Biomedical Physics & Engineering Express最新文献

{"title":"Interpretable multi-class lung disease classification from chest x-ray images using attention-enhanced deep learning.","authors":"Triwiyanto Triwiyanto, Endro Yulianto, Sari Luthfiyah, Finta Isti Kundarti, I Putu Alit Pawana","doi":"10.1088/2057-1976/aeac33","DOIUrl":"10.1088/2057-1976/aeac33","url":null,"abstract":"<p><p>Accurate and interpretable multi-class recognition of lung diseases from chest x-ray (CXR) images remains challenging because different pulmonary conditions can present with overlapping radiographic patterns, making reliable automated diagnosis difficult in clinical screening and decision support. This study aims to develop an interpretable CXR-based multi-class lung disease classification framework using attention-enhanced deep learning and to compare its performance with standard transfer learning and baseline CNN models. The work contributes a comparative benchmark of attention mechanisms, including convolutional block attention module (CBAM), squeeze-and-excitation (SE), self-attention, and spatial-channel attention, together with statistical significance analysis and explainability using Grad-CAM, Score-CAM, and attention heatmaps. It also provides a structured evaluation pipeline across EfficientNet-B0, ResNet50, DenseNet121, MobileNetV2, and a basic CNN. Experiments were conducted on a three-class CXR dataset containing Normal, Lung Opacity, and Viral Pneumonia images. Images were divided into training, validation, and test sets, and deep models were trained using transfer learning with optimized hyperparameters. Model performance was assessed using accuracy, precision, recall,<i>F</i>1-score, ROC-AUC, Matthews correlation coefficient, and Cohen's kappa. Statistical validation was performed using McNemar tests, Friedman testing, and module-level comparative analysis. Among the attention-enhanced models, the SE-based model achieved the best test performance, with accuracy of 0.9425 and macro<i>F</i>1-score of 0.9438, followed closely by CBAM and spatial-channel attention. Across the compared models, the Friedman test showed a statistically significant global difference in performance (chi-square = 112.13,<i>p</i>< 0.001). Explainability results further showed that the proposed attention-based models focused on clinically relevant chest regions. Attention-enhanced deep learning is effective for robust and interpretable multi-class lung disease classification from CXR images. In particular, SE-based attention provided the strongest overall performance, while statistical testing confirmed meaningful differences among model groups. These findings support the use of interpretable attention mechanisms for practical AI-assisted thoracic image analysis.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additively manufactured PLA-bioceramic porous orthopedic implants for biomedical applications: from material-structure co-design to clinical translation.","authors":"Aamir Shehzad, Wang Yanen, Ghulam Hassan Askari, Sidra Aslam, Nasar Ali","doi":"10.1088/2057-1976/aeaaeb","DOIUrl":"10.1088/2057-1976/aeaaeb","url":null,"abstract":"<p><p>Porous orthopedic implants are widely studied because they promote osseointegration and reduce stress shielding associated with dense, stiff implants. This review critically examines recent progress in the design and fabrication of additively manufactured porous orthopedic implants, with a focused emphasis on polylactic acid (PLA)-bioceramic systems, particularly hydroxyapatite-, tricalcium phosphate-, and bioactive glass-containing composites. We examine how porosity governs biological outcomes-cell attachment and proliferation, nutrient transport, and bone ingrowth-while simultaneously affecting stiffness, strength, and fatigue resistance. Rather than treating pore size, porosity, and material selection as universally optimal parameters, the review emphasizes that these variables must be interpreted according to implant location, loading environment, degradation profile, and the expected timeline of bone repair. Key structural variables, including pore size, morphology, interconnectivity, and graded architectures, are discussed in the context of meeting load-bearing requirements. Particular attention is given to balancing permeability with mechanical integrity, and to approaches such as ceramic-filled PLA composites and surface modifications that improve bioactivity. Additive manufacturing is presented as an enabling route to couple external geometry with internal architecture, and fused deposition modeling, Stereolithography, and binder jetting are compared with respect to material compatibility, attainable feature resolution, and post-processing needs. Building on these insights, we frame co-design as the joint optimization of material selection and porous structure to maximize overall implant function. Finally, we identify the main barriers to clinical translation, including degradation-healing mismatch, fatigue durability, sterilization, reproducibility of porous architectures, regulatory documentation, and the need for long-term<i>in vivo</i>validation.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149023855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CardioBrain: a novel user-friendly software for assessing dynamic cerebral autoregulation-implementation validation and robustness to signal artefacts.","authors":"Rhenan Bartels, Gabriel Dias Rodrigues, Tiago Peçanha","doi":"10.1088/2057-1976/aea7a2","DOIUrl":"10.1088/2057-1976/aea7a2","url":null,"abstract":"<p><p>Cerebral autoregulation (CA) is a critical mechanism that protects the brain against ischemia and hyperperfusion by maintaining cerebral blood flow (CBF) relatively stable despite fluctuations in arterial blood pressure (ABP). Alterations in CA are linked to adverse outcomes, highlighting the need for reliable analysis methods. This study aimed to present<i>CardioBrain</i>, a free and user-friendly software for assessing dynamic CA (dCA) using transfer function analysis (TFA) of continuously recorded ABP and CBF velocity (CBFv) data, validate its TFA implementation, and evaluate its robustness to simulated signal artefacts and artefact filtering. CardioBrain calculates TFA-derived dCA metrics, including gain, phase, and coherence, across frequency bands (VLF, LF, HF), and allows users to adjust signal-processing parameters following standards recommended by the Cerebrovascular Research Network (CARNet). Validation of the software's TFA implementation was performed by assessing agreement between<i>CardioBrain</i>and the TFA function provided on CARNet's website (<i>tfa_car</i>) using 18 ABP and CBFv signals. Robustness was assessed following the introduction of increasing levels of simulated signal artefacts (1%-5%), with and without Hampel filtering. Bland-Altman analysis showed excellent agreement between CardioBrain and tfa_car for coherence, gain (cm·s^-1·mmHg^-1), and phase (degrees), with mean differences (LoA) indicating negligible errors: VLF_coherence=-0.00(-0.000067-0.000054), VLF_gain=-0.00(-0.000067-0.000066), VLF_phase=-0.00(-0.000066-0.000053), LF_coherence=-0.00(-0.000063-0.000069), LF_gain=-0.00(-0.000061-0.000041), LF_phase=0.00(-0.000059-0.000069), HF_coherence=-0.00(-0.000058-0.000036), HF_gain=-0.00(-0.00007-0.000063), HF_phase= - 0.00(-0.000044-0.000029). Increasing artefact contamination, particularly from 2% onwards, resulted in greater bias and variability in TFA estimates, which were generally attenuated by Hampel filtering. This study presents<i>CardioBrain</i>as a user-friendly software tool for dCA assessment using TFA and demonstrates excellent agreement with an established TFA implementation. Robustness analyses demonstrate the impact of signal artefacts on TFA estimates and the potential of Hampel filtering to mitigate these effects.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time2Vec transformer for robust gesture recognition from low-density sEMG.","authors":"Blagoj Hristov, Hristijan Gjoreski, Vesna Ojleska Latkoska, Gorjan Nadzinski","doi":"10.1088/2057-1976/aea99c","DOIUrl":"10.1088/2057-1976/aea99c","url":null,"abstract":"<p><p><i>Objective.</i>To determine whether temporal embeddings can recover the discriminative information lost when reducing surface electromyography (sEMG) sensing from dense electrode arrays to a sparse two-channel configuration, and under what conditions they can be integrated without degrading spatial features.<i>Approach.</i>Using a publicly available dataset of 8 subjects performing 10 dynamic finger gestures, we develop a hybrid Transformer optimized for two-channel sEMG. We identify a failure mode in standard additive integration, where the two branches differ in latent magnitude by a factor of four, so the spatial branch dominates the sum. We therefore integrate Time2Vec embeddings through a normalized additive fusion strategy that layer-normalizes both latent distributions before integration. A two-stage curriculum (augmentation-driven pre-training followed by clean-signal fine-tuning) supports robust feature extraction under data scarcity.<i>Main results.</i>Under leave-one-subject-out cross-validation the proposed model achieves a mean<i>F</i>1-score of 95.9% ± 0.15%, outperforming standard additive, gated, and cross-attention fusion. A gain sweep and a parameter-free normalization variant identify scale alignment as the operative mechanism, as a learnable basis performs worse than fixed sinusoidal encodings under standard addition but exceeds them once normalized. Zero-shot transfer to unseen subjects yields 21.0% ± 2.98%, and classical hand-crafted-feature models collapse comparably, consistent with a sensing constraint rather than a representational one. Supervised calibration with two trials per gesture recovers performance to 96.9% ± 0.52%. Inference runs in 21.5 ms on a consumer CPU core.<i>Significance.</i>Temporal embeddings can compensate for reduced spatial sensing density provided their integration preserves the scale of both representations, enabling rapidly personalizable myoelectric interfaces.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149001295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-scattered events imaging in TOF-PET via machine learning-based classification.","authors":"R Verma, P Das","doi":"10.1088/2057-1976/aea168","DOIUrl":"10.1088/2057-1976/aea168","url":null,"abstract":"<p><p>Scattered coincidences are typically rejected in conventional imaging in positron emission tomography (PET) despite carrying potentially useful spatial information. We present a novel algorithm for PET imaging from single-scattered (SS) (inside tissue) events with known time-of-flight (TOF) and without energy information, particularly useful for plastic scintillator. The 3D annihilation loci corresponding to individual SS events are modeled as spindle-torus probability distributions constrained by TOF. These event-wise probability volumes are merged using a MATLAB-based reconstruction framework to estimate the source location. In this study, we used GATE-based Monte Carlo simulations of PET acquisitions to generate realistic emission-event data, including true, SS, and multiple-scattered coincidences. Supervised machine learning classifiers, namely random forest (RF) and extreme gradient boosting (XGB), were employed only for the identification of SS events from event-level geometric and timing-related features extracted from the simulated data. The identified SS events were subsequently processed using a dedicated 3D geometric TOF-based reconstruction framework to estimate the annihilation probability distribution and generate SS images. Both RF and XGB achieved SS-event precision of approximately 68%-71%, with recall ranging from 56%-79% across the evaluated scanner configurations. Using the identified SS events, we created a 3D emission image and validated its quality in terms of resolution, contrast, and uniformity. The proposed framework was also evaluated on the NEMA Image Quality phantom under realistic imaging conditions. The results demonstrate that SS events retain meaningful spatial information and that their combination with geometric TOF-based reconstruction provides a feasible pathway for scatter-aware imaging in plastic scintillator PET systems.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148878718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic coarsened spatio-temporal graph convolutional networks for fMRI classification of addiction-induced sleep disorders.","authors":"Jiaqi Shen, Jiaying Meng, Jiusun Zeng, Bo Ye","doi":"10.1088/2057-1976/aea7a0","DOIUrl":"10.1088/2057-1976/aea7a0","url":null,"abstract":"<p><p>Substance use disorder is frequently characterized by persistent sleep disorders that hinder cognitive recovery. Accurately identifying these disruptions requires methods capable of tracking both spatial interactions and the temporal evolution of brain activity. While functional magnetic resonance imaging (fMRI) offers valuable insights into broad neural patterns, conventional fMRI classification methods typically rely on static connectivity and predefined knowledge. These conventional approaches may fail to account for the complex, time-varying nature of brain networks, thereby posing significant challenges for accurate fMRI classification. To overcome these limitations, this paper proposed the Dynamic Coarsened Spatio-Temporal Graph Convolutional Network, a data-driven framework designed to model the brain as a time-evolving directed graph and extract highly distinct data features. The framework achieves this by integrating two key components: a dynamic causal learning module that maps evolving connections over time, and a noise-reducing Gaussian mixture model. Experimental results indicate that the proposed approach outperforms existing methods in classifying fMRI data related to addiction-induced sleep disorders, offering a robust solution for high-dimensional fMRI analysis in clinical neurology.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-guided decoupled complementary network for binary semi-supervised medical image segmentation.","authors":"Dongsheng Wang, Xiaohan Lang","doi":"10.1088/2057-1976/aea90e","DOIUrl":"10.1088/2057-1976/aea90e","url":null,"abstract":"<p><p>Semi-supervised medical image segmentation methods have drawn wide attention as they reduce reliance on heavily annotated data. However, existing models suffer from confirmation bias with limited annotations, and structural or parameter coupling hinders self-correction, especially for medical images with ambiguous boundaries, low contrast and complex backgrounds. To address these issues, we propose an uncertainty-guided decoupling and complementary network (UGDC-Net) for binary medical image segmentation. It uses a contrastive mechanism to filter high-uncertainty regions of sub-networks for avoiding model collapse, and a dynamic competition mechanism to tackle the weight coupling bottleneck in teacher-student frameworks. Reliable pseudo-labels from high-performance sub-networks and supplementary ones from inconsistent high-uncertainty regions are integrated to provide diverse supervision, enhancing the model's exploration of small targets. Experiments on left atrium, Pancreas-CT and ISIC datasets validate that UGDC-Net outperforms state-of-the-art methods on binary segmentation tasks.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"12 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-weighted dual-domain consistency for self-supervised accelerated MRI reconstruction.","authors":"Xinzhen Li, Jinhong Huang, Genjiao Zhou, Zefeng Yu","doi":"10.1088/2057-1976/aea428","DOIUrl":"10.1088/2057-1976/aea428","url":null,"abstract":"<p><p>Accelerated magnetic resonance imaging (MRI) typically relies on fully sampled k-space data to train supervised reconstruction networks, which are difficult to acquire in clinical practice. To eliminate this dependency, we propose a novel dual-domain self-supervised learning (Dual-SSL) framework that rigorously enforces physical consistency in both image and k-space domains. The framework employs a shared-intersection re-undersampling strategy to generate two sub-masks with approximately 50% overlap, preserving essential low-frequency priors, which are subsequently concatenated for efficient single-forward-pass processing. A composite loss function is formulated to enforce image-domain consistency alongside comprehensive k-space consistency, the latter encompassing cross-consistency on mutually unobserved points and self-consistency on originally observed points. To automatically balance these dual-domain constraints without empirical hyperparameter tuning, a homoscedastic uncertainty-based mechanism is incorporated to learn log-variance parameters. The proposed framework was evaluated on fastMRI and MICCAI 2013 datasets under 1D Cartesian and 2D random variable-density undersampling at 4× and 8× accelerations. Results demonstrate that this approach significantly outperforms existing self-supervised techniques in mitigating severe aliasing artifacts and localized error hotspots, thereby yielding superior anatomical fidelity. Crucially, despite training exclusively on undersampled data, the proposed framework achieves reconstruction performance comparable to fully supervised baselines using the identical network architecture, effectively bridging the gap between self-supervised and supervised MRI reconstruction. This provides a robust and scalable solution for clinical MRI acceleration.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"12 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised pelvic CBCT-to-CT translation via cross-slice attention for 3D context modeling.","authors":"Xu Chen, Yunkui Pang, Jun Lian, Pew-Thian Yap","doi":"10.1088/2057-1976/aea90f","DOIUrl":"10.1088/2057-1976/aea90f","url":null,"abstract":"<p><p>Accurate CBCT-to-CT translation is important for image-guided and adaptive radiotherapy in prostate cancer, where treatment-day anatomy can differ substantially from planning CT. Most unsupervised methods are based on 2D slice-wise translation and therefore underuse volumetric context, while fully 3D models are often computationally prohibitive for routine training and deployment. To address this gap, we propose cross-slice attention based medical image translation (CAMIT), an unsupervised framework that incorporates 3D contextual information without full-volume 3D convolution. CAMIT adopts a two-stage strategy: modality-specific autoencoder pretraining to obtain compact latent representations, followed by latent-space domain translation with a cross-slice attention module that models long-range inter-slice dependencies from randomly sampled slices. We evaluated CAMIT on an unpaired pelvic CBCT/CT dataset (40 training, 5 validation, and 10 testing cases) using both quantitative and qualitative analyses. CAMIT achieved a peak signal-to-noise ratio of 27.45 and an structural similarity index of 0.67, outperforming representative state-of-the-art unsupervised baselines; statistical testing further confirmed significant performance gains. These results indicate that CAMIT provides an effective trade-off between 2D efficiency and 3D contextual modeling, supporting treatment-day anatomical assessment and adaptive workflow guidance.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"12 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149036167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contour-guided attention across YOLO architectures for optic structure detection.","authors":"Shih-Hsin Chen, Imiye Franck Eleazar Yaro, Chao-Wei Wu, Yeong-Cheng Liou","doi":"10.1088/2057-1976/ae9bca","DOIUrl":"10.1088/2057-1976/ae9bca","url":null,"abstract":"<p><p><i>Background.</i>Glaucoma is a leading cause of irreversible blindness, and the cup-to-disc ratio (CDR) used to assess it requires localising the optic disc and faintly bounded cup. Attention modules are frequently proposed for this task, but of six such studies meeting a pre-stated rule, none reported their number of training runs or any run-to-run variability.<i>Methods.</i>We evaluate a contour-guided attention module whose spatial map comes from a fixed, non-learned contour operator applied to the input image rather than an intermediate feature map, refined by zero-initialised convolutions and injected at P3, P4 and P5 so that each block is exactly the identity at initialisation. We measure the seed-noise floor over 310 baseline runs across seven YOLOv9, YOLOv12 and YOLO26 architectures, then run pre-registered, fixed-sample, fresh-seed paired comparisons and zero-shot evaluation on REFUGE.<i>Results.</i>The seed alone moves baseline mAP50 across a range of, and seed matching across arms gives essentially no variance reduction, so ten paired seeds resolve onlytomAP50, the order of gains those studies attribute to attention. Against that floor the module improved YOLOv12m in two pre-registered blocks offresh-seed pairs (mAP50,and,;mAP50-95 null in both), entirely optic-cup driven and confined to intersection over union (IoU), where cup AP is nearagainstat IoU.<i>Conclusion.</i>The module yields a small, replicated, optic-cup-specific gain at IoUon one architecture, with no evidence of improved boundary precision and none of generality. No clinical benefit follows: on a box-derived CDR proxy the module is a precise null. The noise floor is the more transferable result, fixing the sample sizes at which such gains become checkable and showing that single-run and few-seed comparisons cannot support them.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}