significantly change the distribution of surface pressure and affect the vorticity intensity in the wake.This alteration effectively reduces the resistance of the tail car.Meanwhile,the micro-ramp vortex generator with its convergent structure at the rear exhibits enhancedflow-guiding capabilities,resulting in a 15.4%reduction in the drag of the tail car.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274046,11874094,and 12147102)Chongqing Natural Science Foundation(Grant No.CSTB2022NSCQ-JQX0018)Fundamental Research Funds for the Central Universities(Grant No.2021CDJZYJH-003).
文摘In the quantum Monte Carlo(QMC)method,the pseudo-random number generator(PRNG)plays a crucial role in determining the computation time.However,the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process.Here,we systematically analyze the performance of different PRNGs on the widely used QMC method known as the stochastic series expansion(SSE)algorithm.To quantitatively compare them,we introduce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms.After testing several representative observables of the Heisenberg model in one and two dimensions,we recommend the linear congruential generator as the best choice of PRNG.Our work not only helps improve the performance of the SSE method but also sheds light on the other Markov-chain-based numerical algorithms.
基金supported by the Hong Kong Polytechnic University through Projects of RCRE(Project No.1-BBEG)sponsored by the Research Grants Council of HongKong and the NationalNatural Science Foundation of China(Project No.N_PolyU513/18).
文摘Thermoelectric generators(TEGs)play a critical role in collecting renewable energy fromthe sun and deep space to generate clean electricity.With their environmentally friendly,reliable,and noise-free operation,TEGs offer diverse applications,including areas with limited power infrastructure,microelectronic devices,and wearable technology.The review thoroughly analyses TEG system configurations,performance,and applications driven by solar and/or radiative cooling,covering non-concentrating,concentrating,radiative cooling-driven,and dual-mode TEGs.Materials for solar absorbers and radiative coolers,simulation techniques,energy storage management,and thermal management strategies are explored.The integration of TEGs with combined heat and power systems is identified as a promising application.Additionally,TEGs hold potential as charging sources for electronic devices.This comprehensive review provides valuable insights into this energy collection approach,facilitating improved efficiency,reduced costs,and expanded applications.It also highlights current limitations and knowledge gaps,emphasizing the importance of further research and development in unlocking the full potential of TEGs for a sustainable and efficient energy future.
基金Foundation of Heilongjiang Bayi Agricultural University(Grant Nos.ZRCPY201916ZRCPY201817).
文摘A Solid Oxide Fuel Cell(SOFC)is an electrochemical device that converts the chemical energy of a substance into electrical energy through an oxidation-reduction mechanism.The electrochemical reaction of a solid oxide fuel cell(SOFC)generates heat,and this heat can be recovered and put to use in a waste heat recovery system.In addition to preheating the fuel and oxidant,producing steam for industrial use,and heating and cooling enclosed rooms,this waste heat can be used for many more productive uses.The large waste heat produced by SOFCs is a worry that must be managed if they are to be adopted as a viable option in the power generation business.In light of these findings,a novel approach to SOFC waste heat recovery is proposed.The SOFC is combined with a“Thermoelectric Generator and an Alkali Metal Thermoelectric Converter(TG-AMTC)”to transform the excess heat generated by both the SOFC and the TG-AMTC.The proposed TG-AMTC is evaluated using a number of performance indicators including power density,operating temperature,heat recovery rate,exergetic efficiency,energy efficiency,and recovery time.The experimental results state that TG-AMTC has provided an exergetic efficiency,energetic efficiency,and recovery time of 97%,98%,and 23%,respectively.The study proves that the proposed TG-AMTC for SOFC is an efficient method of recovering waste heat.
文摘Thermoelectric generators(TEGs)are considered promising devices for waste heat recovery from various systems.The Seebeck effect can be utilized to generate power using the residual heat emitted by the filter dryer receiver(FDR)of an air conditioning(A/C)system,which would otherwise go to waste.The study aims to build a set of thermoelectric generators(TEG)to collect the waste heat of the FDR and generate low-power electricity.A novel electrical circuit with two transformers is designed and fabricated to produce a more stable voltage for operation and charging.The thermoelectric generator(TEGs)was installed on the FDR of the A/C unit.The test showed that climate conditions have a significant impact on the output power generated from the system.The results showed that the peak voltage recorded in the current study is 5.2 V per day(wet,cold,and wind weather)with an output power of 0.2 W.These values are acceptable for powering the load and charging a single battery with 3.5 V as the voltage increases battery 0.1 V/20 min charge.A case study of operating the emergency signs in a building was considered.The current heat recovery system is deemed to be easily installed and can be connected to a network of TEGs to produce more power.
基金the National Key Research and Development Program of China(2021YFF0900800)the National Natural Science Foundation of China(61972276,62206116,62032016)+2 种基金the New Liberal Arts Reform and Practice Project of National Ministry of Education(2021170002)the Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems(20210101)Tianjin University Talent Innovation Reward Program for Literature and Science Graduate Student(C1-2022-010)。
文摘Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0102)+4 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB42010404)the National Natural Science Foundation of China(Grant No.42175049)the Guangdong Meteorological Service Science and Technology Research Project(Grant No.GRMC2021M01)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)for computational support and Prof.Shiming XIANG for many useful discussionsNiklas BOERS acknowledges funding from the Volkswagen foundation.
文摘Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.
基金National Science Fund for Excellent Young Scholars,Grant/Award Number:52022066。
文摘The supercritical CO_(2)(sCO_(2))power cycle could improve efficiencies for a wide range of thermal power plants.The sCO_(2)turbine generator plays an important role in the sCO_(2)power cycle by directly converting thermal energy into mechanical work and electric power.The operation of the generator encounters challenges,including high temperature,high pressure,high rotational speed,and other engineering problems,such as leakage.Experimental studies of sCO_(2)turbines are insufficient because of the significant difficulties in turbine manufacturing and system construction.Unlike most experimental investigations that primarily focus on 100 kW‐or MW‐scale power generation systems,we consider,for the first time,a small‐scale power generator using sCO_(2).A partial admission axial turbine was designed and manufactured with a rated rotational speed of 40,000 rpm,and a CO_(2)transcritical power cycle test loop was constructed to validate the performance of our manufactured generator.A resistant gas was proposed in the constructed turbine expander to solve the leakage issue.Both dynamic and steady performances were investigated.The results indicated that a peak electric power of 11.55 kW was achieved at 29,369 rpm.The maximum total efficiency of the turbo‐generator was 58.98%,which was affected by both the turbine rotational speed and pressure ratio,according to the proposed performance map.
基金supported in part by the NSF of China under Grant 62322106,62071131the Guangdong Basic and Applied Basic Research Foundation under Grant 2022B1515020086+2 种基金the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2022A0505050070in part by the Open Research Fund of the State Key Laboratory of Integrated Services Networks under Grant ISN22-23the National Research Foundation,Singapore University of Technology Design under its Future Communications Research&Development Programme“Advanced Error Control Coding for 6G URLLC and mMTC”Grant No.FCP-NTU-RG-2022-020.
文摘This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.
文摘The utilization of vortex generators to increase heat transfer from cylinders installed inside a duct is investigated.In particular,a channel containing eight cylinders with volumetric heat sources is considered for different values of the Reynolds number.The effective possibility to use vortex generators with different sizes to increase heat transfer and,consequently,reduce the surface temperature of the cylinders is examined.Also,the amount of pressure drop inside the channel due to the presence of vortex generators is considered and compared with the cases without vortex generators.The results show that although the addition of generators increases the pressure drop,it strongly contributes to increase the heat transfer coefficient inside the duct(up to 80–90%).
文摘Our community currently deals with issues such as rising electricity costs,pollution,and global warming.Scientists work to improve energy harvesting-based power generators in order to reduce their impacts.The Seebeck effect has been used to illustrate the capacity of thermoelectric generators(TEGs)to directly convert thermal energy to electrical energy.They are also ecologically beneficial since they do not include chemical products,function quietly because they lack mechanical structures and/or moving components,and may be built using different fabrication technologies such as three-dimentional(3D)printing,silicon technology,and screen printing,etc.TEGs are also position-independent and have a long operational lifetime.TEGs can be integrated into bulk and flexible devices.This review gives further investigation of TEGs,beginning with a full discussion of their operating principle,kinds,materials utilized,figure of merit,and improvement approaches,which include various thermoelectric material arrangements and utilised technologies.This paper also discusses the use of TEGs in a variety of disciplines such as automobile and biomedical.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20220421)the State Key Program of the National Natural Science Foundation of China(Grant No.42230702)the National Natural Science Foundation of China(Grant No.82302352).
文摘Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide.Accurately predicting landslide displacement enables effective early warning and risk management.However,the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models,such as state-of-the-art machine learning(ML)models.To address these challenges,this study proposes a data augmentation framework that uses generative adversarial networks(GANs),a recent advance in generative artificial intelligence(AI),to improve the accuracy of landslide displacement prediction.The framework provides effective data augmentation to enhance limited datasets.A recurrent GAN model,RGAN-LS,is proposed,specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data.A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data.Then,the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory(LSTM)networks and particle swarm optimization-support vector machine(PSO-SVM)models for landslide displacement prediction tasks.Results on two landslides in the Three Gorges Reservoir(TGR)region show a significant improvement in LSTM model prediction performance when trained on augmented data.For instance,in the case of the Baishuihe landslide,the average root mean square error(RMSE)increases by 16.11%,and the mean absolute error(MAE)by 17.59%.More importantly,the model’s responsiveness during mutational stages is enhanced for early warning purposes.However,the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM.Further analysis indicates that an optimal synthetic-to-real data ratio(50%on the illustration cases)maximizes the improvements.This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results.By using the powerful generative AI approach,RGAN-LS can generate high-fidelity synthetic landslide data.This is critical for improving the performance of advanced ML models in predicting landslide displacement,particularly when there are limited training data.Additionally,this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.
基金supported by the Spanish Government(ISCIII-FEDER)PI20/01063by Navarra Government(PC 060-061 and PC 192-193)Fundación Gangoiti(to MSA).LA was funded by FPU19/03255.
文摘Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.U22B2005,62072109)the Natural Science Foundation of Fujian Province(Grant No.2021J01625)the Major Science and Technology Project of Fuzhou(Grant No.2023-ZD-003).
文摘As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.
文摘In traditional electricity generation plants,large powerful synchronous,induction,and direct current generators were used.With the proliferation of microgrids focused on electricity generation from renewable energy sources in today’s power grids,studies have been conducted on different types of generators.Instead of the traditional generator architecture,generators with brushless structures,particularly those utilizing magnets for excitation,have found broad applications.Fluxswitching generators(FSGs)are innovative types owing to their robust structure,active stator design,and high power density capabilities.However,designs have typically relied on rare-earth element magnets.Rare-earth magnets possess negative characteristics such as price uncertainty,the potential risk of scarcity in the future,and limited geographical production,leading to research on FSGs that do not depend on rare-earth magnets.This study comprehensively examines FSGs that do not use rare-earth element magnets.The study delves into the usage areas,operational mechanisms,structural diversities,and counterparts in the literature of these generators.
基金Project supported by the National Natural Science Foundation of China(Grant No.62001383)。
文摘Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD)has been extensively researched due to its simplicity and security.For practical security of an LLO CVQKD system,there are two main attack modes referred to as reference pulse attack and polarization attack presently.However,there is currently no general defense strategy against such attacks,and the security of the system needs further investigation.Here,we employ a deep learning framework called generative adversarial networks(GANs)to detect both attacks.We first analyze the data in different cases,derive a feature vector as input to a GAN model,and then show the training and testing process of the GAN model for attack classification.The proposed model has two parts,a discriminator and a generator,both of which employ a convolutional neural network(CNN)to improve accuracy.Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance.It only establishes a detection model by monitoring features of the pulse without adding additional devices.
文摘In this paper, we study the second-order nonlinear differential systems of Liénard-type x˙=1a(x)[ h(y)−F(x) ], y˙=−a(x)g(x). Necessary and sufficient conditions to ensure that all nontrivial solutions are oscillatory are established by using a new nonlinear integral inequality. Our results substantially extend and improve previous results known in the literature.