Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making co...The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.展开更多
Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
Influenza,an acute respiratory infectious disease caused by the influenza virus,exhibits distinct seasonal patterns in China,with peak activity occurring in winter and spring in northern regions,and in winter and summ...Influenza,an acute respiratory infectious disease caused by the influenza virus,exhibits distinct seasonal patterns in China,with peak activity occurring in winter and spring in northern regions,and in winter and summer in southern areas[1].The World Health Organization(WHO)emphasizes that early warning and epidemic intensity assessments are critical public health strategies for influenza prevention and control.Internet-based flu surveillance,with real-time data and low costs,effectively complements traditional methods.The Baidu Search Index,which reflects flu-related queries,strongly correlates with influenza trends,aiding in regional activity assessment and outbreak tracking[2].展开更多
Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manu...Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manually designed neural network models, find it difficult to maintain sustained and excellent performance in such a complex and changing environment. To address the problem of the adaptability of neural network models in complex environments, we propose a lightweight Transformer model(TransATIR) with a strong adaptive adjustment capability, based on the characteristics of air target intent recognition and the neural network architecture search technique. After conducting extensive experiments, it has been proved that TransATIR can efficiently extract the deep feature information from battlefield situation data by utilizing the neural architecture search algorithm, in order to quickly and accurately identify the real intention of the target. The experimental results indicate that TransATIR significantly improves recognition accuracy compared to the existing state-of-the-art methods, and also effectively reduces the computational complexity of the model.展开更多
Purpose–For the commonly used concrete mix for railway tunnel linings,concrete model specimens were made,and springback and core drilling tests were conducted at different ages.The springback strength was measured to...Purpose–For the commonly used concrete mix for railway tunnel linings,concrete model specimens were made,and springback and core drilling tests were conducted at different ages.The springback strength was measured to the compressive strength of the core sample with a diameter of 100mm and a height-to-diameter ratio of 1:1.By comparing the measured strength values,the relationship between the measured values under different strength measurement methods was analyzed.Design/methodology/approach–A comparative test of the core drilling method and the rebound method was conducted on the side walls of tunnel linings in some under-construction railways to study the feasibility of the rebound method in engineering quality supervision and inspection.Findings–Tests showed that the rebound strength was positively correlated with the core drill strength.The core drill test strength was significantly higher than the rebound test strength,and the strength still increased after 56 days of age.The rebound method is suitable for the general survey of concrete strength during the construction process and is not suitable for direct supervision and inspection.Originality/value–By studying the correlation of test strength of tunnel lining concrete using two methods,the differences in test results of different methods are proposed to provide a reference for the test and evaluation of tunnel lining strength in railway engineering.展开更多
Accurate estimation of the Direction-of-Arrival(DoA)of incident plane waves is essential for modern wireless communication,radar,sonar,and localization systems.Precise DoA information enables adaptive beamforming,spat...Accurate estimation of the Direction-of-Arrival(DoA)of incident plane waves is essential for modern wireless communication,radar,sonar,and localization systems.Precise DoA information enables adaptive beamforming,spatial filtering,and interference mitigation by steering antenna array beams toward desired sources while suppressing unwanted signals.Traditional one-dimensional Uniform Linear Arrays(ULAs)are limited to elevation angle estimation due to geometric constraints,typically within the range[0,π].To capture full spatial characteristics in environments with multipath and angular spread,joint estimation of both elevation and azimuth angles becomes necessary.However,existing 2D and 3D array geometries often entail increased hardware complexity and computational cost.This work proposes a novel and efficient framework for joint elevation and azimuth angle estimation using three spatially separated,parallel ULAs.The array configuration exploits spatial diversity and orthogonal projections to capture complete directional information with minimal structural overhead.A customized objective function based on the mean square error between measured and reconstructed array outputs is formulated to guide the estimation process.To solve the resulting non-convex optimization problem,three strategies are investigated:a global Genetic Algorithm(GA),a local Pattern Search(PS),and a hybrid GA-PS method that combines global exploration with local refinement.The proposed framework supports automatic pairing of elevation and azimuth angles,eliminating the need for manual post-processing.Extensive simulations validate the robustness,convergence,and accuracy of all three methods under varying signal-to-noise ratio conditions.Results confirm that the hybrid GA-PS approach achieves superior estimation performance and reduced computational complexity,making it well-suited for real-time and resource-constrained applications in next-generation sensing and communication systems.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment w...Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.展开更多
This article presents four techniques for assessing verticality:the plumb line approach,the total station distance technique,the three-point centering method,and the centroid method.Given the significant error associa...This article presents four techniques for assessing verticality:the plumb line approach,the total station distance technique,the three-point centering method,and the centroid method.Given the significant error associated with the total station horizontal distance technique when measuring circular piers,this paper proposes the centroid method.This method calculates verticality by determining the coordinates of the center points at both ends of the pier.Experimental findings indicate that the centroid method achieves accuracy in measuring the verticality of circular piers comparable to the three-point centering method,while offering a faster inspection process.Furthermore,the paper explores the concept of composite verticality and validates the effectiveness of the centroid method in measuring composite verticality and its practical applications through comparative experiments.展开更多
Dairy wastewater,a kind of high concentration organic wastewater,is produced in large quantities and difficult to treat,and has a negative impact on the ecological environment.In this study,the source,composition,wate...Dairy wastewater,a kind of high concentration organic wastewater,is produced in large quantities and difficult to treat,and has a negative impact on the ecological environment.In this study,the source,composition,water quality characteristics of dairy wastewater and its impact on the ecological environment were analyzed,and the treatment methods of dairy wastewater at home and abroad in recent years were summarized,in order to provide a reference for the treatment of dairy wastewater.展开更多
The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and e...The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and efficacy of drugs.Unlike the inherent molecular structures of active pharmaceutical ingredients(APIs)and excipients,the microstructures of pharmaceutical preparations are developed during the formulation process,presenting unique analytical challenges.In this review,we primarily focus on presenting the research methods used to elucidate the microstructures of pharmaceutical preparations,including X-ray imaging(XRI),scanning electron microscopy(SEM),atomic force microscopy(AFM),Raman spectroscopy,infrared(IR)spectroscopy,and rheometer technology.Subsequently,we highlight the applications,advantages,and limitations of these methods.Finally,we discuss the current challenges and future perspectives in this field.This review aims to provide a comprehensive reference for understanding the microstructures of pharmaceutical preparations,offering new insights and potential advancements in their development.展开更多
Neurosurgical patients present complex clinical conditions with high nursing risks.Under traditional teaching models,nursing interns often exhibit challenges such as weak clinical reasoning,insufficient reflective cap...Neurosurgical patients present complex clinical conditions with high nursing risks.Under traditional teaching models,nursing interns often exhibit challenges such as weak clinical reasoning,insufficient reflective capabilities,and inadequate operational proficiency,which fail to meet clinical nursing demands.This study focuses on neurosurgical nursing interns as research subjects,systematically exploring the application value and implementation pathways of serious game pedagogy in cultivating clinical competencies.展开更多
This study achieves a notable enhancement in the thermoelectric performance of copper selenide compounds exhibiting liquid-like characteristics via an innovative processing method.A KCl flux-assisted high-temperature ...This study achieves a notable enhancement in the thermoelectric performance of copper selenide compounds exhibiting liquid-like characteristics via an innovative processing method.A KCl flux-assisted high-temperature melting and slow-cooling strategy was employed to fabricate nanolayered Cu_(2)Se(KCl)_(x)materials(x=0-3,denoted as S_(0)-S_(3)).Systematic characterization reveals that the coexistence ofαandβphases at room temperature creates favorable conditions for optimizing carrier transport.XPS analysis confirms the substitution of low-binding-energy Se_(2)-by high-binding-energy Cl^(-)ions within the lattice,effectively suppressing copper ion migration and remarkably improving the material's structural stability.Microstructural investigations demonstrate that all samples exhibit nanolayered stacking architectures abundant with edge dislocations.This multiscale defect architecture induces strong phonon scattering effects.Hall measurements indicate that the KCl flux-assisted processing facilitates the formation of highly ordered nanostructures,thereby enhancing carrier mobility and structural stability.Although the carrier concentration exhibits a slight decrease compared with the flux-free samples,the significant improvement in microstructural quality plays a crucial role in the synergistic optimization of electrical conductivity and the Seebeck coefficient.Notably,sample S_(2)exhibited a considerable electrical conductivity,reaching approximately 1.0×10^(5)S·m^(-1)at 300 K.More strikingly,the cooperative effect of high-density edge dislocations and dopant atoms elevates material entropy,enabling sample S_(3)to attain an ultralow lattice thermal conductivity of 0.55 W·m^(-1)·K^(-1)at 350 K.Through multi-mechanism coordination,sample S_(2)achieved a high ZT value of 1.45 at 700 K,representing a 2.7-fold improvement compared with traditional synthesis methods.This work provides new insights into performance optimization of liquid-like thermoelectric materials through defect engineering and entropy manipulation.展开更多
Erratum to:Research Methods Used for Developing Academic Wordlists:A Systematic Review of Studies Published Between 2000 and 2020,Chinese Journal of Applied Linguistics,Volume 48,Issue 3,2025,pp.425-450,doi:10.1515/CJ...Erratum to:Research Methods Used for Developing Academic Wordlists:A Systematic Review of Studies Published Between 2000 and 2020,Chinese Journal of Applied Linguistics,Volume 48,Issue 3,2025,pp.425-450,doi:10.1515/CJAL-2025-0210.展开更多
This study examines the translation methods employed by Xu Yuanchong in his English translation of the Chu ci with a specific focus on the treatment of reduplicatives.Reduplicatives in the Chinese language,known for t...This study examines the translation methods employed by Xu Yuanchong in his English translation of the Chu ci with a specific focus on the treatment of reduplicatives.Reduplicatives in the Chinese language,known for their intricate nature of meaning and rhythmic qualities,pose a great challenge in translation due to the lack of equivalent structures in English.The paper investigates how Xu Yuanchong navigates these challenges by employing various strategies,including repetition,onomatopoeia,paraphrase,and literal translation without formal equivalence.Through an analysis of selected examples,the research highlights the difficulties of balancing Xu’s Three Beauties Principle,namely the beauty in sense,sound,and form in translating reduplicatives.The research findings are that,while Xu’s translations sometimes require compromises in sound and form,his nuanced approach ensures that the essence and emotional depth of the original text are effectively conveyed to target readers.This study may contribute to a deeper understanding of the complexities involved in translating classical Chinese poetry and offer insights into the interplay between linguistic and cultural elements in literary translation.展开更多
Recent years have witnessed significant advances in the development of novel techniques and methodologies for identifying active ingredients in traditional Chinese medicine(TCM),substantially advancing research and de...Recent years have witnessed significant advances in the development of novel techniques and methodologies for identifying active ingredients in traditional Chinese medicine(TCM),substantially advancing research and development efforts.Spectrum-effect correlation analysis,affinity ultrafiltration,high-content screening(HCS)imaging,and cell membrane chromatography(CMC)have emerged as essential tools,effectively linking TCM chemical constituents to their biological effects,thereby enabling efficient active ingredient screening.Additionally,molecular interaction analysis provides deeper insights into TCM-biomolecule interaction mechanisms,enhancing understanding of its therapeutic potential.Computer-aided techniques facilitate TCM active ingredient identification,optimizing the screening process for efficiency and cost-effectiveness.Molecular probe technology,as an emerging methodology,enables precise and rapid screening for novel therapeutic drug discovery.Ongoing technological advancement in this field indicates promising future developments,potentially leading to more effective and targeted TCM-based therapies.展开更多
The learning of English academic vocabulary has been the focus of numerous studies from the time Coxhead(2000)developed the academic word list to the present day.Various researchers have emphasized the importance of p...The learning of English academic vocabulary has been the focus of numerous studies from the time Coxhead(2000)developed the academic word list to the present day.Various researchers have emphasized the importance of possessing academic vocabulary knowledge for academic success.Recognizing this importance,it is crucial for researchers,teachers,and learners to understand the progress made in academic word lists.This systematic review first identifies,describes,appraises,and synthesizes the development of academic word lists from 2000 to 2020.It then examines the methods used by researchers in developing academic word lists among 56 studies that meet the pre-established criteria.The word lists were classified based on some criteria such as word counting units,corpora types/sizes,and exclusion criteria.Limitations,suggestions for further study,and implications are also discussed.Additionally,recommendations for future word list establishment are provided to help advance the field of word list development.展开更多
As a key sector in advancing China’s“carbon neutrality”goal,the machinery manufacturing industry has achieved remarkable development in recent years.Against this backdrop,the scientific and objective evaluation of ...As a key sector in advancing China’s“carbon neutrality”goal,the machinery manufacturing industry has achieved remarkable development in recent years.Against this backdrop,the scientific and objective evaluation of the financial performance of machinery manufacturing enterprises has become a pressing issue in financial research.This topic is not only crucial for optimizing enterprise management and improving operational efficiency but also essential for enhancing overall industry performance and promoting sustainable development.This paper first introduces the concept of financial performance,followed by an analysis of related financial performance evaluation theories.It then focuses on the application of the entropy method in evaluating the financial performance of machinery manufacturing enterprises,detailing its analytical steps.Finally,a financial performance evaluation index system is constructed based on four dimensions:profitability,solvency,operational efficiency,and growth potential.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
基金co-supported by the Foundation of Shanghai Astronautics Science and Technology Innovation,China(No.SAST2022-114)the National Natural Science Foundation of China(No.62303378),the National Natural Science Foundation of China(Nos.124B2031,12202281)the Foundation of China National Key Laboratory of Science and Technology on Test Physics&Numerical Mathematics,China(No.08-YY-2023-R11)。
文摘The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
基金supported by the National Key Research and Development Program of China(Project No.2023YFC2307500).
文摘Influenza,an acute respiratory infectious disease caused by the influenza virus,exhibits distinct seasonal patterns in China,with peak activity occurring in winter and spring in northern regions,and in winter and summer in southern areas[1].The World Health Organization(WHO)emphasizes that early warning and epidemic intensity assessments are critical public health strategies for influenza prevention and control.Internet-based flu surveillance,with real-time data and low costs,effectively complements traditional methods.The Baidu Search Index,which reflects flu-related queries,strongly correlates with influenza trends,aiding in regional activity assessment and outbreak tracking[2].
基金co-supported by the National Natural Science Foundation of China(Nos.61806219,61876189 and 61703426)the Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Nos.20190108 and 20220106)the Innovation Talent Supporting Project of Shaanxi,China(No.2020KJXX-065).
文摘Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manually designed neural network models, find it difficult to maintain sustained and excellent performance in such a complex and changing environment. To address the problem of the adaptability of neural network models in complex environments, we propose a lightweight Transformer model(TransATIR) with a strong adaptive adjustment capability, based on the characteristics of air target intent recognition and the neural network architecture search technique. After conducting extensive experiments, it has been proved that TransATIR can efficiently extract the deep feature information from battlefield situation data by utilizing the neural architecture search algorithm, in order to quickly and accurately identify the real intention of the target. The experimental results indicate that TransATIR significantly improves recognition accuracy compared to the existing state-of-the-art methods, and also effectively reduces the computational complexity of the model.
文摘Purpose–For the commonly used concrete mix for railway tunnel linings,concrete model specimens were made,and springback and core drilling tests were conducted at different ages.The springback strength was measured to the compressive strength of the core sample with a diameter of 100mm and a height-to-diameter ratio of 1:1.By comparing the measured strength values,the relationship between the measured values under different strength measurement methods was analyzed.Design/methodology/approach–A comparative test of the core drilling method and the rebound method was conducted on the side walls of tunnel linings in some under-construction railways to study the feasibility of the rebound method in engineering quality supervision and inspection.Findings–Tests showed that the rebound strength was positively correlated with the core drill strength.The core drill test strength was significantly higher than the rebound test strength,and the strength still increased after 56 days of age.The rebound method is suitable for the general survey of concrete strength during the construction process and is not suitable for direct supervision and inspection.Originality/value–By studying the correlation of test strength of tunnel lining concrete using two methods,the differences in test results of different methods are proposed to provide a reference for the test and evaluation of tunnel lining strength in railway engineering.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504)。
文摘Accurate estimation of the Direction-of-Arrival(DoA)of incident plane waves is essential for modern wireless communication,radar,sonar,and localization systems.Precise DoA information enables adaptive beamforming,spatial filtering,and interference mitigation by steering antenna array beams toward desired sources while suppressing unwanted signals.Traditional one-dimensional Uniform Linear Arrays(ULAs)are limited to elevation angle estimation due to geometric constraints,typically within the range[0,π].To capture full spatial characteristics in environments with multipath and angular spread,joint estimation of both elevation and azimuth angles becomes necessary.However,existing 2D and 3D array geometries often entail increased hardware complexity and computational cost.This work proposes a novel and efficient framework for joint elevation and azimuth angle estimation using three spatially separated,parallel ULAs.The array configuration exploits spatial diversity and orthogonal projections to capture complete directional information with minimal structural overhead.A customized objective function based on the mean square error between measured and reconstructed array outputs is formulated to guide the estimation process.To solve the resulting non-convex optimization problem,three strategies are investigated:a global Genetic Algorithm(GA),a local Pattern Search(PS),and a hybrid GA-PS method that combines global exploration with local refinement.The proposed framework supports automatic pairing of elevation and azimuth angles,eliminating the need for manual post-processing.Extensive simulations validate the robustness,convergence,and accuracy of all three methods under varying signal-to-noise ratio conditions.Results confirm that the hybrid GA-PS approach achieves superior estimation performance and reduced computational complexity,making it well-suited for real-time and resource-constrained applications in next-generation sensing and communication systems.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
基金supported by China Postdoctoral Science Foundation,Grant 2024M753544Science and Technology Project of CSG,Grant GDKJXM2022106.
文摘Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.
文摘This article presents four techniques for assessing verticality:the plumb line approach,the total station distance technique,the three-point centering method,and the centroid method.Given the significant error associated with the total station horizontal distance technique when measuring circular piers,this paper proposes the centroid method.This method calculates verticality by determining the coordinates of the center points at both ends of the pier.Experimental findings indicate that the centroid method achieves accuracy in measuring the verticality of circular piers comparable to the three-point centering method,while offering a faster inspection process.Furthermore,the paper explores the concept of composite verticality and validates the effectiveness of the centroid method in measuring composite verticality and its practical applications through comparative experiments.
文摘Dairy wastewater,a kind of high concentration organic wastewater,is produced in large quantities and difficult to treat,and has a negative impact on the ecological environment.In this study,the source,composition,water quality characteristics of dairy wastewater and its impact on the ecological environment were analyzed,and the treatment methods of dairy wastewater at home and abroad in recent years were summarized,in order to provide a reference for the treatment of dairy wastewater.
文摘The microstructures of pharmaceutical preparations play a pivotal role in determining their critical quality attributes(CQAs),such as drug release,content uniformity,and stability,which greatly impact the safety and efficacy of drugs.Unlike the inherent molecular structures of active pharmaceutical ingredients(APIs)and excipients,the microstructures of pharmaceutical preparations are developed during the formulation process,presenting unique analytical challenges.In this review,we primarily focus on presenting the research methods used to elucidate the microstructures of pharmaceutical preparations,including X-ray imaging(XRI),scanning electron microscopy(SEM),atomic force microscopy(AFM),Raman spectroscopy,infrared(IR)spectroscopy,and rheometer technology.Subsequently,we highlight the applications,advantages,and limitations of these methods.Finally,we discuss the current challenges and future perspectives in this field.This review aims to provide a comprehensive reference for understanding the microstructures of pharmaceutical preparations,offering new insights and potential advancements in their development.
基金Humanities and Social Science Research Project of Sichuan Nursing Vocational College,“Strategies and Research on the Improvement of Clinical Reasoning and Reflection Ability of Nursing Students in Neurosurgery Internship through Serious Game Teaching Method”(Project No.:2023RWSY42)。
文摘Neurosurgical patients present complex clinical conditions with high nursing risks.Under traditional teaching models,nursing interns often exhibit challenges such as weak clinical reasoning,insufficient reflective capabilities,and inadequate operational proficiency,which fail to meet clinical nursing demands.This study focuses on neurosurgical nursing interns as research subjects,systematically exploring the application value and implementation pathways of serious game pedagogy in cultivating clinical competencies.
基金Project supported by the National Natural Science Foundation of China(Grant No.62464013)。
文摘This study achieves a notable enhancement in the thermoelectric performance of copper selenide compounds exhibiting liquid-like characteristics via an innovative processing method.A KCl flux-assisted high-temperature melting and slow-cooling strategy was employed to fabricate nanolayered Cu_(2)Se(KCl)_(x)materials(x=0-3,denoted as S_(0)-S_(3)).Systematic characterization reveals that the coexistence ofαandβphases at room temperature creates favorable conditions for optimizing carrier transport.XPS analysis confirms the substitution of low-binding-energy Se_(2)-by high-binding-energy Cl^(-)ions within the lattice,effectively suppressing copper ion migration and remarkably improving the material's structural stability.Microstructural investigations demonstrate that all samples exhibit nanolayered stacking architectures abundant with edge dislocations.This multiscale defect architecture induces strong phonon scattering effects.Hall measurements indicate that the KCl flux-assisted processing facilitates the formation of highly ordered nanostructures,thereby enhancing carrier mobility and structural stability.Although the carrier concentration exhibits a slight decrease compared with the flux-free samples,the significant improvement in microstructural quality plays a crucial role in the synergistic optimization of electrical conductivity and the Seebeck coefficient.Notably,sample S_(2)exhibited a considerable electrical conductivity,reaching approximately 1.0×10^(5)S·m^(-1)at 300 K.More strikingly,the cooperative effect of high-density edge dislocations and dopant atoms elevates material entropy,enabling sample S_(3)to attain an ultralow lattice thermal conductivity of 0.55 W·m^(-1)·K^(-1)at 350 K.Through multi-mechanism coordination,sample S_(2)achieved a high ZT value of 1.45 at 700 K,representing a 2.7-fold improvement compared with traditional synthesis methods.This work provides new insights into performance optimization of liquid-like thermoelectric materials through defect engineering and entropy manipulation.
文摘Erratum to:Research Methods Used for Developing Academic Wordlists:A Systematic Review of Studies Published Between 2000 and 2020,Chinese Journal of Applied Linguistics,Volume 48,Issue 3,2025,pp.425-450,doi:10.1515/CJAL-2025-0210.
文摘This study examines the translation methods employed by Xu Yuanchong in his English translation of the Chu ci with a specific focus on the treatment of reduplicatives.Reduplicatives in the Chinese language,known for their intricate nature of meaning and rhythmic qualities,pose a great challenge in translation due to the lack of equivalent structures in English.The paper investigates how Xu Yuanchong navigates these challenges by employing various strategies,including repetition,onomatopoeia,paraphrase,and literal translation without formal equivalence.Through an analysis of selected examples,the research highlights the difficulties of balancing Xu’s Three Beauties Principle,namely the beauty in sense,sound,and form in translating reduplicatives.The research findings are that,while Xu’s translations sometimes require compromises in sound and form,his nuanced approach ensures that the essence and emotional depth of the original text are effectively conveyed to target readers.This study may contribute to a deeper understanding of the complexities involved in translating classical Chinese poetry and offer insights into the interplay between linguistic and cultural elements in literary translation.
基金supported by the National Natural Science Foundation of China (Nos. 22175078, 52373287, 82404846, and 22467002)the Natural Science Foundation of Jiangsu Province of China (No. BK20241597)the Fundamental Research Funds for the Central Universities (No. 2632024TD05)
文摘Recent years have witnessed significant advances in the development of novel techniques and methodologies for identifying active ingredients in traditional Chinese medicine(TCM),substantially advancing research and development efforts.Spectrum-effect correlation analysis,affinity ultrafiltration,high-content screening(HCS)imaging,and cell membrane chromatography(CMC)have emerged as essential tools,effectively linking TCM chemical constituents to their biological effects,thereby enabling efficient active ingredient screening.Additionally,molecular interaction analysis provides deeper insights into TCM-biomolecule interaction mechanisms,enhancing understanding of its therapeutic potential.Computer-aided techniques facilitate TCM active ingredient identification,optimizing the screening process for efficiency and cost-effectiveness.Molecular probe technology,as an emerging methodology,enables precise and rapid screening for novel therapeutic drug discovery.Ongoing technological advancement in this field indicates promising future developments,potentially leading to more effective and targeted TCM-based therapies.
文摘The learning of English academic vocabulary has been the focus of numerous studies from the time Coxhead(2000)developed the academic word list to the present day.Various researchers have emphasized the importance of possessing academic vocabulary knowledge for academic success.Recognizing this importance,it is crucial for researchers,teachers,and learners to understand the progress made in academic word lists.This systematic review first identifies,describes,appraises,and synthesizes the development of academic word lists from 2000 to 2020.It then examines the methods used by researchers in developing academic word lists among 56 studies that meet the pre-established criteria.The word lists were classified based on some criteria such as word counting units,corpora types/sizes,and exclusion criteria.Limitations,suggestions for further study,and implications are also discussed.Additionally,recommendations for future word list establishment are provided to help advance the field of word list development.
文摘As a key sector in advancing China’s“carbon neutrality”goal,the machinery manufacturing industry has achieved remarkable development in recent years.Against this backdrop,the scientific and objective evaluation of the financial performance of machinery manufacturing enterprises has become a pressing issue in financial research.This topic is not only crucial for optimizing enterprise management and improving operational efficiency but also essential for enhancing overall industry performance and promoting sustainable development.This paper first introduces the concept of financial performance,followed by an analysis of related financial performance evaluation theories.It then focuses on the application of the entropy method in evaluating the financial performance of machinery manufacturing enterprises,detailing its analytical steps.Finally,a financial performance evaluation index system is constructed based on four dimensions:profitability,solvency,operational efficiency,and growth potential.
