Local Deviations Research Articles

Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models

ABSTRACT The spatial structure of local uncertainty of shallow-water satellite-derived bathymetry (SDB) relative to model type, imagery, and geographical adaptability was examined for an area near Key West, Florida (United States). The model types examined were a commonly used quasi-empirical linear regression model and a decision tree-based Categorical Boosting (CatBoost) machine learning (ML) model. Image types examined were (four) cloud-free Sentinel-2 images and a maximum blue band (Band 2) value image composite of the four Sentinel-2 images. Initial models fitted were based on band reflectances alone. Geographical adaptivity was added by including UTM coordinates and refitting the models. Major findings were: 1) The ML/CatBoost models provided substantially better depth estimates than the quasi-empirical models. 2) The geographically adaptive models outperformed the non-geographically adaptive models. 3) The ML/CatBoost models that included non-visible spectral bands including infra-red improved SDB accuracy compared to ML/CatBoost and quasi-empirical models based only on visible spectral bands. 4) Accuracies from ML/CatBoost models were comparable across all individual images and the composite suggesting that CatBoost models eliminate or at least minimize the need to find “the best” cloud-free image nor is it necessary to create a composite image. 5) Localized SDB inaccuracy was spatially random. 6) Significant spatial hotspots where SDB accuracy was consistently higher or lower across all images and models were present. Results suggest that image selection is less important for global and local SDB accuracy than using ML models that detect hidden interactions and non-linear relationships among pixel reflectance and geographic location. The spatially random local deviation from global accuracy suggests a weak ability to infer local accuracy from neighboring accuracies. This lack of spatial autocorrelation among errors is potentially problematic for the use of SDB maps for navigation since error at any location is generally inferred from known uncertainties at neighboring locations. Rigorous and robust uncertainty analysis is necessary in any effort to improve SDB, and the uncertainty analysis techniques employed that characterize SDB uncertainty in both statistical and geographical space could be an important part of quality assurance and continuous improvement.

Thermal Analysis of Jet-In-Crossflow Technique for Hotspot Treatment in Electronics Cooling

Abstract Artificial intelligence and machine learning systems, faster processors, miniaturized computational components, and supercomputer centers are accompanied by larger heat dissipation and the need for innovative cooling methods. In this work, a combined jet-in-crossflow cooling system is introduced and numerically investigated with an application in localized hotspot treatment. A validation study, a grid independence study, and an uncertainty analysis are conducted to ensure the accuracy of the obtained results. Both vertical and angled jet impingement at different jet locations are studied indicating the advantage of using a 45° angled jet placed upstream of the hotspot. In addition, the advantage of jet-in-crossflow in comparison with pure crossflow and pure jet impingement are studied. The results show that the angled jet-in-crossflow setup, in comparison with pure crossflow at the same overall mass flow rate, considerably reduces the temperature values at the heated surface, decreases the temperature standard deviation by 65%, while lowering the required pumping power by 35%. In comparison with pure jet impingement at the same overall mass flow rate, the angled jet-in-crossflow method reduces the required pumping power by 87%, while local temperature and temperature standard deviation values are very comparable. Furthermore, the advantage of structured rib channels in cooling effectiveness is investigated for the jet-in-crossflow setup. Although the addition of a rib slightly increases the pressure drop, the employment of a proper rib size minimizes the increased pressure drop while considerably improves the cooling effectiveness and temperature uniformity.

Nuclear mass predictions with the naive Bayesian model averaging method

A naive Bayesian model averaging (NBMA) method is developed to predict nuclear masses. In the NBMA method, the weights of different models may be different for each nucleus, which are sensitive to the model accuracies to describe the nuclear masses of the isotopes and isotones with the same proton and neutron numbers of that nucleus. Therefore, there are remarkable local structures for the weights of different models on the nuclear chart, which well eliminates the local deviations between the model predictions and the experimental masses and thus achieves better accuracy of mass predictions than the traditional arithmetic mean method (AMM) and weighted average method (WAM). Based on the latest atomic mass evaluation of AME2020, the root-mean-square (rms) mass deviation of the NBMA method is 0.293 MeV, while the rms deviations of AMM and WAM are 0.634 and 0.361 MeV, respectively. This accuracy of the NBMA method is even 28% better than the best accuracy of the mass models used in the NBMA method. The extrapolation ability of the NBMA method is also verified with the experimental nuclear masses which are not used in the training of the NBMA method.

A Circum‐Antarctic Plankton Isoscape: Carbon Export Potential Across the Summertime Southern Ocean

AbstractThe Southern Ocean accounts for ∼30% of the ocean's CO2 sink, partly due to its biological pump that transfers surface‐produced organic carbon to deeper waters. To estimate large‐scale Southern Ocean carbon export potential and characterize its drivers, we measured the carbon and nitrogen isotope ratios of surface suspended particulate matter (δ13CSPM, δ15NSPM) for samples collected in summer 2016/2017 during the Antarctic Circumnavigation Expedition (364 stations). Concurrent measurements of phytoplankton community composition revealed the dominance of large diatoms in the Antarctic and nano‐phytoplankton (mainly haptophytes) in open Subantarctic waters. As expected, δ13CSPM was strongly dependent on pCO2, with local deviations in this relationship explained by phytoplankton community dynamics. δ15NSPM reflected the nitrogen sources consumed by phytoplankton, with higher inferred nitrate (versus recycled ammonium) dependence generally coinciding with higher micro‐phytoplankton abundances. Using δ15NSPM and a two‐endmember isotope mixing model, we quantified the extent of nitrate‐ versus ammonium‐supported growth, which yields a measure of carbon export potential. We estimate that across the Southern Ocean, 41 ± 29% of the surface‐produced organic carbon was potentially exported below the seasonal mixed layer during the growth season, with maximum export potential (50%–99%) occurring in the Antarctic Circumpolar Current's southern Boundary Zone and near the (Sub)Antarctic islands, reaching a minimum in the Subtropical Zone (<33%). Alongside iron, phytoplankton community composition emerged as an important driver of the Southern Ocean's biological pump, with large diatoms dominating regions characterized by high nitrate dependence and elevated carbon export potential and smaller, mainly non‐diatom taxa proliferating in waters where recycled ammonium supported most productivity.

EVALUATION OF THE OPTIC NERVE IN MIGRAINE PATIENTS BY MAGNETIC RESONANCE IMAGING HISTOGRAM ANALYSIS

Background: Migraine is a chronic, paroxysmal neurovascular primary headache disease seen in approximately 15% of the world. In many studies on migraine, changes in the gray and white matter at the microvascular level have been detected. 90% of patients with aura have visual auras. Histogram analysis is an artificial intelligence application that can detect microscopic pathology in tissues before visual pathology. Objective: Histogram analysis was utilized in our investigation to ascertain whether or not the optic nerve, which is ordinarily visible on magnetic resonance images, was impacted. Methods: 120 adult migraine patients and 120 control group were involved. MRI was performed on a Philips 1.5 Tesla Ingenia device. For histogram analysis, optic nerve region of interest (ROI) measurement was performed in the coronal planes from the edges of the intraorbital segment, whose borders can be clearly distinguished, by using the manual drawing tool for both eyes. The data were analyzed through the MatLab program. Results: The average age of individuals diagnosed with migraine was 37.04 ± 9.57 years, whereas the control group had an average age of 37.8 ± 10.8 years. Between the two groups, 27 distinct parameters computed via histogram analysis were compared. In the control and migraine groups, no statistically significant distinction was observed between the right and left eyes. In contrast to the migraine and control groups, the Mean Local Standard Deviation of the Matrix was found to be The histogram's 5th, 10th, 25th, and 75th percentiles, the average squared, the root sum of squared, the size percent M, the most frequently used value, the median, and the average are all quantified using the root sum of squares. The number of pixels is also indicated. It was determined that ROI values were statistically significant (p0.05). Conclusion: In migraine patients, alterations in the optic nerve, which are typically discernible on conventional magnetic resonance images, may be identified via histogram analysis.

Local Reaction Environment Deviations within Gas Diffusion Electrode Pores for CO2 Electrolysis

The local conditions inside a gas diffusion electrode (GDE) pore, especially in the electrical double layer (EDL) region, influence the charge transfer reactions and the selectivity of desired CO2ER products. Most GDE computational models ignore the EDL or are limited in their applicability at high potentials. In this work, we present a continuum model to describe the local environment inside a catalytic pore at varying potentials, electrolyte concentrations and pore diameters. The systems studied in this work are based on an Ag catalyst in contact with KHCO3 solution. Our study shows that steric effects dominate the local environment at high cathodic potentials (≪−25 mV vs pzc at the OHP), leading to a radial drop of CO2 concentration. We also observe a drop in pH value within 1 nm of the reaction plane due to electrostatic repulsion and attraction of OH− and H+ ions, respectively. We studied the influence of pore radii (1–10 nm) on electric field and concentrations. Pores with a radius smaller than 5 nm show a higher mean potential, which lowers the mean CO2 concentration. Pores with a favourable local environment can be designed by regulating the ratio between the pore radius and Debye length.

Tuning the dimensional order in self-assembled magnetic nanostructures: theory, simulations, and experiments.

A major obstacle to building nanoscale magnetic devices or even experimentally studying novel nanomagnetic spin textures is the present lack of a simple and robust method to fabricate various nano-structured alloys. Here, theoretical and experimental investigations were conducted to understand the underlying physical mechanisms of magnetic particle self-assembly in zero applied magnetic field. By changing the amount of NaOH added during the synthesis, we demonstrate that the resulting morphology of the assembled FeCo structure can be tuned from zero-dimensional (0D) nanoparticles to one-dimensional (1D) chains, and even three-dimensional (3D) networks. Two numerical simulations were developed to predict aspects of nanostructure formation by accounting for the magnetic interactions between individual magnetic nanoparticles. The first utilized the Boltzmann distribution to determine the equilibrium structure of a nanochain, iteratively predicting the local deviation angle θ of each particle as it attaches to a forming chain. The second simulation illustrates the differences in nanostructure arrangement and dimensionality (0D, 1D, or 3D) that arise from random interactions at various nanoparticle densities. The simulation results closely match the experimental findings, as seen from SEM images, demonstrating their ability to capture the system's structural properties. In addition, magnetic hysteresis measurements of the samples were performed along two orthogonal directions to show the influence of dimensional order on the magnetic behavior. The normalized remanence (MR/MS||) of the FeCo alloys increases as the dimensions of nanostructures are increased. Of the three cases, the FeCo 3D network structures exhibit the highest normalized nanostructure remanence of 0.33 and an increased coercivity to above 200 Oe at 300 K. This combined numerical and experimental investigation aims to shed light on the preparation of FeCo nanostructures with tailorable dimensional order and it opens new avenues for exploring the complex spin textures and coercive behavior of these multi-dimensional nanomagnetic structures.

UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: LOCAL OUTLIER FACTOR ALGORITHM TO INDONESIAN EARTHQUAKE DATA

Indonesia's location on the "Ring of Fire" poses a high risk for seismic events. Addressing this, our study applied the Local Outlier Factor (LOF) algorithm for advanced seismic anomaly detection, crucial for geotectonic upheaval prediction. The LOF, adept at unsupervised learning in label-scarce datasets, analyzed data from the Indonesian Meteorology, Climatology, and Geophysical Agency, validated for integrity. Our approach, considering local density deviations, offered a refined alternative to conventional threshold-based detection, accommodating seismic data's intrinsic variability. The LOF algorithm successfully pinpointed anomalies, revealing unique seismic events unconstrained by geography or time. A comparative analysis underscored the LOF's superiority in recognizing local deviations and handling disparate data densities. These findings highlight the LOF's utility in strengthening seismic risk mitigation and anticipatory measures. The diverse anomalies identified, varying in magnitude and depth, reflect Indonesia's complex seismic interplay. To conclude, the LOF proves potent for anomaly detection, potentially elevating public safety and disaster preparedness. Future research will compare the LOF with other unsupervised methods, seeking to deepen seismic risk comprehension

Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

Automatic chest computed tomography image noise quantification using deep learning

PurposeThis study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions. MethodsA comprehensive phantom CT dataset (three dose levels, six reconstruction methods, amounting to 9240 slices) was acquired and used to train a convolutional neural network (CNN) to output an estimate of local image noise standard deviations (SD) from a single CT scan input. The CNN model consisting of seven convolutional layers was trained on the phantom image dataset representing a range of scan parameters and was tested with phantom images acquired in a variety of different scan conditions, as well as publicly available chest CT images to produce clinical noise SD maps. ResultsNoise SD maps predicted by the CNN agreed well with the ground truth both visually and numerically in the phantom dataset (errors of < 5 HU for most scan parameter combinations). In addition, the noise SD estimates obtained from clinical chest CT images were similar to running-average based reference estimates in areas without prominent tissue interfaces. ConclusionsPredicting local noise magnitudes without the need for repeated scans is feasible using DL. Our implementation trained with phantom data was successfully applied to open-source clinical data with heterogeneous tissue borders and textures. We suggest that automatic DL noise mapping from clinical patient images could be used as a tool for objective CT image quality estimation and protocol optimization.

CI Traces the Disk Atmosphere in the IM Lup Protoplanetary Disk

The central star and its energetic radiation fields play a vital role in setting the vertical and radial chemical structure of planet-forming disks. We present observations that, for the first time, clearly reveal the UV-irradiated surface of a protoplanetary disk. Specifically, we spatially resolve the atomic-to-molecular (C i-to-CO) transition in the IM Lup disk with Atacama Large Millimeter/submillimeter Array archival observations of [C i] 3P1–3P0. We derive a C i emitting height of z/r ≳ 0.5 with emission detected out to a radius of ≈600 au. Compared to other systems with C i heights inferred from unresolved observations or models, the C i layer in the IM Lup disk is at scale heights almost double that of other disks, confirming its highly flared nature. C i arises from a narrow, optically thin layer that is substantially more elevated than that of 12CO (z/r ≈ 0.3–0.4), which allows us to directly constrain the physical gas conditions across the C i-to-CO transition zone. We also compute a radially resolved C i column density profile and find a disk-averaged C i column density of 2 × 1016 cm−2, which is ≈3–20× lower than that of other disks with spatially resolved C i detections. We do not find evidence for vertical substructures or spatially localized deviations in C i due, e.g., to either an embedded giant planet or a photoevaporative wind that have been proposed in the IM Lup disk, but emphasize that deeper observations are required for robust constraints.

American option evaluations using higher moments

PurposeBy using higher moments, this paper extends the quadratic local risk-minimizing approach in a general discrete incomplete financial market. The local optimization subproblems are convex or nonconvex, depending on the moment variants used in the modeling. Inspired by Lai et al. (2006), the authors propose a new multiobjective approach for the combination of moments that is transformed into a multigoal programming problem.Design/methodology/approachThe authors evaluate financial derivatives with American features using local risk-minimizing strategies. The financial structure is in line with Schweizer (1988): the market is discrete, self-financing is not guaranteed, but deviations are controlled and reduced by minimizing the second moment. As for the quadratic approach, the algorithm proceeds backwardly.FindingsIn the context of evaluating American option, a transposition of this multigoal programming leads not only to nonconvex optimization subproblems but also to the undesirable fact that local zero deviations from self-financing are penalized. The analysis shows that issuers should consider some higher moments when evaluating contingent claims because they help reshape the distribution of global cumulative deviations from self-financing.Practical implicationsA detailed numerical analysis that compares all the moments or some combinations of them is performed.Originality/valueThe quadratic approach is extended by exploring other higher moments, positive combinations of moments and variants to enforce asymmetry. This study also investigates the impact of two types of exercise decisions and multiple assets.

Cost optimization model design of fresh food cold chain system in the context of big data

The assessment of cold chain logistics for fresh products can be more precise with high-dimensional information data, providing valuable insights for the optimization of associated costs. Nonetheless, traditional data processing techniques fail to meet the processing efficiency required for such high-dimensional cold chain logistics data. Therefore, this paper proposes a spectral clustering algorithm based on the local standard deviation and optimized initial center, which comprehensively analyzes the fixed, transportation, refrigeration, and cargo damage costs of cold chain logistics. Additionally, this algorithm includes a variation operator based on clustering and introduces a large neighborhood search mechanism for optimizing the individual connectivity gene layer after selecting the gene layer site for variation. Simulation results demonstrate that the proposed algorithm exhibits better convergence in 15 iterations, reduces error rates, and significantly cuts down on the clustering process time. This ultimately leads to a reduction in the total cost of cold chain calculation.

Hierarchical fusion evaluation and optimization of radar intelligent tracking algorithm via hybrid weight design mechanism

In this paper, a hybrid weight design and optimization mechanism is proposed to ensure a better application effect in the multi-level weighted evaluation/optimization process of intelligent neural network (NN) guidance algorithm. According to different target tracking scenarios, the tracking index are divided into different periods, a single period score is given according to a linear-nonlinear hybrid scoring mechanism. Furthermore, in a single index, the internal scores of different time periods are weighted and fused for the second time to obtain the index overall score. Finally, the third weighted fusion of the multi-index scores is used to obtain the comprehensive score of the algorithm. Design the algorithm parameter evaluation case sets, repeat the above compound weighting, obtain the case with the highest comprehensive score. Finally, the algorithm is optimized by the highest score case. In the hybrid weight design mechanism, the objective grey correlation first defines the weight range, then the subjective expert decision method with local extreme expert elimination and standard deviation global test for double optimization finally ensures the weight reliability of the hierarchical evaluation optimization. The evaluation optimization experiment using fuzzy NN radar seeker verifies the effectiveness of the method.

White matter integrity in bipolar disorder investigated with diffusion tensor magnetic resonance imaging and fractal geometry

BackgroundGrowing evidence suggests the presence of white matter (WM) alterations in bipolar disorder (BD). In this study we aimed to investigate the state of WM structures, in terms of tissue integrity and morphological complexity, in BD patients compared to healthy controls (HC), in an attempt to better elucidate the microstructural changes associated with BD. MethodsWe collected a dataset of 399 Diffusion Tensor Magnetic Resonance Imaging (167 BD and 232 healthy controls) images, acquired at five different sites, which was processed with Tract-Based Spatial Statistics (TBSS) and fractal analysis. ResultsThe TBSS analysis demonstrated significantly lower FA values in the BD group. Diffusion abnormalities were primarily located in the temporo-parietal network. The Fractal Dimension (FD) analysis did not reveal consistent significant differences in the morphological complexity of WM structures between the groups. When the FD values of patients were considered individually, it is possible to notice some localized significant deviations from the healthy population. LimitationsDTI sequences have not been harmonized before acquisition, samples' sizes are heterogeneous. ConclusionsThis study, by applying both TBSS and FD analyses, allows to evaluate diffusion and structural alterations of WM at the same time. The evaluation of WM integrity from these two different perspectives could be useful to better understand the pathophysiological and morphological changes underpinning bipolar disorder.

Crack initiation and propagation behavior of dissimilar interface with intermetallic compound layer in Al/steel joint using coupled multiscale mechanical testing

We demonstrate multiscale mechanical testing using miniature and microscale tensile tests to elucidate the crack initiation and propagation behaviors corresponding to interfacial nanostructures in dissimilar joints of 6061 aluminum alloy and 304 stainless steel. The initial nucleation and subsequent growth of intermetallic compounds (IMCs) can be controlled by heat treatment after friction stir lap welding. The miniature tensile tests exhibited the relationship between the joint strength and IMC layer thickness with changes in the fracture location. Microscale tensile testing using a single edge-notch tension geometry initiates the crack and subsequent propagations near the interface, which helps identify the crack initiation site and understand the dependence of the local strength on the type of IMCs. Crack deviation behavior toward the Al side, attributed to the difference of local strength, is confirmed by in situ observations, which indicated that continuous failure can easily occur within the grown IMCs, particularly in macroscale joints. It was concluded that the crack propagation is controlled by the local crack deviation toward metal matrix, which tends to occur for the dissimilar interface with thin IMC layer. This miniature/microscale tensile testing approach is expected to clarify the essential relationship between the interfacial microstructure and the fracture behavior.

Concordant GRADE-3 Truncal Ataxia and Ocular Laterodeviation in Acute Medullary Stroke.

Background: Severe truncal ataxia associated with an inability to sit up without assistance (STA grade 3) is frequent in patients with central acute vestibular syndrome (AVS) involving the brainstem or cerebellum. When these patients have nystagmus, central HINTS excludes peripheral lesions; however, additional localization and lateralization signs are helpful, not only to resolve the peripheral versus central vestibular lesion dilemma, but to zero in on a precise lesion localization/lateralization to the lateral medulla, the most common ischemic lesion localization associated with an initially false-negative stroke MRI. Methods: This is a study of AVS patients with additional inclusion criteria: grades 2 or 3 ataxia with an eventual diagnosis of medullary stroke (MS), either involving the lateral medulla (LMS) or the medial medulla (MMS), and horizontal (h) gaze paralysis was the main exclusion criteria. All patients sat on the side of the bed or stretcher, with assistance if needed. A general neurologic examination followed in the sitting position, the testing protocol included the head impulse, spontaneous nystagmus, and skew deviation (HINTS) tests, followed by observation of the effect of brief 3-5 sec eyelid closure on ocular position, and saccade and pursuit eye movement tests. If they could sit, the protocol included the ability to stand with a wide base, then a narrow base, the Romberg test, and tandem gait. Radiographic lesion localization and horizontal gaze deviation concluded the protocol. Results: A total of 34 patients met the entry criteria, 34 MS (13 in the lateral medulla, 12 previously described, and 1 new patient), and 1 new MMS. Among them, n = 10/12 had grade 3 ataxia, and 3 (1 new patient) had grade 2 ataxia. In addition, overt ocular laterodeviation (OLD) was present in thirteen of them (35.3%). All OLD patients had gaze deviation and ipsilateral saccade and truncal lateropulsion, 1 medial medulla stroke patient had grade 3 truncal contrapulsion and contralateral hemiparesis without OLD, n = 20/21 patients with LMS without OLD had grade 3 truncal ataxia, and 1 had grade 2 truncal ataxia. Discussion: AVS patients with severe truncal ataxia (inability to sit without assistance) potentially have brainstem, cerebellum, or subcortical lesions. All patients had central HINTS; however, simultaneous direction-concordant STA 3 and OLD provided greater lateral medulla localization specificity, affecting the ipsilateral medulla. Future work to explore a practical posterior circulation stroke scale that includes HINTS, STA, and OLD will potentially select cases for thrombolysis even in the event of initially false-negative imaging.

On-Machine CIS SoC-Based Layerwise Inspection System for MEX Additive Manufacturing

Additive manufacturing processes build three-dimensional objects usually following a layer-upon-layer strategy. An interesting feature of this strategy is that each layer could be inspected before the next one is deposited. On-machine integration of layerwise inspection systems would not only allow for early characterization of the dimensional and geometric quality of the part, but also for the detection of intralayer defects. Contact image sensors (CIS), such as those used in desktop flatbed scanners, could be used for this purpose since they would provide bi-dimensional digital images of the whole layer and its neighborhood. CIS images combine high resolutions with a reduced acquisition time. In this work, a material extrusion (MEX) additive manufacturing system, with layerwise inspection capabilities is proposed. The system has been equipped with the CIS that Epson uses in its Perfection V39 flatbed scanner. The sensor provides two analog output signals, each one consisting on 2584 voltage levels, that represent the amount of light reflected by the material. This analog information is sent to a parallel AD converter, where an 8-bit encoding is assigned to each one of the pixels on the digitized image. To overcome microcontroller-related problems, a Zynq®-7000 system-on-chip (SoC) has been used. This SoC integrates an ARM® based processor, with the hardware programming of a field programmable gate array (FPGA). This architecture ensures an accurate and controlled readout of the various AD converters. The resultant digital image of each layer could then be then processed using different algorithms to detect defects, extract the geometry of the layer contour and characterize the dimensional and geometric quality of the object. In the example provided, a forced error consisting on 0.2 mm height local deviations, caused by a variation in extrusion temperature, was identified from 2D grayscale images obtained with the CIS sensor.

Dynamic savanna burning emission factors based on satellite data using a machine learning approach

Abstract. Landscape fires, predominantly found in the frequently burning global savannas, are a substantial source of greenhouse gases and aerosols. The impact of these fires on atmospheric composition is partially determined by the chemical breakup of the constituents of the fuel into individual emitted chemical species, which is described by emission factors (EFs). These EFs are known to be dependent on, amongst other things, the type of fuel consumed, the moisture content of the fuel, and the meteorological conditions during the fire, indicating that savanna EFs are temporally and spatially dynamic. Global emission inventories, however, rely on static biome-averaged EFs, which makes them ill-suited for the estimation of regional biomass burning (BB) emissions and for capturing the effects of shifts in fire regimes. In this study we explore the main drivers of EF variability within the savanna biome and assess which geospatial proxies can be used to estimate dynamic EFs for global emission inventories. We made over 4500 bag measurements of CO2, CO, CH4, and N2O EFs using a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems under different seasonal conditions sampled over the course of six fire seasons between 2017 and 2022. We complemented our own data with EFs from 85 fires with locations and dates provided in the literature. Based on the locations, dates, and times of the fires we retrieved a variety of fuel, weather, and fire-severity proxies (i.e. possible predictors) using globally available satellite and reanalysis data. We then trained random forest (RF) regressors to estimate EFs for CO2, CO, CH4, and N2O at a spatial resolution of 0.25∘ and a monthly time step. Using these modelled EFs, we calculated their spatiotemporal impact on BB emission estimates over the 2002–2016 period using the Global Fire Emissions Database version 4 with small fires (GFED4s). We found that the most important field indicators for the EFs of CO2, CO, and CH4 were tree cover density, fuel moisture content, and the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N2O EFs. RF models using satellite observations performed well for the prediction of EF variability in the measured fires with out-of-sample correlation coefficients between 0.80 and 0.99, reducing the error between measured and modelled EFs by 60 %–85 % compared to using the static biome average. Using dynamic EFs, total global savanna emission estimates for 2002–2016 were 1.8 % higher for CO, while CO2, CH4, and N2O emissions were, respectively, 0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we found a spatial redistribution compared to GFED4s with higher CO, CH4, and N2O EFs in mesic regions and lower ones in xeric regions. Over the course of the fire season, drying resulted in gradually lower EFs of these species. Relatively speaking, the trend was stronger in open savannas than in woodlands, where towards the end of the fire season they increased again. Contrary to the minor impact on annual average savanna fire emissions, the model predicts localized deviations from static averages of the EFs of CO, CH4, and N2O exceeding 60 % under seasonal conditions.

Geometric quality assessment of precast concrete (PC) elements based on 3D structural light scanning

ABSTRACT The geometric quality of precast concrete (PC) elements is critical in the construction and management of prefabricated construction. However, traditional manual inspection is time-consuming, inaccurate and costly. Therefore, an effective and low-cost non-contact inspection method is needed to support the geometric quality inspection of PC elements as a way to improve inspection efficiency and accuracy. In this study, an intuitive, effective and inexpensive geometric quality inspection method is proposed to evaluate the geometric quality of PC elements using 3D structural light scanning technology. The proposed method fits the point cloud data model of PC elements to the design model, visualizes the range of dimensional deviation of the whole element by the generated deviation chromatogram, and then evaluates the overall geometric quality of PC elements based on the local absolute deviation distance and the average deviation distance. The evaluation results classify PC elements into qualified products, partially unqualified products and severely defective products, which can be directly used to guide the treatment of unqualified products. The method is highly integrated with realistic construction and has a strong practical value. This study enriches the means of assessing the geometric quality of PC elements and shows potential for implementation in real practice.