Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric ana...Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.展开更多
China’s environmental governance strategy provides a distinctive pathway for integrating sustainable development into national policy.Understanding its policy trajectory is essential for assessing China’s contributi...China’s environmental governance strategy provides a distinctive pathway for integrating sustainable development into national policy.Understanding its policy trajectory is essential for assessing China’s contribution to global sustainable development and the United Nations Sustainable Development Goals(SDGs).This study constructs a comprehensive database of 425 national environmental governance policy documents issued between 1978 and 2022 and applies Latent Dirichlet Allocation(LDA)modeling to examine the evolution of policy themes and discourse.The results show that China’s environmental governance has undergone four stages-initial exploration,detailed development,transformative leap,and diverse prosperity-reflecting a progressive shift toward more integrated and coordinated governance.Policy priorities have evolved from a primary focus on pollution control and energy transition to an emphasis on institutional construction and organizational reform,thereby strengthening alignment with the SDGs.This transformation is characterized by recurring developmental themes and increasingly preventive,forward-looking,and system-oriented governance approaches.Moreover,the co-evolution of policy concepts and implementation has driven a transition from localized,end-of-pipe responses to comprehensive governance frameworks,alongside a shift from normative guidance towards effectiveness-oriented policy design.By employing a data-driven text analysis approach,this study offers a systematic framework for tracing long-term policy evolution and assessing its implications for sustainable development.展开更多
Fiber-to-the-Room(FTTR)has emerged as the core architecture for next-generation home and enterprise networks,offering gigabitlevel bandwidth and seamless wireless coverage.However,the complex multi-device topology of ...Fiber-to-the-Room(FTTR)has emerged as the core architecture for next-generation home and enterprise networks,offering gigabitlevel bandwidth and seamless wireless coverage.However,the complex multi-device topology of FTTR networks presents significant chal⁃lenges in identifying sources of network performance degradation and conducting accurate root cause analysis.Conventional approaches often fail to deliver efficient and precise operational improvements.To address this issue,this paper proposes a Transformer-based multi-task learn⁃ing model designed for automated root cause analysis in FTTR environments.The model integrates multidimensional time-series data col⁃lected from access points(APs),enabling the simultaneous detection of APs experiencing performance degradation and the classification of underlying root causes,such as weak signal coverage,network congestion,and signal interference.To facilitate model training and evaluation,a multi-label dataset is generated using a discrete-event simulation platform implemented in MATLAB.Experimental results demonstrate that the proposed Transformer-based multi-task learning model achieves a root cause classification accuracy of 96.75%,significantly outperform⁃ing baseline models including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Random Forest,and eXtreme Gradient Boost⁃ing(XGBoost).This approach enables the rapid identification of performance degradation causes in FTTR networks,offering actionable in⁃sights for network optimization,reduced operational costs,and enhanced user experience.展开更多
Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ...Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networ...Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.展开更多
In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieram...In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieramhical cluster analysis （HCA）. The results showed that the infrared spectra were similar on the whole, but there were differences in peak position, peak shape and peak absorption intensity in the range of 1 800-700 cm-1. The infrared spectra in the range of 1 800-700 cm-1 were selected to perform continuous wavelet transform （CWT） and discrete wavelet transform （DWT）. The 15th-Ievel decomposition coefficients of CWT and the 5=-level detail coefficients of DWT were classified by PCA and HCA. The cumulative contri- bution rates of the first three principal components of CWT and DWT were 93.12% and 89.78%, respectively. The accurate recognition rates of PCA and HCA were all 100%. It is proved that FTIR combined with WT can be used to distinguish different kinds of rhizome crops.展开更多
Wavelet transforms (WT) are proposed as an alternative tool to overcome the limitations of Fourier transforms (FFT) in the analysis of electrochemical noise (EN) data. The most relevant feature of this method of analy...Wavelet transforms (WT) are proposed as an alternative tool to overcome the limitations of Fourier transforms (FFT) in the analysis of electrochemical noise (EN) data. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients, which contain information about the time scale characteristic of the associated corrosion event. In this context, the potential noise fluctuations during the free corrosion of pure aluminum in sodium chloride solution was recorded and analyzed with wavelet transform technique. The typical results showed that the EN signal is composed of distinct type of events, which can be classified according to their scales, i.e. their time constants. Meanwhile, the energy distribution plot (EDP) can be used as 'fingerprints' of EN signals and can be very useful for analyzing EN data in the future.展开更多
It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent...It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.展开更多
Bamboo was carbonized at different temperatures ranging from 200℃to 600℃.The dependence of the change of hemicellulose,cellulose,and lignin on the temperature was investigated by means of elemental analysis and Four...Bamboo was carbonized at different temperatures ranging from 200℃to 600℃.The dependence of the change of hemicellulose,cellulose,and lignin on the temperature was investigated by means of elemental analysis and Fourier Transform Infrared(FTIR)spectra of the residual solid products.The results showed:(1)Below 200℃,hemicellulose in bamboo was de-composed and a large amount of hydroxyl groups are dislocated from hemicellulose and cellulose,accompanied by the evolu-tion of water to escape.(2)200℃-250℃,cellulose in bamboo was drastically decomposed whereas the net structure of lignin keep stable,with the except of the dislocation of methoxyl groups from lignin.(3)250℃~400℃,the net structure of lignin col-lapse,up to 400’℃,followed by that the more position in aryl groups are substituted.(4)For bamboo carbonization,the aroma-tization of residual carbon has approximately completed at the temperature as high as 600℃.But the fusion of aromatic rings possibly does not occur.展开更多
We present a new reliable analytical study for solving the discontinued problems arising in nanotechnology. Such problems are presented as nonlinear differential-difference equations. The proposed method is based on t...We present a new reliable analytical study for solving the discontinued problems arising in nanotechnology. Such problems are presented as nonlinear differential-difference equations. The proposed method is based on the Laplace trans- form with the homotopy analysis method （HAM）. This method is a powerful tool for solving a large amount of problems. This technique provides a series of functions which may converge to the exact solution of the problem. A good agreement between the obtained solution and some well-known results is obtained.展开更多
With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode an...With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.展开更多
After brief describing the Principle of wavelet transform (WT) of signals, a new signals analysis system based on wavelet transform is introduced. The design and development of the instryment of wavelet transform are ...After brief describing the Principle of wavelet transform (WT) of signals, a new signals analysis system based on wavelet transform is introduced. The design and development of the instryment of wavelet transform are described. A number of practical uses of this system demonstrate that wavelet transform system is specially functional in identifying and processing impulse, singular and non-smooth signals, so that it should be evaluated the most advanced signal analyzing system.展开更多
The oxidation behavior of three biodiesels of different origins,viz.rapeseed oil derived biodiesel,soybean oil derived biodiesel and waste oil based biodiesel,were tested on an oxidation tester.The chemical compositio...The oxidation behavior of three biodiesels of different origins,viz.rapeseed oil derived biodiesel,soybean oil derived biodiesel and waste oil based biodiesel,were tested on an oxidation tester.The chemical compositions of the biodiesels were characterized by gas chromatography.Thereafter,the structural transformation of fatty acid methyl ester(FAME)of the biodiesels was analyzed by an infrared spectrometer and an ultraviolet absorption spectrometer.The results demonstrated that the oxidation behavior of biodiesels of different origins was closely related to the composition and distribution of FAMEs.Higher concentration of unsaturated FAME with multi-double bonds exhibited poorer oxidation resistance.Furthermore,cis-trans isomerization transformation occurred in the unsaturated FAME molecules and conjugated double-bond produced during the oxidation process of biodiesel.Greater cis-trans variations corresponded to deeper oxidation degree.The higher the content of unsaturated FAME with multi-double bonds in a biodiesel,the more the conjugated double bonds was formed.展开更多
Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT...Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.展开更多
Objective:There is vast literature on transformative learning,which is an important aspect of nursing education,but its meaning remains unclear.It is therefore important to clarify the meaning of transformative learni...Objective:There is vast literature on transformative learning,which is an important aspect of nursing education,but its meaning remains unclear.It is therefore important to clarify the meaning of transformative learning,identify its attributes,antecedents and consequences to increase its use in nursing education,practice and research.Methods:Walker and Avant's method was used,and the process provided a structured way to analyse the concept of'transfonnative leaming'.Nursing education dictionaries,encyclopaedias,conference papers,research articles,dissertations,theses,journal articles,thesauri and relevant books through the database library and intemet searches were reviewed.One hundred and two literature sources were reviewed,and data saturation was reached.Results:The results of the concept analysis of transformative learning within the context of nursing education identified three categories,namely,1)Antecedents as cognitive and affective perspective,democratic education principles and inspiration;2)Process through three phases,namely i)awareness through self-reflection,ii)the meaningful interactive,integrative and democratic construction process,and iii)metacognitive reasoning abilities;and 3)Outcomes.A theoretical definition of transformative learning was formulated.Theoretical validity was ensured.Conclusion:The results of the concept analysis of transformative learning were used to describe a model to facilitate transformative learning within the context of nursing education.展开更多
Starting from the diffraction imaging process,we have discussed the relationship between optical imaging system and fractional Fourier transform, and proposed a specific system which can form an inverse amplified imag...Starting from the diffraction imaging process,we have discussed the relationship between optical imaging system and fractional Fourier transform, and proposed a specific system which can form an inverse amplified image of input function.展开更多
High frequency transformer is used in many applications among the Switch Mode Power Supply (SMPS), high voltage pulse power and etc can be mentioned. Regarding that the core of these transformers is often the ferrite ...High frequency transformer is used in many applications among the Switch Mode Power Supply (SMPS), high voltage pulse power and etc can be mentioned. Regarding that the core of these transformers is often the ferrite core;their functions partly depend on this core characteristic. One of the characteristics of the ferrite core is thermal behavior that should be paid attention to because it affects the transformer function and causes heat generation. In this paper, a typical high frequency transformer with ferrite core is designed and simulated in ANSYS software. Temperature rise due to winding current (Joule-heat) is considered as heat generation source for thermal behavior analysis of the transformer. In this simulation, the temperature rise and heat distribution are studied and the effects of parameters such as flux density, winding loss value, using a fan to cool the winding and core and thermal conductivity are investigated.展开更多
基金supported by grants for development of new faculty staff,Ratchadaphiseksomphot Fund,Chulalongkorn University,Thailand.
文摘Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.
基金supported by the Key Project of Jiangsu Social Science Fund and the Key Project of Jiangsu Research Center for Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era(Grant No.26ZXZA017).
文摘China’s environmental governance strategy provides a distinctive pathway for integrating sustainable development into national policy.Understanding its policy trajectory is essential for assessing China’s contribution to global sustainable development and the United Nations Sustainable Development Goals(SDGs).This study constructs a comprehensive database of 425 national environmental governance policy documents issued between 1978 and 2022 and applies Latent Dirichlet Allocation(LDA)modeling to examine the evolution of policy themes and discourse.The results show that China’s environmental governance has undergone four stages-initial exploration,detailed development,transformative leap,and diverse prosperity-reflecting a progressive shift toward more integrated and coordinated governance.Policy priorities have evolved from a primary focus on pollution control and energy transition to an emphasis on institutional construction and organizational reform,thereby strengthening alignment with the SDGs.This transformation is characterized by recurring developmental themes and increasingly preventive,forward-looking,and system-oriented governance approaches.Moreover,the co-evolution of policy concepts and implementation has driven a transition from localized,end-of-pipe responses to comprehensive governance frameworks,alongside a shift from normative guidance towards effectiveness-oriented policy design.By employing a data-driven text analysis approach,this study offers a systematic framework for tracing long-term policy evolution and assessing its implications for sustainable development.
基金supported in part by the National Key R&D Program of China under Grant No.2024YFE0200504NSFC key international joint project under Grant No.62120106007Interdisciplinary Research Program of HUST under Grant No.2024JCYJ022.
文摘Fiber-to-the-Room(FTTR)has emerged as the core architecture for next-generation home and enterprise networks,offering gigabitlevel bandwidth and seamless wireless coverage.However,the complex multi-device topology of FTTR networks presents significant chal⁃lenges in identifying sources of network performance degradation and conducting accurate root cause analysis.Conventional approaches often fail to deliver efficient and precise operational improvements.To address this issue,this paper proposes a Transformer-based multi-task learn⁃ing model designed for automated root cause analysis in FTTR environments.The model integrates multidimensional time-series data col⁃lected from access points(APs),enabling the simultaneous detection of APs experiencing performance degradation and the classification of underlying root causes,such as weak signal coverage,network congestion,and signal interference.To facilitate model training and evaluation,a multi-label dataset is generated using a discrete-event simulation platform implemented in MATLAB.Experimental results demonstrate that the proposed Transformer-based multi-task learning model achieves a root cause classification accuracy of 96.75%,significantly outperform⁃ing baseline models including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Random Forest,and eXtreme Gradient Boost⁃ing(XGBoost).This approach enables the rapid identification of performance degradation causes in FTTR networks,offering actionable in⁃sights for network optimization,reduced operational costs,and enhanced user experience.
基金supported by the Technology Innovation Program(20023566,‘Development and Demonstration of Industrial IoT and AI-Based Process Facility Intelligence Support System in Small and Medium Manufacturing Sites’)funded by the Ministry of Trade,Industry,&Energy(MOTIE,Republic of Korea).
文摘Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金the Deanship of Graduate Studies and Scientific Research at Najran University,Saudi Arabia,for their financial support through the Easy Track Research program,grant code(NU/EFP/MRC/13).
文摘Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.
基金Supported by National Natural Science Foundation of China(30960179)Program for Innovative Research Team in Science and Technology in University of Yunnan Province~~
文摘In order to distinguish 8 kinds of rhizome crops, the 40 samples were studied by Fourier transform infrared spectroscopy （FTIR） combined with wavelet transform （WT）, principal component analysis （PCA） and hieramhical cluster analysis （HCA）. The results showed that the infrared spectra were similar on the whole, but there were differences in peak position, peak shape and peak absorption intensity in the range of 1 800-700 cm-1. The infrared spectra in the range of 1 800-700 cm-1 were selected to perform continuous wavelet transform （CWT） and discrete wavelet transform （DWT）. The 15th-Ievel decomposition coefficients of CWT and the 5=-level detail coefficients of DWT were classified by PCA and HCA. The cumulative contri- bution rates of the first three principal components of CWT and DWT were 93.12% and 89.78%, respectively. The accurate recognition rates of PCA and HCA were all 100%. It is proved that FTIR combined with WT can be used to distinguish different kinds of rhizome crops.
基金the financial support of the National Key Basic Research Foundation of China (Project G19990650), the National Natural Science Foundation of China (Project 50071054) and the financial support of State Key
文摘Wavelet transforms (WT) are proposed as an alternative tool to overcome the limitations of Fourier transforms (FFT) in the analysis of electrochemical noise (EN) data. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients, which contain information about the time scale characteristic of the associated corrosion event. In this context, the potential noise fluctuations during the free corrosion of pure aluminum in sodium chloride solution was recorded and analyzed with wavelet transform technique. The typical results showed that the EN signal is composed of distinct type of events, which can be classified according to their scales, i.e. their time constants. Meanwhile, the energy distribution plot (EDP) can be used as 'fingerprints' of EN signals and can be very useful for analyzing EN data in the future.
基金supported by the National Natural Science Foundation of China(61571088)the State High-Tech Development Plan(the 863 Program)(2015AA7031093B2015AA8098088B)
基金This project is supported by National Natural Science Foundation of China (No.50275154) Municipal Natural Science Foundation of Chongqing, China (No.8773).
文摘It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.
基金This paper was supported by the Innovation Project of Nanjing Forestry University.
文摘Bamboo was carbonized at different temperatures ranging from 200℃to 600℃.The dependence of the change of hemicellulose,cellulose,and lignin on the temperature was investigated by means of elemental analysis and Fourier Transform Infrared(FTIR)spectra of the residual solid products.The results showed:(1)Below 200℃,hemicellulose in bamboo was de-composed and a large amount of hydroxyl groups are dislocated from hemicellulose and cellulose,accompanied by the evolu-tion of water to escape.(2)200℃-250℃,cellulose in bamboo was drastically decomposed whereas the net structure of lignin keep stable,with the except of the dislocation of methoxyl groups from lignin.(3)250℃~400℃,the net structure of lignin col-lapse,up to 400’℃,followed by that the more position in aryl groups are substituted.(4)For bamboo carbonization,the aroma-tization of residual carbon has approximately completed at the temperature as high as 600℃.But the fusion of aromatic rings possibly does not occur.
文摘We present a new reliable analytical study for solving the discontinued problems arising in nanotechnology. Such problems are presented as nonlinear differential-difference equations. The proposed method is based on the Laplace trans- form with the homotopy analysis method （HAM）. This method is a powerful tool for solving a large amount of problems. This technique provides a series of functions which may converge to the exact solution of the problem. A good agreement between the obtained solution and some well-known results is obtained.
文摘With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.
基金This project is supported by National Natural Science Foundation of China
文摘After brief describing the Principle of wavelet transform (WT) of signals, a new signals analysis system based on wavelet transform is introduced. The design and development of the instryment of wavelet transform are described. A number of practical uses of this system demonstrate that wavelet transform system is specially functional in identifying and processing impulse, singular and non-smooth signals, so that it should be evaluated the most advanced signal analyzing system.
基金the financial support from the National Natual Science Foundation of China(No.51375491)the Natural Science Foundation of Chongqing(Project No.2011JJA90020)the Science Foundation for Young Teachers of Logistical Engineering University
文摘The oxidation behavior of three biodiesels of different origins,viz.rapeseed oil derived biodiesel,soybean oil derived biodiesel and waste oil based biodiesel,were tested on an oxidation tester.The chemical compositions of the biodiesels were characterized by gas chromatography.Thereafter,the structural transformation of fatty acid methyl ester(FAME)of the biodiesels was analyzed by an infrared spectrometer and an ultraviolet absorption spectrometer.The results demonstrated that the oxidation behavior of biodiesels of different origins was closely related to the composition and distribution of FAMEs.Higher concentration of unsaturated FAME with multi-double bonds exhibited poorer oxidation resistance.Furthermore,cis-trans isomerization transformation occurred in the unsaturated FAME molecules and conjugated double-bond produced during the oxidation process of biodiesel.Greater cis-trans variations corresponded to deeper oxidation degree.The higher the content of unsaturated FAME with multi-double bonds in a biodiesel,the more the conjugated double bonds was formed.
基金Supported by Tianjin Municipal Science and Technology Commission (No.09JCYBJC02200)
文摘Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.
基金The research study was financially supported by the researcher and the partial funding of Supervisor bursaries as awarded by the University of Johannesburg.
文摘Objective:There is vast literature on transformative learning,which is an important aspect of nursing education,but its meaning remains unclear.It is therefore important to clarify the meaning of transformative learning,identify its attributes,antecedents and consequences to increase its use in nursing education,practice and research.Methods:Walker and Avant's method was used,and the process provided a structured way to analyse the concept of'transfonnative leaming'.Nursing education dictionaries,encyclopaedias,conference papers,research articles,dissertations,theses,journal articles,thesauri and relevant books through the database library and intemet searches were reviewed.One hundred and two literature sources were reviewed,and data saturation was reached.Results:The results of the concept analysis of transformative learning within the context of nursing education identified three categories,namely,1)Antecedents as cognitive and affective perspective,democratic education principles and inspiration;2)Process through three phases,namely i)awareness through self-reflection,ii)the meaningful interactive,integrative and democratic construction process,and iii)metacognitive reasoning abilities;and 3)Outcomes.A theoretical definition of transformative learning was formulated.Theoretical validity was ensured.Conclusion:The results of the concept analysis of transformative learning were used to describe a model to facilitate transformative learning within the context of nursing education.
文摘Starting from the diffraction imaging process,we have discussed the relationship between optical imaging system and fractional Fourier transform, and proposed a specific system which can form an inverse amplified image of input function.
文摘High frequency transformer is used in many applications among the Switch Mode Power Supply (SMPS), high voltage pulse power and etc can be mentioned. Regarding that the core of these transformers is often the ferrite core;their functions partly depend on this core characteristic. One of the characteristics of the ferrite core is thermal behavior that should be paid attention to because it affects the transformer function and causes heat generation. In this paper, a typical high frequency transformer with ferrite core is designed and simulated in ANSYS software. Temperature rise due to winding current (Joule-heat) is considered as heat generation source for thermal behavior analysis of the transformer. In this simulation, the temperature rise and heat distribution are studied and the effects of parameters such as flux density, winding loss value, using a fan to cool the winding and core and thermal conductivity are investigated.
