Search Results (59)

Trajectory prediction is a critical component of autonomous driving, intelligent transportation systems, and human–robot interactions, particularly in complex environments like intersections, where diverse road constraints and multi-agent interactions significantly increase the risk of collisions. To address these challenges, a Heterogeneous Risk-Aware Graph Encoder with Continuous Parameterized Decoder for Trajectory Prediction (HRGC) is proposed. The architecture integrates a heterogeneous risk-aware local graph attention encoder, a low-rank temporal transformer, a fusion lane and global interaction encoder layer, and a continuous parameterized decoder. First, a heterogeneous risk-aware edge-enhanced local attention encoder is proposed, which enhances edge features using risk metrics, constructs graph structures through graph optimization and spectral clustering, maps these enhanced edge features to corresponding graph structure indices, and enriches node features with local agent-to-agent attention. Risk-aware edge attention is aggregated to update node features, capturing spatial and collision-aware representations, embedding crucial risk information into agents’ features. Next, the low-rank temporal transformer is employed to reduce computational complexity while preserving accuracy. By modeling agent-to-lane relationships, it captures critical map context, enhancing the understanding of agent behavior. Global interaction further refines node-to-node interactions via attention mechanisms, integrating risk and spatial information for improved trajectory encoding. Finally, a trajectory decoder utilizes the aforementioned encoder to generate control points for continuous parameterized curves. These control points are multiplied by dynamically adjusted basis functions, which are determined by an adaptive knot vector that adjusts based on velocity and curvature. This mechanism ensures precise local control and the superior handling of sharp turns and speed variations, resulting in more accurate real-time predictions in complex scenarios. The HRGC network achieves superior performance on the Argoverse 1 benchmark, outperforming state-of-the-art methods in complex urban intersections. Full article

Bladder cancer (BC) currently ranks as the 9th most common cancer worldwide. It is characterised by very high rates of recurrence and metastasis. Most cases of BC are of urothelial origin, and due to its ability to penetrate muscle tissue, BC is divided into non-muscle-invasive BC (NMIBC) and muscle-invasive BC (MIBC). The current diagnosis of BC is still based primarily on invasive cystoscopy, which is an expensive and invasive method that carries a risk of various complications. Urine sediment cytology is often used as a complementary test, the biggest drawback of which is its very low sensitivity concerning the detection of BC at early stages, which is crucial for prompt implementation of appropriate treatment. Therefore, there is a great need to develop innovative diagnostic techniques that would enable early detection and accurate prognosis of BC. Great potential in this regard is shown by epigenetic changes, which are often possible to observe long before the onset of clinical symptoms of the disease. In addition, these changes can be detected in readily available biological material, such as urine or blood, indicating the possibility of constructing non-invasive diagnostic tests. Over the past few years, many studies have emerged using epigenetic alterations as novel diagnostic and prognostic biomarkers of BC. This review provides an update on promising diagnostic biomarkers for the detection and prognosis of BC based on epigenetic changes such as DNA methylation and expression levels of selected non-coding RNAs (ncRNAs), taking into account the latest literature data. Full article

As an extension of non-negative matrix factorization (NMF), graph-regularized non-negative matrix factorization (GNMF) has been widely applied in data mining and machine learning, particularly for tasks such as clustering and feature selection. Traditional GNMF methods typically rely on predefined graph structures to guide the decomposition process, using fixed data graphs and feature graphs to capture relationships between data points and features. However, these fixed graphs may limit the model’s expressiveness. Additionally, many NMF variants face challenges when dealing with complex data distributions and are vulnerable to noise and outliers. To overcome these challenges, we propose a novel method called sparse feature-weighted double Laplacian rank constraint non-negative matrix factorization (SFLRNMF), along with its extended version, SFLRNMTF. These methods adaptively construct more accurate data similarity and feature similarity graphs, while imposing rank constraints on the Laplacian matrices of these graphs. This rank constraint ensures that the resulting matrix ranks reflect the true number of clusters, thereby improving clustering performance. Moreover, we introduce a feature weighting matrix into the original data matrix to reduce the influence of irrelevant features and apply an $L_{\mathrm{2,1}/2}$ norm sparsity constraint in the basis matrix to encourage sparse representations. An orthogonal constraint is also enforced on the coefficient matrix to ensure interpretability of the dimensionality reduction results. In the extended model (SFLRNMTF), we introduce a double orthogonal constraint on the basis matrix and coefficient matrix to enhance the uniqueness and interpretability of the decomposition, thereby facilitating clearer clustering results for both rows and columns. However, enforcing double orthogonal constraints can reduce approximation accuracy, especially with low-rank matrices, as it restricts the model’s flexibility. To address this limitation, we introduce an additional factor matrix R, which acts as an adaptive component that balances the trade-off between constraint enforcement and approximation accuracy. This adjustment allows the model to achieve greater representational flexibility, improving reconstruction accuracy while preserving the interpretability and clustering clarity provided by the double orthogonality constraints. Consequently, the SFLRNMTF approach becomes more robust in capturing data patterns and achieving high-quality clustering results in complex datasets. We also propose an efficient alternating iterative update algorithm to optimize the proposed model and provide a theoretical analysis of its performance. Clustering results on four benchmark datasets demonstrate that our method outperforms competing approaches. Full article

Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers. In this work, we chose to reduce the models to detect falls using images as input data. We proceeded to use image sequences as video frames, using data from two open source datasets, and we applied the Sparse Low Rank Method to reduce certain layers of the Convolutional Neural Networks that were the backbone of the models. Additionally, we chose to replace a convolutional block with Long Short Term Memory to consider the latest updates of these data sequences. The results showed that performance was maintained decently while significantly reducing the parameter size of the resulting models. Full article

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm’s possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots. Full article

The deterioration of physical spaces and changes in the social environment have led to significant challenges and low life satisfaction among residents in post-industrial neighborhoods. While resident satisfaction is closely linked to the built environment, physical attributes alone do not directly influence human feelings. The perception and processing of urban environments, or city images, play a critical mediating role. Previous studies have often explored the impact of either city image perception or physical space attributes on resident satisfaction separately, lacking an integrated approach. This study addresses this gap by examining the interplay between subjective perceptions and objective environmental attributes. Unlike previous studies that use the whole neighborhood area for human perception, our study uses the actual activity ranges of residents to represent the living environment. Utilizing data from Shenyang, China, and employing image semantic segmentation technology and multiple regression methods, we analyze how subjective city image factors influence resident satisfaction and how objective urban spatial indicators affect these perceptions. We integrate these aspects to rank objective spatial indicators by their impact on resident satisfaction. The results demonstrate that all city image factors significantly and positively influence resident satisfaction, with the overall impression of the area’s appearance having the greatest impact (β = 0.362). Certain objective spatial indicators also significantly affect subjective city image perceptions. For instance, traffic lights are negatively correlated with the perception of greenery (β = −0.079), while grass is positively correlated (β = 0.626). Key factors affecting resident satisfaction include pedestrian flow, traffic flow, open spaces, sky openness, and green space levels. This study provides essential insights for urban planners and policymakers, helping prioritize sustainable updates in post-industrial neighborhoods. By guiding targeted revitalization strategies, this research contributes to improving the quality of life and advancing sustainable urban development. Full article

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances. Full article

In existing image retrieval algorithms, negative samples often appear at the forefront of retrieval results. To this end, in this paper, we propose a feature fusion-based re-ranking method for home textile image retrieval, which utilizes high-level semantic similarity and low-level texture similarity information of an image and strengthens the feature expression via late fusion. Compared with single-feature re-ranking, the proposed method combines the ranking diversity of multiple features to improve the retrieval accuracy. In our re-ranking process, Markov random walk is used to update the similarity metrics, and we propose local constraint diffusion based on contextual similarity. Finally, the fusion–diffusion algorithm is used to optimize the sorted list via combining multiple similarity metrics. We set up a large-scale home textile image dataset, which contains $89k$ home textile product images from $12k$ categories, and evaluate the image retrieval performance of the proposed model with the Recall@k and mAP@K metrics. The experimental results show that the proposed re-ranking method can effectively improve the retrieval results and enhance the performance of home textile image retrieval. Full article

Population exposure to endocrine disrupting chemical- bisphenols, which are used commonly in food containers and drinking water pipes in Europe, is above acceptable health and safety levels, according to updated research data. In order to evaluate the most abundant potential migrants in canned sweetened beverages marketed in Poland, we performed the HPLC-MS screening test of the migrants present in the can coating material. The analyzed samples represented the three top-ranked companies of the global soft drink market; it is reasonable to assume that the obtained data are of global validity. The tested can coatings and beverages contained bisphenols conjugates such as five butoxyethanol (BuOEtOH) adducts with bisphenol A diglycidyl ether (BADGE), one butoxyethanol adduct with bisphenol A monoglycidyl ether (BAMGE), and cyclo-di-BADGE. The performed HPLC-MS/MS analysis in the MRM mode enabled evaluation of the concentrations of the detected conjugates in canned beverages which were found to be very low, namely at the level of 1 µg/L. On the other hand, the high consumption of canned beverages may yield a risk associated with the presence of these compounds in the diet. The subsequent HPLC-QTOF-MS/MS experiments allowed, for the first time, a detailed determination of the fragmentation pathways of the detected migrants as well as detection of the isomers of the two migrants, namely BADGE + BuOEtOH and BADGE + BuOEtOH + HCl. Full article

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations ${\tau }_i$, weighted nuclear norm, and the $L_{2,1}$ norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm $\left(L_{w,\ast }\right)$ to assign weights to singular values to each retinal images and utilize the $L_{2,1}$ norm to eliminate correlated samples and outliers in the retinal images. Moreover, ${\tau }_i$ is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including ${\tau }_i$, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method’s superiority over existing state-of-the-art methods across various datasets. Full article

The preservation of historical traditional architectural ensembles faces multifaceted challenges, and the need for facade renovation and updates has become increasingly prominent. In conventional architectural updating and renovation processes, assessing design schemes and the redesigning component are often time-consuming and labor-intensive. The knowledge-driven method utilizes a wide range of knowledge resources, such as historical documents, architectural drawings, and photographs, commonly used to guide and optimize the conservation, restoration, and management of architectural heritage. Recently, the emergence of artificial intelligence-generated content (AIGC) technologies has provided new solutions for creating architectural facades, introducing a new research paradigm to the renovation plans for historic districts with their variety of options and high efficiency. In this study, we propose a workflow combining Grasshopper with Stable Diffusion: starting with Grasshopper to generate concise line drawings, then using the ControlNet and low-rank adaptation (LoRA) models to produce images of traditional Minnan architectural facades, allowing designers to quickly preview and modify the facade designs during the renovation of traditional architectural clusters. Our research results demonstrate Stable Diffusion’s precise understanding and execution ability concerning architectural facade elements, capable of generating regional traditional architectural facades that meet architects’ requirements for style, size, and form based on existing images and prompt descriptions, revealing the immense potential for application in the renovation of traditional architectural groups and historic districts. It should be noted that the correlation between specific architectural images and proprietary term prompts still requires further addition due to the limitations of the database. Although the model generally performs well when trained on traditional Chinese ancient buildings, the accuracy and clarity of more complex decorative parts still need enhancement, necessitating further exploration of solutions for handling facade details in the future. Full article

The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value. Full article

Large-scale symmetric and asymmetric matrices have emerged in predicting the relationship between genes and diseases. The emergence of large-scale matrices increases the computational complexity of the problem. Therefore, using low-rank matrices instead of original symmetric and asymmetric matrices can greatly reduce computational complexity. In this paper, we propose an approximation conjugate gradient method for solving the low-rank matrix recovery problem, i.e., the low-rank matrix is obtained to replace the original symmetric and asymmetric matrices such that the approximation error is the smallest. The conjugate gradient search direction is given through matrix addition and matrix multiplication. The new conjugate gradient update parameter is given by the F-norm of matrix and the trace inner product of matrices. The conjugate gradient generated by the algorithm avoids SVD decomposition. The backtracking linear search is used so that the approximation conjugate gradient direction is computed only once, which ensures that the objective function decreases monotonically. The global convergence and local superlinear convergence of the algorithm are given. The numerical results are reported and show the effectiveness of the algorithm. Full article

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method. Full article

In Mexico, the evaluations of environmental flows are regulated by the Mexican Norm NMX-AA-159-SCFI-2012, and they warrant the establishment of water reserves for the environment. However, the pressure or demand for water use limits the establishment of said reserves because their implementation is generally conditioned to water availability. This research aimed to evaluate the changes through time of the variables that serve as a basis for the implementation strategy by the Mexican government. A geographical information system was built with updated information on water availability, conservation values, and pressures for all basins nationwide. Their desired conservation status was analyzed, and the potential reserves were estimated based on the reference values. The results were examined according to the ranking changes in environmental water reserves enactment feasibility and desired conservation status of Mexican basins, the progress achieved to date, and the potential contribution to the conservation of protected areas and their connectivity if the gaps of reserves were implemented. The outcomes point towards an administrative implementation strategy with positive results despite the growing demand for water use, with a change rate higher than the one for the creation of new protected areas. Currently, basins with low demand and high conservation value have the potential to meet people’s and the environment’s water needs, and contribute to 86% of the goal set by the present administration without affecting water availability. Finally, reserving water in the priority basins would guarantee the legal protection of the flow regime in 48–50% of the hydrographic network (63,760–66,900 km) in a desired conservation status, 43–49% of wetlands of international importance (48,650–49,600 km²) and other protected areas (128,700–136,500 km²) in 85–89% of the global ecoregions represented in Mexico (780,500–852,200 km²). Full article