Search Results (12,482)

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness. Full article

Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral camera to acquire the electromagnetic characteristics and material categories of the target environment and simultaneously employs light detection and ranging (LiDAR) and an optical camera to achieve a three-dimensional spatial reconstruction of the environment. Specifically, the millimeter-wave radar sensor adopts a multiple input multiple output (MIMO) array and obtains 3D synthetic aperture radar images through 1D mechanical scanning perpendicular to the array, thereby capturing the electromagnetic properties of the environment. The multispectral camera, equipped with nine channels, provides rich spectral information for material identification and clustering. Additionally, LiDAR is used to obtain a 3D point cloud, combined with the RGB images captured by the optical camera, enabling the construction of a three-dimensional geometric model. By fusing the data from four sensors, a comprehensive geometric-physical model of the environment can be constructed. Experiments conducted in indoor environments demonstrated excellent spatial-geometric-physical reconstruction results. This system can play an important role in various applications, such as environment modeling and planning. Full article

Stakeholders in multimodal freight transport encounter significant challenges due to the multitude of unknowns and inherent risks that can adversely affect operations. The subjective nature of the information complicates the identification and assessment of these risks, making them particularly challenging in the context of multimodal transport, where the potential consequences can be substantial. This research intends to provide a comprehensive understanding of the asymmetries in risks associated with multimodal freight transport by identifying and evaluating quantitative hazards. By integrating fuzzy set theory and failure mode and effects analysis (FMEA), the study offers a structured approach to statistically forecast risks, addressing imprecision in traditional risk assessments. Qualitative interviews conducted with multimodal freight transport operators in Thailand reveal critical insights, including the identification of high-priority risks such as delays from regulatory compliance, inadequate infrastructure, and inefficiencies in stakeholder communication. The findings of this study not only highlight these pressing issues but also provide actionable strategies to mitigate risks, thereby enhancing the operational resilience of multimodal freight transport systems. Full article

Clinical trial results indicate that statin therapy aimed at normalising the lipid profile can prevent and reduce the risk of cardiovascular events. Both LDL and HDL consist of several subfractions, with only the smallest and densest subfractions being the most atherogenic. We examine the effect of Atorvastatin treatment not only on basic lipid profile parameters but also atherogenic lipoprotein subfractions and 25(OH)D levels in patients after the first acute myocardial infarction. The study population had not previously received lipid-lowering medications. Serum 25(OH)D concentration was determined by direct competitive immunochemiluminescent assays. Lipoprotein subfractions, including VLDL, IDL-C, IDL-B, and IDL-A, as well as LDL1, LDL2 (large LDL), and LDL3-7 (sdLDL), were measured in serum (Lipoprint^® system). Almost all patients had 25(OH)D deficiency. Atorvastatin primarily reduced strongly atherogenic sdLDL and decreased the less atherogenic large LDL subfractions. A statistically significant reduction in VLDL cholesterol and IDL fractions was also observed. Analysing LDL subfractions provides a more detailed insight into lipid metabolism and enables the identification of patients with a more atherogenic phenotype. LDL subfractions may thus become not only more accurate prognostic biomarkers but also targets for lipid-lowering therapy. Vitamin D deficiency is associated with atherogenic dyslipidaemia, particularly high levels of sdLDL. Full article

Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline’s efficiency and accuracy offer valuable contributions to future advancements in point cloud processing. Full article

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom. Full article

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems. Full article

The worm Caenorhabditis elegans, with its short lifecycle and well-known genetic and metabolic pathways, stands as an exemplary model organism for biological research. Its simplicity and genetic tractability make it an ideal system for investigating the effects of different conditions on its metabolism. The chemical analysis of this nematode was performed to identify specific metabolites produced by the worms when fed with either self- or nonself-DNA. A standard diet with OP50 feeding was used as a control. Different development stages were sampled, and their chemical composition was assessed by liquid chromatography–mass spectrometry combined with chemometrics, including both principal component analysis and orthogonal partial least squares discriminant analysis tools. The obtained data demonstrated that self-DNA-treated larvae, when arrested in their cycle, showed significant decreases in dynorphin, an appetite regulator of the nematode, and in N-formyl glycine, a known longevity promoter in C. elegans. Moreover, a substantial decrease was also recorded in the self-DNA-fed adults for the FMRF amide neuropeptide, an embryogenesis regulator, and for a dopamine derivative modulating nematode locomotion. In conclusion, this study allowed for the identification of key metabolites affected by the self-DNA diet, providing interesting hints on the main molecular pathways involved in its biological inhibitory effects. Full article

Background/Objectives: Metabolic dysfunction-associated fatty liver disease (MAFLD) is currently the most common cause of chronic liver disease. Systemic inflammatory status and peripheral metabolic symptoms in the clinical picture have an impact on gut commensal bacteria. Methods: Our designed clinical trial was based on a cohort of patients with MAFLD whose diet included the daily consumption of 400 g of “Navelina” oranges for 28 days, compared with a control group of patients with the same pathologic conditions whose diet did not include the consumption of oranges and other foods containing similar nutrients/micronutrients. We used 16S metataxonomics and GC/MS analyses to identify taxa and urine/fecal VOCs, respectively. Results: A set of micronutrients from the diet were inspected, and some specific fatty acids were identified as the main contributors in terms of cluster sample separation. Metataxonomics and metabolomics profiles were obtained, and a stringent statistical approach allowed for the identification of significant taxa/VOCs, which emerged from pairwise group comparisons in both fecal and urine samples. Conclusions: In conclusion, a set of taxa/VOCs can be directly referred to as a marker of dysbiosis status and other comorbidities that, together, make up the pathologic burden associated with MAFLD. The investigated variables can be a target of therapeutic strategies. Full article

Sustainable economic development serves society but requires taking over space, often at the expense of areas occupied by single trees or even parts of forest areas. Techniques for transplanting adult trees used in various conflict situations at the interface of economy and nature work as a tool for sustainable management of urbanized and industrial areas, as well as, in certain circumstances, forest or naturally valuable areas. This study aimed to evaluate the effectiveness of ground-penetrating radar (GPR) in determining the horizontal and vertical extent of tree root systems before transplantation. Employing this non-invasive method to map root system architecture aids in the appropriate equipment selection and helps define the dimensions and depth of trenches to minimize root damage during excavation. This study specifically focused on the root systems of wild service trees (Sorbus torminalis (L.) Crantz) found in a limestone mine area, where some specimens were planned to be transplanted, as the species is protected under law in Poland. The root systems were scanned with a ground-penetrating radar equipped with a 750 MHz antenna. Then, the root balls were dug out, and the root parameters and other dendrometric parameters were measured. The GPR survey and manual root analyses provided rich comparative graphic material. The number of the main roots detected by the GPR was comparable to those inventoried after extracting the stump. The research was carried out in problematic soil, causing non-standard deformations of the root systems. Especially in such conditions, identifying unusually arranged roots using the GPR method is valuable because it helps in a detailed planning of the transplanting process, minimizing root breakage during the activities carried out, which increases the survival chances of the transplanted tree in a new location. Full article

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data. Full article

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance. Full article

Rammed earth construction, an ancient and sustainable building technique, faces significant preservation challenges, particularly in historical contexts. This study aims to enhance the preventive preservation of rammed earth historical heritage through a comprehensive methodology combining continuous monitoring, architectural inspections, and data fusion. By integrating nondestructive testing techniques such as ultrasound, thermography, and ground-penetrating radar with operational modal analysis and modeling, the proposed approach allows for early detection and assessment of structural vulnerabilities. This methodology was applied to the Tower of Muhammad in the Alhambra of Granada, Spain, demonstrating its effectiveness in identifying and quantifying damage and predicting structural health. Using multi-source data (documentation, inspections, nondestructive tests, and continuous monitoring), a finite element model was built, calibrated (achieving an avg. error in modal frequencies of 1.28% and a minimum modal assurance criterion value of 0.94), and used to develop a surrogate model able to predict the modal properties of the tower in 0.02 $\mathrm{s}$, becoming compatible with continuous system identification. The presented results highlight the importance of continuous data acquisition and advanced diagnostic tools for safeguarding rammed earth structures against environmental and anthropogenic threats. This study advocates for the adoption of digital twins in historical preservation, facilitating informed decision-making and sustainable management of cultural heritage. Full article

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations. Full article

Universities, as hubs of economic, technological, and social knowledge development, have increasingly adopted metric-based strategies to guide resource management and monitor their growth. The Sustainable University World Ranking, UI GreenMetric, is widely applied for this purpose, measuring performance across six categories aligned with the United Nations Agenda 2030—Sustainable Development Goals (SDGs). This study focused on assessing information concerning the water category of this ranking, or the five water management indicators, at the School of Arts, Sciences, and Humanities of the University of São Paulo, to estimate its classification. The methodology involves assessing the current situation of the university in terms of each indicator, and classifying it according to the ranking guidelines. The information obtained is treated as evidence for posterior validation with the ranking. The findings indicate satisfactory performance in the indicators of water 1, 3, and 5. Notably, the implementation of rainwater collection and storage systems has been successful, alongside maintaining potable water parameters for consumption within the campus, as well as the use of efficient water-saving devices. Indicators 2 and 4, related to effluent treatment and water reuse, are expected to achieve higher classifications with the reactivation of the wastewater treatment system’s operation. Over the period from May 2023 to June 2024, the average daily water consumption was measured at 52.89 ± 25.23 m³ day⁻¹, with a per capita consumption rate of 10.28 L consumer agent⁻¹ day⁻¹. An anticipated 20% reduction in water consumption is expected upon the incorporation of water reuse initiatives. The use of the UI GreenMetric framework has been found strategic and useful as a diagnostic tool, facilitating the identification of areas requiring improvement and guiding efforts toward enhancing the sustainability of the institution. Full article