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A VISION BASED AUTONOMOUS UAV REALTIME OIL SPILL SAMPLING
No full textOil spills represent a significant environmental and economic hazard to marine and coastal ecosystems. Rapid detection and containment are vital, as even short delays can lead to spill expansion, causing ecological damage and increasing cleanup costs. This work presents the design and development of a vision-based autonomous UAV platform capable of real-time oil spill sample collection and autonomous recovery, addressing the urgent need for rapid, unmanned incident response.
The proposed platform incorporates autonomous UAV navigation to target areas, performs simulated sampling, and executes precision landing using fiducial AprilTags. The AprilTag2 package, integrated within the Robot Operating System (ROS) framework, enables fiducial detection using image data from an Intel RealSense D435 camera mounted on the UAV. A custom landing algorithm computes the tag’s centroid as the target landing point, transforms its coordinates from the camera to the robot reference frame, and guides the UAV to land precisely via a PID-based controller.
Simulation and Real-world experiments validated the full operational cycle: the UAV autonomously takes off from a platform, navigates to a waypoint representing a simulated spill site (marked by an AprilTag), descends to a predetermined altitude for mock sampling, and returns to the home platform for precision landing on a second AprilTag (representing the oil spill location)
The relative importance of wind and hydroclimate drivers in modulating wind-blown dust emissions in Earth system models
No full textWindblown dust emissions are subject to large uncertainties in Earth system models (ESMs), yet model discrepancies in dust variability and its physical drivers remain poorly understood. This study evaluates the consistency of 21 ESMs in simulating the climatological distribution and interannual variability of global dust emissions and applies dominance analysis to quantify the relative influence of near-surface wind speed and five hydroclimate variables (precipitation, soil moisture, specific humidity, air temperature, leaf area index) across different climate zones. In hyperarid regions, the models exhibit poor agreement in dust variability, with only 10 % of pairwise comparisons showing significant positive correlations. Most models capture the dominant wind control except GFDL-ESM4 which display dominant hydroclimate influence (wind contributing 42 %) and high spatial variability. In arid and semiarid regions, dust variability is shaped by a dual effect of land surface memory: models with consistent hydroclimate variability converge in dust responses, while those with divergent hydroclimate representations show increased disagreement. While all models capture the expected increase of hydroclimate influence with decreasing aridity, the extent of this transition varies by model, resulting in greater model disagreement regarding the relative importance of wind and hydroclimate drivers in arid/semiarid regions. Implementing the Kok et al. (2014) scheme in CESM reduces the wind contribution from 86 % to 64 % in hyperarid regions and from 56 % to 46 % in arid regions, indicating enhanced hydroclimate influence compared to the Zender et al. (2003) scheme. These findings underscore the importance of improving hydroclimate and land surface representations for reducing uncertainties in dust emission responses to climate variability and change
The Four Pillars of Manufacturing Knowledge: A Contemporary Perspective on Practical Implementation
No full textThe Four Pillars of Manufacturing Knowledge has undergone a substantial revision to encompass the changing landscape of advanced manufacturing, incorporating concepts such as Industry 4.0, Additive Manufacturing (AM), etc. This revised version of the Four Pillars is aligned with the original purpose to be used by industry and academia to represent the breadth and scope of manufacturing engineering based on accreditation criteria and SME’s Body of Knowledge for the Certified Manufacturing Engineer and Technologist (CMfgE and CMfgT). The revised Four Pillars has changes in eleven of the twelve knowledge blocks. The Automated Systems and Control knowledge block, now renamed as Industry 4.0 and Automated Systems and Control, included the most revised topics, with only two remaining unchanged. Process Design is another knowledge block that went through major revisions with the addition of digital manufacturing topics such as: Digital Twin and Computer Aided Process Planning. Overall, this paper presents each of the new and revised topics to gain a better understanding of the implications for ongoing research and education in manufacturing worldwide. Practical examples of teaching methods, as well as laboratory applications, are provided for some of the topics that are most critical to the future of the manufacturing industry
H(curl2)-conforming triangular spectral element method for quad-curl problems
No full textIn this paper, we consider the H(curl2)-conforming triangular spectral element method to solve the quad-curl problems. We first explicitly construct the H(curl2)-conforming elements on triangles through the contravariant transform and the affine mapping from the reference element to physical elements. These constructed elements possess a hierarchical structure and can be categorized into the kernel space and non-kernel space of the curl operator. We then establish H(curl2)-conforming triangular spectral element spaces and the corresponding mixed formulated spectral element approximation scheme for the quad-curl problems and related eigenvalue problems. Subsequently, we present the best spectral element approximation theory in H(curl2;Ω)-seminorms. Notably, the degrees of polynomials in the kernel space solely impact the convergence rate of the (L2(Ω))2-norm of uh, without affecting the semi-norm of H(curl;Ω) and H(curl2;Ω). This observation enables us to derive eigenvalue approximations from either the upper or lower side by selecting different degrees of polynomials for the kernel space and non-kernel space of the curl operator. Finally, numerical results demonstrate the effectiveness and efficiency of our method
Energy balance and scale truncation of the approximate deconvolution with correction
No full textWe present the similarity theory of Approximate Deconvolution with Correction (ADC) - a member of the recently proposed family of turbulence models called LES-C. This model stems from a combination of a well-known Approximate Deconvolution Model (ADM) with a defect correction procedure. We derive the energy equality for the ADC in a way that highlights the enhanced accuracy of the ADC, as compared to the ADM. Using the proposed definitions of the ADC energy and dissipation rates, we investigate the energy cascade of the ADC; we show that it correctly predicts the energy cascade in the inertial range, and acts to dissipate the small scales at a higher rate than the ADM. The micro-scale of the ADC is then found; we prove it to be larger than the micro-scale of the ADM, which also explains the previously established efficiency of the ADC
Influence of build direction on the fracture mechanism of 3D printed octet lattices
No full textWe investigate the effects of 3D printing build directions on the fracture properties of octet lattice metamaterials made of polylactic acid (PLA) and how these effects vary with the relative density of the lattices. Single-edge notch bend samples are 3D printed in two orthogonal build directions at various relative densities. Our results show that the work of fracture for octet lattices with build directions parallel to the crack plane is significantly higher than those with build directions perpendicular to the crack plane. We also observed that the ratio of specific work of fracture between the two build directions remains nearly constant across different relative densities. In contrast, the ratio of peak load between the two build directions decreases as relative density increases. Furthermore, the build direction dictates the fracture mechanism. While the perpendicular build direction predominantly results in a brittle (mode I) fracture, the parallel build direction leads to a complex, delamination-dominated (mode II type) fracture. This phenomenon is largely governed by the weak interfaces formed between the printed layers and their interaction with the lattice geometry. These results reveal that the build direction governs the fracture mechanism and work of fracture of these lattice metamaterials and is therefore an important design consideration
Winter Fungi of the North Woods
Get PDFA simple field guide to assist in identifying winter fungi in northern hardwood forests. This guide is focused on woody species easily found persisting in winter above snow level and with few look-a-likes found in northeastern and north-central North America. Photos and descriptions are from the Upper Peninsula, Michigan.https://digitalcommons.mtu.edu/oabooks/1009/thumbnail.jp
A climatically significant abiotic mechanism driving carbon loss and nitrogen limitation in peat bogs
Get PDFSphagnum-dominated bogs are climatically impactful systems that exhibit two puzzling characteristics: CO2:CH4 ratios are greater than those predicted by electron balance models and C decomposition rates are enigmatically slow. We hypothesized that Maillard reactions partially explain both phenomena by increasing apparent CO2 production via eliminative decarboxylation and sequestering bioavailable nitrogen (N). We tested this hypothesis using incubations of sterilized Maillard reactants, and live and sterilized bog peat. Consistent with our hypotheses, CO2 production in the sterilized peat was equivalent to 8–13% of CO2 production in unsterilized peat, and the increased formation of aromatic N compounds decreased N-availability. Numerous sterility assessments rule out biological contamination or extracellular enzyme activity as significant sources of this CO2. These findings suggest a need for a reevaluation of the fixed CO2:CH4 production ratios commonly used in wetland biogeochemical models, which could be improved by incorporating abiotic sources of CO2 production and N sequestration
Augmenting general-purpose large-language models with domain-specific multimodal knowledge graph for question-answering in construction project management
No full textCurrent studies on Question-Answering of Construction Project Management (CPM-QA) face challenges, including the small-scale CPM-related knowledge repositories, the limited effectiveness of QA methods using grammar rules or tiny machine-learning models, and the shortage of testing sets for comparing QA performance. Hence, this research augments general-purpose large-language models (GLMs) with the multimodal CPM knowledge graph (CPM-KG) for CPM-QA. It encompasses (i) building the multimodal CPM-KG covering 36 CPM subfields, (ii) combining CPM-KG and GLMs through three stages, (iii) developing a 2435-question CPM-QA testing set, and (iv) assessing and comparing CPM-QA accuracies for eight pairs of original and CPM-KG-augmented GLMs. The results demonstrate that CPM-KG-augmented GLMs’ CPM-QA accuracy rate is 30.0 % superior to original GLMs on average, and top-performing CPM-KG-augmented GLMs (e.g., ERNIE-Bot 4.0) pass CRCEEs. Within 36 CPM subfields, CPM-QA accuracy enhancements resulting from CPM-KG are between 12.2 % and 57.8 %. Furthermore, CPM-KG leads to CPM-QA accuracy enhancements of 19.6 % for single-answer, 48.0 % for multiple-answer, 30.6 % for text-only, and 20.4 % for image-embedded questions. The multimodal CPM-KG also outperforms the text-only single-modal CPM-KG in enhancing CPM-QA performance. This work contributes to unveiling the significance of CPM-specific knowledge in augmenting GLMs, sharing a reusable multimodal CPM-KG-formatted knowledge repository, and delivering a testing set of CPM-QA
Data-driven Identification of Bandgaps in Flexural Metastructures using Component Mode Synthesis and FRF Based Substructuring
No full textMetastructures, characterized by their periodic unit cells, are known for their ability to block the propagation of elastic waves within specific frequency ranges, known as “bandgaps”. To estimate the wave propagation characteristics of these systems, two primary approaches are employed: physics-based methods and data-driven techniques. Physics-based methods depend on the material properties and geometry of the unit cells, while data-driven approaches utilize experimental data, such as steady-state dynamic response data.
This study assesses the effectiveness of data-driven techniques, particularly Component Mode Synthesis (CMS) and Frequency Response Function-Based Substructuring (FBS), in identifying bandgaps in metastructures composed of multiple unit cells. The focus is on metastructures consisting of 1D beams that exhibit flexural wave behavior. Within these structures, two significant challenges arise when using frequency response functions based on out-of-plane response data: the absence of rotational degrees of freedom (dofs) and the presence of rigid-body modes. Both factors critically impact the dispersion relationship and, by extension, the bandgap estimation. Traditionally, capturing rotational dynamics has been difficult due to limitations in direct experimental measurement, necessitating the inference of rotational dofs from translational measurements. Furthermore, rigid-body modes are estimated from experimental data. To overcome these challenges, we propose the estimation of rotational dofs by curve-fitting of translational dofs. In addition, this study explores a novel approach to the estimation of rigid body modes from the modal parameters acquired using the well-known Polymax algorithm. The discussed methodologies are also applied to derive dispersion relations for infinite metastructures