Search Results (622)

Feature extraction plays a vital role in pattern recognition and computer vision. In recent years, low-rank representation (LRR) has been widely used in feature extraction, due to its robustness against noise. However, existing methods often overlook the impact of a well-constructed low-rank coefficient matrix on projection learning. This paper introduces a novel feature extraction method, i.e., robust discriminative non-negative and symmetric low-rank projection learning (RDNSLRP), where a coefficient matrix with better properties, such as low-rank, non-negativity, symmetry and block-diagonal structure, is utilized as a graph matrix for learning the projection matrix. Additionally, a discriminant term is introduced to increase inter-class divergence while decreasing intra-class divergence, thereby extracting more discriminative features. An iterative algorithm for solving the proposed model was designed by using the augmented Lagrange multiplier method, and its convergence and computational complexity were analyzed. Our experimental results on multiple data sets demonstrate the effectiveness and superior image-recognition performance of the proposed method, particularly on data sets with complex intrinsic structures. Furthermore, by investigating the effects of noise corruption and feature dimension, the robustness against noise and the discrimination of the proposed model were further verified. Full article

In this paper, we present mathematical geometric models and recursive algorithms to generate and design complex patterns using fractal structures. By applying analytical, iterative methods, iterative function systems (IFS), and L-systems to create geometric models of complicated fractals, we developed fractal construction models, visualization tools, and fractal measurement approaches. We introduced a novel recursive fractal modeling (RFM) method designed to generate intricate fractal patterns with enhanced control over symmetry, scaling, and self-similarity. The RFM method builds upon traditional fractal generation techniques but introduces adaptive recursion and symmetry-preserving transformations to produce fractals with applications in domains such as medical imaging, textile design, and digital art. Our approach differs from existing methods like Barnsley’s IFS and Jacquin’s fractal coding by offering faster convergence, higher precision, and increased flexibility in pattern customization. We used the RFM method to create a mathematical model of fractal objects that allowed for the viewing of polygonal, Koch curves, Cayley trees, Serpin curves, Cantor set, star shapes, circulars, intersecting circles, and tree-shaped fractals. Using the proposed models, the fractal dimensions of these shapes were found, which made it possible to create complex fractal patterns using a wide variety of complicated geometric shapes. Moreover, we created a software tool that automates the visualization of fractal structures. This tool may be used for a variety of applications, including the ornamentation of building items, interior and exterior design, and pattern construction in the textile industry. Full article

The early detection of lameness in elephants is essential for effective treatment and welfare enhancement, but subtle gait abnormalities are often difficult to identify visually. This study aimed to evaluate vertical movement symmetry in Asian elephants using cross-correlation analysis of data from inertial measurement units (IMUs). Six elephants were assessed, including individuals with normal gait and one exhibiting an abnormal gait. IMU sensors were attached to the proximal and distal segments of the forelimbs and hindlimbs to record vertical movements during straight-line walking. Cross-correlation coefficients were calculated to quantify the symmetry between the left and right limbs, providing an objective measure of gait patterns. This method provided an objective assessment of gait patterns and demonstrated potential in detecting lameness in elephants. This approach could facilitate the early identification of gait abnormalities, enabling timely interventions and potentially improving treatment outcomes and the welfare of captive elephant populations. Further studies involving a larger number of elephants with confirmed gait abnormalities are necessary to validate the robustness and reliability of this approach. Full article

In the face of escalating environmental degradation and dwindling resources, the imperatives of prioritizing environmental protection, and conserving resources have come sharply into focus. Therefore, remanufacturing processing, as the core of remanufacturing, becomes a key step in solving the above problems. However, with the increasing number of failing products and the advent of Industry 5.0, there is a heightened request for remanufacturing in the context of environmental protection. In response to these shortcomings, this study introduces a novel remanufacturing process planning model to address these gaps. Firstly, the failure characteristics of the used parts are extracted by the fault tree method, and the failure characteristics matrix is established by the numerical coding method. This matrix includes both symmetry and asymmetry, thereby reflecting each attribute of each failure feature, and the remanufacturing process is expeditiously generated. Secondly, a multi-objective optimization model is devised, encompassing the factors of time, cost, energy consumption, and carbon emission. This model integrates considerations of failure patterns inherent in used parts and components, alongside the energy consumption and carbon emissions entailed in the remanufacturing process. To address this complex optimization model, an improved teaching–learning-based optimization (TLBO) algorithm is introduced. This algorithm amalgamates Pareto and elite retention strategies, complemented by local search techniques, bolstering its efficacy in addressing the complexities of the proposed model. Finally, the validity of the model is demonstrated by means of a single worm gear. The proposed algorithm is compared with NSGA-III, MPSO, and MOGWO to demonstrate the superiority of the algorithm in solving the proposed model. Full article

Emergency resource scheduling is a critical facet of disaster management, particularly within the complex environments of chemical parks. A model with multiple disaster sites, multiple rescue sites, and multiple emergency resources was constructed considering the problem of resource scheduling in chemical parks during disasters. The optimization objectives include minimizing the emergency rescuing time and the total scheduling expense. An improved bacterial foraging optimization (IBFO) algorithm was proposed to satisfy these two objectives simultaneously. This algorithm leverages the symmetry inherent in the structure of resource scheduling problems, particularly in balancing the trade-off between local exploitation and global search. The loop structure was enhanced, information interaction between bacteria was incorporated to provide better guidance in the chemotaxis operator, and the migration operator was reconstructed to strengthen the local exploitation in potential optima areas while maintaining global searching capability. The symmetrical nature of the problem allows for more efficient optimization by better exploiting patterns within the solution space. The experimental results show that the IBFO algorithm demonstrates improved convergence accuracy and faster convergence speed compared with the original bacterial foraging optimization, particle swarm optimization, and genetic algorithm. These findings confirm that the IBFO algorithm effectively solves the emergency resource scheduling problem in chemical industry parks by utilizing symmetries to enhance performance. Full article

In this work, one new approach for RSTB-invariant object representation is presented based on the modified Mellin–Fourier Transform (MFT). For this, in the well-known steps of MFT, the logarithm operation in the log-polar transform is replaced by the operation “rising on a power”. As a result, the central part of the processed area is represented by a significantly larger number of points (transform coefficients), which permits us to give a more accurate description of the main part of the object. The symmetrical properties of the complex conjugated transform coefficients were used, and as a result, the number of coefficients participating in the object representation can be halved without deteriorating the quality of the restored image. The invariant representation is particularly suitable when searching for objects in large databases, which comprise different classes of objects. To verify the performance of the algorithm, object search experiments using the K-Nearest Neighbors (KNN) algorithm were performed, which confirmed this idea. As a result of the analysis, it can be concluded that the complexity of the solutions based on the proposed method depends on the applications, and the inclusion of neural networks is suggested. The neural networks have no conflict with the proposed idea and can only support decision making. Full article

Background: The attractive inflorescence of Chrysanthemum morifolium, its capitulum, is always composed of ray (female, zygomorphy) and disc (bisexual, actinomorphy) florets, but the formation mechanism remains elusive. The gene diversification pattern of the ECE (CYC/TB1) clade has been speculated to correlate with the capitulum. Within the three subclades of ECE, the involvement of CYC2 in defining floret identity and regulating flower symmetry has been demonstrated in many species of Asteraceae, including C. morifolium. Differential expression of the other two subclade genes, CYC1 and CYC3, in different florets has been reported in other Asteraceae groups, yet their functions in flower development have not been investigated. Methods: Here, a CYC1 gene, CmCYC1a, was isolated and its expression pattern was studied in C. morifolium. The function of CmCYC1a was identified with gene transformation in Arabidopsis thaliana and yeast two-hybrid (Y2H) assays were performed to explore the interaction between CmCYC1 and CmCYC2. Results: CmCYC1a was expressed at higher levels in disc florets than in ray florets and the expression of CmCYC1a was increased in both florets during the flowering process. Overexpression of CmCYC1a in A. thaliana changed flower symmetry from actinomorphic to zygomorphic, with fewer stamens. Furthermore, CmCYC1a could interact with CmCYC2b, CmCYC2d, and CmCYC2f in Y2H assays. Conclusions: The results provide evidence for the involvement of CmCYC1a in regulating flower symmetry and stamen development in C. morifolium and deepen our comprehension of the contributions of ECE genes in capitulum formation. Full article

This paper aims to adapt and apply genetic distance metrics in biomedical signal processing to improve the classification and monitoring of neurological disorders, specifically Alzheimer’s disease and frontotemporal dementia. The primary objectives are: (1) to quantify the variability in EEG signal patterns among the distinct subtypes of neurodegenerative disorders and healthy individuals, and (2) to explore the potential of a novel genetic similarity metric in establishing correlations between brain signal dynamics and clinical progression. Using a dataset of resting-state EEG recordings (eyes closed) from 88 subjects (36 with Alzheimer’s disease, 23 with frontotemporal dementia, and 29 healthy individuals), a comparative analysis of brain activity patterns was conducted. Symmetry plays a critical role in the proposed genetic similarity metric, as it captures the balanced relationships between intra- and inter-group EEG signal patterns. Our findings demonstrate that this approach significantly improves disease subtype identification and highlights the potential of the genetic similarity metric to optimize the predictive models. Furthermore, this methodology supports the development of personalized therapeutic interventions tailored to individual patient profiles, making a novel contribution to the field of neurological signal analysis and advancing the application of EEG in personalized medicine. Full article

Objectives: This study aimed to compare ultrasound (US) findings between automated and handheld breast ultrasound (ABUS and HHUS, respectively) in small breast cancers, based on the breast imaging reporting and data system (BI-RADS) category. Methods: We included 51 women (mean age: 52 years; range: 39–66 years) with breast cancer (invasive or DCIS), all of whom underwent both ABUS and HHUS. Patients with tumors measuring ≤1 cm on either modality were enrolled. Two breast radiologists retrospectively evaluated multiple imaging features, including shape, orientation, margin, echo pattern, and posterior characteristics and assigned BI-RADS categories. Lesion sizes were compared between US and pathological findings. Statistical analyses were performed using Bowker’s test of symmetry, a paired t-test, and a cumulative link mixed model. Results: ABUS assigned lower BI-RADS categories than HHUS while still maintaining malignancy suspicion in categories 4A or higher (54.8% consistent with HHUS; 37.3% downcategorized in ABUS, p = 0.005). While ABUS demonstrated less aggressive margins in some cases (61.3% consistent with HHUS; 25.8% showing fewer suspicious margins in ABUS), this difference was not statistically significant (p = 0.221). Similarly, ABUS exhibited slightly greater height–width ratios compared to HHUS (median, interquartile range: 0.98, 0.7–1.12 vs. 0.86, 0.74–1.10, p = 0.166). No significant differences were observed in other US findings or tumor sizes between the two modalities (all p > 0.05). Conclusions: Small breast cancers exhibited suspicious US features on both ABUS and HHUS, yet they were assigned lower BI-RADS assessment categories on ABUS compared to HHUS. Therefore, when conducting breast cancer screening with ABUS, it is important to remain attentive to even subtle suspicious findings, and active consideration for biopsy may be warranted. Full article

Graph Neural Network (GNN) is an effective model for processing graph-structured data. Most GNNs are designed to solve homophilic graphs, where all nodes belong to the same category. However, graph data in real-world applications are mostly heterophilic, and homophilic GNNs cannot handle them well. To address this, we propose a novel hybrid-learning framework based on Node Homophily and Spectral Heterophily (NHSH) for node classification in graph networks. NHSH is designed to achieve state-of-the-art or superior performance on both homophilic and heterophilic graphs. It includes three core modules: homophilic node extraction (HNE), heterophilic spectrum extraction (HSE) and node feature fusion (NFF). More specifically, HNE identifies symmetric neighborhoods of nodes with the same category, extracting local features that reflect these symmetrical structures. Then, HSE uses filters to analyze the high and low-frequency information of nodes in the graph and extract the global features of the nodes. Finally, NFF fuses the above two node features to obtain the final node features in graphs. Moreover, an elaborate loss function drives the network to preserve critical symmetries and structural patterns in the graph. Experiments on eight benchmark datasets validate that NHSH performs comparably or better than existing methods across diverse graph types. Full article

Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-end deep learning approach that autonomously extracts features from joint angle trajectories for a gait cycle, minimizing human intervention. A total of 74 sub-acute post-stroke hemiplegic patients with lower limb impairments were included in the analysis. The dataset comprised 219 sagittal plane joint angle and angular velocity trajectories from the hip, knee, and ankle joints during gait cycles. Deep temporal clustering was employed to cluster them in an end-to-end manner by simultaneously optimizing feature extraction and clustering, with hyperparameter tuning tailored for kinematic gait cycle data. Through this method, six optimal clusters were selected with a silhouette score of 0.2831, which is a relatively higher value compared to other clustering algorithms. To clarify the characteristics of the selected groups, in-depth statistics of spatiotemporal, kinematic, and clinical features are presented in the results. The results demonstrate the effectiveness of end-to-end deep learning-based clustering, yielding significant performance improvements without the need for manual feature extraction. While this study primarily utilizes sagittal plane data, future analysis incorporating coronal and transverse planes as well as muscle activity and gait symmetry could provide a more comprehensive understanding of gait patterns. Full article

With the rapid expansion of urban subway networks, vibrations induced by subway operations have become an increasingly significant concern for nearby structures. To assess the influence of subway-induced vibrations on nearby structures, it is essential to predict the vibration effects accurately prior to the construction of the subway system. By combining an improved Long Short-Term Memory (LSTM) model with a spectral analysis, this paper proposes a hybrid method to enhance the accuracy and efficiency of predicting structural vibrations induced by subway operations. The improved LSTM model is composed of BiLSTM, an attention mechanism, and the DBO algorithm. The symmetry inherent in the vibration propagation paths and the structural layouts of subway systems is leveraged to improve the feature extraction and modeling accuracy. Additionally, the hybrid method utilizes the symmetric properties of vibration signals in the spectral domain to enhance prediction robustness and efficiency. Then, the hybrid method is utilized to rapidly achieve highly accurate vibration responses induced by subway operations. The verification results demonstrate the following: (1) The improved LSTM model enhances the ability to recognize patterns in time-series vibration data, leading to improved model convergence and generalization. The improved LSTM mode has a significant improvement in prediction accuracy compared to the standard LSTM network. For numerical simulation and real-world measured signals, values of R² increased by 3% and 49.37%. (2) The proposed hybrid method significantly reduces computational time while ensuring results consistent with those obtained from the time-history analysis method. Applying the proposed hybrid method for data augmentation enhances the accuracy of the spectral analysis. The hybrid method achieves an improvement of 7% for the prediction accuracy. Full article

In this paper, the short-range ordering structures of Ga melts has been investigated using the Wulff cluster model (WCM). The structures with a Wulff shape outside and crystal symmetry inside have been derived as the equivalent system to describe the short-range-order (SRO) distribution of the Ga melts. It is observed that the simulated HTXRD patterns of the Ga WCM are in excellent agreement with the experimental data at various temperatures (523 K, 623 K, and 723 K). This agreement includes first and second peak positions, widths, and relative intensities of patterns, particularly at temperatures significantly above the melting point. A minor deviation in the second peak position has been observed at 523 K, attributed to the starting of the pre-nucleation stage. These findings demonstrate that the WCM can effectively describe the SRO structure in melt systems exhibiting a certain extent of covalency. Full article

In order to achieve high accuracy in interferometric direction-finding systems, antennas with a stable phase center in the working bandwidth are required. This article proposes a miniaturized loaded open-boundary quad-ridge horn (LOQRH) antenna with dimensions of 40 mm × 40 mm × 49 mm. First, to stabilize the phase center of the antenna, the design builds on the foundation of a quad-ridge horn antenna, where measures such as optimizing the ridge structure and introducing resistive loading were implemented to achieve size reduction. Second, electrically small-sized antennas are more susceptible to the effects of common-mode currents (CMCs), which can reduce the symmetry of the radiation pattern and the stability of the phase center. To avoid the generation of common-mode currents during operation, a self-balanced feed structure was introduced into the proposed antenna design. This structure establishes a balanced circuit and routes the feedline at the voltage null point, effectively suppressing the common-mode current. As a result, the miniaturization of the LOQRH antenna was achieved while ensuring the suppression of the common-mode current, thereby maintaining the stability of the antenna’s electromagnetic performance. The measured results show that the miniaturized antenna has a small phase center change of less than 20.3 mm within 2–18 GHz, while the simulated phase center fluctuation is only 14.6 mm. In addition, when taking 18.5 mm in front of the antenna’s feed point as the phase center, the phase fluctuation is less than 22.5° within the required beam width. Along with the desired stable phase center, the miniaturized design makes the proposed antenna suitable for interferometric direction-finding systems. Full article

The architecture in the title refers to physical buildings, spaces, and walls. Dominant architectural culture prefers minimalist environments that contradict the information setting needed for the infant brain to develop. Much of world architecture after World War II is therefore unsuitable for raising children. Data collected by technological tools, including those that use AI for processing signals, indicate a basic misfit between cognition and design. Results from the way AI software works in general, together with mobile robotics and neuroscience, back up this conclusion. There exists a critical research gap: the systematic investigation of how the geometry of the built environment influences cognitive development and human neurophysiology. While previous studies have explored environmental effects on health (other than from pathogens and pollutants), they largely focus on factors such as acoustics, color, and light, neglecting the fundamental role of spatial geometry. Geometrical features in the ancestral setting shaped neural circuits that determine human cognition and intelligence. However, the contemporary built environment consisting of raw concrete, plate glass, and exposed steel sharply contrasts with natural geometries. Traditional and vernacular architectures are appropriate for life, whereas new buildings and urban spaces adapt to human biology and are better for raising children only if they follow living geometry, which represents natural patterns such as fractals and nested symmetries. This study provides a novel, evidence-based framework for adaptive and empathetic architectural design. Full article