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
This study examines the impact of political polarization on youth engagement in Brazil,focusing on the intersection of historical legacies,cultural dynamics,and modern political influences.Tracing the evolution of pol...This study examines the impact of political polarization on youth engagement in Brazil,focusing on the intersection of historical legacies,cultural dynamics,and modern political influences.Tracing the evolution of polarization from the military dictatorship era to contemporary politics under Jair Bolsonaro,the study explores how polarized rhetoric,misinformation,and digital media have reshaped the political landscape for young Brazilians.Data for the study were collected through a survey of 159 college students aged 18-25 from public and private institutions across Brazil,complemented by 10 interviews with youth activists and party leaders from the Workers’Party(PT)and the Liberal Party(PL).These methods aimed to evaluate how polarization affects young people’s beliefs,political identities,behaviors,engagement levels,and ability to participate in constructive political discourse.The findings reveal that while polarization fosters hostility and deepens ideological divides,it also inspires youth activism by creating a sense of identity and belonging.Education,digital information channels,and the lack of representation in leadership roles emerge as key factors shaping youth engagement.Despite challenges,the research underscores hope:young Brazilians display resilience,critical awareness,and a shared desire for accountability and inclusivity.This study highlights the potential of youth to bridge divides and lead Brazil toward a more collaborative and democratic future.展开更多
Background: For nursing students, gathering social information is essential for understanding healthcare and social issues and developing critical thinking and decision-making skills. However, the choice of informatio...Background: For nursing students, gathering social information is essential for understanding healthcare and social issues and developing critical thinking and decision-making skills. However, the choice of information sources varies by age and individual habits. With the widespread use of the internet, there are notable differences between younger and older generations in their reliance on the internet versus traditional media sources like newspapers and television. Given the wide age range and diverse backgrounds of nursing students, understanding generational differences in information-gathering methods is important for implementing effective education. Purpose: The purpose of this study is to identify how nursing students in different age groups obtain social information and to examine media usage trends by age group. Additionally, we aim to use the findings to provide insights into effective information dissemination methods in nursing education. Results: The results showed that nursing students in their teens to forties, regardless of gender, primarily relied on the internet as their main information source, with television playing a secondary role. In contrast, students in their fifties tended to obtain information more often from newspapers and television than from the internet. This highlights an age-related difference in preferred information sources, with older students showing a greater reliance on traditional media. Conclusions: This study demonstrates that nursing students use different information-gathering methods based on their age, suggesting a need to custo-mize information dissemination strategies in nursing education. Digital media may be more effective for younger students, while traditional media or printed materials might better serve older students. Educational institutions should consider these generational differences in media usage and adopt strategies that meet the diverse needs of their student populations.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
The generalized rheological tests on sandstone were conducted under both dynamic stress and seepage fields.The results demonstrate that the rheological strain of the specimen under increased stress conditions is great...The generalized rheological tests on sandstone were conducted under both dynamic stress and seepage fields.The results demonstrate that the rheological strain of the specimen under increased stress conditions is greater than that under creep conditions,indicating that the dynamic stress field significantly influences the rheological behaviours of sandstone.Following the rheological tests,the number of small pores in the sandstone decreased,while the number of medium-sized pores increased,forming new seepage channels.The high initial rheological stress accelerated fracture compression and the closure of seepage channels,resulting in reduction in the permeability of sandstone.Based on the principles of generalized rheology and the experimental findings,a novel rock rheological constitutive model incorporating both the dynamic stress field and seepage properties has been developed.Numerical simulations of surrounding rock deformation in geotechnical engineering were carried out using a secondary development version of this model,which confirmed the applicability of the generalized rheological numerical simulation method.These results provide theoretical support for the long-term stability evaluation of engineering rock masses and for predicting the deformation of surrounding rock.展开更多
As the penetration rate of distributed energy increases,the transient power angle stability problem of the virtual synchronous generator(VSG)has gradually become prominent.In view of the situation that the grid impeda...As the penetration rate of distributed energy increases,the transient power angle stability problem of the virtual synchronous generator(VSG)has gradually become prominent.In view of the situation that the grid impedance ratio(R/X)is high and affects the transient power angle stability of VSG,this paper proposes a VSG transient power angle stability control strategy based on the combination of frequency difference feedback and virtual impedance.To improve the transient power angle stability of the VSG,a virtual impedance is adopted in the voltage loop to adjust the impedance ratio R/X;and the PI control feedback of the VSG frequency difference is introduced in the reactive powervoltage link of theVSGto enhance the damping effect.Thesecond-orderVSGdynamic nonlinearmodel considering the reactive power-voltage loop is established and the influence of different proportional integral(PI)control parameters on the system balance stability is analyzed.Moreover,the impact of the impedance ratio R/X on the transient power angle stability is presented using the equal area criterion.In the simulations,during the voltage dips with the reduction of R/X from 1.6 to 0.8,Δδ_(1)is reduced from 0.194 rad to 0.072 rad,Δf_(1)is reduced from 0.170 to 0.093 Hz,which shows better transient power angle stability.Simulation results verify that compared with traditional VSG,the proposedmethod can effectively improve the transient power angle stability of the system.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
Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited ...Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.展开更多
Since the release of ChatGPT in late 2022,Generative Artificial Intelligence(GAI)has gained widespread attention because of its impressive capabilities in language comprehension,reasoning,and generation.GAI has been s...Since the release of ChatGPT in late 2022,Generative Artificial Intelligence(GAI)has gained widespread attention because of its impressive capabilities in language comprehension,reasoning,and generation.GAI has been successfully applied across various aspects(e.g.,creative writing,code generation,translation,and information retrieval).In cartography and GIS,researchers have employed GAI to handle some specific tasks,such as map generation,geographic question answering,and spatiotemporal data analysis,yielding a series of remarkable results.Although GAI-based techniques are developing rapidly,literature reviews of their applications in cartography and GIS remain relatively limited.This paper reviews recent GAI-related research in cartography and GIS,focusing on three aspects:①map generation,②geographical analysis,and③evaluation of GAI’s spatial cognition abilities.In addition,the paper analyzes current challenges and proposes future research directions.展开更多
As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as l...As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act ...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.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
In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-b...In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-based alloy(typically pure lead or lead-bismuth eutectic(LBE))is used as the coolant.To clarify the pressure build-up characteristics under water-jet injection,this study conducted several experiments by injecting pressurized water into a molten LBE pool at Sun Yat-sen University.To obtain a further understanding,several new experimental parameters were adopted,including the melt temperature,water subcooling,injection pressure,injection duration,and nozzle diameter.Through detailed analyses,it was found that the pressure and temperature during the water-melt interaction exhibited a consistent variation trend with our previous water-droplet injection mode LBE experiment.Similarly,the existence of a steam explosion was confirmed,which typically results in a much stronger pressure build-up.For the non-explosion cases,increasing the injection pressure,melt-pool temperature,nozzle diameter,and water subcooling promoted pressure build-up in the melt pool.However,a limited enhancement effect was observed when increasing the injection duration,which may be owing to the continually rising pressure in the interaction vessel or the isolation effect of the generated steam cavity.Regardless of whether a steam explosion occurred,the calculated mechanical and kinetic energy conversion efficiencies of the melt were relatively small(not exceeding 4.1%and 0.7%,respectively).Moreover,the range of the conversion efficiency was similar to that of previous water-droplet experiments,although the upper limit of the jet mode was slightly lower.展开更多
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.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.
文摘This study examines the impact of political polarization on youth engagement in Brazil,focusing on the intersection of historical legacies,cultural dynamics,and modern political influences.Tracing the evolution of polarization from the military dictatorship era to contemporary politics under Jair Bolsonaro,the study explores how polarized rhetoric,misinformation,and digital media have reshaped the political landscape for young Brazilians.Data for the study were collected through a survey of 159 college students aged 18-25 from public and private institutions across Brazil,complemented by 10 interviews with youth activists and party leaders from the Workers’Party(PT)and the Liberal Party(PL).These methods aimed to evaluate how polarization affects young people’s beliefs,political identities,behaviors,engagement levels,and ability to participate in constructive political discourse.The findings reveal that while polarization fosters hostility and deepens ideological divides,it also inspires youth activism by creating a sense of identity and belonging.Education,digital information channels,and the lack of representation in leadership roles emerge as key factors shaping youth engagement.Despite challenges,the research underscores hope:young Brazilians display resilience,critical awareness,and a shared desire for accountability and inclusivity.This study highlights the potential of youth to bridge divides and lead Brazil toward a more collaborative and democratic future.
文摘Background: For nursing students, gathering social information is essential for understanding healthcare and social issues and developing critical thinking and decision-making skills. However, the choice of information sources varies by age and individual habits. With the widespread use of the internet, there are notable differences between younger and older generations in their reliance on the internet versus traditional media sources like newspapers and television. Given the wide age range and diverse backgrounds of nursing students, understanding generational differences in information-gathering methods is important for implementing effective education. Purpose: The purpose of this study is to identify how nursing students in different age groups obtain social information and to examine media usage trends by age group. Additionally, we aim to use the findings to provide insights into effective information dissemination methods in nursing education. Results: The results showed that nursing students in their teens to forties, regardless of gender, primarily relied on the internet as their main information source, with television playing a secondary role. In contrast, students in their fifties tended to obtain information more often from newspapers and television than from the internet. This highlights an age-related difference in preferred information sources, with older students showing a greater reliance on traditional media. Conclusions: This study demonstrates that nursing students use different information-gathering methods based on their age, suggesting a need to custo-mize information dissemination strategies in nursing education. Digital media may be more effective for younger students, while traditional media or printed materials might better serve older students. Educational institutions should consider these generational differences in media usage and adopt strategies that meet the diverse needs of their student populations.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金supported and financed by Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (No.2024yjrc96)Anhui Provincial University Excellent Research and Innovation Team Support Project (No.2022AH010053)+2 种基金National Key Research and Development Program of China (Nos.2023YFC2907602 and 2022YFF1303302)Anhui Provincial Major Science and Technology Project (No.202203a07020011)Open Foundation of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining (No.EC2023020)。
文摘The generalized rheological tests on sandstone were conducted under both dynamic stress and seepage fields.The results demonstrate that the rheological strain of the specimen under increased stress conditions is greater than that under creep conditions,indicating that the dynamic stress field significantly influences the rheological behaviours of sandstone.Following the rheological tests,the number of small pores in the sandstone decreased,while the number of medium-sized pores increased,forming new seepage channels.The high initial rheological stress accelerated fracture compression and the closure of seepage channels,resulting in reduction in the permeability of sandstone.Based on the principles of generalized rheology and the experimental findings,a novel rock rheological constitutive model incorporating both the dynamic stress field and seepage properties has been developed.Numerical simulations of surrounding rock deformation in geotechnical engineering were carried out using a secondary development version of this model,which confirmed the applicability of the generalized rheological numerical simulation method.These results provide theoretical support for the long-term stability evaluation of engineering rock masses and for predicting the deformation of surrounding rock.
基金supported by theMajor Science and Technology Projects of China Southern Power Grid(Grant number CGYKJXM20210328).
文摘As the penetration rate of distributed energy increases,the transient power angle stability problem of the virtual synchronous generator(VSG)has gradually become prominent.In view of the situation that the grid impedance ratio(R/X)is high and affects the transient power angle stability of VSG,this paper proposes a VSG transient power angle stability control strategy based on the combination of frequency difference feedback and virtual impedance.To improve the transient power angle stability of the VSG,a virtual impedance is adopted in the voltage loop to adjust the impedance ratio R/X;and the PI control feedback of the VSG frequency difference is introduced in the reactive powervoltage link of theVSGto enhance the damping effect.Thesecond-orderVSGdynamic nonlinearmodel considering the reactive power-voltage loop is established and the influence of different proportional integral(PI)control parameters on the system balance stability is analyzed.Moreover,the impact of the impedance ratio R/X on the transient power angle stability is presented using the equal area criterion.In the simulations,during the voltage dips with the reduction of R/X from 1.6 to 0.8,Δδ_(1)is reduced from 0.194 rad to 0.072 rad,Δf_(1)is reduced from 0.170 to 0.093 Hz,which shows better transient power angle stability.Simulation results verify that compared with traditional VSG,the proposedmethod can effectively improve the transient power angle stability of the system.
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supported by the Yonsei University graduate school Department of Integrative Biotechnology.
文摘Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.
基金National Natural Science Foundation of China(Nos.4210144242394063).
文摘Since the release of ChatGPT in late 2022,Generative Artificial Intelligence(GAI)has gained widespread attention because of its impressive capabilities in language comprehension,reasoning,and generation.GAI has been successfully applied across various aspects(e.g.,creative writing,code generation,translation,and information retrieval).In cartography and GIS,researchers have employed GAI to handle some specific tasks,such as map generation,geographic question answering,and spatiotemporal data analysis,yielding a series of remarkable results.Although GAI-based techniques are developing rapidly,literature reviews of their applications in cartography and GIS remain relatively limited.This paper reviews recent GAI-related research in cartography and GIS,focusing on three aspects:①map generation,②geographical analysis,and③evaluation of GAI’s spatial cognition abilities.In addition,the paper analyzes current challenges and proposes future research directions.
基金supported by the National Natural Science Foundation of China(Grant Nos.22225801,W2441009,22408228)。
文摘As energy demands continue to rise in modern society,the development of high-performance lithium-ion batteries(LIBs)has become crucial.However,traditional research methods of material science face challenges such as lengthy timelines and complex processes.In recent years,the integration of machine learning(ML)in LIB materials,including electrolytes,solid-state electrolytes,and electrodes,has yielded remarkable achievements.This comprehensive review explores the latest applications of ML in predicting LIB material performance,covering the core principles and recent advancements in three key inverse material design strategies:high-throughput virtual screening,global optimization,and generative models.These strategies have played a pivotal role in fostering LIB material innovations.Meanwhile,the paper briefly discusses the challenges associated with applying ML to materials research and offers insights and directions for future research.
基金supported by Interdisciplinary Innova-tion Project of“Bioarchaeology Laboratory”of Jilin University,China,and“MedicineþX”Interdisciplinary Innovation Team of Norman Bethune Health Science Center of Jilin University,China(Grant No.:2022JBGS05).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
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supported by Basic and Applied Basic research foundation of Guangdong province(Nos.2021A1515010343 and 2022A1515011582)the Science and Technology Program of Guangdong Province(Nos.2021A0505030026 and 2022A0505050029).
文摘In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-based alloy(typically pure lead or lead-bismuth eutectic(LBE))is used as the coolant.To clarify the pressure build-up characteristics under water-jet injection,this study conducted several experiments by injecting pressurized water into a molten LBE pool at Sun Yat-sen University.To obtain a further understanding,several new experimental parameters were adopted,including the melt temperature,water subcooling,injection pressure,injection duration,and nozzle diameter.Through detailed analyses,it was found that the pressure and temperature during the water-melt interaction exhibited a consistent variation trend with our previous water-droplet injection mode LBE experiment.Similarly,the existence of a steam explosion was confirmed,which typically results in a much stronger pressure build-up.For the non-explosion cases,increasing the injection pressure,melt-pool temperature,nozzle diameter,and water subcooling promoted pressure build-up in the melt pool.However,a limited enhancement effect was observed when increasing the injection duration,which may be owing to the continually rising pressure in the interaction vessel or the isolation effect of the generated steam cavity.Regardless of whether a steam explosion occurred,the calculated mechanical and kinetic energy conversion efficiencies of the melt were relatively small(not exceeding 4.1%and 0.7%,respectively).Moreover,the range of the conversion efficiency was similar to that of previous water-droplet experiments,although the upper limit of the jet mode was slightly lower.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
