Search Results (43,618)

After vessel preparation, using different strategies such as balloon angioplasty, specialty balloons, atherectomy or intravascular lithotripsy, definitive treatment has emerged as a key feature in endovascular treatment strategies. Based on current guidelines, endovascular treatment is the most common treatment option in patients with claudication. In patients with chronic limb-threatening ischemia (CLTI), on the other hand, the best treatment modality, including bypass surgery and endovascular revascularization, needs to be selected by an interdisciplinary team, focusing on individual anatomic and patient-specific characteristics, on the availability of a vein graft and on cardiovascular and other comorbidities of the patients. With endovascular therapy, currently, a plethora of options are available for the treatment of femoropopliteal lesions, which are increasingly gaining in complexity. Therefore, a practical systematic case-based approach, entailing contemporary treatment options, like drug-coated balloon (DCB) angioplasty tools, self-expanding bare-metal stents (BMSs), drug-eluting stents (DESs), interwoven stents and covered stents, is crucial. Generally, most endovascular operators adhere to the ‘leave nothing behind’ concept, meaning that, after proper lesion preparation, lesions can be treated with DCBs, avoiding the implantation of permanent metallic implants. However, in the case of severe dissections or significant recoil, stent implantation becomes necessary to achieve adequate limb perfusion. The selection between long versus spot stenting and the different stent options depends on the current scientific evidence, guidelines and expert opinion statements. An interdisciplinary expert consensus was recently compiled on how these modalities should be used in specific lesions and patients in the femoropopliteal segment. Herein we present a practical case-based approach, which is based on this algorithm and aims at harmonization of endovascular treatment strategies in daily practice and ultimately at further improvements in limb and patient outcomes. Full article

This paper develops a structured analytical framework and a robust numerical methodology for the spectral stability of time-varying bilateral quaternion differential equations of the form $\dot{q}=A\left(t\right)q+qB\left(t\right)$. By systematically extending classical real matrix theory to non-commutative dynamical systems via exact isometric real representations, this study utilizes the Kronecker product of real adjoint matrices to rigorously elucidate the underlying tensor structure of the bilateral evolution operator. This tensor-based reformulation proves that the Floquet multipliers of the bilaterally coupled system can be strictly decoupled into the product of the spectra corresponding to the left and right unilateral subsystems. Second, a “Scalar-Vector Stability Separation Principle” based on logarithmic norms is proposed, demonstrating that the transient energy evolution of the system is governed exclusively by the Hermitian real parts of the coefficient matrices, remaining entirely independent of the anti-Hermitian imaginary parts (rotation terms). Furthermore, for constant-coefficient and slowly varying systems, the Riesz projection from holomorphic functional calculus is introduced to establish algebraic criteria for exponential dichotomies, thereby revealing a cubic scaling law that relates the robustness threshold to the spectral gap (${\epsilon }_0\propto {\beta }^3$). Numerically, a Quaternion Chebyshev Spectral Collocation Method (Q-CSCM) is embedded within this exact vectorization framework to ensure that the algebraic symmetries of the bilateral system are strictly preserved through the isomorphic mapping. By explicitly constructing the fully discrete Kronecker product matrix via the exact real vectorization isomorphism, discrete energy estimates are utilized to rigorously prove that the numerical scheme successfully inherits the intrinsic spectral accuracy of the Chebyshev approximation. Comprehensive numerical experiments demonstrate that, within the low-dimensional regime, this methodology exhibits substantial temporal approximation efficiency advantages and superior numerical robustness compared to an alternative Legendre spectral baseline, as well as traditional explicit and state-of-the-art implicit symplectic Runge–Kutta methods, particularly when solving stiff and critically stable problems such as nonlinear Riccati oscillators. Full article

In this paper, we propose a novel Width-Adaptive Convolutional Autoencoder (WACAE) that automatically learns the optimal network width. The proposed approach assigns a relevance weight to each channel in the encoder’s hidden layers and leverages these weights to guide architectural adaptation. Based on the learned relevance, the model incrementally introduces new channels when needed and prunes irrelevant ones to achieve an optimal configuration. The WACAE simultaneously trains the network and learns its width in an unsupervised manner. Moreover, a novel cost function is devised to optimize channel relevance weights concurrently with model hyperparameters. Unlike conventional static or widening strategies, the proposed method adaptively enhances feature expressiveness within a single encoder–decoder framework. The model is evaluated on standard benchmark datasets (MNIST and CIFAR-10) and two real-world medical datasets (Brain Tumor MRI and Kvasir-Capsule). Experimental results demonstrate its effectiveness compared to state-of-the-art methods based on empirical tuning and network-width scaling. Furthermore, the proposed inner-product-based relevance weighting mechanism reduces model complexity while achieving high classification accuracy. Full article

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article

The electrodeposition of rare earth metal alloys has attracted considerable interest, not only due to the challenges associated with the reduction in metal ions, but also because of their unique material properties and promising technological applications. This review presents a comprehensive analysis of the state-of-the-art in the electrochemical deposition of these alloys, focusing on various electrolytic systems, including aqueous solutions, organic molecular solvents, ionic liquids, and deep eutectic solvents. Despite inherent problematic factors such as low reduction potentials, competing hydrogen evolution reactions, and difficulties in controlling metal formation, recent advancements have enabled improved control over film formation, typically through the induced codeposition of lanthanides with iron-group metals. The influence of key factors, such as electrolyte composition and current/potential modes, on alloy codeposition, elemental and phase composition, structure, and deposition efficiency is discussed. The magnetic properties, electrocatalytic behavior, and corrosion resistance of the deposited films are also shown, highlighting their relevance for high-performance applications. Full article

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

Referring Remote Sensing Image Segmentation (RRSIS) is a representative multimodal understanding task for remote sensing, which segments designated targets from remote images according to free-form natural language descriptions. However, complex remote sensing characteristics, such as cluttered backgrounds, large-scale variations, small scattered targets and repetitive textures, lead to unstable visual grounding and further spatial grounding drift, resulting in inaccurate segmentation results. Existing approaches typically perform implicit visual–linguistic fusion across encoding and decoding stages, entangling spatial grounding with mask refinement. This tightly coupled formulation lacks explicit structural constraints and is prone to cross-modal ambiguity, especially in complex remote sensing layouts. To address these limitations, we propose a Structurally consistent and Grounding-aware Stagewise Reasoning Framework (SGSRF) that follows a grounding-first, segmentation-second paradigm. The framework decomposes inference into three cascaded stages with progressively imposed structural constraints. First, Cross-modal Consistency Refinement (CCR) lays the foundation for stable spatial grounding by enhancing visual–textual structural alignment via CLIP-based features and Structural Consistency Regularization (SCR), producing well-aligned multimodal representations and reliable grounding cues. Second, Grounding-aware Prompt (GPG) Generation bridges grounding and segmentation by converting aligned representations into complementary sparse and dense prompts, which serve as explicit grounding guidance for the segmentation model. Third, Grounding Modulated Segmentation (GMS) leverages the Segment Anything Model (SAM) to generate fine-grained mask prediction under the joint guidance of prompts and grounding cues, improving spatial grounding stability and robustness to background interference and scale variation. Extensive experiments on three remote sensing benchmarks , namely RefSegRS, RRSIS-D, and RISBench, demonstrate that SGSRF achieves state-of-the-art performance. The proposed stagewise paradigm integrates structural alignment, explicit grounding, and prompt-driven segmentation into a unified framework, providing a practical and robust solution for RRSIS in real-world Earth observation applications. Full article

The digital reconstruction of damaged archeological wall paintings is a challenging task due to severe material degradation, high fragmentation, and the lack of reference images. A crucial preliminary step is the separation and grouping of fragments originating from different wall paintings, which are often found mixed together at archeological sites. To address this issue, we explored the potential of multispectral imaging (MSI) for unsupervised fragment clustering, aiming to assess whether integrating multiple spectral bands can enhance fragment discrimination compared to using the visible band alone. As a test set, we examined five groups of wall painting fragments from a Roman domus (1st c. BC–1st c. AD) provided by the Archaeological Museum of Cremona (Italy). Images were acquired using the Hypercolorimetric Multispectral Imaging (HMI) system developed by Profilocolore^® Srl (Rome, Italy). Specifically, we considered visible reflectance (VIS), infrared reflectance (IR), infrared false color (IRFC), and Ultraviolet-induced Fluorescence (UVF) images. Through a systematic benchmarking study, we compared several state-of-the-art feature extraction and clustering methods across single- and multi-band configurations. Results show that combining MSI data can substantially enhance the system’s ability to correctly separate and group fragments, indicating a promising direction for future research. Full article

Spatially fractionated radiotherapy with X-ray minibeams (x-MBRT) aims to increase normal-tissue tolerance by delivering alternating high- and low-dose regions. We provide a Monte Carlo-based framework to design and optimize multi-slit collimators, quantifying how geometry and material govern peak–valley modulation. A validated digital twin of the SmART X-RAD225Cx irradiator was implemented in TOPAS/Geant4. Various x-MBRT collimators were simulated with parallel or divergent slits. The parameter space covered a slit width w (0.1–0.9 mm), center-to-center spacing CTC (1–3 mm), thickness T (1–5 mm), and acceptance angle θ. Dose was scored in a 2 × 2 × 2 cm³ water phantom at a 1 cm depth. For fixed w/CTC, peak-valley dose ratio PVDR increases with larger CTC via an increase in peak dose, with the valley dose nearly constant. Peak transmission saturated at θ ≈ 3°, indicating minimal benefit from larger acceptance. Divergent slits yielded flatter lateral profiles but higher valley doses than parallel slits, reducing PVDR around the central axis. This Monte Carlo study provides insights for optimizing collimator geometries in x-MBRT using small-animal irradiators, informing the design of more effective collimation systems to enhance treatment precision and normal-tissue sparing. Full article

Infrared and visible image fusion aims to integrate complementary information from heterogeneous images captured by different optical sensors based on distinct imaging principles; however, existing methods often exhibit modality bias, leading to weakened targets or the loss of crucial texture details. To address this, we propose MBFTFuse, an adversarial fusion network based on modality balancing and feature tracing, which consists of a triple-path generator and dual discriminators. The architecture employs a generator with a triple-path structure: a central modality-balancing path for deep feature fusion and dual edge feature-tracing paths for modality-specific enhancement. Specifically, a multi-cognitive modality-balancing module is introduced to achieve feature weight equilibrium, while a Feature-Tracing Attention Module self-enhances single-modality features to compensate for information loss in the fusion results. Furthermore, a pixel loss based on intensity histograms is designed to optimize inter-modal balance at the pixel level. Comparative experiments against nine state-of-the-art methods across three public datasets demonstrate that MBFTFuse effectively highlights infrared targets while preserving intricate visible textures. The superior performance of this method in both quantitative metrics and downstream object detection tasks contributes to extending the boundaries of sensor-driven computer vision technologies. Full article

Age estimation based on facial images is a challenging task due to the complex and nonlinear nature of facial aging, which is influenced by both genetic and environmental factors. To address this challenge, we propose a hybrid ConvNeXt–Transformer framework that combines convolutional local feature extraction with attention-based global contextual modeling within a unified age regression pipeline. The methodological contribution of this work lies in the sequential integration of these two complementary paradigms for facial age estimation, allowing the model to capture both fine-grained textural cues—such as wrinkles and skin spots—and long-range spatial dependencies. We evaluate the proposed framework on benchmark datasets including MORPH II, CACD, UTKFace, and AFAD. The results show competitive performance across these datasets and confirm the effectiveness of the proposed hybrid design through extensive ablation analyses. Experimental results demonstrate that our approach achieves state-of-the-art MAE on MORPH II (2.26), CACD (4.35), and AFAD (3.09) under the adopted benchmark settings while remaining competitive on UTKFace. To address computational efficiency, we employ ImageNet pre-trained backbones and explore different architectural configurations, including fusion strategies and varying depths of the Transformer module, as well as regularization techniques such as stochastic depth and label smoothing. Ablation studies confirm the contribution of each component, particularly the role of attention mechanisms, in enhancing the model’s sensitivity to age-relevant features. Overall, the proposed hybrid framework provides a robust and accurate solution for facial age estimation, effectively balancing performance and computational cost. Full article

Adversarial examples, created by adding small distortions to audio, can fool neural network models and cause automatic speech recognition (ASR) systems to produce incorrect outputs. Most current detection methods rely on the recognition capabilities of ASR systems. As a result, they often fail to detect such examples when ASR performance degrades or when facing an evasion attack—referred to in this paper as a “partial adversarial attack”—that is specifically designed to bypass ASR models. In this paper, we identify a distinct noise energy difference between adversarial examples and their original audio. Moreover, this noise energy difference is typically greater than that between adversarial examples and their re-attacked examples. This finding leads us to propose a novel detection method that fundamentally departs from traditional approaches by operating independently of ASR systems. The proposed method employs a detection strategy that involves re-attacking the input audio and identifies adversarial examples by characterizing the noise energy difference before and after re-attack without relying on ASR systems. The experimental results demonstrate that the proposed method detects various state-of-the-art adversarial attacks. Compared with the baselines, the proposed method achieves substantially better detection performance across standard adversarial examples, noisy adversarial examples, and partial adversarial examples. Full article

Remote sensing (RS) images are often degraded by atmospheric haze, which compromises both visual interpretation and downstream applications. To address this, we introduce CSGL-Former, a novel Cross-Stripes Global–Local Fusion Transformer for RS image dehazing. Our model efficiently captures anisotropic long-range dependencies using cross-stripes attention (CSA) and aggregates hierarchical global semantics via a Multi-Layer Global Aggregation (MLGA) module. In the decoder, global context is adaptively blended with fine-grained local features to restore intricate textures. Finally, inspired by the atmospheric scattering model, a soft reconstruction head restores the clear image by predicting spatially varying affine parameters, strictly preserving content fidelity while effectively removing haze. Trained end-to-end, CSGL-Former demonstrates a compelling balance of accuracy and efficiency. Extensive experiments on the RRSHID and SateHaze1K benchmarks show that our model achieves state-of-the-art or highly competitive performance against representative baselines. Ablation studies further validate the effectiveness of each proposed component. Full article

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization. Full article

This study examines the campus-planning projects (1920–1937) of Kwan, Chu & Yang, Architects & Engineers (KCY), a major Chinese firm, against the backdrop of Sino-American cross-cultural knowledge transfer. It argues that their work exhibited a distinct compositional tendency derived from the partners’ U.S. Beaux-Arts education and contemporary American planning theory. Through historical analysis and case studies of four university projects, this research examines how composition-based spatial unity engaged with specific Chinese site conditions. The results indicate that early projects negotiated irregular boundaries, while later ones grappled with complex topography, such as historic gardens and hills. Although often unrealized, these grand schemes embodied a scientific planning methodology and served as aspirational blueprints. This study concludes that compositional practice was a significant part of China’s architectural modernization, representing both a professional design approach and a cultural response to the quest for modernity and national identity. Full article