Search Results (2,497)

Effective and sustainable planning for future smart transportation systems is hindered by outdated traffic management models that fail to capture real-world dynamics, leading to congestion and significant environmental impact. To address this, advanced machine learning models are required to provide high-fidelity insights into urban mobility. In this work, we propose an adaptive machine learning approach to traffic pattern recognition that synergizes the HDBSCAN and k-means clustering algorithms. By employing a data-driven weighted voting mechanism, our solution provides a robust analytical foundation for sustainable planning, integrating structural analysis with precise cluster refinement. The crafted model was validated using a high-fidelity simulation of the Khmelnytskyi, Ukraine, transport network, where it demonstrated a superior ability to identify distinct traffic modes, achieving a V-measure of 0.79–0.82 and improving cluster compactness by 10–14% over standalone algorithms. It also attained a scenario identification accuracy of 92.8–95.0% with a temporal coherence of 0.94. These findings confirm that our adaptive approach is a foundational technology for intelligent transport systems, enabling the planning and deployment of more responsive, efficient, and sustainable urban mobility solutions. Full article

Accurate identification of modal properties in a steel cantilever beam is crucial for enhancing numerical models and supporting structural health monitoring, particularly when numerical and experimental data are combined. This study investigates the modal system identification of a steel cantilever beam using finite element method (FEM) simulations, which are validated by experimental testing. The beam was bolted to a reinforced concrete block and subjected to dynamic testing, where natural frequencies and mode shapes were extracted through Frequency Domain Decomposition (FDD). The experimental outcomes were compared with FEM predictions from SAP2000, and discrepancies were analysed using the Modal Assurance Criterion (MAC). A model updating procedure was applied, refining boundary conditions and considering sensor mass effects, which improved model accuracy. The updated FEM achieved closer agreement with frequency deviations reduced to less than 4% and MAC values above 0.9 for the first three modes. Beyond validation, the research links the updated FEM results with a Building Information Modelling (BIM) framework to enable the development of a digital twin of the beam. A workflow was designed to connect vibration monitoring data with BIM, providing visualisation of structural performance through colour-coded alerts. The findings confirm the effectiveness of FEM updating in generating reliable modal representations and demonstrate the potential of BIM-based digital twins for advancing structural condition assessment, maintenance planning and decision-making in civil engineering practice. Full article

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration. Full article

The safety and reliability of modern power systems are increasingly challenged by adverse environmental conditions. (1) Background: Ice accumulation on power transmission lines is recognized as a severe threat to grid stability, as tower collapse, conductor breakage, and large-scale outages may be caused, thereby making accurate monitoring essential. (2) Methods: A rule-driven and interpretable stereo vision framework is proposed for three-dimensional (3D) detection and quantitative measurement of transmission line icing. The framework consists of three stages. First, adaptive preprocessing and segmentation are applied using multiscale Retinex with nonlinear color restoration, graph-based segmentation with structural constraints, and hybrid edge detection. Second, stereo feature extraction and matching are performed through entropy-based adaptive cropping, self-adaptive keypoint thresholding with circular descriptor analysis, and multi-level geometric validation. Third, 3D reconstruction is realized by fusing segmentation and stereo correspondences through triangulation with shape-constrained refinement, reaching millimeter-level accuracy. (3) Result: An accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03% were achieved in contour extraction, while a precision of 90%, recall of 82%, and an F1-score of 0.8594 with real-time efficiency (0.014–0.037 s) were obtained in stereo matching. Millimeter-level accuracy (Mean Absolute Error: 1.26 mm, Root Mean Square Error: 1.53 mm, Coefficient of Determination = 0.99) was further achieved in 3D reconstruction. (4) Conclusions: Superior accuracy, efficiency, and interpretability are demonstrated compared with two existing rule-based stereo vision methods (Method A: ROI Tracking and Geometric Validation Method and Method B: Rule-Based Segmentation with Adaptive Thresholding) that perform line icing identification and 3D reconstruction, highlighting the framework’s advantages under limited data conditions. The interpretability of the framework is ensured through rule-based operations and stepwise visual outputs, allowing each processing result, from segmentation to three-dimensional reconstruction, to be directly understood and verified by operators and engineers. This transparency facilitates practical deployment and informed decision making in real world grid monitoring systems. Full article

This paper proposes a method for detecting distorted frames in video footage recorded by an unmanned vehicle. The proposed detection method is performed by analyzing a sequence of video frames, utilizing the contrast aspect of the structural similarity index between previous and current frames. This approach allows for the detection of distortions in the video caused by various types of noise. The scientific novelty lies in the targeted adaptation of the SSIM component to the task of real interframe analysis in conditions of shooting from an unmanned vehicle, in the absence of a reference. The three videos were considered during the simulation. They were distorted by random significant impulse noise, Gaussian noise, and mixed noise. Every 100th frame of the experimental video was subjected to distortion with increasing density. An additional measure was introduced to provide a more accurate assessment of distortion detection quality. This measure is based on the average absolute difference in similarity between video frames. The developed approach allows for effective identification of distortions and is of significant importance for monitoring systems and video data analysis, particularly in footage obtained from unmanned vehicles, where video quality is critical for subsequent processing and analysis. Full article

This study presents the application of Mycelial_Net, a biologically inspired deep learning architecture, to the analysis and classification of mineral images in thin section under optical microscopy. The model, inspired by the adaptive connectivity of fungal mycelium networks, was trained on a test mineral image database to extract structural features and to classify various minerals. The performance of Mycelial_Net was evaluated in terms of accuracy, robustness, and adaptability, and compared against conventional convolutional neural networks. The results demonstrate that Mycelial_Net, properly integrated with Residual Networks (ResNets), offers superior analysis capabilities, interpretability, and resilience to noise and artifacts in petrographic images. This approach holds promise for advancing automated mineral identification and geological analysis through adaptive AI systems. Full article

With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths and enables the capture of high-frequency signals to reveal finer subsurface structural details. However, the insufficient sampling rate of existing petroleum instruments prevents the effective capture of such high-frequency signals. To address this limitation, we employ high-frequency geophones together with high-density and high-frequency acquisition systems to collect the required data. Meanwhile, conventional processing methods such as Fourier transform-based time–frequency analysis are prone to phase instability caused by frequency interval selection. This instability hinders the accurate representation of subsurface structures and reduces the precision of shallow-layer phase identification. To overcome these challenges, this paper proposes a denoising method for high-sampling-rate seismic data based on Variational Mode Decomposition (VMD) optimized by the Starfish Optimization Algorithm (SFOA). The denoising results of simulated signals demonstrate that the proposed method effectively preserves the stability of noise-free regions while maintaining the integrity of peak signals. It significantly improves the signal-to-noise ratio (SNR) and normalized cross-correlation coefficient (NCC) while reducing the root mean square error (RMSE) and relative root mean square error (RRMSE). After denoising the surface mountain drilling-while-drilling signals, the resulting waveforms show a strong correspondence with the low-velocity zone interfaces, enabling clear differentiation of shallow stratigraphic distributions. Full article

Mesoscale convective systems (MCSs) play a pivotal role in the occurrence of severe weather phenomena, with convective cells constituting their fundamental elements. The precise identification of these cells from satellite imagery is crucial yet presents significant challenges, including issues related to merging errors and sensitivity to threshold parameters. This study introduces a novel detection algorithm for convective cells that leverages H-maxima transformation and incorporates multichannel data from the FY-2F satellite. The proposed method utilizes H-maxima transformation to identify seed points while maintaining the integrity of core structural features, followed by a novel neighborhood labeling method, region growing and adaptive merging criteria to effectively differentiate adjacent convective cells. The neighborhood labeling method improves the accuracy of seed clustering and avoids “over-clustering” or “under-clustering” issues of traditional neighborhood criteria. When compared to established methods such as RDT, ETITAN, and SA, the algorithm demonstrates superior performance, attaining a Probability of Detection (POD) of 0.87, a False Alarm Ratio (FAR) of 0.21, and a Critical Success Index (CSI) of 0.71. These results underscore the algorithm’s efficacy in elucidating the internal structures of convective complexes and mitigating false merging errors. Full article

The geometric content of chaos in nonlinear systems with multiple stabilities of high order is a challenge to computation. We introduce a single algorithmic framework to overcome this difficulty in the present study, where a parametrically forced oscillator with cubic–quintic nonlinearities is considered as an example. The framework starts with the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, which is a self-learned algorithm that extracts an interpretable and correct model by simply analyzing time-series data. The resulting parsimonious model is well-validated, and besides being highly predictive, it also offers a solid base on which one can conduct further investigations. Based on this tested paradigm, we propose a unified diagnostic pathway that includes bifurcation analysis, computation of the Lyapunov exponent, power spectral analysis, and recurrence mapping to formally describe the dynamical features of the system. The main characteristic of the framework is an effective algorithm of computational basin analysis, which is able to display attractor basins and expose the fine scale riddled structures and fractal structures that are the indicators of extreme sensitivity to initial conditions. The primary contribution of this work is a comprehensive dynamical analysis of the DM-CQDO, revealing the intricate structure of its stability landscape and multi-stability. This integrated workflow identifies the period-doubling cascade as the primary route to chaos and quantifies the stabilizing effects of key system parameters. This study demonstrates a systematic methodology for applying a combination of data-driven discovery and classical analysis to investigate the complex dynamics of parametrically forced, high-order nonlinear systems. Full article

Since it is difficult to obtain the positions of dynamic loads on structures, this paper suggests a new method to identify the locations of dynamic loads step-by-step based on the correlation coefficients of dynamic responses. First, a recognition model for dynamic load position based on a finite-element scheme is established, with the finite-element domain divided into several regions. Second, virtual loads are applied at the central points of these regions, and acceleration responses are calculated at the sensor measurement points. Third, the maximum correlation coefficient between the calculational and measured accelerations is obtained, and the dynamic load is located in the region with the virtual load corresponding to the maximum correlation coefficient. Finally, this region is continuously subdivided with the refined mesh until the dynamic load is pinpointed in a sufficiently small area. Different virtual load construction methods are proposed according to different types of loads. The frequency response function, unresolvable for the actual problem due to the unknown location of the real dynamic load, can be transformed into a solvable form, involving only known points. This transformation simplifies the analytical process, making it more efficient and applicable to analysis of the dynamic behavior of the system. The identification of the dynamic load position in the entire structure is then transformed into a sub-region approach, focusing on the area where the dynamic load acts. Simulations for case studies are conducted to demonstrate that the proposed method can effectively identify positions of single and multiple dynamic loads. The correctness of the theory and simulation model is verified with experiments. Compared to recent methods that use machine learning and neural networks to identify positions of dynamic loads, the approach proposed in this paper avoids the heavy computational cost and time required for data training. Full article

The architectural heritage of the 20th century has proved to be highly vulnerable to the test of time, with slight variations in different geographical contexts. The lack of value recognition, restrictions imposition, and resulting protection has led to the loss of memory of material and immaterial values. Restoring dignity has been the primary goal of those who have given substance and vitality to the theme of Modern Restoration, inheriting from the past the method that requires, in order to catalogue each work, the essential stages of knowledge and documentation, preliminary to conservation and enhancement. It is precisely in this scenario, after analysing the experiences of institutions, bodies and associations in the field of filing and cataloguing, that the needs brought about by the digital transition were taken on board; the aim is to define, within the PRIN 2022 DIMHENSION project, an innovative operative protocol that is economically, socially and technically sustainable, aimed at the computerised management of 20th century architectural heritage. The steps are the identification of the global description of the history of the building, translation of the entire body of data into information assets (H-BIR), and the possibility of consultation using parametric models (H-BIM). A Digital Register has therefore been designed, initially for an international sample of late 20th century façade systems, which goes well beyond their dynamic documentation, creating the conditions for a platform for consulting the complex of information, structured in an H-BIR archive interfaced with an H-BIM object library. Full article

Avocado (Persea americana Mill.) is one of the most widely consumed fruits worldwide. The tree species is traditionally classified into three botanical races: Mexican, Guatemalan, and West Indian (with a potentially distinct Colombian genepool). However, previous studies using molecular markers, such as AFLPs, microsatellites (SSRs), and GBS-derived SNP markers, have only partially resolved this racial divergence, especially in the hyper agrobiodiverse region of northwest South America. Therefore, in order to confirm genetic identity and origin of “criollo” avocado cultivars in the region, as well as to improve their traceability as rootstocks for the Hass variety, we performed low-coverage whole genome resequencing (lcWGS) on 205 ex situ conserved tree samples, comprising 42 commercial varieties and 163 “criollo” trees from various provinces in Colombia. This characterization yielded a total of 64,310,961 SNPs at an average coverage of 4.69×. Population structure analysis using principal component analysis (PCA) and ADMIXTURE retrieved at least five genetic clusters (K = 5), partly confirmed by Bayesian phylogenetic inference. Three clusters matched the recognized Mesoamerican botanical races (Mexican, Guatemalan, and West Indian), and two clusters reinforced the distinctness of two novel Andean and Caribbean Colombian genetic groups. Finally, in order to retrieve high-quality SNP markers for racial screening, a second genomic dataset was filtered, consisting of 68 avocado tree samples exhibiting more than 80% ancestry to a given racial cluster, and 9826 SNPs with a minimum allele frequency (maf) of 5%, a minimum sequencing depth (SD) of 10× per position, and missing data per variant not exceeding 20% (i.e., variants with genotypes present in at least 80% of the samples). This racially segregating high-quality subset was analyzed against the racial substructure using linear mixed models (LMMs), enabling the identification of 254 SNP markers associated with the five avocado genetic races. The previous candidate SNPs may be leveraged by nurseries and producers through a high-throughput SNP screening system for the racial traceability of seedling donor trees, saplings, and rootstocks. These genomic resources will support the selection of regionally adapted elite rootstocks and represent a landmark in Colombian horticulture as the first large-scale lcWGS-based characterization of a local avocado germplasm collection. Full article

This study addresses the challenges in health monitoring and safety assessment of retaining structures by developing an innovative damage identification system based on the Frequency-Optimized Wavelet Packet Transform (FOWPT) algorithm. The system introduces the Impulse Response Function (IRF) and optimized energy feature characterization to achieve precise damage localization (error ≤ 5%) and quantitative severity assessment. Recognizing the limitations of traditional dynamic methods in explaining damage mechanisms and spatial specificity, this research proposes a Wavelet Packet–Curvature Fusion (WPCF) model that integrates dynamic response signals with static topographic features. Through experimental validation, the WPCF model demonstrates a strong spatial correlation between terrain curvature and damage indicators, enabling damage prediction based solely on topographic data. The results show that the fusion approach significantly improves the accuracy of damage diagnosis and facilitates a transition from post-diagnosis to pre-prediction, offering a reliable technical framework for the intelligent monitoring and maintenance of retaining structures. Full article

Ensuring the structural integrity of mechanical components operating in fluid environments requires precise and reliable monitoring techniques. This study presents a methodology for reconstructing the full-field deformation of a flexible aluminium plate subjected to unsteady water flow in a water tunnel, using a structural modal reconstruction approach informed by experimental data. The experimental setup involves an aluminium lamina (200 mm × 400 mm × 2.5 mm) mounted in a closed-loop water tunnel and exposed to a controlled flow with velocities up to 0.5 m/s, corresponding to Reynolds numbers on the order of ${10}^4$, inducing transient deformations captured through an image-based optical tracking technique. The core of the methodology lies in reconstructing the complete deformation field of the structure by combining a reduced number of vibration modes derived from the geometry and boundary conditions of the system. The novelty of the present work consists in the integration of the Internal Strain Potential Energy Criterion (ISPEC) for mode selection with a data-driven machine learning framework, enabling real-time identification of active modal contributions from sparse experimental measurements. This approach allows for an accurate estimation of the dynamic response while significantly reducing the required sensor data and computational effort. The experimental validation demonstrates strong agreement between reconstructed and measured deflections, with normalised errors below 15% and correlation coefficients exceeding 0.94, confirming the reliability of the reconstruction. The results confirm that, even under complex, time-varying fluid–structure interactions, it is possible to achieve accurate and robust deformation reconstruction with minimal computational cost. This integrated methodology provides a reliable and efficient basis for structural health monitoring of flexible components in hydraulic and marine environments, bridging the gap between sparse measurement data and full-field dynamic characterisation. Full article

Risk identification is a foundational process in construction project management, yet the selection of appropriate identification techniques often lacks empirical guidance. To address this gap, this study adopts a case study design and conducts a comparative evaluation of four established but underutilized methods—Delphi, Nominal Group Technique (NGT), Hazard and Operability Study (HAZOP), and Preliminary Hazard Analysis (PHA)—within the context of a large-scale infrastructure project in Türkiye. The Delphi panel consisted of five senior experts. The NGT session involved six site-level practitioners, and the HAZOP team was composed of four multidisciplinary professionals. Two project-level managers conducted the PHA. Each technique was assessed against seven evaluative criteria: methodological structure, stakeholder engagement, analytical depth, resource intensity, flexibility, decision-support value, and contextual fit. The findings reveal that HAZOP achieved the highest analytical depth and decision-support capacity, while NGT demonstrated the strongest stakeholder engagement and contextual adaptability. Delphi provided robust systemic insights but required substantial time and expert availability, whereas PHA offered rapid screening capacity with limited depth. Drawing on these findings, the study proposes a Contextual Decision Support Model that helps practitioners select the most suitable technique based on project complexity, available resources, and stakeholder conditions. This practical framework enables construction professionals to balance methodological rigor with contextual feasibility, ensuring that risk identification processes are both systematic and adaptable to real-world constraints. Beyond its methodological contribution, the study advances risk management in construction by providing a structured and transparent decision-support approach that bridges academic rigor with on-site practice. By aligning method selection with project-specific attributes and stakeholder dynamics, the model strengthens the integration of analytical precision and practical decision-making across the project lifecycle, thereby contributing to more proactive, evidence-based, and resilient risk management in construction projects. Full article