Search Results (661)

The technological triangle is a non-mathematical representation of the relationship between the characteristics of transport infrastructure, modes of transport, and the operational concept in a specific region. It is only through the synergistic effect of these three vertices that the railway undertaking, infrastructure manager, and authority can achieve optimal resource utilisation. Concurrently, it is imperative to exert pressure on the authorities to implement conceptual, systematic, and predictable measures. The process of implementing changes to transport infrastructure is a protracted one, typically spanning several years from the initial stages of preparation through to the project’s execution. The application of the technological triangle is possible on various parts of the infrastructure. Based on previous research, the authors prepared this Article to address intermediate stations, which were identified as the key focus of this article. Therefore, the authors in this article answer the question of what typical solutions exist for intermediate station configurations in relation to the operational concept and financial costs. Twenty different configurations were selected, and each was examined from the perspectives of financial, operational, planning, automation, and user pillars. The weights of the individual pillars were then assessed from the perspective of the infrastructure manager, the carrier, and the customer. The result is a comprehensive assessment of all wayside station configurations from different perspectives. Each user of this workflow can determine the weights of the individual pillars according to their needs and financial capabilities. This also gives the article a general use. The final part of the article presents specific examples of existing structures in the Czech Republic, which were not built with the perspective of this article in mind. The authors point out that if our method were applied, not only would large platform stations be built, which is the case for many intermediate stations in the Czech Republic; instead, more efficient solutions would be developed and adapted to the specific case. Full article

Facial Expression Recognition (FER) is a critical component of affective computing, with deep learning models dominating performance metrics. In contrast, geometric approaches based on the Facial Action Coding System (FACS) offer explainability through using triangles aligned to facial landmarks. The notable points of these triangles capture the deformation of muscles. However, restricting the feature extraction to notable points may be suboptimal. This paper introduces a novel method for optimizing the extraction of features by searching for optimal inner points in 22 facial triangles applying three metaheuristics: Differential Evolution (DE), Particle Swarm Optimization (PSO), and Convex Partition (CP). This results in a set of 59 geometric-based descriptors that capture muscle deformation more accurately. The proposed method was evaluated using five machine learning classifiers on two benchmark databases: the Karolinska Directed Emotional Faces (KDEF) and the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate significant performance improvements. The combination of DE with a Multi-Layer Perceptron (MLP) achieved an accuracy of $0.91$ on the KDEF database, while Support Vector Machine (SVM) optimized via CP attained an accuracy of $0.81$ on the JAFFE database. Statistical analysis confirms that optimized descriptors yield higher accuracy than previous geometric methods. Full article

The existing methods used for estimating generator matrixes of BCH codes, which are based on Galois Field Fourier transforms, need to exhaustively test all the possible codeword lengths and corresponding primitive polynomials. With the increase of codeword length, the search space exponentially expands. Consequently, the computational complexity of the estimation scheme becomes very high. To overcome this limitation, a fast estimation method is proposed based on Gaussian elimination. Firstly, the encoded bit stream is reshaped into a matrix according to the assumed codeword length. Then, by using Gaussian elimination, the bit matrix is simplified as the upper triangle form. By testing the independent columns of the upper triangle matrix, the assumed codeword length is judged to be right or not. Simultaneously, by using an augmented matrix, the parity check matrix of a BCH code can be estimated from the simplification result in the procedure of Gaussian elimination. Furthermore, the generator matrix is estimated by using the orthogonality between the generator matrix and parity check matrix. To improve the performance of the proposed method in resisting bit errors, soft-decision data is adopted to evaluate the reliability of received bits, and reliable bits are selected to construct the matrix to be analyzed. Experimental results indicate that the proposed method can recognize BCH codes effectively. The robustness of our method is acceptable for application, and the computation required is much less than the existing methods. Full article

The growing threat of unauthorized drones to ground-based critical infrastructure necessitates efficient ground-to-air surveillance systems. This paper proposes a lightweight framework for UAV detection and altitude measurement from a fixed ground perspective. We introduce Ghost-YOLOv11n, an optimized detector that integrates GhostConv modules into YOLOv11n, reducing computational complexity by 12.7% while achieving 98.8% mAP_0.5 on a comprehensive dataset of 8795 images. Deployed on a LuBanCat4 edge device with Rockchip RK3588S NPU acceleration, the model achieves 20 FPS. For stable altitude estimation, we employ an Extended Kalman Filter to refine measurements from a monocular ranging method based on similar-triangle geometry. Experimental results under ground monitoring scenarios show height measurement errors remain within 10% up to 30 m. This work provides a cost-effective, edge-deployable solution specifically for ground-based anti-drone applications. Full article

Background/Objectives: Zenker’s diverticulum (ZD) is a rare but clinically relevant condition. It is a false, pulsion-type diverticulum due to the protrusion of mucosal and submucosal layers through the Killian’s Triangle. Its pathogenesis is multifactorial and entails cricopharyngeus muscle dysfunction and age-related tissue degeneration. This review addresses the current evidence regarding the anatomy, pathophysiology, clinical presentation, diagnostic approach, and therapeutic management of ZD. Methods: For this literature review, we searched the PubMed and Scopus databases using combinations of keywords relevant to Zenker’s diverticulum, including “Zenker’s diverticulum,” “esophageal diverticula,” “diagnosis,” “endoscopic treatment,” and “surgery”. We included articles published in recent decades, with a focus on most recent ones regarding clinical studies, systematic reviews, meta-analyses, and descriptions of new diagnostic and therapeutic techniques. Results: Characteristic symptoms comprise progressive dysphagia, regurgitation of undigested food, halitosis, and, in advanced cases, aspiration-related respiratory complications. Diagnosis of ZD is primarily based on barium swallow esophagography and endoscopic evaluation, complemented by other imaging techniques. Current therapeutic options include traditional open surgery and endoscopic procedures, including newer minimally invasive techniques. Conclusions: ZD is the most common type of esophageal diverticulum and can have a disabling impact on a patient’s quality of life. It is commonly underdiagnosed or misdiagnosed as another condition, and prevalence is expected to increase with the growing population ageing. Improved understanding of its pathophysiology is needed to refine diagnostic and therapeutic strategies and minimize recurrences and risks. Full article

Loop closure detection is essential for improving the long-term consistency and robustness of simultaneous localization and mapping (SLAM) systems. Existing LiDAR-based loop closure approaches often rely on limited or partial geometric features, restricting their performance in complex environments. To address these limitations, this paper introduces a Density Triangle Descriptor (DTD). The proposed method first extracts keypoints from density images generated from LiDAR point clouds, and then constructs a triangle-based global descriptor that is invariant to rotation and translation, enabling robust structural representation. Furthermore, to enhance local discriminative ability, the neighborhood around each keypoint is modeled as a Gaussian distribution, and a local descriptor is derived from the entropy of its probability distribution. During loop closure detection, candidate matches are first retrieved via hash indexing of triangle edge lengths, followed by entropy-based local verification, and are finally refined by singular value decomposition for accurate pose estimation. Extensive experiments on multiple public datasets demonstrate that compared to STD, the proposed DTD improves the average F1 max score and EP by 18.30% and 20.08%, respectively, while achieving a 50.57% improvement in computational efficiency. Moreover, DTD generalizes well to solid-state LiDAR with non-repetitive scanning patterns, validating its robustness and applicability in complex environments. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article

This paper studies higher-order Eulerian numbers based on Stirling permutations and utilizing Eulerian triangles. It primarily focuses on the chain of higher-order Eulerian numbers, higher-order Eulerian polynomials, and higher-order Eulerian fractions, especially their computation. Many results for Eulerian numbers and second-order Eulerian numbers are generalized to higher-order Eulerian numbers. More specifically, we present recurrence relations of high-order Eulerian numbers, row-generating functions, and row sums of higher-order Eulerian triangles. Furthermore, we investigate the higher-order Eulerian fraction and its alternative form. Some properties of higher-order Eulerian fractions are expressed using differentiation and integration. We derive the inversion relations between second-order Eulerian numbers and Stirling numbers of the second and first kinds. Finally, we provide exact expressions and a computational method for higher-order Eulerian numbers. Full article

Examinations are typically used by educational institutions to assess students’ strengths and weaknesses. Unfortunately, exam malpractices like cheating and other forms of academic integrity violations continue to present a serious challenge to the evaluation framework because it seeks to provide a trustworthy assessment. Existing methods involving human invigilators have limitations, as they must be physically present in examination settings and cannot monitor all students who take an exam while successfully ensuring integrity. With the developments in artificial intelligence (AI) and computer vision, we now have novel possibilities to develop methods for detecting students who engage in cheating. This paper presents a practical, real-time detection system based on computer vision techniques for detecting cheating in examination halls. The system utilizes two primary methods: The first method is YOLOv8, a top-of-the-line object detection model, where the model is used to detect students in video footage in real time. After detecting the students, the second aspect of the detection process is to apply pose estimation to extract key points of the detected students. For the first time, this paper proposes to measure angles from the geometry of the key points of detected students by constructing two triangles using the distance from the tip of the nose to both eyes, and the distance from the tip of the nose to both ears; one triangle is sized from the distance to the eyes, and the other triangle contains the measurements to their ears. By continually calculating these angles, it is possible to derive each student’s facial pose. A dynamic threshold is calculated and updated for each frame to better represent the body position in real time. When the left or right angle pass that threshold, it is flagged as suspicious behavior indicating cheating. All detected cheating instances, including duration, timestamps, and captured images, are logged automatically in an Excel file stored on Google Drive. The proposed study presents a computationally cheap approach that does not utilize a GPU or additional computational aspects in any capacity. This implementation is affordable and has higher accuracy than all of those mentioned in prior studies. Analyzing data from exam halls indicated that the proposed system reached 96.18% accuracy and 96.2% precision. Full article

As direct geomorphic evidence and records of earthquakes on the surface, coseismic surface ruptures have long been a key focus in earthquake research. However, compared with strike-slip and normal faults, studies on reverse-fault surface ruptures remain relatively scarce. In this study, surface rupture characteristics of the most recent earthquake on the Kumysh thrust fault in eastern Tianshan were investigated using high-resolution topographic data, including 0.5 m- and 5 cm-resolution Digital Elevation Models (DEMs) generated from the WorldView-2 satellite stereo image pairs and Unmanned Aerial Vehicle (UAV) images, respectively. We carefully mapped the spatial geometry of the surface rupture and measured 120 vertical displacements along the rupture strike. Using the moving-window method and statistical analysis, both moving-mean and moving-maximum coseismic displacement curves were obtained for the entire rupture zone. Results show that the most recent rupture on the Kumysh Fault extends ~25 km with an overall NWW strike, exhibits complex spatial geometry, and can be subdivided into five secondary segments, which are discontinuously distributed in arcuate shapes across both piedmont alluvial fans and mountain fronts. Reverse fault scarps dominate the rupture pattern. The along-strike coseismic displacements generally form three asymmetric triangles, with an average displacement of 0.9–1.1 m and a maximum displacement of 2.8–3.2 m, yielding an estimated earthquake magnitude of M_w 6.6–6.7. This study not only highlights the strong potential of high-resolution remote sensing data for investigating surface earthquake ruptures, but also provides an additional example to the relatively underexplored reverse-fault surface ruptures. Full article

The sensitivity analysis for the design of an automotive push-rod suspension is discussed. Conventional iterative design cycles rely heavily on repeated CAD and Finite Element Method (FEM) analyses. Here, the initial design is based on an alternative and uncommon approach. A pre-CAD diagram of the entire vehicle (for FSAE competition) integrated with drivers is fully parameterized. A series of simulations in which the virtual driver inputs are repeated while the geometry of the suspension varies is executed. A database with isolated geometric effects on suspension loads and performance is obtained. By employing multivariate regression techniques, specifically Response Surface Methodology (RSM), the complex (often nonlinear) relationship between design inputs and structural outputs is mapped. The geometric inputs for this optimization include the coordinates that define the lengths and angles of the suspension triangles, the kingpin angle, the hub length, and aerodynamic downforce coefficients. The key performance indicators analyzed include corner exit speed loss, load and force distribution on the tires and main suspension joints, and the roll and pitch angles of the chassis. This methodology allows for the rapid identification of an optimal design configuration, avoiding trial and error and reducing development time and costs. The proposed framework demonstrates how RSM can enable the configuration of an optimal push-rod design with enhanced performance characteristics and improved manufacturing efficiency. Different case studies based on the mentioned input–output are analyzed to validate the approach in a practical manner. Full article

Adjoint-based optimization methods, that were previously in the realm of computational fluid dynamics (CFD) research, are now available in commercial software. This work explores the use of adjoint-based optimization to maximize mixing and combustion efficiencies for a supersonic combustor. To this end, a two-dimensional combustor was considered with parallel hydrogen injection. Simulations were carried out based on the steady Reynolds-Averaged Navier–Stokes equations and optimization was performed using a simplified passive scalar field instead of the full reactive flow problem. The optimization of a triangle-shaped mixing element is considered in addition to a case allowing the entire bottom of the combustor to deform. The relatively small mixing element could not boost efficiency significantly. By comparison, the optimization of the combustor wall resulted in both mixing and combustion efficiency gains accompanied by total pressure loss penalty. The optimization achieved higher efficiency compared to the baseline by extending the total volume of the reaction zone. The presented proof-of-concept results are relevant for the design of hypersonic vehicle propulsion systems, such as scramjets. Full article

Typhoons pose severe threats to coastal regions through destructive winds and extreme rainfall, with rainfall-induced flooding often causing more casualties and economic damage than wind damage alone. Accurate precipitation forecasting is therefore paramount for effective disaster risk management. This study proposes a trajectory-based framework for predicting cumulative rainfall from typhoon events, based on the premise that cyclones with similar tracks yield comparable precipitation due to topographic interactions. An extensive dataset of typhoons over East Asia (1979–2022) is analyzed, and two new similarity metrics—the Kernel Density Similarity Index (KDSI) and the Comprehensive Index (CI)—are introduced to quantify track resemblance. Their predictive skill is benchmarked against existing indices, including fuzzy C-means, convex hull area, and triangle mesh methods. Optimal performance is achieved using an ensemble of 13 analogous cyclones, which minimizes root-mean-square error (RMSE). Validation across a large sample demonstrates that the proposed model overcomes limitations of earlier approaches, providing a robust and efficient tool for forecasting typhoon-induced rainfall. Full article

Low, small, and slow (LSS) unmanned aerial vehicles (UAVs) pose great challenges for conventional guidance methods. However, existing deep reinforcement learning (DRL)-based interception guidance law has mostly focused on simplified two-dimensional planes and requires strict initial launch scenarios (constructing collision triangles). Designing more robust guidance laws has therefore become a key research focus. In this paper, we propose a novel recurrent proximal policy optimization (RPPO)-based guidance law framework. Specifically, we first design initial launch conditions in three-dimensional space that are more applicable and realistic, without requiring to form a collision triangle at the initial launch. Then, considering the temporal continuity of the seeker’s observations, we introduce the long short-term memory (LSTM) networks into the proximal policy optimization (PPO) algorithm to extract hidden temporal information from the observation sequences, thus supporting the policy training. Finally, we propose a reward function based on velocity prediction and overload constraints. Simulation experiments show that the proposed RPPO framework achieves an interception rate of 95.3% and a miss distance of 1.2935 m under broader launch conditions. Moreover, the framework demonstrates strong generalization ability, effectively coping with unknown maneuvers of UAVs. Full article

Counting and listing triangles in graphs is a fundamental task in network analysis, supporting applications such as community detection, clustering coefficient computation, k-truss decomposition, and triangle centrality. We introduce the cover-edge set, a novel concept that eliminates unnecessary edges during triangle enumeration, thereby improving efficiency. This compact cover-edge set is rapidly constructed using a breadth-first search (BFS) strategy. Using this concept, we develop both sequential and parallel triangle-counting algorithms and conduct comprehensive comparisons with state-of-the-art methods. We also design a benchmarking framework to evaluate our sequential and parallel algorithms in a systematic and reproducible manner. Extensive experiments on the latest Intel Xeon 8480+ processor reveal clear performance differences among algorithms, demonstrate the benefits of various optimization strategies, and show how graph characteristics, such as diameter and degree distribution, affect algorithm performance. Our source code is available on GitHub. Full article