工程技术最新文献

{"title":"The phonation test can distinguish the patient with Parkinson's disease via Bayes inference.","authors":"Yifeng Liu, Hongjie Gong, Meimei Mouse, Fan Xu, Xianwei Zou, Jingsheng Yang, Qingping Xue, Min Huang","doi":"10.1007/s11571-024-10194-x","DOIUrl":"10.1007/s11571-024-10194-x","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a neurodegenerative disease with various clinical manifestations caused by multiple risk factors. However, the effect of different factors and relationships between different features related to PD and the extent of those factors leading to the incidence of PD remains unclear. we employed Bayesian network to construct a prediction model. The prediction system was trained on the data of 35 patients and 26 controls. The structure learning and parameter learning of Bayesian Network was completed through the tree-augmented network (TAN) and Netica software, respectively. We employed four Bayesian Networks in terms of the syllable, including monosyllables, disyllables, multisyllables and unsegmented syllables. The area under the curve (AUC) of monosyllabic, disyllabic, multisyllabic, and unsegmented-syllable models were 0.95, 0.83, 0.80 and 0.84, respectively. In the monosyllabic tests, the best predictor of PD was duration, the posterior probability of which was 92.70%. Meanwhile, minimum f0 (61.60%) predicted best in the disyllabic tests and the variables that predicted best in multisyllables and unsegmented syllables were end f0 (59.40%) and maximum f0 (58.40%). In the cross-sectional comparison, the prediction effect of each variable in the monosyllabic tests was generally higher than that of other test groups. The monosyllabic models had the highest predicted performance of PD. Among acoustic parameters, duration was the strongest feature in predicting the prevalence of PD in monosyllabic tests. We believe that this network methodology will be a useful tool for the clinical prediction of Parkinson's disease.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10194-x.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"18"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal time-frequency localized wavelet filters for identification of Alzheimer's disease from EEG signals.","authors":"Digambar V Puri, Jayanand P Gawande, Pramod H Kachare, Ibrahim Al-Shourbaji","doi":"10.1007/s11571-024-10198-7","DOIUrl":"10.1007/s11571-024-10198-7","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a chronic disability that occurs due to the loss of neurons. The traditional methods to detect AD involve questionnaires and expensive neuro-imaging tests, which are time-consuming, subjective, and inconvenient to the target population. To overcome these limitations, Electroencephalogram (EEG) based methods have been developed to classify AD patients from normal controlled (NC) and mild cognitive impairment (MCI) subjects. Most of the EEG-based methods involved entropy-based feature extraction and discrete wavelet transform. However, the existing AD classification methods failed to provide promising classification accuracy. Here, we proposed a wavelet-machine learning (ML) framework to detect AD using a newly designed biorthogonal filter bank by optimization of frequency and time localization of triplet halfband filter banks (OTFL-THFB). The OTFL-THFB decomposes EEG signals into various EEG sub- bands. Hjorth Parameters (HP) and Higuchi's Fractal Dimension (HFD) have been investigated to extract features from each EEG subband. Subsequently, ML models are trained and tested using different features such as OTFL-THFB with HFD, OTFL-THFB with HP, and OTFL-THFB with HFD and HP used for detecting AD with 10-fold cross-validation. This method was applied to two publicly available datasets. Our model achieved an accuracy of <math><mrow><mn>98.91</mn> <mo>%</mo></mrow> </math> for AD versus NC and <math><mrow><mn>98.65</mn> <mo>%</mo></mrow> </math> for AD versus MCI versus NC using the least square support vector machine. Results indicate that this framework surpassed existing state-of-the-art techniques for classifying AD from NC.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"12"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of memristive emotion associative learning circuit.","authors":"Zhangzhi Zhou, Mi Lin, Xuanxuan Zhou, Chong Zhang","doi":"10.1007/s11571-024-10211-z","DOIUrl":"10.1007/s11571-024-10211-z","url":null,"abstract":"<p><p>Psychological studies have demonstrated that the music can affect memory by triggering different emotions. Building on the relationships among music, emotion, and memory, a memristor-based emotion associative learning circuit is designed by utilizing the nonlinear and non-volatile characteristics of memristors, which includes a music judgment module, three emotion generation modules, three emotional homeostasis modules, and a memory module to implement functions such as learning, second learning, forgetting, emotion generation, and emotional homeostasis. The experimental results indicate that the proposed circuit can simulate the learning and forgetting processes of human under different music circumstances, demonstrate the feasibility of memristors in biomimetic circuits, verify the impact of music on memory, and provide a foundation for in-depth research and application development of the interaction mechanism between emotion and memory.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"13"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view domain adaption based multi-scale convolutional conditional invertible discriminator for cross-subject electroencephalogram emotion recognition.","authors":"Sivasaravana Babu S, Prabhu Venkatesan, Parthasarathy Velusamy, Saravana Kumar Ganesan","doi":"10.1007/s11571-024-10193-y","DOIUrl":"10.1007/s11571-024-10193-y","url":null,"abstract":"<p><p>Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people. Accurately predicting emotions poses challenges due to dynamic traits. Models struggle with feature extraction, convergence, and negative transfer issues, hindering cross subject emotion recognition. The proposed model employs thorough signal preprocessing, Short-Time Geodesic Flow Kernel Fourier Transform (STGFKFT) for feature extraction, enhancing classifiers' accuracy. Multi-view sheaf attention improves feature discrimination, while the Multi-Scale Convolutional Conditional Invertible Puma Discriminator Neural Network (MSCCIPDNN) framework ensures generalization. Efficient computational techniques and the puma optimization algorithm enhance model robustness and convergence. The suggested framework demonstrates extraordinary success with high accuracy, of 99.5%, 99% and 99.50% for SEED, SEED-IV, and DEAP dataset sequentially. By incorporating these techniques, the proposed method aims to precisely recognition emotions, and accurately captures the features, thereby overcoming the limitations of existing methodologies.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"23"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An effective encryption approach using a combination of a non-chain ring and a four-dimensional chaotic map.","authors":"Muhammad Umair Safdar, Tariq Shah, Asif Ali","doi":"10.1007/s11571-025-10217-1","DOIUrl":"10.1007/s11571-025-10217-1","url":null,"abstract":"<p><p>Algebraic structures are highly effective in designing symmetric key cryptosystems; however, if the key space is not sufficiently large, such systems become vulnerable to brute-force attacks. To address this challenge, our research focuses on enlarging the key space in symmetric key schemes by integrating the non-chain ring with a four-dimensional chaotic system. While chaotic maps offer significant potential for data processing, relying solely on them does not fully leverage their operational advantages. Therefore, it is essential to incorporate algebraic structures that enhance the complexity of the scheme. In the proposed technique, four-dimensional chaotic sequences are employed for image pixel permutation, diffusion, and exclusive-or operations. The scheme is further strengthened against chosen and known plaintext attacks by incorporating pixel values during the exclusive-or operation, where images are XORed with hashed images and keys generated from chaotic sequences. The effectiveness of the technique, its resilience to various forms of attack, and its feasibility for practical implementation are demonstrated through extensive testing and a comprehensive security analysis.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"27"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data.","authors":"V Kavitha, R Siva","doi":"10.1007/s11571-024-10202-0","DOIUrl":"10.1007/s11571-024-10202-0","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"22"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroenhancement by repetitive transcranial magnetic stimulation (rTMS) on DLPFC in healthy adults.","authors":"Elias Ebrahimzadeh, Seyyed Mostafa Sadjadi, Mostafa Asgarinejad, Amin Dehghani, Lila Rajabion, Hamid Soltanian-Zadeh","doi":"10.1007/s11571-024-10195-w","DOIUrl":"10.1007/s11571-024-10195-w","url":null,"abstract":"<p><p>The term \"neuroenhancement\" describes the enhancement of cognitive function associated with deficiencies resulting from a specific condition. Nevertheless, there is currently no agreed-upon definition for the term \"neuroenhancement\", and its meaning can change based on the specific research being discussed. As humans, our continual pursuit of expanding our capabilities, encompassing both cognitive and motor skills, has led us to explore various tools. Among these, repetitive Transcranial Magnetic Stimulation (rTMS) stands out, yet its potential remains underestimated. Historically, rTMS was predominantly employed in studies focused on rehabilitation objectives. A small amount of research has examined its use on healthy subjects with the goal of improving cognitive abilities like risk-seeking, working memory, attention, cognitive control, learning, computing speed, and decision-making. It appears that the insights gained in this domain largely stem from indirect outcomes of rehabilitation research. This review aims to scrutinize these studies, assessing the effectiveness of rTMS in enhancing cognitive skills in healthy subjects. Given that the dorsolateral prefrontal cortex (DLPFC) has become a popular focus for rTMS in treating psychiatric disorders, corresponding anatomically to Brodmann areas 9 and 46, and considering the documented success of rTMS stimulation on the DLPFC for cognitive improvement, our focus in this review article centers on the DLPFC as the focal point and region of interest. Additionally, recognizing the significance of theta burst magnetic stimulation protocols (TBS) in mimicking the natural firing patterns of the brain to modulate excitability in specific cortical areas with precision, we have incorporated Theta Burst Stimulation (TBS) wave patterns. This inclusion, mirroring brain patterns, is intended to enhance the efficacy of the rTMS method. To ascertain if brain magnetic stimulation consistently improves cognition, a thorough meta-analysis of the existing literature has been conducted. The findings indicate that, after excluding outlier studies, rTMS may improve cognition when compared to appropriate control circumstances. However, there is also a considerable degree of variation among the researches. The navigation strategy used to reach the stimulation site and the stimulation location are important factors that contribute to the variation between studies. The results of this study can provide professional athletes, firefighters, bodyguards, and therapists-among others in high-risk professions-with insightful information that can help them perform better on the job.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"34"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The dynamic impact of adult neurogenesis on pattern separation within the dentate gyrus neural network.","authors":"Kai Yang, Xiaojuan Sun, Zengbin Wang","doi":"10.1007/s11571-025-10244-y","DOIUrl":"10.1007/s11571-025-10244-y","url":null,"abstract":"<p><p>Pattern separation in the dentate gyrus (DG) is crucial for distinguishing similar memories. The DG continues to undergo neurogenesis throughout the lifespan, and adult hippocampus neurogenesis leads to the incorporation of thousands of adult-born granule cells (adult-born GCs) into the existing DG circuitry. These newborn GCs exhibit high excitability and are easier to respond to novel stimuli, which seems to be contrary to the requirement of pattern separation for high input specificity. Meanwhile, the changes brought about by the growth of adult-born GCs can not be ignored. Here, we build a biologically relevant model of the DG containing adult-born GCs and test it using the Modified National Institute of Standards and Technology (MNIST) database. By analyzing this model, the results show that the net effect of adult-born GCs to GCs is inhibition, thereby improving the sparsity of GCs and pattern separation. This provides computational evidence for \"indirect encoding\" of adult-born GCs. In addition, as adult-born GCs transition toward maturity, they have the following growth characteristics: decreased activity, increased coupling strength with feedback inhibition, and enhanced synaptic plasticity. We find that the decreased activity reduces pattern separation efficiency while the other characteristics increase pattern separation efficiency. Finally, given that the firing rate of entorhinal cortex (EC) neurons is influenced by numerous factors (such as the complexity of memory tasks), the input frequency to the DG should be within a range rather than being fixed. To address this, we gradually increase the input frequency and notice that the presence of adult-born GCs increases the adaptability of the DG neural network and thus improves the robustness of pattern separation.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"57"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of brain network effective connectivity in juvenile myoclonic epilepsy.","authors":"Ming Ke, Xin Kang, Di Xu, Guangyao Liu","doi":"10.1007/s11571-025-10256-8","DOIUrl":"10.1007/s11571-025-10256-8","url":null,"abstract":"<p><p>Juvenile Myoclonic Epilepsy (JME) is a prevalent idiopathic generalized epilepsy whose neurophysiological mechanisms remain elusive. This study aims to elucidate the aberrant brain network patterns in JME through a multi-modal fMRI approach combining local consistency, functional connectivity, and causal interaction analysis. Resting-state fMRI data were acquired from 37 JME patients and 35 healthy controls. Regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFF) analyses identified eight brain regions with significant between-group differences (FDR-corrected p < 0.05), including the right middle frontal gyrus, right insula, right medial/paracingulate gyrus, bilateral superior frontal gyri, left postcentral gyrus, and left superior occipital gyrus. These regions served as regions of interest (ROIs) for subsequent functional and effective connectivity analyses. Functional connectivity analysis revealed increased connectivity strength between the right middle frontal gyrus and right medial or paracingulate gyrus, as well as between the right insula and right medial/paracingulate gyrus (two-sample <i>t</i> test, p < 0.01), despite decreased local synchrony in these regions. Dynamic causal modeling (DCM) demonstrated bidirectional enhancement of effective connectivity between the right insula and right medial or paracingulate gyrus in patients (Bayesian posterior probability > 0.95). These findings suggest that the observed decoupling of local neuronal synchronization and long-range connectivity may reflect compensatory neuroadaptive processes, particularly involving the salience network (insula) and cognitive control circuitry (cingulate regions).The integration of ReHo/ALFF mapping with DCM provides a novel framework for understanding the neurodevelopmental trajectory of JME, highlighting the critical role of cortico-subcortical dysregulation in its pathogenesis.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"69"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12052659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic analysis of multi-spiral chaotic inertia model with a cyclic configuration involving four homogeneous HNN cells: stability analysis, analog and digital verifications.","authors":"Jean Baptiste Koinfo, Donghua Jiang, Jean Chamberlain Chedjou, Jacques Kengne, Khabibullo Nosirov","doi":"10.1007/s11571-025-10240-2","DOIUrl":"10.1007/s11571-025-10240-2","url":null,"abstract":"<p><p>This paper investigates the behavior of a Hopfield neural network consisting of four interconnected inertial neurons arranged in a loop configuration. The mathematical equation that governs the overall dynamic of the model is consists of a set of eight first-order ordinary differential equations (ODEs) with odd symmetry. The system has 81 equilibrium points, some of which undergo multiple Hopf bifurcations as a control parameter is varied. The maximum number of coexisting states is related to the maximum number of active equilibrium points. Through numerical investigations, intriguing nonlinear properties are discovered, including both homogeneous and heterogeneous multistability and the coexistence of up to sixteen bifurcation branches, the presence of multi-spiral chaos, crisis phenomenon, period splitting and the oscillation death phenomenon. In order to obtain a comprehensive understanding of the dynamics, various tools are used, such as phase portraits, bifurcation diagrams, Poincare maps, frequency spectra, Lyapunov exponent spectra, and attraction basins. A Significant achievement of this study is the demonstration that coupling inertial neurons can be an effective method to generate multi-spiral chaotic signals. The overall dynamics is non-hidden and meticulous adjustment of the gradient connected to the fourth neuron allows to complete annihilate oscillations (no motion) in the neural network in a particular interval. Finally, an electronic circuit inspired by the coupled inertial neuron system is designed using Orcad-PSpice software and implemented using an Arduino-based microcontroller. The simulation results from PSpice and microcontroller confirm the findings from the theoretical analysis.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"63"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}