With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding ...Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future.展开更多
Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to comm...Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates.展开更多
Micro RNA-133a(mi RNA-133a) and cardiac troponin I(c Tn I) are different-type crucial biomarkers of acute myocardial infarction(AMI), whose levels are great significance for AMI diagnosis and treatment. Herein,a novel...Micro RNA-133a(mi RNA-133a) and cardiac troponin I(c Tn I) are different-type crucial biomarkers of acute myocardial infarction(AMI), whose levels are great significance for AMI diagnosis and treatment. Herein,a novel photoelectrochemical-electrochemical(PEC-EC) dual-mode biosensing platform for dual-target assays of mi RNA-133a and c Tn I was developed. In which, a PEC-EC dual-mode sensing platform for mi RNA-133a was constructed based on the changes of the photocurrent inhibition effect and the electrochemical signal of Fc on the Fc-hairpin DNA probe(Fc-HP)/Zn Cd S-quantum dots(QDs)/ITO electrode. Furthermore, under magnetic separation and the specific interaction between c Tn I and its aptamer, the N-doped porous carbon-Zn O polyhedra(NPC-Zn O)-hemin-capture DNA probe hybrid(NH-CP) was obtained and introduced to the Fc-HP/Zn Cd S-QDs/ITO electrode via hybridization between NH-CP and Fc-HP. The hemin molecules encapsulated in NH-CP could effectively induce the photocurrent-polarity-switching of the FcHP/Zn Cd S-QDs/ITO electrode and generate a new electrochemical signal originating from hemin. Thus,c Tn I was assayed sensitively and selectively by the PEC-EC dual-mode biosensing platform. Here, Fc and hemin not only serve as the electrochemical indicators, but also respectively inhibit the photocurrent and switch the photocurrent polarity of Zn Cd S-QDs. Furthermore, the proposed biosensing platform could be easily expanded to the detection of other multiplex-type biomarkers via the change of the sequences of the related DNA probes, implying its significant potential in clinical diagnosis and biological analysis.展开更多
Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetec...Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.展开更多
High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal im...High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.展开更多
The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geomet...The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geometries.We investigate the range of validity of the approximation by random walk simulations of restricted diffusion in a cylinder using isotropic diffusion encoding sequences as well as conventional single gradient pulse pairs and oscillating gradient waveforms.The results show that clear deviations from the approximation may be observed at relative signal attenuations below 0.1 for onedimensional sequences with few oscillation periods.Increasing the encoding dimensionality and/or number of oscillations while extending the total duration of the waveform diminishes the non-Gaussian effects while preserving the low apparent diffusivities characteristic of restriction.展开更多
This paper describes an experimental study investigating the effects of sinusoidal pulsed injection on the combustion mode transition in a dual-mode supersonic combustor.The results are obtained under inflow condition...This paper describes an experimental study investigating the effects of sinusoidal pulsed injection on the combustion mode transition in a dual-mode supersonic combustor.The results are obtained under inflow conditions of 2.9 MPa stagnation pressure,1900 K stagnation temperature,and Mach number of 3.0.It has been observed that,at the same equivalence ratio,the combustion mode and flow field structure undergo irreversible changes from a weak combustion state to a strong combustion state at a specific pulsed jet frequency compared to steady jet.For steady jet,the combustion mode is dual-mode.As the frequency of the unsteady jet changes,the combustion mode also changes:it becomes a transition mode at frequencies of 171 Hz and 260 Hz,and a ramjet mode at 216 Hz.Combustion instability under steady jet manifests as a transition in flame stabilization mode.In contrast,under pulsed jet,combustion instability appears either as a transition in flame stabilization mode or as flame blow-off and flashback.The flow field oscillation frequency in the non-reacting flow is 171 Hz,which may resonate with the 171 Hz pulsed jet frequency,making the combustion oscillations most pronounced at this frequency.When the jet frequency is increased to 216 Hz,the combustion intensity significantly increases,and the combustion mode transfers to the ramjet mode.However,further increasing the frequency to 260 Hz results in a decrease in combustion intensity,returning to the transition mode.The frequency of the flow field oscillations varies with the coupling of the pulsed injection frequency,shock wave,and flame,and if the system reaches an unstable state,that is,pre-combustion shock train moves far upstream of the isolator during the pulsed jet period,strong combustion state can be achieved,and this process is irreversible.展开更多
Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the t...Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.展开更多
Deep learning(DL)methods like multilayer perceptrons(MLPs)and convolutional neural networks(CNNs)have been applied to predict the complex traits in animal and plant breeding.However,improving the genomic prediction ac...Deep learning(DL)methods like multilayer perceptrons(MLPs)and convolutional neural networks(CNNs)have been applied to predict the complex traits in animal and plant breeding.However,improving the genomic prediction accuracy still presents signifcant challenges.In this study,we applied CNNs to predict swine traits using previously published data.Specifcally,we extensively evaluated the CNN model's performance by employing various sets of single nucleotide polymorphisms(SNPs)and concluded that the CNN model achieved optimal performance when utilizing SNP sets comprising 1,000 SNPs.Furthermore,we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into sets of eight binary variables.This innovative encoding method signifcantly enhanced the CNN's prediction accuracy for swine traits,outperforming the traditional one-hot encoding techniques.Our fndings suggest that the expanded one-hot encoding method can improve the accuracy of DL methods in the genomic prediction of swine agricultural economic traits.This discovery has significant implications for swine breeding programs,where genomic prediction is pivotal in improving breeding strategies.Furthermore,future research endeavors can explore additional enhancements to DL methods by incorporating advanced data pre-processing techniques.展开更多
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
Norovirus(NoV)is regarded as one of the most common causes of foodborne diarrhea in the world.It is urgent to identify the pathogenic microorganism of the diarrhea in short time.In this work,we developed an electroche...Norovirus(NoV)is regarded as one of the most common causes of foodborne diarrhea in the world.It is urgent to identify the pathogenic microorganism of the diarrhea in short time.In this work,we developed an electrochemical and colorimetric dual-mode detection for NoV based on the excellent dual catalytic properties of copper peroxide/COF-NH_(2)nanocomposite(CuO_(2)@COF-NH_(2)).For the colorimetric detection,NoV can be directly detected by the naked eye based on CuO_(2)@COF-NH_(2)as a laccase-like nonazyme using“peptide-NoV-antibody”recognition mode.The colorimetric assay displayed a wide and quality linear detection range from 1 copy/mL to 5000 copies/mL of NoV with a low limit of detection(LOD)of 0.125 copy/mL.For the electrochemical detection of NoV,CuO_(2)@COF-NH_(2)showed an oxidation peak of copper ion from Cu^(+)to Cu^(2+)using“peptide-NoV-antibody”recognition mode.The electrochemical assay showed a linear detection range was 1-5000 copies/mL with a LOD of 0.152 copy/mL.It's worthy to note that this assay does not need other electrical signal molecule,which provide the stable and sensitive electrochemial detection for NoV.The electrochemical and colorimetric dual-mode detection was used to detect NoV in foods and faceal samples,which has the potential for improving food safety and diagnosing of NoV-infected diarrhea.展开更多
Sweat loss monitoring is important for understanding the body’s thermoregulation and hydration status,as well as for comprehensive sweat analysis.Despite recent advances,developing a low-cost,scalable,and universal m...Sweat loss monitoring is important for understanding the body’s thermoregulation and hydration status,as well as for comprehensive sweat analysis.Despite recent advances,developing a low-cost,scalable,and universal method for the fabrication of colorimetric microfluidics designed for sweat loss monitoring remains challenging.In this study,we propose a novel laserengraved surface roughening strategy for various flexible substrates.This process permits the construction of microchannels that show distinct structural reflectance changes before and after sweat filling.By leveraging these unique optical properties,we have developed a fully laser-engraved microfluidic device for the quantification of naked-eye sweat loss.This sweat loss sensor is capable of a volume resolution of 0.5µL and a total volume capacity of 11µL,and can be customized to meet different performance requirements.Moreover,we report the development of a crosstalk-free dual-mode sweat microfluidic system that integrates an Ag/AgCl chloride sensor and a matching wireless measurement flexible printed circuit board.This integrated system enables the real-time monitoring of colorimetric sweat loss signals and potential ion concentration signals without crosstalk.Finally,we demonstrate the potential practical use of this microfluidic sweat loss sensor and its integrated system for sports medicine via on-body studies.展开更多
Dual-excitation and dual-emission Y_(4)GeO_(8):Bi^(3+),Sm^(3+)phosphors were manufactured by traditional solidphase sintering technique.The X-ray diffraction,morphology,photoluminescence,energy transfer process and te...Dual-excitation and dual-emission Y_(4)GeO_(8):Bi^(3+),Sm^(3+)phosphors were manufactured by traditional solidphase sintering technique.The X-ray diffraction,morphology,photoluminescence,energy transfer process and temperature sensing properties of Y_(4)GeO_(8):Bi^(3+),Sm^(3+)samples were comprehensively evaluated.The Y4GeO_(8):Bi^(3+),Sm^(3+)phosphors exhibit characteristic emissions of Bi^(3+)(^(3)P_(1)→^(1)S_(0)) and Sm^(3+)(^(4)G_(5/2)→^(6)H)under both 290 and 347 nm excitations.In fluorescence intensity ratio and Commission International de L'Eclairage coordinates modes,Y_(4)GeO_(8):Bi^(3+),Sm^(3+)samples present excellent temperature measurement performance.The maximum relative sensitivity(S_(r-max)) values of the former are 1.55%/K(460 K,290 nm excitation) and 0.82%/K(506 K,347 nm excitation).The S_(r-max)(x) values of the latter are 0.21 %/K(437 K,290 nm excitation) and 0.15%/K(513 K,347 nm excitation).These results illustrate that Y_(4)GeO_(8):Bi^(3+),Sm^(3+)phospho rs can be used as a candidate material for a dual-mode optical thermometer under dual-excitation.展开更多
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related diseases.In this study,a novel colorimetric-fluorescent dual-mode biosensor based on...Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related diseases.In this study,a novel colorimetric-fluorescent dual-mode biosensor based on highly dispersive trimetal-modified graphite-phase carbon nitride nanocomposites for acetylcholinesterase detection was designed and synthesized by phosphorus doping and a mixed-metal MOF strategy.The specific surface area of trimetal-modified graphite-phase carbon nitride nanocomposites increased from 15.81 to 96.69 g m^(-2),and its thermal stability,interfacial charge transfer,and oxidation-reduction capability were enhanced compared with those of graphite-phase carbon nitride.First-principles density functional theory calculations and steady-state kinetic analysis are applied to investigate the electronic structures and efficient peroxidase-mimicking properties of trimetal-modified graphite-phase carbon nitride nanocomposites.The oxidation of 3,3',5,5'-tetramethylbenzidin was inhibited by thiocholine,which originates from the decomposition of thiocholine iodide by Acetyl-cholinesterase(AChE),resulting in changes in fluorescence and absorbance intensity.Due to the independence and complementarity of the signals,a highly precise colorimetric-fluorescent dual-mode biosensor with a linear range for detecting AChE of 4-20μU mL^(-1) and detection limits of 0.13μU mL^(-1)(colorimetric)and 0.04μU mL^(-1)(fluorescence)was developed.The spiking recovery of AChE in actual samples was 99.0%-100.4%.Therefore,a highly accurate,specific,and stable dual-mode biosensor is available for AChE detection,and this biosensor has the potential for the analysis of other biomarkers.展开更多
Nowadays, the global climate is constantly being destroyed and the fluctuations in ambient temperature are becoming more frequent. However, conventional single-mode thermal management strategies(heating or cooling) fa...Nowadays, the global climate is constantly being destroyed and the fluctuations in ambient temperature are becoming more frequent. However, conventional single-mode thermal management strategies(heating or cooling) failed to resolve such dynamic temperature changes. Moreover, developing thermal management devices capable of accommodating these temperature variations while remaining simple to fabricate and durable has remained a formidable obstacle. To address these bottlenecks, we design and successfully fabricate a novel dual-mode hierarchical(DMH) composite film featuring a micronanofiber network structure, achieved through a straightforward two-step continuous electrospinning process. In cooling mode, it presents a high solar reflectivity of up to 97.7% and an excellent atmospheric transparent window(ATW) infrared emissivity of up to 98.9%. Noted that this DMH film could realize a cooling of 8.1 ℃ compared to the ambient temperature outdoors. In heating mode, it also exhibits a high solar absorptivity of 94.7% and heats up to 11.9 ℃ higher than black cotton fabric when utilized by individuals. In practical application scenarios, a seamless transition between efficient cooling and heating is achieved by simply flipping the film. More importantly, the DMH film combining the benefits of composites demonstrates portability, durability, and easy-cleaning, promising to achieve large-scale production and use of thermally managed textiles in the future. The energy savings offered by film applications provide a viable solution for the early realization of carbon neutrality.展开更多
Magnetic resonance imaging(MRI)is a non-invasive medical imaging technique that has been widely applied in the clinical diagnosis of diseases across various fields.Currently,there is a dearth of high-performance dual-...Magnetic resonance imaging(MRI)is a non-invasive medical imaging technique that has been widely applied in the clinical diagnosis of diseases across various fields.Currently,there is a dearth of high-performance dual-mode contrast agents that provide precise diagnostic information for complex diseases.In this study,iron oxide-gadolinium nanoparticles(IO-Gd NPs)bridged by hydrophilic ligand ethylenedi-amine tetramethylenephosphonic acid(EDTMP)are designed inspired by Solomon-Bloembergen-Morgan(SBM)theory,where T_(1)and T_(2)relaxivity increase with a reduction in Gd content.In particular,the NPs with minimum Gd content exhibit excellent dual-mode contrast performance(r_(1)=94.42 mM^(−1)s^(−1),r_(2)=343.62 mM^(−1)s^(−1)).The underlying mechanism is that the bridged EDTMP coordinate limits Gd tum-bling and forces Gd^(3+)to expose itself to the surface of the NPs.This provides more opportunities for water molecules to coordinate with Gd^(3+)and significantly enhances proton relaxivity.Moreover,the hy-drophilicity of the ligand enhances the water dispersion stability of the NPs and increases the exchange rate of water protons near the NPs,further enhancing the dual-mode contrast effect.Finally,the biocom-patibility and in vitro/vivo imaging performances of the IO@EDTMP-Gd NPs are systematically evaluated,and the results demonstrate their potential as dual-mode contrast agents.This study provides a new strategy for developing dual-mode MRI contrast agents that can further improve the accuracy of MRI in the diagnosis of complex diseases.展开更多
Optical reservoir computing(ORC)offers advantages,such as high computational speed,low power consumption,and high training speed,so it has become a competitive candidate for time series analysis in recent years.The cu...Optical reservoir computing(ORC)offers advantages,such as high computational speed,low power consumption,and high training speed,so it has become a competitive candidate for time series analysis in recent years.The current ORC employs single-dimensional encoding for computation,which limits input resolution and introduces extraneous information due to interactions between optical dimensions during propagation,thus constraining performance.Here,we propose complex-value encoding-based optoelectronic reservoir computing(CE-ORC),in which the amplitude and phase of the input optical field are both modulated to improve the input resolution and prevent the influence of extraneous information on computation.In addition,scale factors in the amplitude encoding can fine-tune the optical reservoir dynamics for better performance.We built a CE-ORC processing unit with an iteration rate of up to∼1.2 kHz using high-speed communication interfaces and field programmable gate arrays(FPGAs)and demonstrated the excellent performance of CE-ORC in two time series prediction tasks.In comparison with the conventional ORC for the Mackey–Glass task,CE-ORC showed a decrease in normalized mean square error by∼75%.Furthermore,we applied this method in a weather time series analysis and effectively predicted the temperature and humidity within a range of 24 h.展开更多
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
基金supported by the National Natural Science Foundation of China(Grant No.62001440).
文摘Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future.
基金sponsored by the National Natural Science Foundation of China No.U24B201114,6247070859,62302114 and No.62172353Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1331007 and No.1311022Natural Science Foundation of Guangdong Province No.2024A1515010177.
文摘Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates.
基金financially supported by National Natural Science Foundation of China (Nos. 22074033, 22374035)。
文摘Micro RNA-133a(mi RNA-133a) and cardiac troponin I(c Tn I) are different-type crucial biomarkers of acute myocardial infarction(AMI), whose levels are great significance for AMI diagnosis and treatment. Herein,a novel photoelectrochemical-electrochemical(PEC-EC) dual-mode biosensing platform for dual-target assays of mi RNA-133a and c Tn I was developed. In which, a PEC-EC dual-mode sensing platform for mi RNA-133a was constructed based on the changes of the photocurrent inhibition effect and the electrochemical signal of Fc on the Fc-hairpin DNA probe(Fc-HP)/Zn Cd S-quantum dots(QDs)/ITO electrode. Furthermore, under magnetic separation and the specific interaction between c Tn I and its aptamer, the N-doped porous carbon-Zn O polyhedra(NPC-Zn O)-hemin-capture DNA probe hybrid(NH-CP) was obtained and introduced to the Fc-HP/Zn Cd S-QDs/ITO electrode via hybridization between NH-CP and Fc-HP. The hemin molecules encapsulated in NH-CP could effectively induce the photocurrent-polarity-switching of the FcHP/Zn Cd S-QDs/ITO electrode and generate a new electrochemical signal originating from hemin. Thus,c Tn I was assayed sensitively and selectively by the PEC-EC dual-mode biosensing platform. Here, Fc and hemin not only serve as the electrochemical indicators, but also respectively inhibit the photocurrent and switch the photocurrent polarity of Zn Cd S-QDs. Furthermore, the proposed biosensing platform could be easily expanded to the detection of other multiplex-type biomarkers via the change of the sequences of the related DNA probes, implying its significant potential in clinical diagnosis and biological analysis.
基金supported by the Fundamental Research Funds for the Central Universities(xxj022019009)。
文摘Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.
基金supported by the Fundamental Research Funds for the Central Universities(No.2024JBZX027)the National Natural Science Foundation of China(No.52375078).
文摘High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.
基金financially supported by the Swedish Research Council(2022-04422_VR)。
文摘The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geometries.We investigate the range of validity of the approximation by random walk simulations of restricted diffusion in a cylinder using isotropic diffusion encoding sequences as well as conventional single gradient pulse pairs and oscillating gradient waveforms.The results show that clear deviations from the approximation may be observed at relative signal attenuations below 0.1 for onedimensional sequences with few oscillation periods.Increasing the encoding dimensionality and/or number of oscillations while extending the total duration of the waveform diminishes the non-Gaussian effects while preserving the low apparent diffusivities characteristic of restriction.
基金supported by the Program of Key Laboratory of Cross-Domain Flight Interdisciplinary Technology,China(No.2023-ZY0205)。
文摘This paper describes an experimental study investigating the effects of sinusoidal pulsed injection on the combustion mode transition in a dual-mode supersonic combustor.The results are obtained under inflow conditions of 2.9 MPa stagnation pressure,1900 K stagnation temperature,and Mach number of 3.0.It has been observed that,at the same equivalence ratio,the combustion mode and flow field structure undergo irreversible changes from a weak combustion state to a strong combustion state at a specific pulsed jet frequency compared to steady jet.For steady jet,the combustion mode is dual-mode.As the frequency of the unsteady jet changes,the combustion mode also changes:it becomes a transition mode at frequencies of 171 Hz and 260 Hz,and a ramjet mode at 216 Hz.Combustion instability under steady jet manifests as a transition in flame stabilization mode.In contrast,under pulsed jet,combustion instability appears either as a transition in flame stabilization mode or as flame blow-off and flashback.The flow field oscillation frequency in the non-reacting flow is 171 Hz,which may resonate with the 171 Hz pulsed jet frequency,making the combustion oscillations most pronounced at this frequency.When the jet frequency is increased to 216 Hz,the combustion intensity significantly increases,and the combustion mode transfers to the ramjet mode.However,further increasing the frequency to 260 Hz results in a decrease in combustion intensity,returning to the transition mode.The frequency of the flow field oscillations varies with the coupling of the pulsed injection frequency,shock wave,and flame,and if the system reaches an unstable state,that is,pre-combustion shock train moves far upstream of the isolator during the pulsed jet period,strong combustion state can be achieved,and this process is irreversible.
基金supported by the National Key Research and Development Project(Grant No.2023YFB4705300)the National Natural Science Foundation of China(NSFC)(Grant Nos.62205200 and 62375168)the Natural Science Foundation of Shanghai(Grant No.22ZR1431600)。
文摘Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.
基金supported by the National Natural Science Foundation of China(32102513)the National Key Scientific Research Project(2023YFF1001100)+1 种基金the Shenzhen Innovation and Entrepreneurship PlanMajor Special Project of Science and Technology,China(KJZD20230923115003006)the Innovation Project of Chinese Academy of Agricultural Sciences(CAAS-ZDRW202006)。
文摘Deep learning(DL)methods like multilayer perceptrons(MLPs)and convolutional neural networks(CNNs)have been applied to predict the complex traits in animal and plant breeding.However,improving the genomic prediction accuracy still presents signifcant challenges.In this study,we applied CNNs to predict swine traits using previously published data.Specifcally,we extensively evaluated the CNN model's performance by employing various sets of single nucleotide polymorphisms(SNPs)and concluded that the CNN model achieved optimal performance when utilizing SNP sets comprising 1,000 SNPs.Furthermore,we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into sets of eight binary variables.This innovative encoding method signifcantly enhanced the CNN's prediction accuracy for swine traits,outperforming the traditional one-hot encoding techniques.Our fndings suggest that the expanded one-hot encoding method can improve the accuracy of DL methods in the genomic prediction of swine agricultural economic traits.This discovery has significant implications for swine breeding programs,where genomic prediction is pivotal in improving breeding strategies.Furthermore,future research endeavors can explore additional enhancements to DL methods by incorporating advanced data pre-processing techniques.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
基金financially supported by National Key Research and Development Program of China(2022YFC2601604)Major science and technology project of Yunnan Province(202202AE090085)+9 种基金the National Natural Science Foundation of China(3216059732160236)Science and technology talent and platform plan of YunnanKey Scientific and Technology Project of Yunnan(202203AC100010)Spring City Plan:the High-level Talent Promotion and Training Project of Kunming(2022SCP001)the second phase of“Double-First Class”program construction of Yunnan Universitygrants from State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan,Yunnan University(2021KF005)Key Scientific and Technology Project of Yunnan(202002AE320005)Program for Excellent Young Talents of Yunnan Universitythe Program for Donglu Scholars of Yunnan University。
文摘Norovirus(NoV)is regarded as one of the most common causes of foodborne diarrhea in the world.It is urgent to identify the pathogenic microorganism of the diarrhea in short time.In this work,we developed an electrochemical and colorimetric dual-mode detection for NoV based on the excellent dual catalytic properties of copper peroxide/COF-NH_(2)nanocomposite(CuO_(2)@COF-NH_(2)).For the colorimetric detection,NoV can be directly detected by the naked eye based on CuO_(2)@COF-NH_(2)as a laccase-like nonazyme using“peptide-NoV-antibody”recognition mode.The colorimetric assay displayed a wide and quality linear detection range from 1 copy/mL to 5000 copies/mL of NoV with a low limit of detection(LOD)of 0.125 copy/mL.For the electrochemical detection of NoV,CuO_(2)@COF-NH_(2)showed an oxidation peak of copper ion from Cu^(+)to Cu^(2+)using“peptide-NoV-antibody”recognition mode.The electrochemical assay showed a linear detection range was 1-5000 copies/mL with a LOD of 0.152 copy/mL.It's worthy to note that this assay does not need other electrical signal molecule,which provide the stable and sensitive electrochemial detection for NoV.The electrochemical and colorimetric dual-mode detection was used to detect NoV in foods and faceal samples,which has the potential for improving food safety and diagnosing of NoV-infected diarrhea.
基金support from the National Natural Science Foundation of China(No.62174152)。
文摘Sweat loss monitoring is important for understanding the body’s thermoregulation and hydration status,as well as for comprehensive sweat analysis.Despite recent advances,developing a low-cost,scalable,and universal method for the fabrication of colorimetric microfluidics designed for sweat loss monitoring remains challenging.In this study,we propose a novel laserengraved surface roughening strategy for various flexible substrates.This process permits the construction of microchannels that show distinct structural reflectance changes before and after sweat filling.By leveraging these unique optical properties,we have developed a fully laser-engraved microfluidic device for the quantification of naked-eye sweat loss.This sweat loss sensor is capable of a volume resolution of 0.5µL and a total volume capacity of 11µL,and can be customized to meet different performance requirements.Moreover,we report the development of a crosstalk-free dual-mode sweat microfluidic system that integrates an Ag/AgCl chloride sensor and a matching wireless measurement flexible printed circuit board.This integrated system enables the real-time monitoring of colorimetric sweat loss signals and potential ion concentration signals without crosstalk.Finally,we demonstrate the potential practical use of this microfluidic sweat loss sensor and its integrated system for sports medicine via on-body studies.
基金supported by the National Natural Science Foundation of China (11974315)。
文摘Dual-excitation and dual-emission Y_(4)GeO_(8):Bi^(3+),Sm^(3+)phosphors were manufactured by traditional solidphase sintering technique.The X-ray diffraction,morphology,photoluminescence,energy transfer process and temperature sensing properties of Y_(4)GeO_(8):Bi^(3+),Sm^(3+)samples were comprehensively evaluated.The Y4GeO_(8):Bi^(3+),Sm^(3+)phosphors exhibit characteristic emissions of Bi^(3+)(^(3)P_(1)→^(1)S_(0)) and Sm^(3+)(^(4)G_(5/2)→^(6)H)under both 290 and 347 nm excitations.In fluorescence intensity ratio and Commission International de L'Eclairage coordinates modes,Y_(4)GeO_(8):Bi^(3+),Sm^(3+)samples present excellent temperature measurement performance.The maximum relative sensitivity(S_(r-max)) values of the former are 1.55%/K(460 K,290 nm excitation) and 0.82%/K(506 K,347 nm excitation).The S_(r-max)(x) values of the latter are 0.21 %/K(437 K,290 nm excitation) and 0.15%/K(513 K,347 nm excitation).These results illustrate that Y_(4)GeO_(8):Bi^(3+),Sm^(3+)phospho rs can be used as a candidate material for a dual-mode optical thermometer under dual-excitation.
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
基金supported by the Hubei University of Technology Graduate Research Innovation Project(No.2022048)the Natural Science Foundation Project of Hubei Province(grant No.2022CFB533)+1 种基金the Scientific Research Plan of Education Department of Hubei Province(grant No.D20222702)the Natural Science Project of Xiaogan city(grant No.XGKJ20210100014).
文摘Highly sensitive and stable acetylcholinesterase detection is critical for diagnosing and treating various neurotransmission-related diseases.In this study,a novel colorimetric-fluorescent dual-mode biosensor based on highly dispersive trimetal-modified graphite-phase carbon nitride nanocomposites for acetylcholinesterase detection was designed and synthesized by phosphorus doping and a mixed-metal MOF strategy.The specific surface area of trimetal-modified graphite-phase carbon nitride nanocomposites increased from 15.81 to 96.69 g m^(-2),and its thermal stability,interfacial charge transfer,and oxidation-reduction capability were enhanced compared with those of graphite-phase carbon nitride.First-principles density functional theory calculations and steady-state kinetic analysis are applied to investigate the electronic structures and efficient peroxidase-mimicking properties of trimetal-modified graphite-phase carbon nitride nanocomposites.The oxidation of 3,3',5,5'-tetramethylbenzidin was inhibited by thiocholine,which originates from the decomposition of thiocholine iodide by Acetyl-cholinesterase(AChE),resulting in changes in fluorescence and absorbance intensity.Due to the independence and complementarity of the signals,a highly precise colorimetric-fluorescent dual-mode biosensor with a linear range for detecting AChE of 4-20μU mL^(-1) and detection limits of 0.13μU mL^(-1)(colorimetric)and 0.04μU mL^(-1)(fluorescence)was developed.The spiking recovery of AChE in actual samples was 99.0%-100.4%.Therefore,a highly accurate,specific,and stable dual-mode biosensor is available for AChE detection,and this biosensor has the potential for the analysis of other biomarkers.
基金financially Fundamental Research Funds for the Central Universities (2232021G-04 and 2232020D-20)Student Innovation Fund of Donghua University (GSIF-DH-M-2021003)。
文摘Nowadays, the global climate is constantly being destroyed and the fluctuations in ambient temperature are becoming more frequent. However, conventional single-mode thermal management strategies(heating or cooling) failed to resolve such dynamic temperature changes. Moreover, developing thermal management devices capable of accommodating these temperature variations while remaining simple to fabricate and durable has remained a formidable obstacle. To address these bottlenecks, we design and successfully fabricate a novel dual-mode hierarchical(DMH) composite film featuring a micronanofiber network structure, achieved through a straightforward two-step continuous electrospinning process. In cooling mode, it presents a high solar reflectivity of up to 97.7% and an excellent atmospheric transparent window(ATW) infrared emissivity of up to 98.9%. Noted that this DMH film could realize a cooling of 8.1 ℃ compared to the ambient temperature outdoors. In heating mode, it also exhibits a high solar absorptivity of 94.7% and heats up to 11.9 ℃ higher than black cotton fabric when utilized by individuals. In practical application scenarios, a seamless transition between efficient cooling and heating is achieved by simply flipping the film. More importantly, the DMH film combining the benefits of composites demonstrates portability, durability, and easy-cleaning, promising to achieve large-scale production and use of thermally managed textiles in the future. The energy savings offered by film applications provide a viable solution for the early realization of carbon neutrality.
基金supported by the National Key R&D Program of China(Nos.2020YFA0710800,2022YFE0124500)the National Natural Science Foundation of China(Nos.32271467,32071364)+1 种基金the Outstanding Postdoctoral Program of Jiangsu Province(No.2022ZB367)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Magnetic resonance imaging(MRI)is a non-invasive medical imaging technique that has been widely applied in the clinical diagnosis of diseases across various fields.Currently,there is a dearth of high-performance dual-mode contrast agents that provide precise diagnostic information for complex diseases.In this study,iron oxide-gadolinium nanoparticles(IO-Gd NPs)bridged by hydrophilic ligand ethylenedi-amine tetramethylenephosphonic acid(EDTMP)are designed inspired by Solomon-Bloembergen-Morgan(SBM)theory,where T_(1)and T_(2)relaxivity increase with a reduction in Gd content.In particular,the NPs with minimum Gd content exhibit excellent dual-mode contrast performance(r_(1)=94.42 mM^(−1)s^(−1),r_(2)=343.62 mM^(−1)s^(−1)).The underlying mechanism is that the bridged EDTMP coordinate limits Gd tum-bling and forces Gd^(3+)to expose itself to the surface of the NPs.This provides more opportunities for water molecules to coordinate with Gd^(3+)and significantly enhances proton relaxivity.Moreover,the hy-drophilicity of the ligand enhances the water dispersion stability of the NPs and increases the exchange rate of water protons near the NPs,further enhancing the dual-mode contrast effect.Finally,the biocom-patibility and in vitro/vivo imaging performances of the IO@EDTMP-Gd NPs are systematically evaluated,and the results demonstrate their potential as dual-mode contrast agents.This study provides a new strategy for developing dual-mode MRI contrast agents that can further improve the accuracy of MRI in the diagnosis of complex diseases.
基金the National Natural Science Foundation of China(Grant Nos.62375171,62305208,62205189,62105203,and 62405182)the Shanghai Pujiang Program(Grant No.22PJ1407500)+4 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SJTU)(Grant No.SL2022ZD205)the National Key Research and Development Program of China(Grant No.2022YFC2806600)the Science Foundation of Donghai Laboratory(Grant Nos.DH-2022KF01001 and DH-2022KF01005)the Startup Fund for Young Faculty at SJTU(Grant No.24X010500120)the Science and Technology Commission of Shanghai Municipality(Grant No.20DZ2220400).
文摘Optical reservoir computing(ORC)offers advantages,such as high computational speed,low power consumption,and high training speed,so it has become a competitive candidate for time series analysis in recent years.The current ORC employs single-dimensional encoding for computation,which limits input resolution and introduces extraneous information due to interactions between optical dimensions during propagation,thus constraining performance.Here,we propose complex-value encoding-based optoelectronic reservoir computing(CE-ORC),in which the amplitude and phase of the input optical field are both modulated to improve the input resolution and prevent the influence of extraneous information on computation.In addition,scale factors in the amplitude encoding can fine-tune the optical reservoir dynamics for better performance.We built a CE-ORC processing unit with an iteration rate of up to∼1.2 kHz using high-speed communication interfaces and field programmable gate arrays(FPGAs)and demonstrated the excellent performance of CE-ORC in two time series prediction tasks.In comparison with the conventional ORC for the Mackey–Glass task,CE-ORC showed a decrease in normalized mean square error by∼75%.Furthermore,we applied this method in a weather time series analysis and effectively predicted the temperature and humidity within a range of 24 h.
