Objective: To investigate the efficacy of multidimensional protective nursing interventions in reducing occupational exposure risks to hydrofluoric acid in dental departments. Methods: The study was conducted between ...Objective: To investigate the efficacy of multidimensional protective nursing interventions in reducing occupational exposure risks to hydrofluoric acid in dental departments. Methods: The study was conducted between January 2024 and January 2025, involving 60 healthcare workers who were randomly assigned to groups. The control group (n = 30) received conventional occupational protective nursing care, while the observation group underwent multidimensional protective nursing interventions. Results: Following intervention, the observation group demonstrated significantly higher scores than the control group in knowledge of hydrofluoric acid occupational exposure, occupational exposure risk perception, and protective skill operation (p < 0.05). Emergency response capability scores and protective behavior implementation scores were also higher in the observation group (p < 0.05). Compared to the control group, the observation group exhibited higher occupational health satisfaction rates and lower occupational exposure incidence rates (p < 0.05). Conclusion: Multidimensional protective nursing interventions effectively enhance dental healthcare personnel’s awareness and protective capabilities regarding hydrofluoric acid occupational exposure, improve compliance with protective measures, and significantly reduce occupational exposure risks. This approach warrants promotion and application in occupational protective nursing interventions within dental departments.展开更多
Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimen...Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed...The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed out from such logic system. This article aims to rearticulate the underlying lucid multi-dimensional logic system, which faded in obscurity only because of time-out loss of the mid-right concept. Retracing this past tacit but important concept could uncover a multi-dimensional system over a point relating to all matters while capturing the central core of the matter. The seemingly unmanageable multidimensional logic was strengthened by verification processes which affirmed its further extensions, and made up the language of the people, the concepts of yin-yang(阴阳), and the development of extensions of Ba Gua(八卦) derivatives, which furthered the interpretation of the space-time properties and Chinese medicine.展开更多
Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial...Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial for acquiring stable NiSe2-based materials for sodium-ion batteries(SIBs),Herein,a stress dissipation strategy driven by architecture engineering is proposed,which can achieve ultrafast and ultralong sodium storage properties.Different from the conventional sphere-like or rod-like architecture,the three-dimensional(3D)flower-like NiSe_(2)@C composite is delicately designed and assembled with onedimensional nanorods and carbon framework.More importantly,the fundamental mechanism of improved structure stability is unveiled by simulations and experimental results simultaneously.It demonstrates that this designed multidimensional flower-like architecture with dispersed nanorods can balance the structural mismatch,avoid concentrated local strain,and relax the internal stress,mainly induced by the unavoidable volume variation during the repeated conversion processes.Moreover,it can provide more Na^(+)-storage sites and multi-directional migration pathways,leading to a fast Na^(+)-migration channel with boosted reaction kinetic.As expected,it delivers superior rate performance(441 mA h g^(-1)at 5.0 A g^(-1))and long cycling stability(563 mA h g^(-1)at 1.0 A g^(-1)over 1000 cycles)for SIBs.This work provides useful insights for designing high-performance conversion-based anode materials for SIBs.展开更多
This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses ...This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses the construction of multi-dimensional collaborative theoretical models,public space streamline organization,and other aspects,emphasizing the importance of multi-dimensional collaboration.Additionally,it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.展开更多
The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themse...The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.展开更多
During the critical transformation period of landscape architecture major after the adjustment of disciplinary structure and the changes in market demand,private colleges and universities,as important places for culti...During the critical transformation period of landscape architecture major after the adjustment of disciplinary structure and the changes in market demand,private colleges and universities,as important places for cultivating local talents,have pain points such as uneven quality of teachers and students and weak innovation and practice.The practice system with“multi-dimensional Integration”integrates four dimensions:interdisciplinary integration,spatial and temporal intersection,historical inheritance,and behavioral activity,deepens the disciplinary connotation,and integrates the three elements of nature,humanity,and technology,aiming to provide a new path for private colleges and universities to cultivate application-oriented and compound talents with innovative capabilities.In terms of optimizing talent cultivation and adapting to industry changes,this system provides thinking and reference for landscape architecture major,helping the major reshape its core competitiveness and promoting educational innovation and industry development.展开更多
This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with...This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.展开更多
Constructing multi-dimensional hydrogen bond(H-bond)regulated single-molecule systems with multiemission remains a challenge.Herein,we report the design of a new excited-state intramolecular proton transfer(ESIPT)feat...Constructing multi-dimensional hydrogen bond(H-bond)regulated single-molecule systems with multiemission remains a challenge.Herein,we report the design of a new excited-state intramolecular proton transfer(ESIPT)featured chromophore(HBT-DPI)that shows flexible emission tunability via the multidimensional regulation of intra-and intermolecular H-bonds.The feature of switchable intramolecular Hbonds is induced via incorporating several hydrogen bond acceptors and donors into one single HBT-DPI molecule,allowing the“turn on/off”of ESIPT process by forming isomers with distinct intramolecular Hbonds configurations.In response to different external H-bonding environments,the obtained four types of crystal/cocrystals vary in the contents of isomers and the molecular packing modes,which are mainly guided by the intermolecular H-bonds,exhibiting non-emissive features or emissions ranging from green to orange.Utilizing the feature of intermolecular H-bond guided molecular packing,we demonstrate the utility of this fluorescent material for visualizing hydrophobic/hydrophilic areas on large-scale heterogeneous surfaces of modified poly(1,1-difluoroethylene)(PVDF)membranes and quantitatively estimating the surface hydrophobicity,providing a new approach for hydrophobicity/hydrophilicity monitoring and measurement.Overall,this study represents a new design strategy for constructing multi-dimensional hydrogen bond regulated ESIPT-based fluorescent materials that enable multiple emissions and unique applications.展开更多
To break the limitations of the multi-dimensional(M-D)vibration isolation(VI)platforms with the Stewart-Gough design,such as strongly coupling motions,excessive friction in connections,heavy weight,and limited workspa...To break the limitations of the multi-dimensional(M-D)vibration isolation(VI)platforms with the Stewart-Gough design,such as strongly coupling motions,excessive friction in connections,heavy weight,and limited workspace,this study processes a novel platform integrated by a stiffness-adjustable origami spring sub-structure and a parallel mechanism.The origami-based stiffness-adjustable spring realizes low-frequency VI,and the parallel mechanism symmetry design realizes motions decoupling.In the origami-based sub-leg,the parallel-stack-assembly(PSA)design mechanism with two Miura origami configurations is proposed to generate a symmetrical negative stiffness property.Paired with a linear positive stiffness spring,the origami-based sub-leg has wide-amplitude-high-static-low-dynamic stiffness(WA-HSLDS)characteristics in one direction.Then,with construction of the parallel mechanism connected with origami-based sub-legs,an M-D VI platform is achieved,whose motions in the vertical direction and yaw direction are decoupled with the motions in the other directions.Based on the dynamic model and incremental harmonic balance(IHB)with the arc-length continuation method,appropriate structural parameters in the parallel mechanism part are figured out,and the accurate transmissibility in different directions is defined,which gives the parametric influencing investigations for realization of low-frequency VI performances.Finally,experiments are conducted to validate the accuracy and feasibility of the theoretical methods,and to demonstrate the performance of M-D low-frequency isolation with load-carrying capacity of the proposed VI platform.The integration of the origami into the parallel mechanism results in a compact,efficient,and flexible platform with nonlinear adjustability,offering new possibilities for lightweight M-D VI,and developing the practical applications in high-precision platforms in ocean and aerospace environments.展开更多
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware...The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differ...The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.展开更多
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-li...In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-link capacitors.The voltage pulses required by inductance-based initial position detection can cause unequal discharge of the series capacitors,shifting the neutral-point voltage away from half of DC-link voltage(U_(dc)/2).This neutral-point drift breaks the spatial symmetry of the inverter voltage vectors,so the 360°electrical period can no longer be evenly partitioned into six sectors during initial rotor position detection.To address this issue,this paper proposes a detection-pulse injection sequence that explicitly accounts for the asymmetric voltage vectors of the FSTP inverter.With the proposed sequence,the initial rotor position can be identified within a 30°electrical sector.The method requires no additional voltage or current sensors,and experimental results confirm its feasibility.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
基金Construction of Standardized Protective Nursing Plan for Hydrofluoric Acid Operations in Dentistry and Research on Injury Prevention Effects(Project No.:FZ2025101)。
文摘Objective: To investigate the efficacy of multidimensional protective nursing interventions in reducing occupational exposure risks to hydrofluoric acid in dental departments. Methods: The study was conducted between January 2024 and January 2025, involving 60 healthcare workers who were randomly assigned to groups. The control group (n = 30) received conventional occupational protective nursing care, while the observation group underwent multidimensional protective nursing interventions. Results: Following intervention, the observation group demonstrated significantly higher scores than the control group in knowledge of hydrofluoric acid occupational exposure, occupational exposure risk perception, and protective skill operation (p < 0.05). Emergency response capability scores and protective behavior implementation scores were also higher in the observation group (p < 0.05). Compared to the control group, the observation group exhibited higher occupational health satisfaction rates and lower occupational exposure incidence rates (p < 0.05). Conclusion: Multidimensional protective nursing interventions effectively enhance dental healthcare personnel’s awareness and protective capabilities regarding hydrofluoric acid occupational exposure, improve compliance with protective measures, and significantly reduce occupational exposure risks. This approach warrants promotion and application in occupational protective nursing interventions within dental departments.
基金supported by the National Natural Science Foundation of China(22072048)the Guangdong Provincial Department of Science and Technology(2021A1515010128 and 2022A0505050013).
文摘Succinonitrile(SN)-based polymer plastic crystal electrolytes(PPCEs)are regarded as promising candidates for lithium metal batteries but suffer from serious side reactions with Li metal.Herein,we propose a multi-dimensional optimization strategy to alleviate the side reactions between SN and Li metal,and develop a highly stable poly-vinylethylene carbonate-based PPCE(PPCE-VEC).Moreover,we identify the intrinsic factors of multi-dimensional polymer structures on the electrolyte stability by three typical classes of polyesters.The PPCE-VEC constructed by in situ polymerization exhibits much better stability than poly-vinylene carbonate-based PPCE(PPCE-VCA)and poly-trifluoroethyl acrylate-based PPCE(PPCE-TFA),which is verified by its fewer SN-decomposition species in X-ray photoelectron spectroscopy(XPS)and outstanding full cell performance.The PPCE-VEC-enabled LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2)full cell achieve 73.7%capacity retention after 1400 cycles,which outperforms PPCE-VCA-and PPCE-TFA-enabled full cells(61.9%and 46.9%).Spectral analysis and theoretical calculation reveal that the high solvation ability of the carbonyl site,flexible polymer chain,and homogeneous electrolyte phase of PPCE-VEC are favorable to maximizing competition coordination with Li^(+)to weaken the Li^(+)–SN binding and shape an anion-rich solvation structure.This optimized polymer-involved Li^(+)solvation enhances SN stability and facilitates the formation of B/F enriched solid-electrolyte interphase(SEI),thus significantly improving PPCE stability.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
文摘The ancient tacit knowledge behind the logic system permeated the culture and promoted numerous impactful inventions throughout the history. Traditional Chinese medicine with its effectiveness should also have stemmed out from such logic system. This article aims to rearticulate the underlying lucid multi-dimensional logic system, which faded in obscurity only because of time-out loss of the mid-right concept. Retracing this past tacit but important concept could uncover a multi-dimensional system over a point relating to all matters while capturing the central core of the matter. The seemingly unmanageable multidimensional logic was strengthened by verification processes which affirmed its further extensions, and made up the language of the people, the concepts of yin-yang(阴阳), and the development of extensions of Ba Gua(八卦) derivatives, which furthered the interpretation of the space-time properties and Chinese medicine.
基金the financial support from the Guangxi Natural Science Foundation(grant no.2021GXNSFDA075012,2023GXNSFGA026002)National Natural Science Foundation of China(52104298,22075073,52362027,52462029)Fundamental Research Funds for the Central Universities(531107051077).
文摘Stress accumulation is a key factor leading to sodium storage performance deterioration for NiSe_(2)-based anodes.Therefore,inhibiting the concentrated local stress during the sodiataion/desodiation process is crucial for acquiring stable NiSe2-based materials for sodium-ion batteries(SIBs),Herein,a stress dissipation strategy driven by architecture engineering is proposed,which can achieve ultrafast and ultralong sodium storage properties.Different from the conventional sphere-like or rod-like architecture,the three-dimensional(3D)flower-like NiSe_(2)@C composite is delicately designed and assembled with onedimensional nanorods and carbon framework.More importantly,the fundamental mechanism of improved structure stability is unveiled by simulations and experimental results simultaneously.It demonstrates that this designed multidimensional flower-like architecture with dispersed nanorods can balance the structural mismatch,avoid concentrated local strain,and relax the internal stress,mainly induced by the unavoidable volume variation during the repeated conversion processes.Moreover,it can provide more Na^(+)-storage sites and multi-directional migration pathways,leading to a fast Na^(+)-migration channel with boosted reaction kinetic.As expected,it delivers superior rate performance(441 mA h g^(-1)at 5.0 A g^(-1))and long cycling stability(563 mA h g^(-1)at 1.0 A g^(-1)over 1000 cycles)for SIBs.This work provides useful insights for designing high-performance conversion-based anode materials for SIBs.
文摘This paper explores whole-process engineering consulting,including its application models in public buildings and elderly-friendly projects,such as service integration and whole lifecycle management.It also addresses the construction of multi-dimensional collaborative theoretical models,public space streamline organization,and other aspects,emphasizing the importance of multi-dimensional collaboration.Additionally,it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.
文摘The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.
基金Sponsored by the Quality Engineering Project of Education Department of Anhui Province(2022jyxm671)Research Team Project of Anhui Xinhua University(kytd202202)+1 种基金Key Project of Scientific Research(Natural Science)of Higher Education Institutions in Anhui Province(2022AH051861)Teaching Reform Research and Practice Quality Engineering Project of Anhui Xinhua University(2024jy035).
文摘During the critical transformation period of landscape architecture major after the adjustment of disciplinary structure and the changes in market demand,private colleges and universities,as important places for cultivating local talents,have pain points such as uneven quality of teachers and students and weak innovation and practice.The practice system with“multi-dimensional Integration”integrates four dimensions:interdisciplinary integration,spatial and temporal intersection,historical inheritance,and behavioral activity,deepens the disciplinary connotation,and integrates the three elements of nature,humanity,and technology,aiming to provide a new path for private colleges and universities to cultivate application-oriented and compound talents with innovative capabilities.In terms of optimizing talent cultivation and adapting to industry changes,this system provides thinking and reference for landscape architecture major,helping the major reshape its core competitiveness and promoting educational innovation and industry development.
基金supported by the National Natural Science Foundation of China(72101025,72271049),the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities,FRF-IDRY-24-024)the Hebei Natural Science Foundation(F2023501011)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-20-073A1)the R&D Program of Beijing Municipal Education Commission(KM202411232015).
文摘This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.
基金supported by the National Key R&D Program of China(No.2021YFC2103600)the National Natural Science Foundation of China(Nos.21878156,21978131,22275085,and 22278224)+2 种基金the Natural Science Foundation of Jiangsu Province(Nos.BK20200089 and BK20200691)the Project of Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the State Key Laboratory of Materials-Oriented Chemical Engineering(No.KL21-08).
文摘Constructing multi-dimensional hydrogen bond(H-bond)regulated single-molecule systems with multiemission remains a challenge.Herein,we report the design of a new excited-state intramolecular proton transfer(ESIPT)featured chromophore(HBT-DPI)that shows flexible emission tunability via the multidimensional regulation of intra-and intermolecular H-bonds.The feature of switchable intramolecular Hbonds is induced via incorporating several hydrogen bond acceptors and donors into one single HBT-DPI molecule,allowing the“turn on/off”of ESIPT process by forming isomers with distinct intramolecular Hbonds configurations.In response to different external H-bonding environments,the obtained four types of crystal/cocrystals vary in the contents of isomers and the molecular packing modes,which are mainly guided by the intermolecular H-bonds,exhibiting non-emissive features or emissions ranging from green to orange.Utilizing the feature of intermolecular H-bond guided molecular packing,we demonstrate the utility of this fluorescent material for visualizing hydrophobic/hydrophilic areas on large-scale heterogeneous surfaces of modified poly(1,1-difluoroethylene)(PVDF)membranes and quantitatively estimating the surface hydrophobicity,providing a new approach for hydrophobicity/hydrophilicity monitoring and measurement.Overall,this study represents a new design strategy for constructing multi-dimensional hydrogen bond regulated ESIPT-based fluorescent materials that enable multiple emissions and unique applications.
基金Project supported by the National Natural Science Foundation of China(Nos.U2441202,12372043,and 12372022)the Fundamental Research Funds for Central Universities。
文摘To break the limitations of the multi-dimensional(M-D)vibration isolation(VI)platforms with the Stewart-Gough design,such as strongly coupling motions,excessive friction in connections,heavy weight,and limited workspace,this study processes a novel platform integrated by a stiffness-adjustable origami spring sub-structure and a parallel mechanism.The origami-based stiffness-adjustable spring realizes low-frequency VI,and the parallel mechanism symmetry design realizes motions decoupling.In the origami-based sub-leg,the parallel-stack-assembly(PSA)design mechanism with two Miura origami configurations is proposed to generate a symmetrical negative stiffness property.Paired with a linear positive stiffness spring,the origami-based sub-leg has wide-amplitude-high-static-low-dynamic stiffness(WA-HSLDS)characteristics in one direction.Then,with construction of the parallel mechanism connected with origami-based sub-legs,an M-D VI platform is achieved,whose motions in the vertical direction and yaw direction are decoupled with the motions in the other directions.Based on the dynamic model and incremental harmonic balance(IHB)with the arc-length continuation method,appropriate structural parameters in the parallel mechanism part are figured out,and the accurate transmissibility in different directions is defined,which gives the parametric influencing investigations for realization of low-frequency VI performances.Finally,experiments are conducted to validate the accuracy and feasibility of the theoretical methods,and to demonstrate the performance of M-D low-frequency isolation with load-carrying capacity of the proposed VI platform.The integration of the origami into the parallel mechanism results in a compact,efficient,and flexible platform with nonlinear adjustability,offering new possibilities for lightweight M-D VI,and developing the practical applications in high-precision platforms in ocean and aerospace environments.
基金co-supported by the National Natural Science Foundation of China(Grant Nos.62222404,T2450054,62304084,62504087,62361136587 and 92248304)the National Key Research and Development Plan of China(Grant No.2021YFB3601200)+3 种基金the Major Program of Hubei Province(Grant No.2023BAA009)the Research Grants Council of Hong Kong Postdoctoral Fellowship Scheme(Grant No.PDFS2223-4S06)the China Postdoctoral Science Foundation funded project(Grant No.2025M770530)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20250136).
文摘The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by the National Key R&D Program of China under Grant No.2023YFA1008702the National Natural Science Foundation of China under Grant No.12571300。
文摘The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.
基金supported by grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金supported in part by the National Natural Science Foundation of China under Grant 52477060in part by the Tianjin Natural Science Foundation Project under Grant 24JCZDJC00250in part by the Zhejiang Leading Innovation and Entrepreneurship Team Project under Grant 2024R01012.
文摘In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-link capacitors.The voltage pulses required by inductance-based initial position detection can cause unequal discharge of the series capacitors,shifting the neutral-point voltage away from half of DC-link voltage(U_(dc)/2).This neutral-point drift breaks the spatial symmetry of the inverter voltage vectors,so the 360°electrical period can no longer be evenly partitioned into six sectors during initial rotor position detection.To address this issue,this paper proposes a detection-pulse injection sequence that explicitly accounts for the asymmetric voltage vectors of the FSTP inverter.With the proposed sequence,the initial rotor position can be identified within a 30°electrical sector.The method requires no additional voltage or current sensors,and experimental results confirm its feasibility.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
