Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air poll...Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.展开更多
Correction to:Nano-Micro Lett.(2023)15:233 https://doi.org/10.1007/s40820-023-01201-7 Following publication of the original article[1],the authors reported that the first two lines of the introduction were accidentall...Correction to:Nano-Micro Lett.(2023)15:233 https://doi.org/10.1007/s40820-023-01201-7 Following publication of the original article[1],the authors reported that the first two lines of the introduction were accidentally placed in the right-hand column of the page in the PDF,which affects the readability.展开更多
With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presen...With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator(Stiff-TENG)for variable inclusions in soft objects detection.The Stiff-TENG employs a stacked structure comprising an indium tin oxide film,an elastic sponge,a fluorinated ethylene propylene film with a conductive ink electrode,and two acrylic pieces with a shielding layer.Through the decoupling method,the Stiff-TENG achieves stiffness detection of objects within 1.0 s.The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed.The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures,enabling effective recognition of variable inclusions in soft object,reaching a recognition accuracy of 99.7%.Furthermore,its adaptability makes it well-suited for the detection of pathological conditions within the human body,as pathological tissues often exhibit changes in the stiffness of internal organs.This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.展开更多
Responsivity is a critical parameter for sensors utilized in industrial miniaturized sensors and biomedical implants,which is typically constrained by the size and the coupling with external reader,hindering their wid...Responsivity is a critical parameter for sensors utilized in industrial miniaturized sensors and biomedical implants,which is typically constrained by the size and the coupling with external reader,hindering their widespread applications in our daily life.Here,we propose a highly-responsive sensing method based on Hamiltonian hopping,achieving the responsivity enhancement by 40 folds in microscale sensor detection compared to the standard method.We implement this sensing method in a nonlinear system with a pair of coupled resonators,one of which has a nonlinear gain.Surprisingly,our method surpasses the sensing performance at an exceptional point(EP)—simultaneous coalescence of both eigenvalues and eigenvectors.The responsivity of our method is notably enhanced thanks to the large frequency response at a Hamiltonian hopping point(HHP)in the strong coupling,far from the EP.Our study also reveals a linear HHP shift under different perturbations and demonstrates the detection capabilities down to sub-picofarad(<1 pF)of the microscale pressure sensors,highlighting their potential applications in biomedical implants.展开更多
Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential applications.To further optimize the performance of metalens,it is necessary t...Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential applications.To further optimize the performance of metalens,it is necessary to characterize the phase modulation of the metalens.In this study,we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and robustness.Our findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently long.This system enables the analysis of focal length and aberration by utilizing the computed phase distribution.We extend our investigation to measure the phase distribution of the metalens operating in the near-infrared(NIR)spectrum and identify the impact of defects in the sample on the phase.Additionally,we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working mediums.Our system provides a straightforward method for the phase characterization of metalens,aiding in optimizing the metalens design and functionality.展开更多
A meta-lens array-based Shack-Hartmann wavefront sensor has been developed to break the limits imposed by the size and curvature of traditional micro-lenses,which significantly improves both sampling density and angul...A meta-lens array-based Shack-Hartmann wavefront sensor has been developed to break the limits imposed by the size and curvature of traditional micro-lenses,which significantly improves both sampling density and angular resolution of phase measurement.Metasurface advances the field of optical phase measurement to smaller-scale complex wavefront characterization.展开更多
The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent years.However,a critical challenge in this evolution is the efficient and accurate convers...The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent years.However,a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram(POH)generation.Existing computational models often struggle to address the inherent complexities of optical phenomena,compromising the conversion process.In this study,we present the cross-domain fusion network(CDFN),an architecture designed to tackle the complexities involved in POH generation.The CDFN employs a multi-stage(MS)mechanism to progressively learn the translation from amplitude to phase domain,complemented by the deep supervision(DS)strategy of middle features to enhance task-relevant feature learning from the initial stages.Additionally,we propose an infinite phase mapper(IPM),a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of holography.Through simulations,our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset,achieving an average PSNR of 31.68 dB and SSIM of 0.944.Furthermore,we realize high-quality color image reconstruction in optical experiments.The experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.展开更多
In this study,a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multiobjective energy resource management.Addressing the need for sustainable energy solutions,this technique integra...In this study,a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multiobjective energy resource management.Addressing the need for sustainable energy solutions,this technique integrated neural network models as fitness functions,representing an advancement in artificial intelligencedriven optimization.Data collected in the European Union covered greenhouse gas emissions,energy consumption by sources,energy imports,and Levelized Cost of Energy.Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions,costs,and imports,neural network prediction models were used to project the effect of new energy combinations on these variables.The projections were then fed into the gene modification optimization process to identify optimal configurations.Over 28 generations,simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions.Human bias and subjectivity were mitigated by automating parameter settings,enhancing the objectivity of results.Benchmarking against traditional methods,such as Euclidean Distance,validated the superior performance of this approach.Furthermore,the technique’s ability to visualize chromosomes and gene values offered clarity in optimization processes.These results suggest significant advancements in the energy sector and potential applications in other industries,contributing to the global effort to combat climate change.展开更多
Ferroelectric nanocapacitors have attracted intensive research interest due to their novel functionalities and potential application in nanodevices.However,due to the lack of knowledge of domain evolution in isolated ...Ferroelectric nanocapacitors have attracted intensive research interest due to their novel functionalities and potential application in nanodevices.However,due to the lack of knowledge of domain evolution in isolated nanocapacitors,precise manipulation of topological domain switching in the nanocapacitor is still a challenge.Here,we report unique bubble and cylindrical domains in the well-ordered BiFeO_(3) nanocapacitor array.The transformation of bubble,cylindrical and mono domains in isolated ferroelectric nanocapacitor has been demonstrated via scanning probe microscopy(SPM).The bubble domain can be erased to mono domain or written to cylindrical domain and mono domain by positive and negative voltage,respectively.Additionally,the domain evolution rules,which are mainly affected by the depolarization field,have been observed in the nanocapacitors with different domain structures.This work will be helpful in understanding the domain evolution in ferroelectric nanocapacitors and providing guidance on the manipulation of nanoscale topological domains.展开更多
Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging problem.CNVs,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic ...Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging problem.CNVs,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of diseases.The characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream analysis.As the allelic copy number increases,the error rate of genotyping increases sharply.Some instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the model.Motivated by these,we propose a transfer learning-based method to genotype structural variations accurately considering CNVs.The method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype datasets.It maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct genotyping.By adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be minimized.We applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and Gindel.The experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and efficiency.The source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.展开更多
基金supported by the National Natural Science Foundation of China(42277087,42130708,42471021,42277482,and 42361144876)the Natural Science Foundation of Guangdong Province(2024A1515012550)+3 种基金the Hainan Institute of National Park grant(KY-23ZK01)the Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan(JC2022011)the Shenzhen Science and Technology Program(JCYJ20240813112106009 and ZDSYS20220606100806014)the Scientific Research Start-up Funds(QD2021030C)from Tsinghua Shenzhen International Graduate School。
文摘Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.
文摘Correction to:Nano-Micro Lett.(2023)15:233 https://doi.org/10.1007/s40820-023-01201-7 Following publication of the original article[1],the authors reported that the first two lines of the introduction were accidentally placed in the right-hand column of the page in the PDF,which affects the readability.
基金This work is supported by the grant from the National Natural Science Foundation of China under Grants 62104125 and 62311530102,Guangdong Innovative and Entrepreneurial Research Team Program(2021ZT09L197)Guangdong Basic and Applied Basic Research Foundation(2020A1515110887)+1 种基金Tsinghua Shenzhen International Graduate School-Shenzhen Pengrui Young Faculty Program of Shenzhen Pengrui Foundation(No.SZPR2023005)Shenzhen Science and Technology Program(JCYJ20220530143013030).
文摘With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator(Stiff-TENG)for variable inclusions in soft objects detection.The Stiff-TENG employs a stacked structure comprising an indium tin oxide film,an elastic sponge,a fluorinated ethylene propylene film with a conductive ink electrode,and two acrylic pieces with a shielding layer.Through the decoupling method,the Stiff-TENG achieves stiffness detection of objects within 1.0 s.The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed.The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures,enabling effective recognition of variable inclusions in soft object,reaching a recognition accuracy of 99.7%.Furthermore,its adaptability makes it well-suited for the detection of pathological conditions within the human body,as pathological tissues often exhibit changes in the stiffness of internal organs.This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.
基金Key Research and Development Program of Hunan Province(2023GK2009)Hunan Provincial Major Sci-Tech Program(2023ZJ1010)。
文摘Responsivity is a critical parameter for sensors utilized in industrial miniaturized sensors and biomedical implants,which is typically constrained by the size and the coupling with external reader,hindering their widespread applications in our daily life.Here,we propose a highly-responsive sensing method based on Hamiltonian hopping,achieving the responsivity enhancement by 40 folds in microscale sensor detection compared to the standard method.We implement this sensing method in a nonlinear system with a pair of coupled resonators,one of which has a nonlinear gain.Surprisingly,our method surpasses the sensing performance at an exceptional point(EP)—simultaneous coalescence of both eigenvalues and eigenvectors.The responsivity of our method is notably enhanced thanks to the large frequency response at a Hamiltonian hopping point(HHP)in the strong coupling,far from the EP.Our study also reveals a linear HHP shift under different perturbations and demonstrates the detection capabilities down to sub-picofarad(<1 pF)of the microscale pressure sensors,highlighting their potential applications in biomedical implants.
基金supported by National Key R&D Program of China(2023YFA1406900 and 2022YFA1404800)the University Grants Committee/Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.AoE/P-502/20,CRF Project:C1015-21E,C5031-22G,and GRF Project:CityU15303521,CityU11305223,CityU11310522,CityU11300123)+7 种基金City University of Hong Kong(Project Nos.9380131,9610628,and 7005867)National Natural Science Foundation of China(Nos.62375232,62305184,12221004,12234007 and 12321161645)Major Program of National Natural Science Foundation of China(Grant Nos.T2394480,T2394481)Science and Technology Commission of Shanghai Municipality(22142200400,21DZ1101500,2019SHZDZX01 and 23DZ2260100)Project funded by China Postdoctoral Science Foundation(BX20220093)Shanghai Yangfan Project(23YF1415300)Applied Basic Research Foundation of Guangdong Province(2023A1515012932)Science,Technology and Innovation Commission of Shenzhen Municipality(WDZC20220818100259004).
文摘Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential applications.To further optimize the performance of metalens,it is necessary to characterize the phase modulation of the metalens.In this study,we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and robustness.Our findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently long.This system enables the analysis of focal length and aberration by utilizing the computed phase distribution.We extend our investigation to measure the phase distribution of the metalens operating in the near-infrared(NIR)spectrum and identify the impact of defects in the sample on the phase.Additionally,we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working mediums.Our system provides a straightforward method for the phase characterization of metalens,aiding in optimizing the metalens design and functionality.
文摘A meta-lens array-based Shack-Hartmann wavefront sensor has been developed to break the limits imposed by the size and curvature of traditional micro-lenses,which significantly improves both sampling density and angular resolution of phase measurement.Metasurface advances the field of optical phase measurement to smaller-scale complex wavefront characterization.
基金National Natural Science Foundation of China(62305184)Basic and Applied Basic Research Foundation of Guangdong Province(2023A1515012932)+4 种基金Science,Technology and Innovation Commission of Shenzhen Municipality(WDZC20220818100259004)Research Grants Council of the Hong Kong Special Administrative Region,China(C5031-22G,CityU11300123,CityU11310522)Guangdong Provincial Department of Science and Technology(2020B1515120073)City University of Hong Kong(9610628)Research Grants Council of Hong Kong(ECS 27212822).
文摘The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent years.However,a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram(POH)generation.Existing computational models often struggle to address the inherent complexities of optical phenomena,compromising the conversion process.In this study,we present the cross-domain fusion network(CDFN),an architecture designed to tackle the complexities involved in POH generation.The CDFN employs a multi-stage(MS)mechanism to progressively learn the translation from amplitude to phase domain,complemented by the deep supervision(DS)strategy of middle features to enhance task-relevant feature learning from the initial stages.Additionally,we propose an infinite phase mapper(IPM),a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of holography.Through simulations,our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset,achieving an average PSNR of 31.68 dB and SSIM of 0.944.Furthermore,we realize high-quality color image reconstruction in optical experiments.The experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.
基金Open Access funding provided by Hungarian Electronic Information Services National Programme(EISZ)-Corvinus University of Budapest。
文摘In this study,a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multiobjective energy resource management.Addressing the need for sustainable energy solutions,this technique integrated neural network models as fitness functions,representing an advancement in artificial intelligencedriven optimization.Data collected in the European Union covered greenhouse gas emissions,energy consumption by sources,energy imports,and Levelized Cost of Energy.Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions,costs,and imports,neural network prediction models were used to project the effect of new energy combinations on these variables.The projections were then fed into the gene modification optimization process to identify optimal configurations.Over 28 generations,simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions.Human bias and subjectivity were mitigated by automating parameter settings,enhancing the objectivity of results.Benchmarking against traditional methods,such as Euclidean Distance,validated the superior performance of this approach.Furthermore,the technique’s ability to visualize chromosomes and gene values offered clarity in optimization processes.These results suggest significant advancements in the energy sector and potential applications in other industries,contributing to the global effort to combat climate change.
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2021A1515110155)National Key Research and Development Program of China(No.2022YFF0706100)+2 种基金the National Natural Science Foundation of China(Nos.92066203,12192213,U22A20117,52002134)the Guangdong Provincial Key Laboratory Program from Guangdong Science and Technology Department(No.2021B1212040001)the Science and Technology Projects in Guangzhou(No.202201000008)。
文摘Ferroelectric nanocapacitors have attracted intensive research interest due to their novel functionalities and potential application in nanodevices.However,due to the lack of knowledge of domain evolution in isolated nanocapacitors,precise manipulation of topological domain switching in the nanocapacitor is still a challenge.Here,we report unique bubble and cylindrical domains in the well-ordered BiFeO_(3) nanocapacitor array.The transformation of bubble,cylindrical and mono domains in isolated ferroelectric nanocapacitor has been demonstrated via scanning probe microscopy(SPM).The bubble domain can be erased to mono domain or written to cylindrical domain and mono domain by positive and negative voltage,respectively.Additionally,the domain evolution rules,which are mainly affected by the depolarization field,have been observed in the nanocapacitors with different domain structures.This work will be helpful in understanding the domain evolution in ferroelectric nanocapacitors and providing guidance on the manipulation of nanoscale topological domains.
基金supported by the National Natural Science Foundation of China (Grant No.31701150)the Fundamental Research Funds for the Central Universities (CXTD2017003).
文摘Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging problem.CNVs,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of diseases.The characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream analysis.As the allelic copy number increases,the error rate of genotyping increases sharply.Some instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the model.Motivated by these,we propose a transfer learning-based method to genotype structural variations accurately considering CNVs.The method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype datasets.It maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct genotyping.By adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be minimized.We applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and Gindel.The experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and efficiency.The source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.
