Polyploidization is a commonly employed strategy in crop breeding to augment genetic diversity,particularly leveraging the distinctive benefits of additional progressive heterosis or multi-generation heterosis unique ...Polyploidization is a commonly employed strategy in crop breeding to augment genetic diversity,particularly leveraging the distinctive benefits of additional progressive heterosis or multi-generation heterosis unique to polyploidy.Despite genetic disparities between polyploids and diploids,challenges stem from reproductive anomalies,complicating genetic investigations in polyploid systems.Through nearly two decades of intensive research,our team has effectively generated a series of fertile tetraploid lines known as neo-tetraploid rice(NTR),facilitating comparative genetic studies between diploid and tetraploid rice.In this study,we identified diploid counterparts(H3d and H8d)for two NTR lines[Huaduo 3(H3)and Huaduo 8(H8)]and utilized them to create diploid and tetraploid fertile F_(2) populations to assess genotype segregation ratios,recombination rates,and their impact on QTL mapping via bulked segregant analysis combined with sequencing(BSA-seq).Additionally,we assessed yield heterosis in F_(1) and F_(2) generations of two tetraploid populations(H3×H8 and T449×H1),revealing evidence of multi-generation heterosis in polyploid rice.These findings provide valuable insights into the advantages and challenges of polyploid rice breeding.展开更多
[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predicto...[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predictors by mean generational function based on the rolling 50- year data of TYs frequency and sunspot number, and was repeated to generate forecasts year after year by optimal subset regression. [ Result] The results showed a reasonably high predictive ability dudng period 2000 -2010, with an average root mean square （RMSE） value of 1.92 and a mean absolute error （MAE） value of 1.64. [ Conclusion] Although the MMGF method needs further validation in the practical operation, it already has strong potential for the improvement of skill at forecasting annual frequency of TYs in the WNP.展开更多
This paper reports on a qualitative research study that examined the experience of expert and novice nurses participating in a new, reflective program of “clinical supervision”, intending to facilitate the transitio...This paper reports on a qualitative research study that examined the experience of expert and novice nurses participating in a new, reflective program of “clinical supervision”, intending to facilitate the transition of new graduate nurses into the workforce. Three patterns emerged during the constructivist inquiry: readiness to reflect, valuing of clinical supervision, and sustainability of the clinical supervision model. The researchers suggest generational sensitivity as a key perspective to consider when developing engaging workplace strategies for millennial nurses. The article offers recommendations for the implementation of clinical supervision and would be of interest to nurse leaders in a clinical setting.展开更多
One of the major concerns in today‘s business world is talent retention and development.Leading and working with multi-generational workforce in the age of digital transformation may seem daunting,but this paper argu...One of the major concerns in today‘s business world is talent retention and development.Leading and working with multi-generational workforce in the age of digital transformation may seem daunting,but this paper argues that it has its advantages in creating opportunities.Through interviews and case studies,this paper discovers that tensions in multi-generational collaboration often occur during the process of setting priorities for a group because different generations might have brought in different levels of capacity and willingness to take risks,and different levels of trust in,and care for people and the organization.The role of design in this context focuses more on capabilities,including observing generational behavioral nuances through practicing empathetic view,inviting people from different age into conversation and actively listening to them through the practice of shifting perspectives,and communicating complex situations through visualization and materialization for people to feel together.If we look at different generations in an organization as natural continuum of knowledge flow,if we see multigenerational workforce as one of driving forces to maintain organizational balance rather than tearing forces,and if we approach generational attributes with honesty,we could steer away from stereotypes and find common grounds to thrive together.展开更多
Climate, weather, and its attributes such as temperature and number of rainy days are essential for the success of many tourism destinations. As climate scientists have determined that climate changes are inevitable, ...Climate, weather, and its attributes such as temperature and number of rainy days are essential for the success of many tourism destinations. As climate scientists have determined that climate changes are inevitable, tourism destinations need to determine how to best manage these changes and mitigate any negative consequences. In addition, the perceived weather and/or climate at a destination can have as much weight on an individual＇s travel experience as the actual weather. The purpose of this study was to examine climate attributes and their importance on a traveler＇s behavior and satisfaction. Two hundred and sixty four surveys were gathered in the Mediterranean regions of Europe in the summer of 2009. Regression analysis revealed that climate attributes play a role in a traveler＇s satisfaction with their choice of a destination, but the traveler does not feel that climate changes are affecting their destinations as a whole. Analysis of variance （ANOVA） determined generational age differences in importance of climate attributes and if climate changes are affecting destinations. Management considerations for destination planners are explored.展开更多
This article explores the social transformation on account of surveillance conception preoccupied through the technology diffusion thanks to the Web 2.0 novel features.The new mediation abstracts the social provisions...This article explores the social transformation on account of surveillance conception preoccupied through the technology diffusion thanks to the Web 2.0 novel features.The new mediation abstracts the social provisions into radical customs of surveillance.It primes the supposition of socialization hitherto to transpire the scrutiny of the mutual rehearses of vertical and horizontal surveillance.Hence,the ultimate conversion keeps on through self-exposition notion in views of social interaction which so far posit the question of the privacy and public boundary owing to the hypothetical undue freedom of self-expression through Web 2.0.Thus,this paper examines the Surveillance Society in a comprehensive scope of the social structure vis-à-vis the ideas of generational submission towards social transformation.Using the dichotomy of digital revolutions,the Digital“Natives”are classified as typically addicted digital consumptions bearing the community outlooks,while Digital“Immigrants”persist in semi obedience along with the traditional adherence.Ultimately,the Panopticon conceptualizes the certainty of social surveillance by dint of proliferation of information flows keen on social conducts automated in both online and offline paths through technological appliances.展开更多
The COVID pandemic has accelerated the growth of ecommerce and reshaped shopping patterns,which in turn impacts trip-making and vehicle miles traveled.The objectives of this study are to define shopping styles and qua...The COVID pandemic has accelerated the growth of ecommerce and reshaped shopping patterns,which in turn impacts trip-making and vehicle miles traveled.The objectives of this study are to define shopping styles and quantify their prevalence in the population,investigate the impact of the pandemic on shopping style transition,understand the generational heterogeneity and other factors that influence shopping styles,and comment on the potential impact of the pandemic on long-term shopping behavior.Two months after the initial shutdown(May/June 2021),we collected ecommerce behavioral data from 313 Sacramento Region households using an online survey.A K-means clustering analysis of shopping behavior across eight commodity types identified five shopping styles,including ecommerce independent,ecommerce dependent,and three mixed modes in-between.We found that the share of ecommerce independent style shifted from 55%pre-pandemic to 27%during the pandemic.Overall,30%kept the same style as pre-pandemic,54%became more ecommerce dependent,and 16%became less ecommerce dependent,with the latter group more likely to view shopping an excuse to get out.Heterogeneity was found across generations.Pre-pandemic,Millennials and Gen Z were the most ecommerce dependent,but during the pandemic they made relatively small shifts toward increased ecommerce dependency.Baby Boomers and the Silent Generation were bimodal,either sticking to in-person shopping or shifting to ecommercedependency during the pandemic.Post-pandemic intentions varied across styles,with households who primarily adopt non-food ecommerce intending to reverse back to in-person shopping,while the highly ecommerce dependent intend to limit future in-store activities.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
In this study,three specific scenarios of a novel accelerator light source mechanism called steady-state microbunching(SSMB)were studied:longitudinal weak focusing,longitudinal strong focusing,and generalized longitud...In this study,three specific scenarios of a novel accelerator light source mechanism called steady-state microbunching(SSMB)were studied:longitudinal weak focusing,longitudinal strong focusing,and generalized longitudinal strong focusing(GLSF).At present,GLSF is the most promising method for realizing high-power short-wavelength coherent radiation with mild requirements on modulation laser power.Its essence is to exploit the ultrasmall natural vertical emittance of an electron beam in a planar storage ring for efficient microbunching formation,like a partial transverse-longitudinal emittance exchange in the optical laser wavelength range.Based on an in-depth investigation of related beam physics,a solution for a GLSF SSMB storage ring that can deliver 1 kW average-power EUV light is presented.The work in this paper,such as the generalized Courant–Snyder formalism,analysis of theoretical minimum emittances,transverse-longitudinal coupling dynamics,and derivation of the bunching factor and modulation strengths for laser-induced microbunching schemes,is expected to be useful not only for the development of SSMB but also for future accelerator light sources in general that demand increasingly precise electron beam phase space manipulations.展开更多
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
This paper proposes a passive control method to reduce peak values of slipstream and turbulent kinetic energy in a high-speed train wake by attaching vortex generators(VGs)onto the upper surface of the tail car.The im...This paper proposes a passive control method to reduce peak values of slipstream and turbulent kinetic energy in a high-speed train wake by attaching vortex generators(VGs)onto the upper surface of the tail car.The impact of the VGs is assessed through the improved delayed detached eddy simulations(IDDES)after validating predictions against previous experimental measurements and other numerical predictions for the base case.The simulations indicate that strategically installed VGs can reduce the average slipstream velocity(U slipstream)and the upper limit of slipstream velocity(U_(slipstream,max))by~17%and~15%,respectively,as well as moving the peaks downstream by approximately train height,thus reducing the danger posed by slipstream to waiting passengers and trackside workers.Analysis shows that the wake turbulent kinetic energy diminishes as the vortex generators decelerate the downwash flow and reduce shear production in the wake.It is also found that the presence of VGs significantly impacts the flow on the upper surface near the tail by modifying the unsteady trailing longitudinal vortices through the formation of additional counter-rotating longitudinal vortices from the VGs.These latter vortices prevent the merging of vortical airflow around the trailing nose tip,which is otherwise induced by the longitudinal vortex of the train.They also reduce vortex intensity through cross-annihilation and cross diffusion as the wake advects downstream,limiting outwards advection through interaction with the image pair,and contributing to a decrease in the peak slipstream value.The method proposed offers a simple approach to wake control leading to significant slipstream benefits.展开更多
This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow con...This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per...To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.展开更多
Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global...Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.展开更多
This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint...This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs,while robust optimization techniques manage electricity and thermal price volatilities.Building upon this foundation,a hierarchical Nash-Stackelberg game model is established across multiple VPPs.Within each VPP,a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers(PVP).Among VPPs,a cooperative Nash bargaining model coordinates alliance formation.The problem is decomposed into two subproblems:maximizing coalitional benefits,and allocating cooperative surpluses via payment bargaining,solved distributively using the alternating direction method of multipliers(ADMM).Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.展开更多
With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided b...With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.展开更多
Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and ...Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.展开更多
基金supported by the National Key Resarch and Development Program of China(Grant No.2023YFD1200802)the Base Bank of Lingnan Rice Germplasm Resources Project,China(Grant No.2024B1212060009).
文摘Polyploidization is a commonly employed strategy in crop breeding to augment genetic diversity,particularly leveraging the distinctive benefits of additional progressive heterosis or multi-generation heterosis unique to polyploidy.Despite genetic disparities between polyploids and diploids,challenges stem from reproductive anomalies,complicating genetic investigations in polyploid systems.Through nearly two decades of intensive research,our team has effectively generated a series of fertile tetraploid lines known as neo-tetraploid rice(NTR),facilitating comparative genetic studies between diploid and tetraploid rice.In this study,we identified diploid counterparts(H3d and H8d)for two NTR lines[Huaduo 3(H3)and Huaduo 8(H8)]and utilized them to create diploid and tetraploid fertile F_(2) populations to assess genotype segregation ratios,recombination rates,and their impact on QTL mapping via bulked segregant analysis combined with sequencing(BSA-seq).Additionally,we assessed yield heterosis in F_(1) and F_(2) generations of two tetraploid populations(H3×H8 and T449×H1),revealing evidence of multi-generation heterosis in polyploid rice.These findings provide valuable insights into the advantages and challenges of polyploid rice breeding.
基金Supported by the Natural Science Fund of Education Department of Anhui Province (KJ2012Z097)
文摘[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predictors by mean generational function based on the rolling 50- year data of TYs frequency and sunspot number, and was repeated to generate forecasts year after year by optimal subset regression. [ Result] The results showed a reasonably high predictive ability dudng period 2000 -2010, with an average root mean square （RMSE） value of 1.92 and a mean absolute error （MAE） value of 1.64. [ Conclusion] Although the MMGF method needs further validation in the practical operation, it already has strong potential for the improvement of skill at forecasting annual frequency of TYs in the WNP.
文摘This paper reports on a qualitative research study that examined the experience of expert and novice nurses participating in a new, reflective program of “clinical supervision”, intending to facilitate the transition of new graduate nurses into the workforce. Three patterns emerged during the constructivist inquiry: readiness to reflect, valuing of clinical supervision, and sustainability of the clinical supervision model. The researchers suggest generational sensitivity as a key perspective to consider when developing engaging workplace strategies for millennial nurses. The article offers recommendations for the implementation of clinical supervision and would be of interest to nurse leaders in a clinical setting.
文摘One of the major concerns in today‘s business world is talent retention and development.Leading and working with multi-generational workforce in the age of digital transformation may seem daunting,but this paper argues that it has its advantages in creating opportunities.Through interviews and case studies,this paper discovers that tensions in multi-generational collaboration often occur during the process of setting priorities for a group because different generations might have brought in different levels of capacity and willingness to take risks,and different levels of trust in,and care for people and the organization.The role of design in this context focuses more on capabilities,including observing generational behavioral nuances through practicing empathetic view,inviting people from different age into conversation and actively listening to them through the practice of shifting perspectives,and communicating complex situations through visualization and materialization for people to feel together.If we look at different generations in an organization as natural continuum of knowledge flow,if we see multigenerational workforce as one of driving forces to maintain organizational balance rather than tearing forces,and if we approach generational attributes with honesty,we could steer away from stereotypes and find common grounds to thrive together.
文摘Climate, weather, and its attributes such as temperature and number of rainy days are essential for the success of many tourism destinations. As climate scientists have determined that climate changes are inevitable, tourism destinations need to determine how to best manage these changes and mitigate any negative consequences. In addition, the perceived weather and/or climate at a destination can have as much weight on an individual＇s travel experience as the actual weather. The purpose of this study was to examine climate attributes and their importance on a traveler＇s behavior and satisfaction. Two hundred and sixty four surveys were gathered in the Mediterranean regions of Europe in the summer of 2009. Regression analysis revealed that climate attributes play a role in a traveler＇s satisfaction with their choice of a destination, but the traveler does not feel that climate changes are affecting their destinations as a whole. Analysis of variance （ANOVA） determined generational age differences in importance of climate attributes and if climate changes are affecting destinations. Management considerations for destination planners are explored.
文摘This article explores the social transformation on account of surveillance conception preoccupied through the technology diffusion thanks to the Web 2.0 novel features.The new mediation abstracts the social provisions into radical customs of surveillance.It primes the supposition of socialization hitherto to transpire the scrutiny of the mutual rehearses of vertical and horizontal surveillance.Hence,the ultimate conversion keeps on through self-exposition notion in views of social interaction which so far posit the question of the privacy and public boundary owing to the hypothetical undue freedom of self-expression through Web 2.0.Thus,this paper examines the Surveillance Society in a comprehensive scope of the social structure vis-à-vis the ideas of generational submission towards social transformation.Using the dichotomy of digital revolutions,the Digital“Natives”are classified as typically addicted digital consumptions bearing the community outlooks,while Digital“Immigrants”persist in semi obedience along with the traditional adherence.Ultimately,the Panopticon conceptualizes the certainty of social surveillance by dint of proliferation of information flows keen on social conducts automated in both online and offline paths through technological appliances.
基金funded by the University of California Institute of Transportation Studies'California Senate Bill 1 research program and the US Department of Transportation's Telemobility Tier 1 University Transportation Center(UTC).
文摘The COVID pandemic has accelerated the growth of ecommerce and reshaped shopping patterns,which in turn impacts trip-making and vehicle miles traveled.The objectives of this study are to define shopping styles and quantify their prevalence in the population,investigate the impact of the pandemic on shopping style transition,understand the generational heterogeneity and other factors that influence shopping styles,and comment on the potential impact of the pandemic on long-term shopping behavior.Two months after the initial shutdown(May/June 2021),we collected ecommerce behavioral data from 313 Sacramento Region households using an online survey.A K-means clustering analysis of shopping behavior across eight commodity types identified five shopping styles,including ecommerce independent,ecommerce dependent,and three mixed modes in-between.We found that the share of ecommerce independent style shifted from 55%pre-pandemic to 27%during the pandemic.Overall,30%kept the same style as pre-pandemic,54%became more ecommerce dependent,and 16%became less ecommerce dependent,with the latter group more likely to view shopping an excuse to get out.Heterogeneity was found across generations.Pre-pandemic,Millennials and Gen Z were the most ecommerce dependent,but during the pandemic they made relatively small shifts toward increased ecommerce dependency.Baby Boomers and the Silent Generation were bimodal,either sticking to in-person shopping or shifting to ecommercedependency during the pandemic.Post-pandemic intentions varied across styles,with households who primarily adopt non-food ecommerce intending to reverse back to in-person shopping,while the highly ecommerce dependent intend to limit future in-store activities.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金supported by the National Key Research and Development Program of China(No.2022YFA1603401)National Natural Science Foundation of China(Nos.12035010 and 12342501)+1 种基金Beijing Outstanding Young Scientist Program(No.JWZQ20240101006)the Tsinghua University Dushi Program.
文摘In this study,three specific scenarios of a novel accelerator light source mechanism called steady-state microbunching(SSMB)were studied:longitudinal weak focusing,longitudinal strong focusing,and generalized longitudinal strong focusing(GLSF).At present,GLSF is the most promising method for realizing high-power short-wavelength coherent radiation with mild requirements on modulation laser power.Its essence is to exploit the ultrasmall natural vertical emittance of an electron beam in a planar storage ring for efficient microbunching formation,like a partial transverse-longitudinal emittance exchange in the optical laser wavelength range.Based on an in-depth investigation of related beam physics,a solution for a GLSF SSMB storage ring that can deliver 1 kW average-power EUV light is presented.The work in this paper,such as the generalized Courant–Snyder formalism,analysis of theoretical minimum emittances,transverse-longitudinal coupling dynamics,and derivation of the bunching factor and modulation strengths for laser-induced microbunching schemes,is expected to be useful not only for the development of SSMB but also for future accelerator light sources in general that demand increasingly precise electron beam phase space manipulations.
基金supported by Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
基金Project(52372370)supported by the National Natural Science Foundation of ChinaProject(2023ZZTS0379)supported by the Graduate Student Independent Innovation Project of Central South University,ChinaProject(202206370058)supported by the China Scholarship Council。
文摘This paper proposes a passive control method to reduce peak values of slipstream and turbulent kinetic energy in a high-speed train wake by attaching vortex generators(VGs)onto the upper surface of the tail car.The impact of the VGs is assessed through the improved delayed detached eddy simulations(IDDES)after validating predictions against previous experimental measurements and other numerical predictions for the base case.The simulations indicate that strategically installed VGs can reduce the average slipstream velocity(U slipstream)and the upper limit of slipstream velocity(U_(slipstream,max))by~17%and~15%,respectively,as well as moving the peaks downstream by approximately train height,thus reducing the danger posed by slipstream to waiting passengers and trackside workers.Analysis shows that the wake turbulent kinetic energy diminishes as the vortex generators decelerate the downwash flow and reduce shear production in the wake.It is also found that the presence of VGs significantly impacts the flow on the upper surface near the tail by modifying the unsteady trailing longitudinal vortices through the formation of additional counter-rotating longitudinal vortices from the VGs.These latter vortices prevent the merging of vortical airflow around the trailing nose tip,which is otherwise induced by the longitudinal vortex of the train.They also reduce vortex intensity through cross-annihilation and cross diffusion as the wake advects downstream,limiting outwards advection through interaction with the image pair,and contributing to a decrease in the peak slipstream value.The method proposed offers a simple approach to wake control leading to significant slipstream benefits.
基金the Scientific Research Projects Unit of Erciyes University under contract no:FDS-2022-11532 and FOA-2025-14773.
文摘This paper examines a model that combines vortex generators and leading-edge tubercles for controlling the laminar separation bubble(LSB)over an airfoil at low Reynolds numbers(Re).This new concept of passive flow control technique utilizing a tubercle and vortex generator(VG)close to the leading edge was analyzed numerically for a NACA0015 airfoil.In this study,the Shear Stress Transport(SST)turbulence model was employed in the numerical modelling.Numerical modelling was completed using the ANSYS-Fluent 18.2 solver.Analyses were conducted to investigate the flow pattern and understand the underlying LSB control phenomena that enabled the new passive flow control method to provide this significant performance benefit.The findings indicated that the new concept of passive flow control technique suppressed the formation of an LSB at the suction surface of the NACA0015 airfoil,resulting in a higher lift coefficient and improved aerodynamic performance.Improvements in LSB dynamics and aerodynamic performance through the passive flow control method lead to increased energy output and enhanced stability.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金supported by the Jiangsu Engineering Research Center of the Key Technology for Intelligent Manufacturing Equipment and the Suqian Key Laboratory of Intelligent Manufacturing(Grant No.M202108).
文摘To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.
基金supported by the National Natural Science Foundation of China(Grant No.62172123)the Key Research and Development Program of Heilongjiang Province,China(GrantNo.2022ZX01A36).
文摘Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.
基金supported by Science and Technology Project of SGCC(Research on Distributed Cooperative Control of Virtual Power Plants Based on Hybrid Game)(5700-202418337A-2-1-ZX).
文摘This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs,while robust optimization techniques manage electricity and thermal price volatilities.Building upon this foundation,a hierarchical Nash-Stackelberg game model is established across multiple VPPs.Within each VPP,a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers(PVP).Among VPPs,a cooperative Nash bargaining model coordinates alliance formation.The problem is decomposed into two subproblems:maximizing coalitional benefits,and allocating cooperative surpluses via payment bargaining,solved distributively using the alternating direction method of multipliers(ADMM).Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.
基金financially supported by the Talent Initiation Fund of Wuxi University(550220008).
文摘With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.
基金the National Natural Science Foundation of China(Grant No.:52508343)the Fundamental Research Funds for the Central Universities(Grant No.:B250201004).
文摘Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.
