Objective Many combinatorial optimization problems can be regarded as graph coloring problems. A classic topic in this field is total coloring, which combines vertex coloring and edge coloring. Previous studies and current research focus on the Total Coloring Conjecture (TCC), proposed in the 1960s. For graphs, including planar graphs, with maximum degree less than six, the correctness of the TCC has been verified through case enumeration. For planar graphs with maximum degree greater than six, the discharging technique has been used to confirm the conjecture by identifying reducible configurations and establishing detailed discharging rules. This method becomes limited when applied to planar graphs with maximum degree exactly six. Only certain restricted classes of graphs have been shown to satisfy the TCC, such as graphs without 4-cycles and graphs without adjacent triangles. More recent work demonstrates that the TCC holds for planar graphs without 4-fan subgraphs and for planar graphs with maximum average degree less than twenty-three fifths. Thus, it remains unclear whether planar graphs with maximum degree six that contain a 4-fan subgraph or have maximum average degree at least twenty-three fifths satisfy the conjecture. To address this question, this paper studies total coloring of a class of planar graphs known as nested n-pointed stars and aims to show that the TCC holds for these graphs. Methods The study relies on theoretical methods, including mathematical induction, constructive techniques, and case enumeration. An n-pointed star is obtained by connecting each edge of an n-polygon (n >= 3) to a triangle and then joining the triangle vertices not on the polygon to form a new n-polygon. Repeating this operation produces a nested n-pointed star with l layers, denoted by G(n)(l). These graphs have maximum degree exactly six. Their structural properties, including the presence of 4-fan subgraphs and maximum average degree greater than twenty-three fifths, are established. Induction on the number of layers is then used to show that G(n)(l) has a total 8-coloring: (1) G(n)(l) has a total 8-coloring; (2)G(n)(l -1) Suppose that has a total 8-coloring; (3) prove that has a G(l) (n) total 8-coloring. A graph G(n)(l) is defined as a type I graph if it has a total 7-coloring. When n= 3k , constructive arguments show that G(3k)(l) is a type I graph. The value of is considered in two cases, (k= 2m - 1)and (k = 2m) . In both cases, a total 7-coloring of G(3k)(l) is obtained by directly assigning colors to all vertices and edges. Results and Discussions Induction on the number of layers of G(n)(l) that nested n-pointed stars satisfy the Total Coloring Conjecture (Fig. 5). Five colors are assigned to the vertices and edges of to obtain a total 5-coloring (Fig. 6(a) and Fig. 8(a)). Two additional colors are then applied alternately to the edges connecting the polygons in layers 1 and 2. This produces a total 7-coloring of G(3k)(2) (Fig. 7(a) and Fig. 9(a)). After a permutation of the colors, another total 7-coloring of G(3k)(3) is obtained (Fig. 7(b) and Fig. 9(b)). The coloring pattern on the outermost layer is identical to that of G(3k)(1), which allows the same extension to construct total 7-colorings for G(3k)(4), G(3k)(5),... , G(3k)(l). Therefore, G(3k)(l) is a type I graph. Conclusions This study verifies that the Total Coloring Conjecture holds for nested n-pointed stars, which have maximum degree six and contain 4-fan subgraphs. It shows that G(3k)(l) is a type I graph. A further question arises regarding whether G(n)(l) is a type I graph when n not equal 3k. A total 7-coloring can be constructed when or n = 5, and therefore both G(4)(l) and G (l)(5) are type I graphs. For other values of n not equal 3k , whether G(n)(l) is a type I graph remains open.
Objective Low-Dose Computed Tomography (LDCT) reduces patient radiation exposure but introduces substantial noise and artifacts into reconstructed images. Convolutional Neural Network (CNN)-based denoising approaches are limited by local receptive fields, which restrict their abilities to capture long-range dependencies. Transformer-based methods alleviate this limitation but incur quadratic computational complexity relative to image size. In contrast, State Space Model (SSM)-based Mamba frameworks achieve linear complexity for long-range interactions. However, existing Mamba-based methods often suffer from information loss and insufficient noise suppression. To address these limitations, we propose the Wave-MambaCT model. Methods The proposed Wave-MambaCT model adopts a multi-scale framework that integrates Discrete Wavelet Transform (DWT) with a Mamba module based on the SSM. First, DWT performs a two-level decomposition of the LDCT image, decoupling noise from Low-Frequency (LF) content. This design directs denoising primarily toward the High-Frequency (HF) components, facilitating noise suppression while preserving structural information. Second, a residual module combined with a Spatial-Channel Mamba (SCM) module extracts both local and global features from LF and HF bands at different scales. The noise-free LF features are then used to correct and enhance the corresponding HF features through an attention-based Cross-Frequency Mamba (CFM) module. Finally, inverse wavelet transform is applied in stages to progressively reconstruct the image. To further improve denoising performance and network stability, multiple loss functions are employed, including L1 loss, wavelet-domain LF loss, and adversarial loss for HF components. Results and Discussions Extensive experiments on the simulated Mayo Clinic datasets, the real Piglet datasets, and the hospital clinical dataset DeepLesion show that Wave-MambaCT provides superior denoising performance and generalization. On the Mayo dataset, a PSNR of 31.652 8 dB is achieved, which is higher than that of the suboptimal method DenoMamba (PSNR 31.421 9 dB), while MSE is reduced to 0.000 74 and SSIM and VIF are improved to 0.885 1 and 0.462 9, respectively (Table 1). Visual results (Figs. 4 similar to 6) demonstrate that edges and fine details such as abdominal textures and lesion contours are preserved, with minimal blurring or residual artifacts compared with competing methods. Computational efficiency analysis (Table 2) indicates that Wave-MambaCT maintains low FLOPs (17.213 5 G) and parameters (5.391 3 M). FLOPs are lower than those of all networks except RED-CNN, and the parameter count is higher only than those of RED-CNN and CTformer. During training, 4.12 minutes per epoch are required, longer only than RED-CNN. During testing, 0.146 3 seconds are required per image, which is at a medium level among the compared methods. Generalization tests on the Piglet datasets (Figs. 7, 8, Tables 3, 4) and DeepLesion (Fig. 9) further confirm the robustness and generalization capacity of Wave-MambaCT.In the proposed design, HF sub-bands are grouped, and noise-free LF information is used to correct and guide their recovery. This strategy is based on two considerations. First, it reduces network complexity and parameter count. Second, although the sub-bands correspond to HF information in different orientations, they are correlated and complementary as components of the same image. Joint processing enhances the representation of HF content, whereas processing them separately would require a multi-branch architecture, inevitably increasing complexity and parameters. Future work will explore approaches to reduce complexity and parameters when processing HF sub-bands individually, while strengthening their correlations to improve recovery. For structural simplicity, SCM is applied to both HF and LF feature extraction. However, redundancy exists when extracting LF features, and future studies will explore the use of different Mamba modules for HF and LF features to further optimize computational efficiency. Conclusions Wave-MambaCT integrates DWT for multi-scale decomposition, a residual module for local feature extraction, and an SCM module for efficient global dependency modeling to address the denoising challenges of LDCT images. By decoupling noise from LF content through DWT, the model enables targeted noise removal in the HF domain, facilitating effective noise suppression. The designed RSCM, composed of residual blocks and SCM modules, captures fine-grained textures and long-range interactions, enhancing the extraction of both local and global information. In parallel, the Cross-band Enhancement Module (CEM) employs noise-free LF features to refine HF components through attention-based CFM, ensuring structural consistency across scales. Ablation studies (Table 5) confirm the essential contributions of both SCM and CEM modules to maintaining high performance. Importantly, the model's staged denoising strategy achieves a favorable balance between noise reduction and structural preservation, yielding robustness to varying radiation doses and complex noise distributions.
Objective Visible and infrared images are two commonly used modalities in intelligent transportation scenarios and play a key role in vehicle re-identification.However,differences in imaging mechanisms and spectral responses lead to inconsistent visual characteristics between these modalities,which limits cross-modal vehicle re-identification.To address this problem,this paper proposes a Channel Relation Spectra Alignment(CaRS-Align)method that uses channel relation spectra,rather than channel-wise features,as the alignment target.This strategy reduces interference caused by imaging style differences at the relational-structure level.Within each modality,a channel relation spectrum is constructed to capture stable and semantically coordinated channel-to-channel relationships through correlation modeling.At the cross-modal level,the correlation between the corresponding channel relation spectra of the two modalities is maximized to achieve consistent alignment of relational structures.Experiments on the public MSVR310 and RGBN300 datasets show that CaRS-Align outperforms existing state-of-the-art methods.For example,on MSVR310,under infrared-to-visible retrieval,CaRS-Align achieves a Rank-1 accuracy of 64.35％,which is 2.58％higher than advanced existing methods. Methods CaRS-Align adopts a hierarchical optimization paradigm:(1)for each modality,a channel-channel relation spectrum is constructed by mining inter-channel dependencies,yielding a semantically coordinated relation matrix that preserves the organizational structure of semantic cues;(2)cross-modal consistency is achieved by maximizing the correlation between the relation spectra of the two modalities,enabling progressive optimization from intra-modal construction to cross-modal alignment;and(3)relation spectrum alignment is integrated with standard classification and retrieval objectives commonly used in re-identification to supervise backbone training for the vehicle re-identification model. Results and Discussions Compared with several state-of-the-art cross-modal re-identification methods on the RGBN300 and MSVR310 datasets,CaRS-Align demonstrates strong performance and achieves best or second-best results across both retrieval modes.As shown in(Table 1),on RGBN300 it attains 75.09％Rank-1 accuracy and 55.45％mean Average Precision(mAP)in the infrared-to-visible mode,and 76.60％Rank-1 accuracy and 56.12％mAP in the visible-to-infrared mode.As shown in(Table 2),similar advantages are observed on MSVR310,with 64.54％Rank-1 accuracy and 41.25％mAP in the visible-to-infrared mode,and 64.35％Rank-1 accuracy and 40.99％mAP in the infrared-to-visible mode.(Fig.4)presents Top-10 retrieval results,where CaRS-Align reduces identity mismatches in both directions(Fig.5)illustrates feature distance distributions,showing substantial overlap between intra-class and inter-class distances without CaRS-Align(Fig.5(a)),whereas clearer separation is observed with CaRS-Align(Fig.5(b)),confirming improved feature discrimination.These results indicate that modeling channel-level relational structures improves both retrieval modes,increases adaptability to modality shifts,and effectively reduces mismatches caused by cross-modal differences. Conclusions This paper proposes a visible-infrared cross-modal vehicle re-identification method based on CaRS-Align.Within each modality,a channel relation spectrum is constructed to preserve semantic co-occurrence structures.A CaRS-Align function is then designed to maximize the correlation between modalities,thereby achieving consistent alignment and improving cross-modal performance.Experiments on the MSVR310 and RGBN300 datasets demonstrate that CaRS-Align outperforms existing state-of-the-art methods in key metrics,including Rank-1 accuracy and mAP.
Objective Data-driven deep learning methods are widely applied to cancer subtyping,yet their performance depends on large training datasets with fine-grained annotations.For gigapixel Whole Slide Images(WSI),such annotations are labor-intensive and costly.Clinical data are typically stored in isolated data silos,and sharing procedures raise privacy concerns.Federated Learning(FL)enables a global model to be trained from data distributed across multiple medical centers without transmitting local data.However,in conventional FL,substantial heterogeneity across centers reduces the performance and stability of the global model. Methods A privacy-preserving FL method is proposed for gigapixel WSI in computational pathology.Weakly supervised attention-based Multiple Instance Learning(MIL)is integrated with differential privacy to support training when only slide-level labels are available.Within each client,a multi-scale attention-based MIL method is used to conduct local training on histopathology WSIs,reducing the need for costly pixel-level annotation through a weakly supervised setting.During the federated update,local differential privacy is applied to limit the risk of sensitive information leakage.Random noise drawn from a Gaussian or Laplace distribution is added to model parameters after each client's local training.Furthermore,a federated adaptive reweighting strategy is introduced to address the heterogeneity of pathological images across clients by dynamically balancing the influence of local data quantity and quality on each client's aggregation weight. Results and Discussions The proposed FL framework is evaluated on two clinical diagnostic tasks:Non-small Cell Lung Cancer(NSCLC)histologic subtyping and Breast Invasive Carcinoma(BRCA)histologic subtyping.As shown in(Table 1,Table 2,and Fig.4),the proposed FL method(Ours with DP and Ours w/o DP)achieves higher accuracy and stronger generalization than localized models and other FL approaches.Its classification performance remains competitive even when compared with the centralized model(Fig.3).These results indicate that privacy-preserving FL is a feasible and effective strategy for multicenter histopathology images and may reduce the performance degradation typically caused by data heterogeneity across centers.When the magnitude of added noise is controlled within a limited range,stable classification can also be achieved(Table 3).The two main components,the multiscale representation attention network and the federated adaptive reweighting strategy,each contribute to consistent performance improvement(Table 4).In addition,the proposed FL method maintains stable classification performance across different hyperparameter settings(Table 5,Table 6),confirming its robustness. Conclusions The proposed FL method addresses two central challenges in multicenter computational pathology:the presence of data silos and concerns over privacy.It also alleviates the performance degradation caused by inter-center data heterogeneity.As balancing model accuracy with privacy protection remains a key challenge,future work focuses on developing methods that preserve privacy while sustaining stable classification performance.
ObjectiveA novel cross-modal hashing framework driven by large language models (LLMs) is proposed to address the semantic misalignment between medical images and their corresponding textual reports. The objective is to enhance cross-modal semantic representation and improve retrieval accuracy by effectively mining and matching semantic associations between modalities.MethodsThe generative capacity of LLMs is first leveraged to produce high-quality textual descriptions of medical images. These descriptions are integrated with diagnostic reports and structured clinical data using a dual-stream semantic enhancement module, designed to reinforce inter-modality alignment and improve semantic comprehension. A structural similarity-guided hashing scheme is then developed to encode both visual and textual features into a unified Hamming space, ensuring semantic consistency and enabling efficient retrieval. To further enhance semantic alignment, a prompt-driven attention template is introduced to fuse image and text features through fine-tuned LLMs. Finally, a contrastive loss function with hard negative mining is employed to improve representation discrimination and retrieval accuracy.Results and DiscussionsExperiments are conducted on a multimodal medical dataset to compare the proposed method with existing cross-modal hashing baselines. The results indicate that the proposed method significantly outperforms baseline models in terms of precision and mean Average Precision (mAP) (Table 3; Table 4). On average, a 7.21% improvement in retrieval accuracy and a 7.72% increase in mAP are achieved across multiple data scales, confirming the effectiveness of the LLM-driven semantic mining and hashing approach.ConclusionsExperiments are conducted on a multimodal medical dataset to compare the proposed method with existing cross-modal hashing baselines. The results indicate that the proposed method significantly outperforms baseline models in terms of precision and mean Average Precision (mAP) (Table 3; Table 4). On average, a 7.21% improvement in retrieval accuracy and a 7.72% increase in mAP are achieved across multiple data scales, confirming the effectiveness of the LLM-driven semantic mining and hashing approach.
Objective Multicenter validation is a growing requirement in clinical research,yet strict privacy regulations,heterogeneous cross-institutional data distributions,and scarce pixel-level annotations limit the use of conventional centralized medical image segmentation models.This study develops a federated semi-supervised framework that uses labeled and unlabeled prostate MRI data from multiple hospitals,considers dynamic client participation and Non-Independent and Identically Distributed(Non-IID)data,and aims to improve segmentation accuracy and robustness under real-world constraints. Methods A cross-silo Federated Semi-Supervised Learning(FSSL)paradigm is used.Clients with pixel-wise annotations act as labeled clients,and those without annotations act as unlabeled clients.Each client maintains a local student network for prostate segmentation.On unlabeled clients,a teacher network with the same architecture is updated using the exponential moving average of student parameters and generates perturbed pseudo-labels to supervise the student through a hybrid consistency loss that combines Dice and binary cross-entropy terms.To reduce the effect of heterogeneous and low-quality updates,a performance-driven dynamic client selection and aggregation strategy is applied.At each communication round,clients are evaluated on their local validation sets,and only those whose Dice scores exceed a threshold are retained.A top-K subset is then aggregated with normalized contribution weights derived from validation Dice,with bounds to avoid gradient vanishing and single-client dominance.For unlabeled clients,a penalty factor down-weights unreliable pseudo-labeled updates.The segmentation backbone is a Multi-scale Feature Fusion U-Net(MFF-UNet).Starting from a standard encoder-decoder U-Net,an FPN-like pyramid is added to the encoder,where multi-level feature maps are channel-aligned using 1*1 convolutions,fused in a top-down pathway through upsampling and element-wise addition,and refined using 3*3 convolutions.The decoder upsamples these fused features and combines them with encoder features through skip connections,enabling joint modeling of global semantics and fine-grained boundaries.The framework is evaluated on T2-weighted prostate MRI from six centers,comprising three labeled and three unlabeled clients.All 3D volumes are resampled,sliced into 2D axial images,resized,and augmented.The Dice coefficient and 95th percentile Hausdorff distance(HD95)are used as evaluation metrics. Results and Discussions On the six-center dataset,the method achieves average Dice scores of 0.840 5 on labeled clients and 0.786 8 on unlabeled clients,with corresponding HD95 values of 8.04 and 8.67 pixel.These results are superior to or comparable with several representative federated semi-supervised or mixed-supervision methods,with the largest gains on distribution-shifted unlabeled centers.Visualization shows that the method generates more complete and smoother prostate contours with fewer false positives in low-contrast or small-volume cases than the baselines.Attention heatmaps from the final decoder layer indicate that UNet exhibits attention drift,SegMamba produces diffuse responses,and nnU-Net shows weak activations for small lesions,whereas MFF-UNet focuses more precisely on the prostate region with stable high responses,indicating improved discriminative capability and interpretability. Conclusions A federated semi-supervised prostate MRI segmentation framework that integrates teacher-student consistency learning,multi-scale feature fusion,and performance-driven dynamic client selection is presented.The method preserves privacy by keeping data local,reduces annotation scarcity by using unlabeled clients,and addresses client heterogeneity through reliability-aware aggregation.Experiments on a six-center dataset show that the framework achieves competitive or superior overlap and boundary accuracy compared with state-of-the-art federated semi-supervised methods,particularly on distribution-shifted unlabeled centers.The framework is model-agnostic and can be applied to other organs,imaging modalities,and cross-institutional segmentation tasks under strict privacy and regulatory constraints.
Objective Twisted Generalized Reed-Solomon (TGRS) codes have attracted considerable attention in coding theory due to their flexible structural properties. However, studies on their extended codes remain limited. Existing results indicate that only a small number of works examine extended TGRS codes, leaving gaps in the understanding of their error-correcting capability, duality properties, and applications. In addition, previously proposed parity-check matrix forms for TGRS codes lack clarity and do not cover all parameter ranges. In particular, the case h = 0 is not addressed, which limits applicability in scenarios requiring diverse parameter settings. Constructing non-Generalized Reed-Solomon (non-GRS) codes is of interest because such codes resist Sidelnikov-Shestakov and Wieschebrink attacks, whereas GRS codes are vulnerable. Maximum Distance Separable (MDS) codes, self-orthogonal codes, and almost self-dual codes are valued for their error-correcting efficiency and structural properties. MDS codes achieve the Singleton bound and are essential for distributed storage systems that require data reliability under node failures. Self-orthogonal and almost self-dual codes, due to their duality structures, are applied in quantum coding, secret sharing schemes, and secure multi-party computation. Accordingly, this paper aims to: (1) characterize the MDS and Almost MDS (AMDS) properties of double-twisted GRS codes C-k,C-h,C-eta(alpha, v)and their extended codes C-k,C-h,C-eta(alpha, v, infinity); (2)derive explicit and unified parity-check matrices for all valid parameter ranges, including h = 0; (3) establish non-GRS properties under specific parameter conditions; (4) provide necessary and sufficient conditions for self-orthogonality of the extended codes and almost self-duality of the original codes; and (5) construct a class of almost self-dual double-twisted GRS codes with flexible parameters for secure and reliable communication systems. Methods The study is based on algebraic coding theory and finite field methods. Explicit parity-check matrices are derived using properties of polynomial rings over F-q, Vandermonde matrix structures, and polynomial interpolation. The Schur product method is applied to determine non-GRS properties by comparing the dimensions of the Schur squares of the codes and their duals with those of GRS codes. Linear algebra and combinatorial techniques are used to characterize MDS and AMDS properties. Conditions are obtained by analyzing the nonsingularity of generator-matrix submatrices and solving systems involving symmetric sums of finite field elements. These conditions are expressed using the sets S-k(alpha, eta),L-k(alpha, eta) , and D-k(alpha, eta). Duality theory is used to study orthogonality. A code C is self-orthogonal if C subset of C(perpendicular to )and its generator matrix satisfies GG(T) = O. For almost self-dual codes with odd length and dimension-(n-1)/2, this condition is combined with the structure of the dual code and symmetric sum relations of alpha i to obtain necessary and sufficient conditions. Results and Discussions For MDS and AMDS properties, the following results are obtained. The extended double-twisted GRS code C-k,C-h,C-eta(alpha, v, infinity) is MDS if and only if 1 is not an element of S-k(alpha, eta) and 1 is not an element of L-k(alpha, eta). The double-twisted GRS code C-k,C-h,C-eta(alpha, v) is AMDS if and only if 1 is an element of S-k(alpha, eta) and (0, 1) is not an element of D-k(alpha, eta). The code C-k,C-h,C-eta(alpha, v) (0, 1) is an element of (alpha, eta). Unified parity-check matrices of C-k,C-h,C-eta(alpha, v) and C-k,C-h,C-eta(alpha, v, infinity) are derived for all 0 <= h <= k - 1, removing previous restrictions that exclude h = 0. For non-GRS properties, when k >= 4 and n - k >= 4, both C-k,C-h,C-eta(alpha, v) and its extended code C-k,C-h,C-eta(alpha, v, infinity) are non-GRS for both 2k >= n or 2k < n. This conclusion follows from the fact that the dimensions of their Schur squares exceed those of the corresponding GRS codes, which ensures resistance to Sidelnikov-Shestakov and Wieschebrink attacks. Regarding orthogonality, the extended code C-k,C-h,C-eta(alpha, v, infinity) with h = k -1 is self-orthogonal under specific algebraic conditions. The code C-k,C-h,C-eta(alpha, v) with h = k -1 and n = 2k + 1 is almost self-dual if and only if there exists lambda is an element of F-q* such that lambda u(j) = v(j)(2 )(j = 1, 2, . . . 2k + 1) together with a symmetric sum condition on alpha(i )involving eta(1) and eta(2). For odd prime power q, an almost self-dual code with parameters [q - t - 1, (q - t - 2)/2, >= (q - t - 2)/2] is constructed using the roots of m(x) = (x(q) - x)/f(x) where f(x) = x(t+1) - x. An example over F-11 yields [5, 2, >= 2] code. Conclusions The study advances the theory of double-twisted GRS codes and their extensions through five contributions: (1) complete characterization of MDS and AMDS properties using sets Sk, Lk, Dk; (2) unified parity-check matrices for all 0 <= h <= k - 1; (3) non-GRS properties are established for k >= 4, ensuring resistance to known structural attacks; (4) necessary and sufficient conditions for self-orthogonal extended codes and almost self-dual original codes are obtained; (5) a flexible construction of almost self-dual double-twisted GRS codes is proposed. These results extend the theoretical understanding of TGRS-type codes and support the design of secure and reliable coding systems.
Objective ColoRectal Cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Approximately 25 similar to 50% of patients with CRC develop liver metastases during the course of their disease, which increases the disease burden. Although the MultiDisciplinary Team (MDT) model improves survival in ColoRectal Liver Metastases (CRLM), its broader implementation is limited by delayed knowledge updates and regional differences in medical standards. Large Language Models (LLMs) can integrate multimodal data, clinical guidelines, and recent research findings, and can generate structured diagnostic and therapeutic recommendations. These features suggest potential to support MDT-based care. However, the actual effectiveness of LLMs in MDT decision-making for CRLM has not been systematically evaluated. This study assesses the performance of DeepSeek-V3.1 and ChatGPT-5 in supporting MDT decisions for CRLM and examines the consistency of their recommendations with MDT expert consensus. The findings provide evidence-based guidance and identify directions for optimizing LLM applications in clinical practice. Methods Six representative virtual CRLM cases are designed to capture key clinical dimensions, including colorectal tumor recurrence risk, resectability of liver metastases, genetic mutation profiles (e.g., KRAS/BRAF mutations, HER2 amplification status, and microsatellite instability), and patient functional status. Using a structured prompt strategy, MDT treatment recommendations are generated separately by the DeepSeek-V3.1 and ChatGPT-5 models. Independent evaluations are conducted by four MDT specialists from gastrointestinal oncology, gastrointestinal surgery, hepatobiliary surgery, and radiation oncology. The model outputs are scored using a 5-point Likert scale across seven dimensions: accuracy, comprehensiveness, frontier relevance, clarity, individualization, hallucination risk, and ethical safety. Statistical analysis is performed to compare the performance of DeepSeek-V3.1 and ChatGPT-5 across individual cases, evaluation dimensions, and clinical disciplines. Results and Discussions Both LLMs, DeepSeek-V3.1 and ChatGPT-5, show robust performance across all six virtual CRLM cases, with an average overall score of >= 4.0 on a 5-point scale. This performance indicates that clinically acceptable decision support is provided within a complex MDT framework. DeepSeek-V3.1 shows superior overall performance compared with ChatGPT-5 (4.27 +/- 0.77 vs. 4.08 +/- 0.86, P=0.03). Case-by-case analysis shows that DeepSeek-V3.1 performs significantly better in Cases 1, 4, and 6 (P=0.04, P<0.01, and P =0.01, respectively), whereas ChatGPT-5 receives higher scores in Case 2 (P<0.01). No significant differences are observed in Cases 3 and 5 (P=0.12 and P=1.00, respectively), suggesting complementary strengths across clinical scenarios (Table 3). In the multidimensional assessment, both models receive high scores (range: 4.12$ \sim $4.87) in clarity, individualization, hallucination risk, and ethical safety, confirming that readable, patient-tailored, reliable, and ethically sound recommendations are generated. Improvements are still needed in accuracy, comprehensiveness, and frontier relevance (Fig. 1). DeepSeek-V3.1 shows a significant advantage in frontier relevance (3.90 +/- 0.65 vs. 3.24 +/- 0.72, P=0.03) and ethical safety (4.87 +/- 0.34 vs. 4.58 +/- 0.65, P= 0.03) (Table 4), indicating more effective incorporation of recent evidence and more consistent delivery of ethically robust guidance. For the case with concomitant BRAF V600E and KRAS G12D mutations, DeepSeek-V3.1 accurately references a phase III randomized controlled study published in the New England Journal of Medicine in 2025 and recommends a triple regimen consisting of a BRAF inhibitor + EGFR monoclonal antibody + FOLFOX. By contrast, ChatGPT-5 follows conventional recommendations for RAS/BRAF mutant populations-FOLFOXIRI+bevacizumab-without integrating recent evidence on targeted combination therapy. This difference shows the effect of timely knowledge updates on the clinical value of LLM-generated recommendations. For MSI-H CRLM, ChatGPT-5's recommendation of "postoperative immunotherapy" is not supported by phase III evidence or existing guidelines. Direct use of such recommendations may lead to overtreatment or ineffective therapy, representing a clear ethical concern and illustrating hallucination risks in LLMs. Discipline-specific analysis shows notable variation. In radiation oncology, DeepSeek-V3.1 provides significantly more precise guidance on treatment timing, dosage, and techniques than ChatGPT-5 (4.55 +/- 0.67 vs. 3.38 +/- 0.91, P<0.01), demonstrating closer alignment with clinical guidelines. In contrast, ChatGPT-5 performs better in gastrointestinal surgery (4.48 +/- 0.67 vs. 4.17 +/- 0.85, P=0.02), with experts rating its recommendations on surgical timing and resectability as more concise and accurate. No significant differences are identified in gastrointestinal oncology and hepatobiliary surgery (P=0.89 and P=0.14, respectively), indicating comparable performance in these areas (Table 5). These findings show a performance bias across medical sub-specialties, demonstrating that LLM effectiveness depends on the distribution and quality of training data. Conclusions Both DeepSeek-V3.1 and ChatGPT-5 demonstrated strong capabilities in providing reliable recommendations for CRLM-MDT decision-making. Specifically, DeepSeek-V3.1 showed notable advantages in integrating cutting-edge knowledge, ensuring ethical safety, and performing in the field of radiation oncology, whereas ChatGPT-5 excelled in gastrointestinal surgery, reflecting a complementary strength between the two models. This study confirms the feasibility of leveraging LLMs as "MDT collaborators", offering a readily applicable and robust technical solution to bridge regional disparities in clinical expertise and enhance the efficiency of decision-making. However, model hallucination and insufficient evidence grading remain key limitations. Moving forward, mechanisms such as real-world clinical validation, evidence traceability, and reinforcement learning from human feedback are expected to further advance LLMs into more powerful auxiliary tools for CRLM-MDT decision support.
Objective The rapid advancement of 5G communication technology has expanded the use of antennas in aviation, radar, medical, and other wireless systems. Wearable antennas have gained attention because of their conformability. Loading an Artificial Magnetic Conductor (AMC) is an effective way to enhance wearable antenna performance. This method increases gain, improves the Front-to-Back Ratio (FBR), and provides radiation isolation between the antenna and the human body. This study presents a tri-band wearable antenna loaded with an AMC for the ISM band and 5G frequency bands. Methods A trident-structured tri-band monopole antenna operating at 2.5 GHz, 3.5 GHz, and 5.8 GHz is designed together with a ring-shaped tri-band AMC tuned to the same frequency bands. Both structures use semi-flexible Rogers 4003 substrate. A 4x5 AMC array is placed on the back of the antenna to form a wearable integrated antenna. Simulation, physical measurement, and human safety assessment are performed. Results and Discussions The integrated antenna shows simulated operating bands of 2.40 similar to 2.50 GHz, 3.15 similar to 3.80 GHz, and 5.56 similar to 6.02 GHz, and measured bands of 2.38 similar to 2.52 GHz, 3.30 similar to 3.86 GHz, and 5.54 similar to 7.86 GHz (Fig. 4). These bands cover the ISM scientific band (2.40 similar to 2.4835 GHz), the 5G-n78 band (3.3 similar to 3.8 GHz), and the 5G-WiFi 5.8 GHz band (5.72 similar to 55.875 GHz). Measured gains at 2.5 GHz, 3.5 GHz, and 5.8 GHz increase by 5.3 dB, 4.6 dB, and 2.2 dB compared with the unloaded state (Fig. 15). The FBR values reach 20.8 dB, 18.0 dB, and 18.8 dB, corresponding to improvements of 19.8 dB, 16.7 dB, and 12.4 dB relative to the unloaded AMC (Table 4). The AMC reflector reduces the Specific Absorption Rate (SAR), and all integrated antennas show SAR values below 0.025 W/kg/g (Table 6), well below the FCC and ETSI limits. Performance is also measured when the antenna is placed on the chest, back, and thigh (Fig. 16), confirming safe and flexible on-body use. Conclusions A tri-band wearable antenna incorporating an AMC array is developed using semi-flexible Rogers 4003 substrate. With a 4x5 AMC array integrated behind the antenna, the measured operating bands cover the ISM scientific band (2.40 similar to 2.483 5 GHz), the 5G-n78 band (3.3 similar to 3.8 GHz), and the 5G-WiFi 5.8 GHz band (5.72 similar to 55.875 GHz). The results confirm high gain, high FBR, and stable wearable performance suitable for human-worn devices.
Objective Cardiac-cycle synchronization is required in Cardiovascular Magnetic Resonance(CMR)to reduce motion artifacts and preserve quantitative accuracy.At high field strengths,the ElectroCardioGram(ECG)trigger is affected by magnetohydrodynamic effects and scanner-generated ElectroMagnetic Interference(EMI).Electrode placement and lead routing add setup burden.Contact-based mechanical sensors still require skin contact,and optical photoplethysmography introduces long physiological delay.A fully contactless and EMI-robust mechanical surrogate is therefore needed.This study develops a split-architecture,non-contact optical SeismoCardioGraphy(SCG)triggering system for CMR and evaluates its availability,beatwise detection performance,and timing characteristics under practical body-coil coverage. Methods The split-architecture system consists of a near-magnet optical acquisition unit and a far-magnet computation-and-triggering unit connected by fiber-optic links to minimize conductive pathways near the scanner(Fig.2).The acquisition unit uses a defocused industrial camera and laser illumination to record speckle-pattern dynamics on the anterior chest without physical contact(Fig.3).Dense optical flow is computed in a chest region of interest,and the displacement field is projected onto a principal motion direction to form a one-dimensional SCG sequence(Fig.4).Drift suppression,smoothing,and short-window normalization are applied.Trigger timing is refined with a valley-constrained gradient search within a physiologically bounded window to reduce spurious detections and improve temporal consistency(Fig.4).A benchmark dataset is acquired from 20 healthy volunteers under three coil configurations:no body coil,an ultra-flexible body coil,and a rigid body coil(Fig.5,Fig.6,Table 3).ECG serves as the reference,and CamPPG and radar are recorded for comparison.Beatwise precision,recall,and F1 score are computed against ECG R peaks,and availability is reported as the fraction of usable segments under unified quality criteria(Table 4).Backward and forward physiological delays and delay variability are summarized across subjects and coil conditions(Table 5,Table 6).Key windowing and refractory parameters are tested for sensitivity(Table 2).Runtime is measured to assess real-time feasibility,including the cost of dense optical flow and the overhead of one-dimensional processing and triggering(Table 7). Results and Discussions Under no-coil and ultra-flexible-coil conditions,the optical SCG trigger achieves high availability(about 97.6％)and strong beatwise performance.F1 reaches about 0.91 under the ultra-flexible coil(Table 4,Table 5).The backward physiological delay remains on the order of several tens of milliseconds,and delay jitter is generally within a few tens of milliseconds(Table 5,Table 6).Under the rigid body coil,performance decreases markedly.Mechanical decoupling between the coil surface and the chest wall weakens and distorts the vibration signature,which blurs AO-related features and increases false triggers(Fig.1).This effect appears as lower precision and F1 and as a shift toward longer and more variable delays compared with the other conditions(Table 4,Table 6).Compared with CamPPG,which reflects peripheral blood-volume dynamics and typically lags further behind the ECG R peak,the optical SCG surrogate provides a more proximal mechanical marker with reduced trigger phase lag(Fig.8,Table 5).EMI robustness is supported by representative segments:ECG waveforms show visible distortion under interference,whereas the optical SCG surrogate remains interpretable because acquisition and transmission near the scanner are fully optical and electrically isolated(Fig.8).Parameter analysis supports a moderate processing window and a 0.5 s minimum interbeat interval as a stable choice across subjects(Table 2).Runtime analysis shows that dense optical flow dominates computational cost,whereas one-dimensional processing and triggering add little overhead.Throughput exceeds the acquisition frame rate,supporting real-time triggering(Table 7). Conclusions A split-architecture,non-contact optical SCG triggering system is developed and validated under three representative body-coil configurations.Fiber-optic separation between near-magnet acquisition and far-magnet processing improves EMI robustness while maintaining real-time trigger output.High availability,strong beatwise performance,and short physiological delay are demonstrated under no-coil and ultra-flexible-coil conditions(Table 4,Table 5).Rigid-coil coverage exposes a clear limitation caused by reduced mechanical coupling,which motivates further optimization for mechanically decoupled or heavily occluded scenarios(Fig.1,Table 6).
Objective Accurate localization of the Region Of Interest (ROI) is a prerequisite for high-precision palmprint recognition. In contactless and uncontrolled application scenarios, complex background illumination and diverse hand postures frequently cause ROI localization offsets. Most existing deep learning-based recognition methods rely on a single fixed-size ROI as input. Although some approaches adopt multi-scale convolution kernels, fusion at the ROI level is not performed, which makes these methods highly sensitive to localization errors. Therefore, small deviations in ROI extraction often result in severe performance degradation, which restricts practical deployment. To overcome this limitation, a Multi-scale ROI Feature Fusion Mechanism is proposed, and a corresponding model, termed ROI3Net, is designed. The objective is to construct a recognition system that is inherently robust to localization errors by integrating complementary information from multiple ROI scales. This strategy reinforces shared intrinsic texture features while suppressing scale-specific noise introduced by positioning inaccuracies. Methods The proposed ROI3Net adopts a dual-branch architecture consisting of a Feature Extraction Network and a lightweight Weight Prediction Network (Fig. 4). The Feature Extraction Network employs a sequence of Multi-Scale Residual Blocks (MSRBs) to process ROIs at three progressive scales (1.00 & times;, 1.25 & times;, and 1.50 & times;) in parallel. Within each MSRB, dense connections are applied to promote feature reuse and reduce information loss (Eq. 3). Convolutional Block Attention Modules (CBAMs) are incorporated to adaptively refine features in both the channel and spatial dimensions. The Weight Prediction Network is implemented as an end-to-end lightweight module. It takes raw ROI images as input and processes them using a serialized convolutional structure (Conv2d-BN-GELU-MaxPool), followed by a Multi-Layer Perceptron (MLP) head, to predict a dynamic weight vector for each scale. This subnetwork is optimized for efficiency, containing 2.38 million parameters, which accounts for approximately 6.2% of the total model parameters, and requiring 103.2 MFLOPs, which corresponds to approximately 2.1% of the total computational cost. The final feature representation is obtained through a weighted summation of multi-scale features (Eq. 1 and Eq. 2), which mathematically maximizes the information entropy of the fused feature vector. Results and Discussions Experiments are conducted on six public palmprint datasets: IITD, MPD, NTU-CP, REST, CASIA, and BMPD. Under ideal conditions with accurate ROI localization, ROI3Net demonstrates superior performance compared with state-of-the-art single-scale models. For instance, a Rank-1 accuracy of 99.90% is achieved on the NTU-CP dataset, and a Rank-1 accuracy of 90.17% is achieved on the challenging REST dataset (Table 1). Model robustness is further evaluated by introducing a random 10% localization offset. Under this condition, conventional models exhibit substantial performance degradation. For example, the Equal Error Rate (EER) of the CO3Net model on NTU-CP increases from 2.54% to 15.66%. In contrast, ROI3Net maintains stable performance, with the EER increasing only from 1.96% to 5.01% (Fig. 7, Table 2). The effect of affine transformations, including rotation (+/- 30 degrees) and scaling (0.85 similar to 1.15 & times;), is also analyzed. Rotation causes feature distortion because standard convolution operations lack rotation invariance, whereas the proposed multi-scale mechanism effectively compensates for translation errors by expanding the receptive field (Table 3). Generalization experiments further confirm that embedding this mechanism into existing models, including CCNet, CO3Net, and RLANN, significantly improves robustness (Table 6). In terms of efficiency, although the theoretical computational load increases by approximately 150%, the actual GPU inference time increases by only about 20% (6.48 ms) because the multi-scale branches are processed independently and in parallel (Table 7). Conclusions A Multi-scale ROI Feature Fusion Mechanism is presented to reduce the sensitivity of palmprint recognition systems to localization errors. By employing a lightweight Weight Prediction Network to adaptively fuse features extracted from different ROI scales, the proposed ROI3Net effectively combines fine-grained texture details with global semantic information. Experimental results confirm that this approach significantly improves robustness to translation errors by recovering truncated texture information, whereas the efficient design of the Weight Prediction Network limits computational overhead. The proposed mechanism also exhibits strong generalization ability when integrated into different backbone networks. This study provides a practical and resilient solution for palmprint recognition in unconstrained environments. Future work will explore non-linear fusion such as neural to further cross-scale feature interactions.
Significance The Ryanodine Receptor(RyR)is a central regulator of intracellular calcium(Ca2+)homeostasis in cardiomyocytes through its control of Ca2+release from the Sarcoplasmic Reticulum(SR).Abnormal RyR activity,including excessive activation or impaired gating,is a key mechanism underlying Early Afterdepolarizations(EADs)and Delayed Afterdepolarizations(DADs),thereby increasing arrhythmia risk.The coupling between membrane electrophysiology and Ca2+cycling in cardiomyocytes depends on spatially organized and rapidly evolving processes that are difficult to resolve experimentally.Conventional approaches,including animal models and pharmacological interventions,are constrained by high cost and limited control of experimental variables.Mathematical modeling and computer simulation of the RyR have therefore become essential tools for studying RyR regulation under physiological and pathological conditions and for elucidating arrhythmogenic mechanisms.This review provides an overview of RyR biology and modeling.It first summarizes structural features and core functional properties to establish the mechanistic basis of RyR gating and regulation.It then evaluates current and emerging modeling approaches,outlining their strengths and limitations.The review next describes the integration of RyR models into cardiomyocyte Ca2+cycling frameworks and their application across different cardiomyocyte subtypes.It further examines arrhythmogenic mechanisms arising from RyR dysfunction and assesses drug strategies designed to stabilize RyR activity.Finally,it highlights artificial intelligence and cardiac digital twins as emerging directions for advancing RyR modeling and therapeutic development. Progress The growing availability of RyR structural data has enabled continued refinement of modeling strategies.Early RyR models relied primarily on phenomenological formulations that were computationally practical but limited in mechanistic detail.Markov models have become the dominant framework for simulating RyR gating behavior and enable detailed representation of Ca2+sparks and related events through discrete state transitions.Deterministic integration of Markov models offers high computational efficiency and adaptability across different cardiomyocyte types.However,this approach neglects the stochastic nature of RyR opening and fails to reproduce random fluctuations in intracellular Ca2+concentration,which can lead to discrepancies between simulations and physiological behavior.Stochastic Markov models capture these random processes and are therefore essential for investigating arrhythmogenic phenomena such as Ca2+waves.Their application,however,requires extensive experimental data and substantial computational resources,which limits large-scale implementation.Recent artificial intelligence approaches,including deep neural networks that compress Markov models into single governing equations,have improved computational efficiency.Advances in structural biology have further clarified RyR conformational dynamics and subunit cooperativity during gating,particularly in relation to diastolic Ca2+leak.These insights have motivated more detailed models that incorporate subunit interactions or molecular dynamics.Numerous RyR models have been incorporated into cardiac action potential frameworks and applied to the study of EADs and DADs.These integrated models enhance understanding of electrical disturbances caused by RyR dysfunction and provide a useful platform for drug screening and mechanistic investigation. Conclusion Multiple RyR models have been developed that successfully reproduce key physiological processes,including Ca2+sparks,and are widely applied in studies of cardiomyocyte Ca2+cycling.Nevertheless,several challenges remain.(1)A unified modeling framework is still lacking.No single RyR model can accurately simulate Ca2+dynamics across the full spectrum of physiological and pathological conditions.Careful evaluation is therefore required when selecting models for intracellular Ca2+handling.(2)Computational burden limits multiscale integration.Multiscale models are necessary to connect cellular Ca2+dynamics with tissue-level electrical propagation by incorporating spatial heterogeneity,but their high computational cost restricts application in clinically relevant scenarios.(3)Pacemaker cell models remain underdeveloped.Current research focuses primarily on ventricular and atrial cardiomyocytes,whereas pacemaker cell models are less mature and often rely on common-pool formulations that do not represent spatial Ca2+gradients.Future studies should prioritize the development of detailed pacemaker cell models that explicitly represent Ca2+release unit networks and incorporate realistic RyR dynamics.Artificial intelligence and cardiac digital twins,although still at an early stage in RyR modeling,offer substantial potential to advance mechanistic research and support precision-medicine applications. Prospects Future RyR research will increasingly depend on integrating advances from structural biology,biophysics,and computational science.Such efforts are required to connect molecular-scale RyR conformational changes with organ-level cardiac function and to enable scalable,clinically actionable models.These models can strengthen mechanistic understanding and accelerate translational progress in precision cardiology.Artificial intelligence and cardiac digital twins provide a pathway toward multi-scale cardiac models that incorporate patient-specific electrophysiology and Ca2+cycling.These approaches may substantially improve understanding of arrhythmia mechanisms and heart failure pathophysiology and serve as predictive platforms for the development of mechanism-based personalized antiarrhythmic therapies.
Objective ElectroCardioGram(ECG)signals are key indicators of human health.However,their complex composition and diverse features make visual recognition prone to errors.This study proposes a classification algorithm for ECG pathological signals based on data generation.A Diffusion Generative Network(DGN),also known as a diffusion model,progressively adds noise to real ECG signals until they approach a noise distribution,thereby facilitating model processing.To improve generation speed and reduce memory usage,a Knowledge Distillation-Diffusion Generative Network(KD-DGN)is proposed,which demonstrates superior memory efficiency and generation performance compared with the traditional DGN.This work compares the memory usage,generation efficiency,and classification accuracy of DGN and KD-DGN,and analyzes the characteristics of the generated data after lightweight processing.In addition,the classification effects of the original MIT-BIH dataset and an extended dataset(MIT-BIH-PLUS)are evaluated.Experimental results show that convolutional networks extract richer feature information from the extended dataset generated by DGN,leading to improved recognition performance of ECG pathological signals. Methods The generative network-based ECG signal generation algorithm is designed to enhance the performance of convolutional networks in ECG signal classification.The process begins with a Gaussian noise-based image perturbation algorithm,which obscures the original ECG data by introducing controlled randomness.This step simulates real-world variability,enabling the model to learn more robust representations.A diffusion generative algorithm is then applied to reconstruct and reproduce the data,generating synthetic ECG signals that preserve the essential characteristics of the original categories despite the added noise.This reconstruction ensures that the underlying features of ECG signals are retained,allowing the convolutional network to extract more informative features during classification.To improve efficiency,the approach incorporates knowledge distillation.A teacher-student framework is adopted in which a lightweight student model is trained from the original,more complex teacher ECG data generation model.This strategy reduces computational requirements and accelerates the data generation process,improving suitability for practical applications.Finally,two comparative experiments are designed to validate the effectiveness and accuracy of the proposed method.These experiments evaluate classification performance against existing approaches and provide quantitative evidence of its advantages in ECG signal processing. Results and Discussions The data generation algorithm yields ECG signals with a Signal-to-Noise Ratio(SNR)comparable to that of the original data,while presenting more discernible signal features.The student model constructed through knowledge distillation produces ECG samples with the same SNR as those generated by the teacher model,but with substantially reduced complexity.Specifically,the student model achieves a 50％reduction in size,37.5％lower memory usage,and a 57％shorter runtime compared with the teacher model(Fig.6).When the convolutional network is trained with data generated by the KD-DGN,its classification performance improves across all metrics compared with a convolutional network trained without KD-DGN.Precision reaches 95.7％,and the misidentification rate is reduced to approximately 3％(Fig.9). Conclusions The DGN provides an effective data generation strategy for addressing the scarcity of ECG datasets.By supplying additional synthetic data,it enables convolutional networks to extract more diverse class-specific features,thereby improving recognition performance and reducing misidentification rates.Optimizing DGN with knowledge distillation further enhances efficiency,while maintaining SNR equivalence with the original DGN.This optimization reduces computational cost,conserves machine resources,and supports simultaneous task execution.Moreover,it enables the generation of new data without LOSS,allowing convolutional networks to learn from larger datasets at lower cost.Overall,the proposed approach markedly improves the classification performance of convolutional networks on ECG signals.Future work will focus on further algorithmic optimization for real-world applications.
Objective Diabetes mellitus and its complications are recognized as major global health challenges, causing severe morbidity, high healthcare costs, and reduced quality of life. Accurate joint prediction of these conditions is essential for early intervention but is hindered by data heterogeneity, sparsity, and complex inter-entity relationships. To address these challenges, a Representation Learning Enhanced Knowledge Graph-based Multi-Disease Prediction (REKG-MDP) model is proposed. Electronic Health Records (EHRs) are integrated with supplementary medical knowledge to construct a comprehensive Medical Knowledge Graph (MKG), and higher-order semantic reasoning combined with relation-aware representation learning is applied to capture complex dependencies and improve predictive accuracy across multiple diabetes-related conditions. Methods The REKG-MDP framework consists of three modules. First, an MKG is constructed by integrating structured EHR data from the MIMIC-IV dataset with external disease knowledge. Patient-side features include demographics, laboratory indices, and medical history, whereas disease-side attributes cover comorbidities, susceptible populations, etiological factors, and diagnostic criteria. This integration mitigates data sparsity and enriches semantic representation. Second, a relation-aware embedding module captures four relational patterns: symmetric, antisymmetric, inverse, and compositional. These patterns are used to optimize entity and relation embeddings for semantic reasoning. Third, a Hierarchical Attention-based Graph Convolutional Network (HA-GCN) aggregates multi-hop neighborhood information. Dynamic attention weights capture both local and global dependencies, and a bidirectional mechanism enhances the modeling of patient-disease interactions. Results and Discussions Experiments demonstrate that REKG-MDP consistently outperforms four baselines: two machine learning models (DCKD-RF and bSES-AC-RUN-FKNN) and two graph-based models (KGRec and PyRec). Compared with the strongest baseline, REKG-MDP achieves average improvements in P, F1, and NDCG of 19.39%, 19.67%, and 19.39% for single-disease prediction (n = 1); 16.71%, 21.83%, and 23.53% for n = 3; and 22.01%, 20.34%, and 20.88% for n= 5 (Table 4). Ablation studies confirm the contribution of each module. Removing relation-pattern modeling reduces performance metrics by approximately 12%, removing hierarchical attention decreases them by 5'6%, and excluding disease-side knowledge produces the largest decline of up to 20% (Fig. 5). Sensitivity analysis indicates that increasing the embedding dimension from 32 to 128 enhances performance by more than 11%, whereas excessive dimensionality (256) leads to over-smoothing (Fig. 6). Adjusting the beta parameter strengthens sample discrimination, improving P, F1, and NDCG by 9.28%, 27.9%, and 8.08%, respectively (Fig. 7). Conclusions REKG-MDP integrates representation learning with knowledge graph reasoning to enable multi-disease prediction. The main contributions are as follows: (1) integrating heterogeneous EHR data with disease knowledge mitigates data sparsity and enhances semantic representation; (2) modeling diverse relational patterns and applying hierarchical attention improves the capture of higher-order dependencies; and (3) extensive experiments confirm the model's superiority over state-of-the-art baselines, with ablation and sensitivity analyses validating the contribution of each module. Remaining challenges include managing extremely sparse data and ensuring generalization across broader populations. Future research will extend REKG-MDP to model temporal disease progression and additional chronic conditions.
Objective Cancer mortality in China continues to rise,and pathological image classification has become central to diagnosis.Pathological images have a multilevel structure,yet many existing methods focus only on the highest resolution or use simple feature concatenation for multi-scale fusion.These strategies do not make effective use of hierarchical information.In addition,most approaches rely on random pseudo-bag division to handle high-resolution images.Because cancerous regions in positive slides are sparse,random sampling often produces incorrect pseudo-labels and low signal-to-noise ratios,which reduce classification accuracy.This study proposes a Hierarchical Fusion Multi-Instance Learning(HFMIL)method that integrates multilevel feature fusion with a pseudo-bag division strategy based on an attention evaluation function to improve accuracy and interpretability in pathological image classification. Methods A weakly supervised multilevel classification method is proposed to use the hierarchical characteristics of pathological images and improve cancer image classification performance.The method has three main steps.First,multilevel features are extracted.Blank regions are removed,low-resolution images are divided into patches,and these patches are indexed to their corresponding high-resolution regions.Semantic features capture low-resolution tissue structure and high-resolution cellular detail.Second,pseudo-bags are constructed using an attention-based evaluation function.Class activation mapping is used to compute patch-level scores.Patches are ranked,and high-scoring ones are selected as potential positive samples.Low-scoring patches are discarded to maintain pseudo-label relevance.High-resolution pseudo-bags are then generated using index mapping,which reduces incorrect pseudo-labels and improves the signal-to-noise ratio.Third,a two-stage classification model is developed.Low-resolution pseudo-bags are aggregated with a gated attention mechanism for preliminary classification.A cross-attention mechanism then fuses the most informative low-resolution features with their corresponding high-resolution features.The fused representation is concatenated with aggregated high-resolution pseudo-bags to form an image-level feature vector for final prediction.Training uses a two-stage loss that combines low-resolution and overall cross-entropy losses.Experiments on three pathological image datasets confirm the effectiveness of the method in weakly supervised settings. Results and Discussions The proposed method is compared with several recent weakly supervised classification approaches,including ABMIL,CLAM,TransMIL,and DTFD,using three pathological image datasets:the publicly available Camelyon16 and TCGA-LUNG datasets and a private skin cancer dataset,NBU-Skin.The results show clear performance gains.On Camelyon16,the method achieves 88.3％accuracy and an AUC of 0.979(Table 2).On TCGA-LUNG,accuracy reaches 86.0％and AUC 0.931(Table 2),exceeding the comparative methods.On the NBU-Skin dataset,accuracy reaches 90.5％and AUC 0.976 for multiclass tasks(Table 2).Ablation studies further examine the necessity of the multilevel feature fusion and pseudo-bag division modules.The combination of these modules improves classification performance.On the skin cancer dataset,removing the pseudo-bag division module reduces accuracy from 93.8％to 90.7％,and removing the multilevel feature fusion module reduces accuracy further to 80.0％(Table 3).These results confirm that each component contributes to the effectiveness of the method. Conclusions A weakly supervised pathological image classification method that integrates multilevel feature fusion and an attention-based pseudo-bag division strategy is proposed.The method uses hierarchical information effectively and reduces errors caused by incorrect pseudo-labels and low signal-to-noise ratios.Experiments show consistent improvements in accuracy and AUC across three datasets.The main contributions are:(1)a multilevel feature extraction and fusion strategy that uses a cross-attention mechanism to combine features across scales;(2)an attention-based pseudo-bag division method that identifies potential positive regions and improves pseudo-label correctness through a top-k strategy while reducing background noise;and(3)superior performance compared with recent weakly supervised classifiers.Future work may include optimizing cross-level attention mechanisms,extending the framework to prognosis prediction or lesion segmentation,and developing more efficient feature extraction and fusion modules for broader clinical use.
Objective Fully Programmable Valve Array(FPVA)biochips,a recent type of flow-based microfluidic biochip,offer high flexibility and programmability,which enables them to meet different and complex experimental needs.Component placement is a critical stage in FPVA architectural synthesis because it affects several performance metrics,including assay completion time,total fluid-transport length,and cross-contamination.Cell reuse,an essential feature of FPVA programmability,requires special consideration during placement.However,existing studies have largely ignored the effect of reagent type differences in cell reuse on these metrics Methods This study presents a component placement algorithm for FPVA biochips that accounts for reagent type differences during cell reuse.The algorithm first introduces a cell reuse complexity metric that quantifies reuse complexity by considering the effects of reagent-type differences and component overlap on cross-contamination.It then integrates constraints,including placement-area limits and non-overlapping conditions for concurrent components,to ensure valid placement.The reward function is optimized to minimize reuse complexity and reduce the distance between components that use the same reagent type.The goal is to lower cross-contamination,total fluid-transport length,and assay completion time. Results and Discussions The algorithm is evaluated on benchmark FPVA instances with different chip sizes and functional requirements and compared with related methods.It reduces cell reuse complexity by 34.2％,assay completion time by 2.8％,and total fluid-transport length by 9.2％on average(Table 2).It also reduces the reagent-aware distance metric by 29.9％on average(Fig.6).The learning agent's decision trajectories show clear spatial structure,which reflects global placement awareness. Conclusions This study is the first to investigate FPVA component placement with attention to reagent type differences in cell reuse.The main contributions are as follows:(1)a cell reuse complexity metric is proposed to assess reuse intensity in placement,(2)the FPVA placement problem is modeled as a Markov decision process to enable the use of double deep Q-networks for safe and efficient placement policy learning,and(3)compared with existing work,the model improves FPVA biochemical assay performance and reliability.
Objective With the rapid deployment of deep learning in edge computing, the demand for efficient Convolutional Neural Network (CNN) accelerators continues to increase. Although traditional CPUs and GPUs provide strong computational capability, they incur high power consumption, long latency, and limited scalability in real-time embedded scenarios. FPGA-based accelerators, due to their reconfigurability and parallelism, provide a viable alternative. However, current designs often show low resource utilization, memory access bottlenecks, and difficulty in balancing throughput and energy efficiency. To address these issues, a systolic array-based CNN accelerator with inter-layer fusion optimization is proposed. The design integrates an enhanced memory hierarchy and optimized computation scheduling. Hardware-oriented convolution mapping and lightweight quantization are adopted to improve computational efficiency and reduce resource consumption, while meeting real-time inference requirements for applications such as intelligent surveillance and autonomous driving. Methods This study addresses core challenges in FPGA-based CNN accelerators, including data transfer overhead, insufficient resource utilization, and low processing unit efficiency. A hybrid accelerator architecture based on systolic array-assisted inter-layer fusion is proposed. Computation-intensive adjacent layers are tightly coupled and executed sequentially within a single systolic array, which reduces frequent off-chip memory accesses for intermediate results. This reduces data transfer overhead and power consumption and improves computation speed and overall energy efficiency. A dynamically reconfigurable systolic array is further developed to support multi-dimensional matrix multiplications with varying scales. This design avoids resource waste caused by fixed-function hardware and reduces FPGA logic consumption, thereby improving hardware adaptability and flexibility. A streaming systolic array computation scheme is also introduced through coordinated computation flow and control logic. Processing elements maintain a high-efficiency operating state, and data flows continuously through the computation engine in a pipelined and parallel manner. This improves processing unit utilization, reduces idle cycles, and increases overall throughput. Results and Discussions To determine appropriate quantization precision, experiments are conducted on the MNIST dataset using VGG16 and ResNet50 under fixed-point quantization with 12-bit, 10-bit, 8-bit, and 6-bit precision. As shown in Table 1, inference accuracy decreases significantly when precision falls below 8 bits, indicating that excessively low precision weakens model representational capacity. On the proposed accelerator, VOO16, ResNet50, and YOLOv8n achieve peak computational performances of 390.25 OOPS, 360.27 OOPS, and 348.08 OOPS, respectively. Performance comparisons with FPOA accelerators reported in the literature are summarized in Table 4. Table 5 presents comparisons with CPU and OPU platforms in terms of throughput and energy efficiency. For VOO16, ResNet50, and YOLOv8n, the proposed accelerator delivers throughput that is 1.76 & times;, 3.99 & times;, and 2.61 & times; higher than the corresponding CPU platforms. Energy efficiency improves by 3.1 & times; (VOO16), 2.64 & times; (ResNet50), and 2.96 & times; (YOLOv8n) compared with OPU platforms, demonstrating superior energy utilization. Conclusions A systolic array-assisted inter-layer fusion CNN accelerator architecture is proposed. A theoretical analysis of computational density confirms the performance advantages of the design. To address variation in convolution window sizes in the second layer, a dynamically reconfigurable systolic array method is developed. A streaming systolic array scheme is also implemented to sustain pipelined and parallel data flow within the computation engine. This design reduces idle cycles and improves throughput. Experimental results show that the accelerator achieves high computational performance with minimal loss in inference accuracy. Peak performances of 390.25 OOPS, 360.27 OOPS, and 348.08 OOPS are achieved for VOO16, ResNet50, and YOLOv8n, respectively. Compared with CPU and OPU platforms, the proposed accelerator shows superior energy efficiency and is suitable for resource-constrained and energy-sensitive edge computing scenarios.
Objective ElectroEncephaloGraphy (EEG) is a noninvasive technique for recording neural signals and provides rich emotional and cognitive information for brain science research and affective computing. Although Transformer-based models demonstrate strong global modeling capability in EEG emotion recognition, their multi-head self-attention mechanisms do not reflect the characteristics of brain-generated signals that exhibit a forgetting effect. In human cognition, emotional or cognitive states from distant time points gradually decay, whereas existing Transformer-based approaches emphasize temporal relevance only and neglect this forgetting behavior. This limitation reduces recognition performance. Therefore, a model is designed to account for both temporal relevance and the intrinsic forgetting effect of brain activity. Methods A novel EEG emotion recognition model, termed Memory Self-Attention (MSA), is proposed by embedding a memory-based forgetting mechanism into the standard self-attention framework. The MSA mechanism integrates global semantic modeling with a biologically inspired memory decay component. For each attention head, a memory forgetting score is learned through two independent linear decay curves to represent natural attenuation over time. These scores are combined with conventional attention weights so that temporal relationships are adjusted by distance-aware forgetting behavior. This design improves performance with a negligible increase in model parameters and computational cost. An Aggregated Convolutional Neural Network (ACNN) is first applied to extract spatiotemporal features across EEG channels. The MSA module then captures global dependencies and memory-aware interactions. The refined representations are finally passed to a classification head to generate predictions. Results and Discussions The proposed model is evaluated on several benchmark EEG emotion recognition datasets. On the DEAP binary classification task, classification accuracies of 98.87% for valence and 98.30% for arousal are achieved. On the SEED three-class task, an accuracy of 97.64% is obtained, and on the SEED-IV four-class task, the accuracy reaches 95.90%. These results (Figs. 3'5, Tables 3'5) exceed those of most mainstream methods, indicating the effectiveness and robustness of the proposed approach across different datasets and emotion classification settings. Conclusions An effective and biologically informed method for EEG-based emotion recognition is presented by incorporating a memory forgetting mechanism into a Transformer architecture. The proposed MSA model captures both temporal correlations and forgetting characteristics of brain signals, providing a lightweight and accurate solution for multi-class emotion recognition. Experimental results confirm its strong performance and generalizability.
Objective The battery voltage sampling circuit is a key component of the Battery Management Integrated Circuit (BMIC). It performs real-time monitoring of cell voltages, and its performance directly affects the safety of series battery packs. Traditional resistive voltage sampling circuits exhibit channel leakage current, which affects cell-voltage consistency and sampling accuracy. In addition, the level-shifting circuit in the high-voltage domain contains high-voltage operational amplifiers, and the use of many high-voltage MOSFETs increases area overhead. Methods This study proposes a low-leakage-current battery voltage sampling circuit for 14-series lithium batteries. Based on the traditional resistive sampling structure, channel leakage current is reduced to the pA level by designing an operational-amplifier-isolated active-drive technique. Voltage conversion methods are selected according to the voltage domain of each cell group. The first section of the battery uses a unity-gain buffer for isolation and then performs voltage conversion through resistive division. Sections 2 to 13 use operational-amplifier-isolated active driving to follow each cell voltage synchronously, after which the followed voltage is converted to a ground-referenced level through a level-shifting circuit. The voltage sampling process of the highest-section battery draws power from the entire battery stack and does not affect pack consistency; therefore, this section directly adopts the level-shifting circuit for voltage conversion. Results and Discussions The circuit was designed and verified using a 0.35 mm high-voltage BCD process. The overall layout area of the proposed sampling circuit is 3 105 mu m & times; 638 mu m (Fig. 10). Verification results show that, across different process corners and temperatures, the maximum channel leakage current after applying the isolated active-drive technique is only 48.9 pA. In contrast, the minimum leakage current of the traditional sampling circuit is 1.169 & times; 10(6) pA ( Fig. 12, Fig. 13). The effect of the sampling process on cell-voltage inconsistency is reduced from 18.56% to 2.122 ppm ( Fig. 14). Under full PVT verification, the maximum measurement error of the proposed sampling circuit is 0.9 mV (Fig. 15, Fig. 16, Fig. 17). Conclusions This study proposes an operational-amplifier-isolated active-drive technique to address the channel leakage issue in traditional resistive voltage sampling circuits, which affects cell-voltage consistency and measurement accuracy. Using the proposed circuit, the maximum channel leakage current is 48.9 pA, the cell-voltage inconsistency is 2.122 ppm, and the maximum measurement error is 1.25 mV. The circuit achieves very low leakage current while maintaining sampling accuracy. The proposed low-leakage-current sampling circuit is suitable for 14-series lithium battery management chips.
Objective Reconfigurable Intelligent Surfaces (RIS) attract increasing attention due to their ability to controllably manipulate electromagnetic wave propagation. A typical RIS consists of a dense array of Reflecting Elements (REs) with inter-element spacing no greater than half a wavelength, under which electromagnetic mutual coupling inevitably occurs between adjacent REs. This effect becomes more pronounced when the element spacing is smaller than half a wavelength and can significantly affect the performance and efficiency of RIS-assisted systems. Accurate modeling of mutual coupling is therefore essential for RIS optimization. However, existing mutual-coupling-aware channel models usually suffer from high computational complexity because of the large dimensionality of the mutual-impedance matrix, which restricts their practical use. To address this limitation, a simplified mutual-coupling-aware channel model based on a sparse neighboring mutual-coupling matrix is proposed, together with an efficient optimization method for configuring RIS tunable impedances. Methods First, a simplified mutual-coupling-aware channel model is established through two main steps. (1) A neighboring mutual-coupling matrix is constructed by exploiting the exponential decay of mutual impedance with inter-element distance. (2) A closed-form approximation of the mutual impedance between the transmitter or receiver and the REs is derived under far-field conditions. By taking advantage of the rapid attenuation of mutual impedance as spacing increases, only eight or three mutual-coupling parameters, together with one self-impedance parameter, are retained. These parameters are arranged into a neighboring mutual-coupling matrix using predefined support matrices. To further reduce computational burden, the distance term in the mutual-impedance expression is approximated by a central value under far-field assumptions, which allows the original integral formulation to be simplified into a compact analytical expression. Based on the resulting channel model, an efficient optimization method for RIS tunable impedances is developed. Through impedance decomposition, a closed-form expression for the optimal tunable-impedance matrix is derived, enabling low-complexity RIS configuration with computational cost independent of the number of REs. Results and Discussions The accuracy and computational efficiency of the proposed simplified models, as well as the effectiveness of the proposed impedance optimization method, are validated through numerical simulations. First, the two simplified models are evaluated against a reference model. The first simplified model accounts for mutual coupling among elements separated by at most one intermediate unit, whereas the second model considers only immediately adjacent elements. Results indicate that channel gain increases as element spacing decreases, with faster growth observed at smaller spacings (Fig. 4). The modeling error between the simplified models and the reference model remains below 0.1 when the spacing does not exceed lambda /4, but increases noticeably at larger spacings. Error curves further show that the modeling errors of both simplified models become negligible when the spacing is below lambda/4, indicating that the second model can be adopted to further reduce complexity (Fig. 6). Second, the computational complexity of the proposed models is compared with that of the reference model. When the number of REs exceeds four, the complexity of computing the mutual-coupling matrix in the reference model exceeds that of the proposed neighboring mutual-coupling model. As the number of REs increases, the complexity of the reference model grows rapidly, whereas that of the proposed model remains constant ( Fig. 5). Finally, the proposed impedance optimization method is compared with benchmark method ( Fig. 7, Fig. 8). When the element spacing is no greater than lambda /4, the channel gain achieved by the proposed method approaches that of the benchmark method. As the spacing increases beyond this range, a clear performance gap emerges. In all cases, the proposed method yields higher channel gain than the coherent phase-shift optimization method. Conclusions The integration of a large number of densely arranged REs in an RIS introduces notable mutual coupling effects, which can substantially influence system performance and therefore must be considered in channel modeling and impedance optimization. A simplified mutual-coupling-aware channel model based on a neighboring mutual-coupling matrix has been proposed, together with an efficient tunable-impedance optimization method. By combining the neighboring mutual-coupling matrix with a simplified mutual-impedance expression derived under far-field assumptions, a low-complexity channel model is obtained. Based on this model, a closed-form solution for the optimal RIS tunable impedances is derived using impedance decomposition. Simulation results confirm that the proposed channel model and optimization method maintain satisfactory accuracy and effectiveness when the element spacing does not exceed lambda/4. The proposed framework provides practical theoretical support and useful design guidance for analyzing and optimizing RIS-assisted systems under mutual coupling effects.
