1. CSAS China Solid State Ughting Alliance - Standardization Committee （CSAS） is the standardization organization of Chinese solid state lighting alliance （CSA）. Adhering to the principles of ＂openness, transpar...1. CSAS China Solid State Ughting Alliance - Standardization Committee （CSAS） is the standardization organization of Chinese solid state lighting alliance （CSA）. Adhering to the principles of ＂openness, transparency, consultation and consensus＂, CSAS conducts standardization work in the following aspects：展开更多
Context:In the dynamic and constantly evolving world of agriculture,promoting innovation and ensuring sustainable growth are crucial.A planned division of tasks and responsibilities within agricultural systems,known a...Context:In the dynamic and constantly evolving world of agriculture,promoting innovation and ensuring sustainable growth are crucial.A planned division of tasks and responsibilities within agricultural systems,known as efficient role allocation,is necessary to make this vision a reality.Climate-smart agriculture(CSA)movement enjoys widespread support from the research and development community because it seeks to improve livelihoods in response to climate change.Objective:This study explores an innovative approach to optimizing role assignment within agricultural frameworks to effectively scale AI-driven innovations.By leveraging advanced algorithms and machine learning techniques,the research aims to streamline the allocation of tasks and responsibilities among various stakeholders,including farmers,agronomists,technicians,and AI systems.Methods:The methodology involves the development of a dynamic role assignment model that considers factors such as expertise,resource availability,and real-time environmental data.This model is tested in various agricultural scenarios to evaluate its impact on operational efficiency and innovation scalability.The findings demonstrate that optimized role assignment not only enhances the performance of AI applications but also fosters a collaborative ecosystem that is adaptable to changing agricultural demands.Results:&Discussion:This research finds a number of elements that affect how well duties are distributed within agricultural frameworks,including organizational frameworks,leadership,resource accessibility,and cooperative efforts through AI.In addition to advocating for its comprehensive integration into the sector's culture,this paper offers a collection of best practices and techniques for optimizing role allocation in agriculture.Additionally,the study gives a thorough overview,summary,and analysis of a few papers that are specifically concerned with scaling innovation in the field of agricultural research for development.Significance:Furthermore,the study highlights the potential of AI to transform traditional farming practices,reduce labor-intensive processes,and improve decision-making accuracy.The proposed approach serves as a blueprint for agricultural enterprises aiming to adopt AI technologies while ensuring optimal utilization of human and technological resources.By addressing the challenges of role ambiguity and resource allocation,this research contributes to the broader goal of achieving sustainable and resilient agricultural systems through technological innovation.展开更多
In this paper,we present a novel unimodular sequence design algorithm based on the coordinate descent(CD)algorithm,aimed at countering electronic surveillance(ES)systems based on cyclostationary analysis.Our algorithm...In this paper,we present a novel unimodular sequence design algorithm based on the coordinate descent(CD)algorithm,aimed at countering electronic surveillance(ES)systems based on cyclostationary analysis.Our algorithm not only provides resistance against cyclostationary analysis(CSA)but also maintains low integrated sidelobe(ISL)characteristics.Initially,we derive the expression of the cyclostationary feature(CSF)detector and simplify it into an iterative quadratic form.Additionally,we derive a quadratic form to ensure the similarity of the autocorrelation sidelobes.To balance the minimization of the detection probability and the ISL values,we introduce a Pareto scalar that transforms the multiobjective optimization problem into a convex combination of objective functions.This approach allows us to find an optimal trade-off between the two objectives.Finally,we propose a monotonic algorithm based on the CD algorithm to counter CSA analysis.This algorithm efficiently solves the optimization problem mentioned earlier.Numerical experiments are conducted to validate the correctness and effectiveness of our proposed algorithm.展开更多
There is a need for more focus in understanding the economic benefits of Climate-Smart Agriculture(CSA)interventions,particularly in sub-Saharan Africa,where extreme climate events are significantly affecting agricult...There is a need for more focus in understanding the economic benefits of Climate-Smart Agriculture(CSA)interventions,particularly in sub-Saharan Africa,where extreme climate events are significantly affecting agriculture and rural livelihoods.This study used the Net Present Value(NPV),Internal Rate of Return(IRR),Benefit-Cost Ratio(BCR),and payback period to evaluate the economic viability of the adopted CSA interventions in the three villages(Doggoh,Jeffiri,and Wulling)of the dryland farming systems of northern Ghana,where CSA interventions were mostly practiced.Data were collected from 161 farm households by the questionnaire survey.The results showed that CSA interventions including livestock-crop integration,mixed cropping,crop rotation,nutrient integration,and tie ridging enhanced crop yield and the household income of smallholder farmers.The five CSA interventions selected by smallholders were in the following order of priority:livestock-crop integration(BCR=2.87),mixed cropping(BCR=2.54),crop rotation(BCR=2.24),nutrient integration(BCR=1.98),and tie ridging(BCR=1.42).Results further showed that livestock-crop integration was the most profitable CSA intervention even under a pessimistic assumption with a long payback period of 5.00 a.Moreover,this study indicated that the implementation of CSA interventions,on average,was relatively profitable and had a nominal financial risk for smallholder farmers.Understanding the economic viability of CSA interventions will help in decision-making process toward selecting the right CSA interventions for resilience development.展开更多
The present review critically examines the role of neglected and underutilized crops(NUCs)in enhancing the resilience of South Asian cropping systems and diets in the context of climate change and nutritional challeng...The present review critically examines the role of neglected and underutilized crops(NUCs)in enhancing the resilience of South Asian cropping systems and diets in the context of climate change and nutritional challenges.This analysis reveals that integrating NUCs,such as millets,sorghums,amaranth,and indigenous legumes,into existing cropping systems can significantly improve the climate resilience,dietary diversity,and ecological sustainability of the food systems.These crops exhibit superior tolerance to abiotic stress and offer higher nutritional density compared to staple cereals,such as rice and wheat.However,their adoption faces challenges,including limited research investment,fragmented value chains,etc.We further identify that complementary cropping strategies and climate-smart agriculture(CSA)practices can optimize resource use while boosting smallholder farmers’income.NUCs are pivotal for the transformation of exist cropping systems towards nutrition-sensitive and climate-resilient agricultural and food systems.Strategic integration of NUCs can simultaneously address food insecurity,biodiversity loss,and rural poverty.Yet,unlocking their potential requires coordinated efforts in genetic improvement,market development,and policy frameworks tailored to regional contexts.This synthesis provides a comprehensive roadmap for policy-makers,researchers,and farmers to leverage NUCs as“Future Smart Food”.By bridging agronomic,nutritional,and socioeconomic perspectives,this study highlights the transformative potential of NUCs in achieving Sustainable Development Goals(SDGs)across South Asian countries.展开更多
文摘1. CSAS China Solid State Ughting Alliance - Standardization Committee （CSAS） is the standardization organization of Chinese solid state lighting alliance （CSA）. Adhering to the principles of ＂openness, transparency, consultation and consensus＂, CSAS conducts standardization work in the following aspects：
文摘Context:In the dynamic and constantly evolving world of agriculture,promoting innovation and ensuring sustainable growth are crucial.A planned division of tasks and responsibilities within agricultural systems,known as efficient role allocation,is necessary to make this vision a reality.Climate-smart agriculture(CSA)movement enjoys widespread support from the research and development community because it seeks to improve livelihoods in response to climate change.Objective:This study explores an innovative approach to optimizing role assignment within agricultural frameworks to effectively scale AI-driven innovations.By leveraging advanced algorithms and machine learning techniques,the research aims to streamline the allocation of tasks and responsibilities among various stakeholders,including farmers,agronomists,technicians,and AI systems.Methods:The methodology involves the development of a dynamic role assignment model that considers factors such as expertise,resource availability,and real-time environmental data.This model is tested in various agricultural scenarios to evaluate its impact on operational efficiency and innovation scalability.The findings demonstrate that optimized role assignment not only enhances the performance of AI applications but also fosters a collaborative ecosystem that is adaptable to changing agricultural demands.Results:&Discussion:This research finds a number of elements that affect how well duties are distributed within agricultural frameworks,including organizational frameworks,leadership,resource accessibility,and cooperative efforts through AI.In addition to advocating for its comprehensive integration into the sector's culture,this paper offers a collection of best practices and techniques for optimizing role allocation in agriculture.Additionally,the study gives a thorough overview,summary,and analysis of a few papers that are specifically concerned with scaling innovation in the field of agricultural research for development.Significance:Furthermore,the study highlights the potential of AI to transform traditional farming practices,reduce labor-intensive processes,and improve decision-making accuracy.The proposed approach serves as a blueprint for agricultural enterprises aiming to adopt AI technologies while ensuring optimal utilization of human and technological resources.By addressing the challenges of role ambiguity and resource allocation,this research contributes to the broader goal of achieving sustainable and resilient agricultural systems through technological innovation.
基金support of the National Natural Science Foundation of China under grant numbers 62101570 and 61901494financial support has played a crucial role in the successful completion of this research.
文摘In this paper,we present a novel unimodular sequence design algorithm based on the coordinate descent(CD)algorithm,aimed at countering electronic surveillance(ES)systems based on cyclostationary analysis.Our algorithm not only provides resistance against cyclostationary analysis(CSA)but also maintains low integrated sidelobe(ISL)characteristics.Initially,we derive the expression of the cyclostationary feature(CSF)detector and simplify it into an iterative quadratic form.Additionally,we derive a quadratic form to ensure the similarity of the autocorrelation sidelobes.To balance the minimization of the detection probability and the ISL values,we introduce a Pareto scalar that transforms the multiobjective optimization problem into a convex combination of objective functions.This approach allows us to find an optimal trade-off between the two objectives.Finally,we propose a monotonic algorithm based on the CD algorithm to counter CSA analysis.This algorithm efficiently solves the optimization problem mentioned earlier.Numerical experiments are conducted to validate the correctness and effectiveness of our proposed algorithm.
文摘There is a need for more focus in understanding the economic benefits of Climate-Smart Agriculture(CSA)interventions,particularly in sub-Saharan Africa,where extreme climate events are significantly affecting agriculture and rural livelihoods.This study used the Net Present Value(NPV),Internal Rate of Return(IRR),Benefit-Cost Ratio(BCR),and payback period to evaluate the economic viability of the adopted CSA interventions in the three villages(Doggoh,Jeffiri,and Wulling)of the dryland farming systems of northern Ghana,where CSA interventions were mostly practiced.Data were collected from 161 farm households by the questionnaire survey.The results showed that CSA interventions including livestock-crop integration,mixed cropping,crop rotation,nutrient integration,and tie ridging enhanced crop yield and the household income of smallholder farmers.The five CSA interventions selected by smallholders were in the following order of priority:livestock-crop integration(BCR=2.87),mixed cropping(BCR=2.54),crop rotation(BCR=2.24),nutrient integration(BCR=1.98),and tie ridging(BCR=1.42).Results further showed that livestock-crop integration was the most profitable CSA intervention even under a pessimistic assumption with a long payback period of 5.00 a.Moreover,this study indicated that the implementation of CSA interventions,on average,was relatively profitable and had a nominal financial risk for smallholder farmers.Understanding the economic viability of CSA interventions will help in decision-making process toward selecting the right CSA interventions for resilience development.
文摘The present review critically examines the role of neglected and underutilized crops(NUCs)in enhancing the resilience of South Asian cropping systems and diets in the context of climate change and nutritional challenges.This analysis reveals that integrating NUCs,such as millets,sorghums,amaranth,and indigenous legumes,into existing cropping systems can significantly improve the climate resilience,dietary diversity,and ecological sustainability of the food systems.These crops exhibit superior tolerance to abiotic stress and offer higher nutritional density compared to staple cereals,such as rice and wheat.However,their adoption faces challenges,including limited research investment,fragmented value chains,etc.We further identify that complementary cropping strategies and climate-smart agriculture(CSA)practices can optimize resource use while boosting smallholder farmers’income.NUCs are pivotal for the transformation of exist cropping systems towards nutrition-sensitive and climate-resilient agricultural and food systems.Strategic integration of NUCs can simultaneously address food insecurity,biodiversity loss,and rural poverty.Yet,unlocking their potential requires coordinated efforts in genetic improvement,market development,and policy frameworks tailored to regional contexts.This synthesis provides a comprehensive roadmap for policy-makers,researchers,and farmers to leverage NUCs as“Future Smart Food”.By bridging agronomic,nutritional,and socioeconomic perspectives,this study highlights the transformative potential of NUCs in achieving Sustainable Development Goals(SDGs)across South Asian countries.
