Deep Boundary Research Articles

Interactions between Dust Aerosols and the Ultra-high Atmospheric Boundary Layer: Case study of vertical observation over the Taklimakan Desert, China

A deep atmospheric boundary layer (ABL) with a height of up to 4,000–6,000 m can develop over the Taklimakan Desert (TD). Nevertheless, the deep ABL in the TD was only studied for clean days. To date, the observational evidence concerning the interaction between the ultra-high ABL and dust aerosols over the TD remains scarce. In May 2022, we conducted the inaugural observational particle-sounding experiment in the TD, selecting representative cases of light and heavy dust days. The results captured a distinctive stratified structure of the daytime ABL induced by dust aerosols characterized by an ultra-thick residual layer (RL) in the afternoon. On heavy dust days, the daytime ABL was divided into convective boundary layer (CBL), capping inversion layer (CIL), RL and residual capping inversion layer (RCIL). Dust aerosols exerted a warming effect in the atmosphere reaching a maximum of about 1.0 K/day at the top of the dust aerosol layer and causing a temperature inversion. A CIL about 1,000 m occurred between the CBL and the RL, which acted as a barrier, restraining CBL development and hindering dust aerosol transport. Furthermore, a deeper stable boundary layer on heavy dust days at night was observed. A unique vertical structure of nocturnal dust aerosol concentrations peaks of 477μg/m3 was also observed in the deep RL at 1800-2600 m. The radiation effects of dust aerosols strengthened and weakened the stability of the RL above and below the dust layer, respectively. The study provided vertical observational evidence of the impact of dust aerosols on the deep ABL structure, thus contributing to a more profound comprehension of aerosol–radiation interactions over the desert arid region.

Source Modeling of Deep Plate Boundary 2021 Miyagi Earthquake (Mw7.0) Employing Modified Semi-Empirical Technique with Site Effects: A Step Forward Towards Hazard Mitigation

ABSTRACT The source modeling of 2021 Miyagi earthquake (Mw7.0) offers a significant tool to understand the initial evaluation of M9 earthquake cycle in this region. This article seeks to simulate the 2021 Miyagi earthquake (Mw7.0) using the modified semi-empirical technique (MSET) after incorporating site effects determined using the Horizontal to Vertical spectral ratio technique. We propose the best-fitting source model of this earthquake from a spectrum of rupture model parameters using MSET. We believe that this effort is the first to use MSET to model this earthquake and will provide significant contribution for seismic hazard assessment of the Miyagi region.

GeGra: Approaching a generic model for quantitative grain size analysis from materials microscopy data using deep learning

Grain size has a significant impact on the properties of materials, and is crucial for predicting material properties. Traditional grain size measurement relies heavily on human operators, leading to subjective results, and existing machine learning methods are typically material-specific, requiring significant labeling and training efforts for each new material. This paper provides insight into developing a deep learning-based generic grain boundary detection model (GeGra) from different material micrographs. The model is trained on 1006 images from various microscopy techniques such as light optical, Kerr, and scanning electron microscopy, acquired at different magnifications for different materials such as copper, austenite, brass, sintered hard magnet, hard metal, bronze, nickel silver, and aluminum. The developed GeGra model effectively handles visual artifacts and substructures such as twin grains, which often pose challenges for material-specific, state-of-the-art grain boundary segmentation models. The developed model achieved an IoU score of 69 % on a diverse test set and enables accurate grain size analysis using external image analysis software in less than one minute, according to ASTM standards, which is more than 5 times faster than the manual method. The developed model prioritizes generality with objective that it can have broader applicability for various materials instead of high-precision grain boundary detection. Additionally, the model has the potential to be a foundational tool for generalized grain size analysis in material microscopy, reducing the effort required for such analysis and assisting both material science experts and machine learning users.

Research on controlled mining of end slope fire-burned area in open-pit mine

To solve the problem of controlling mining in the open-pit mine end slope fire-burned area, applying multivariate function fitting to the roof and floor modeling of multi-coal seam open pit mines, introducing the factor of coal quality changes in the fire-burned area, determining coal quality information at each location through proximate analysis of coal, to establish the net profit model of the mining area, it is determined the net profit of each mining position by numerical integration, the final mining position was determined without failure by calculating the slope stability based on the numerical simulation of strength reduction. Taking the Dananhu No. 2 open-pit mine in Hami, Xinjiang, China as the engineering background, the fire-burned area III within the southern end slope boundary of the first mining area is 240 m. It was finally determined that the optimal mining position is when the advancement degree is 182 m, the ultimate pit slope angle is 25°, the three-dimensional slope stability is 1.305, the profit is 671.96 million yuan, The deep boundary of the southern end slope fire-burned area of the slope is reduced by 58 m. This paper solves the problem of end slope mining in Dananhu No. 2 mine, and maximizes its net profit under the condition of ensuring safe production.

Physical Mechanisms of Deep Convective Boundary Layer Leading to Dust Emission in the Taklimakan Desert

AbstractDeserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large‐scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low‐level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary‐layer processes in desert regions.

Spatiotemporal prediction and characterization of microstructure evolution during in-situ heat treatment using a deep learning algorithm

The austenite grain growth plays a significant role in improving the structures and properties of carbon steels. Modeling, characterizing, and predicting the temporal evolution of microstructures is crucial for understanding the relationship of processing-structure-property. Therefore, in this study, we designed a customized in-situ heating device that could be installed in the scanning electron microscope chamber to observe the temporal evolution process of the microstructures of 45# steel. We collected the in-situ experimental images during heating for downstream deep learning-based grain boundary extraction, spatiotemporal sequence characterization, and prediction. The proposed pipeline was fully verified in three experimental datasets under two distinct heating schemes in terms of (1) short-term and long-term prediction, (2) feeding different lengths of input sequence, and (3) transferring to other datasets. The proposed pipeline presented a high performance in quantitative evaluation metrics and qualitative comparisons between the predicted results and the ground truths in all three verification scenarios. These results are inspiring for researchers as they can preview the evolutional state in advance, thereby saving money, labor, and time with the assistance of deep learning-based predictive models. We believe that this pipeline opens opportunities for modeling, characterizing, and predicting evolutional state, which will aid in the analysis of processing-structure-property relationship, tailoring heat treatment, and improving material properties.

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading.

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

Potassium doping for grain boundary passivation and defect suppression enables highly-efficient kesterite solar cells

The complicated and diverse deep defects, voids, and grain boundary in the CZTSSe absorber are the main reasons for carrier recombination and efficiency degradation. The further improvement of the open-circuit voltage and fill factor so as to increase the efficiency of CZTSSe device is urgent. In this work, we obtained K-doped CZTSSe absorber by a simple solution method. The medium-sized K atoms, which combine the advantages of light and heavy alkali metals, are able to enter the grain interior as well as segregate at grain boundary. The K-Se liquid phase can improve the absorber crystallinity. We find that the accumulation of the wide bandgap compound K2Sn2S5 at grain boundary can increase the contact potential difference of grain boundary, form more effective hole barriers, and enhance the charge separation ability. At the same time, K doping passivates the interface as well as bulk defects and suppresses the non-radiative recombination. The improved crystallinity, enhanced charge transport capability and reduced defect density due to K doping result in a significant enhancement of the carrier lifetime, leading to 13.04% device efficiency. This study provides a new idea for simultaneous realization of grain boundary passivation and defect suppression in inorganic kesterite solar cells.

Mathematical modelling and statistical analysis in designing deep learning-based shot boundary detection and aesthetic assessment of videos

The main goal of this study is to create a strong deep learning-based system for finding shot boundaries and judging the quality of movies by combining mathematical models and statistical analysis. Aesthetic evaluation helps people understand visual material better, and shot border recognition is an important part of video analysis. We come up with a new approach that uses convolutional neural networks (CNNs) to improve the accuracy of shot border recognition by extracting features and using mathematical modeling. The model’s performance is improved through statistical analysis, which makes sure that changes between shots are correctly identified. Proposed method also includes judging how something looks by combining deep learning methods with mathematical models that understand how things look. This two-in-one model gives a full picture of video material, which helps with both content selection and improving the user experience. We test our method on a variety of video datasets and show that it works better than other methods. The way our deep learning approach uses mathematical models and statistical analysis together not only raises the bar for finding shot boundaries, but it also helps the new field of judging the aesthetic quality of video material. This study creates a useful and flexible tool for analyzing videos, which opens the door to better uses in fun, content creation, and multimedia. The proposed method is implemented MATLAB and the performances evaluated based on performance metrics such as such as accuracy, precision, sensitivity, specificity and recall. And the accuracy of the proposed system is obtained as 93.12%. And to assure that the proposed system functions effectively in aesthetic assessment of video a contrast is made with the conventional technique such as Hand-Crafted Feature (HCF) technique, Neural Network (NN) and Support Vector Machine (SVM).

A colorful look at climate sensitivity

Abstract. The radiative response to warming and to changing concentrations of CO2 is studied in spectral space. If, at a particular wavenumber, the emission temperature of the constituent controlling the emission to space does not change its emission temperature, as is the case when water vapor adopts a fixed relative humidity in the troposphere or for CO2 emissions in the stratosphere, spectral emissions become independent of surface temperature, giving rise to the idea of spectral masking. This way of thinking allows one to derive simple, physically informative, and surprisingly accurate expressions for the clear-sky radiative forcing, the radiative response to warming, and hence climate sensitivity. Extending these concepts to include the effects of clouds leads to the expectation that (i) clouds dampen the clear-sky response to forcing; (ii) diminutive clouds near the surface, which are often thought to be unimportant, may be effective at enhancing the clear-sky sensitivity over deep moist tropical boundary layers; (iii) even small changes in high clouds over deep moist regions in the tropics make these regions radiatively more responsive to warming than previously believed; and (iv) spectral masking by clouds may contribute substantially to polar amplification. The analysis demonstrates that the net effect of clouds on warming is ambiguous, if not moderating, justifying the assertion that the clear-sky (fixed relative humidity) climate sensitivity – which, after accounting for surface albedo feedbacks, we estimate to be about 3 K – provides a reasonable prior for Bayesian updates accounting for how clouds are distributed, how they might change, and deviations associated with changes in relative humidity with temperature. These effects are best assessed by quantifying the distribution of clouds and water vapor and how they change in temperature rather than geographic space.

A robust high-resolution potential-field edge detection filter with its application in the Indo-China Peninsula

Potential-field edge detection techniques are effective tools for identification of large-scale tectonic boundaries and geologic target bodies. Although a series of edge detectors have been developed, most of them suffer from a trade-off between edge resolution and noise immunity. To this end, we integrate multiple edge detectors such as normalized Harris filter (NHF) and ILTHG filter to propose a novel edge detector called ILGNH. The ILGNH edge detector takes advantage of both the enhanced noise immunity of NHF and the high resolution of ILTHG. Model experiments without/with noises both show that it is an alternative robust high-resolution edge detector. Afterwards, we apply this edge detection method to the Indo-China Peninsula and its adjacent areas to identify multi-phase tectonic boundaries. Combining the edge detection, residual crustal gravity anomalies (RCGA), and active faults, we identify four major NS transverse suture zones associated with microplates. These suture/boundary zones, together with deep faults, enclose 14 microplates with different interior feature of RCGA, while most of the boundaries are located in the gradient zones or the dislocation/distortion zones of the RCGA. Notably, inside the West Burma microplate, the ILGNH edge detection shows a distinct near NS-oriented arc-shaped deep boundary zone, which indicates that the underlying slab beneath the Indo-Burma Ranges was blocked during its eastward advance and shifted downward to form a high-angle deep subduction. Further, multi-scale edge detection at different depths confirms that the microplate boundary under the western Indo-China Peninsula vertically extends deeper than the eastern part. In addition, the seismicity below 50 km is concentrated to the west of the identified arc-shaped deep boundary zone and near the Naga-Kalaymyo-Arakan-Andaman Suture Zone.

Deep-sea response to interglacial-glacial variability on the South Australian margin over the last 94 ka

The continuous record offered by deep-sea sediments has been extensively used to constrain shifting continental and oceanographic conditions. Yet, past fluctuations in deep-sea benthic conditions and bottom-currents are in numerous parts of the globe scarcely documented, one such example being the South Australian margin. Indeed, though variations in surface water masses and continental aeolian dust and river outflow are well documented in the area, little is known about benthic environments and their dynamics during the last interglacial-glacial cycle. We focus here on benthic foraminiferal assemblages sampled from a sediment core recovered at 2420 m depth from a small plateau south of Kangaroo Island within the underwater Murray Canyons Group (South Australian margin). Benthic foraminiferal assemblages show a distinct separation between interglacial and glacial periods over the last 94 ka, and indicate that the benthic environment was well-ventilated and oligotrophic during glacial periods, whilst being rather marked by reduced oxygenation associated to higher food input during the Holocene and Marine Isotope Stage 5a-c. We demonstrate that autochtonous deep-sea benthic foraminiferal communities neither respond to changes in the Murray River's discharges, nor do they follow variations in aeolian dust input from South Australia. Instead, the deep-sea and the terrestrial realm appear decoupled. Moreover, our observations suggest that bottom-water slope currents were stronger during the Holocene and Marine Isotope Stage 5a-c. We propose that this strengthening was triggered by an intensification of the poleward-circulating deep eastern boundary current transporting carbon-rich Indian Deep Water. In contrast, glacial seafloor conditions, especially during the Last Glacial Maximum, may reflect a greater influence and a shoaling of oxygen-rich Antarctic Bottom Water of South Australia. This bottom-water shift would follow the northward displacement of the Subtropical and Subantarctic Fronts and coincide with a withering influence of the Leeuwin Current within surface waters.

High-Temperature corrosion of P91/T91, 304L, Sanicro 28 and Inconel 625 exposed at 600 °C under continuous KCl deposition

This study investigates the corrosion attack after breakaway oxidation on four commercial alloys (T91/P91, 304L, Sanicro 28 and Inconel 625) in the presence of KCl(g)/KCl(s) at 600 °C. The study suggests that an increase in corrosion resistant alloying elements (mainly nickel and chromium) results in the decrease of the thickness of the general oxide scale and not equally deep alloy grain boundary attack for the austenitic alloys. The corrosion attack in the presence of KCl is suggested to proceed by chromate formation and chlorine-induced acceleration of the diffusion of ions through the scale.

Wake Characterization of Building Clusters Immersed in Deep Boundary Layers

Wind tunnel experiments were conducted to understand the effect of building array size (N), aspect ratio (AR), and the spacing between buildings (WS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_S$$\\end{document}) on the mean structure and decay of their wakes. Arrays of size 3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}3, 4×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}4,and 5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}5, AR = 4, 6, and 8, and WS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_S$$\\end{document} = 0.5WB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_B$$\\end{document}, 1WB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_B$$\\end{document}, 2WB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_B$$\\end{document} and 4WB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_B$$\\end{document} (where WB\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_B$$\\end{document} is the building width) were considered. Three different wake regimes behind the building clusters were identified: near-, transition-, and far-wake regimes. The results suggest that the spatial extent of these wake regimes is governed by the overall array width (WA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$W_A$$\\end{document}). The effects of individual buildings are observed to be dominant in the near-wake regime (0<x/WA<0.45\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0<x/W_A< {0.45}$$\\end{document}) where individual wakes appear behind each building. These wakes are observed to merge in the transition-wake region (0.45<x/WA<1.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${0.45}< x/W_A < 1.5$$\\end{document}), forming a combined wake in which the individual contributions are no longer apparent. In the far-wake regime (x/WA>1.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$x/W_A > 1.5$$\\end{document}), clusters’ wakes are akin to those developing downwind of a single isolated building. Accordingly, new local and global scaling parameters in the near- and far-wake regimes are introduced. The decay of the centreline velocity deficit is then modelled as a function of the three parameters considered in the experiment.

Comparison of a spectral wave model with a fully nonlinear potential flow wave model

This study evaluates the performance of the MIKE 21 Spectral Wave (SW) model by comparing the evolution of the spectrum with the fully nonlinear potential flow model OceanWave3D. We test only the shoaling, nonlinear wave–wave interaction and whitecapping terms by considering a two-dimensional shoaling problem on a mild slope bottom without wind, or bottom friction effects and before reaching the breaking zone at the beach. A JONSWAP spectrum is introduced at the deep-water boundary and allowed to propagate freely up to a relatively shallow shelf. The peak wave is in relative water depth kh=4 at the deep water boundary where k=2π/λ is the wave number, λ the wavelength and h the water depth. For the peak relative water depth greater than about kh=0.8, the spectrum agrees well with the fully nonlinear results, but below this value significant differences appear. In particular, the long-wave difference frequency effects are mostly absent, indicating a need for improvement in the SW triad interaction model.

Characteristics of Lake-Effect Precipitation over the Black River Valley and Western Adirondack Mountains

Abstract Potential factors affecting the inland penetration and orographic modulation of lake-effect precipitation east of Lake Ontario include the environmental (lake, land, and atmospheric) conditions, mode of the lake-effect system, and orographic processes associated with flow across the downstream Tug Hill Plateau (herein Tug Hill), Black River valley, and Adirondack Mountains (herein Adirondacks). In this study we use data from the KTYX WSR-88D, ERA5 reanalysis, New York State Mesonet, and Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine how these factors influence lake-effect characteristics with emphasis on the region downstream of Tug Hill. During an eight-cool-season (16 November–15 April) study period (2012/13–2019/20), total radar-estimated precipitation during lake-effect periods increased gradually from Lake Ontario to upper Tug Hill and decreased abruptly where the Tug Hill escarpment drops into the Black River valley. The axis of maximum precipitation shifted poleward across the northern Black River valley and into the northwestern Adirondacks. In the western Adirondacks, the heaviest lake-effect snowfall periods featured strong, near-zonal boundary layer flow, a deep boundary layer, and a single precipitation band aligned along the long-lake axis. Airborne profiling radar observations collected during OWLeS IOP10 revealed precipitation enhancement over Tug Hill, spillover and shadowing in the Black River valley where a resonant lee wave was present, and precipitation invigoration over the western Adirondacks. These results illustrate the orographic modulation of inland-penetrating lake-effect systems downstream of Lake Ontario and the factors favoring heavy snowfall over the western Adirondacks. Significance Statement Inland penetrating lake-effect storms east of Lake Ontario affect remote rural communities, enable a regional winter-sports economy, and contribute to a snowpack that contributes to runoff and flooding during thaws and rain-on-snow events. In this study we illustrate how the region’s three major geographic features—Tug Hill, the Black River valley, and the western Adirondacks—affect the characteristics of lake-effect precipitation, describe the factors contributing to heavy snowfall over the western Adirondacks, and provide an examples of terrain effects in a lake-effect storm observed with a specially instrumented research aircraft.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

Deep learning-based boundary effect filtering for nanometric roughness of diamond-turned micro-structured surface

The functional performance of micro-structured surfaces manufactured by diamond turning is closely related to their nanometric surface roughness. Evaluating the surface roughness is crucial for determining the workpiece’s functionalities. However, conventional filters like wavelet and Gaussian filters can result in a boundary effect that affects the evaluation result when extracting surface roughness from micro-structured surface. To eliminate this boundary effect, a filter that combines deep learning with spectrum analysis for extracting nanometric surface roughness from micro-structured surfaces is proposed. First, the theoretical modeling of the method used for micro-structured surface filtering which contains spectrum analysis and deep learning structure is described in detail. Second, diamond turning experiment based on the designed micro-structured surface is performed for verifying the proposed method. Finally, the nanometric surface roughness without boundary effect is obtained by the proposed method and the filtering result is compared with the one obtained by the traditional filter. The result not only shows that the proposed method can achieve effective extraction for nanometric surface roughness, but also the values of the evaluation parameters are accurate and reliable compared with those obtained by the Gaussian regression filter which has the boundary effect. Significantly, the proposed method could fundamentally eliminate the boundary effect, thus improving the evaluation for nanometric surface roughness of micro-structured surface.

Decadal changes in Atlantic overturning due to the excessive 1990s Labrador Sea convection

Changes in the Atlantic Meridional Overturning Circulation (AMOC) represent a crucial component of Northern Hemisphere climate variability. In modelling studies decadal overturning variability has been attributed to the intensity of deep winter convection in the Labrador Sea. This linkage is challenged by transport observations at sections across the subpolar gyre. Here we report simulations with an eddy-rich ocean model which captures the observed concentration of downwelling in the northeastern Atlantic and the negligible impact of interannual variations in Labrador Sea convection during the last decade. However, the exceptionally cold winters in the Labrador Sea during the first half of the 1990s induced a positive AMOC anomaly of more than 20%, mainly by augmenting the downwelling in the northeastern North Atlantic. The remote effect of excessive Labrador Sea buoyancy forcing is related to rapid spreading of mid-depth density anomalies into the Irminger Sea and their entrainment into the deep boundary current off Greenland.

Volatile organic compounds measured by proton transfer reaction mass spectrometry over the complex terrain of Quintero Bay, Central Chile

This research provides new evidence regarding the different kinds of air quality episodes, and their underlying mechanisms, that frequently impact the urban area of Quintero Bay in Central Chile, which is located along complex coastal terrain and is surrounded by industries. The monitoring campaign was carried out in January 2022 and encompassed two distinctive meteorological regimes. The first part of the month was dominated by a coastal low centered to the south of Quintero, which resulted in prevailing northerly flow (or weak southerlies) and a deep cloud-topped marine boundary layer. After a 2–3-day transition, the latter collapsed, and a clear-sky regime ensued, which was characterized by a shallow boundary layer and strong southerly winds during the daytime that lasted until the end of the campaign.By using proton transfer reaction time of flight mass spectrometry (PTR–TOF–MS) at a high temporal resolution (1 s), we measured high levels of volatile organic compounds (VOCs) during air quality episodes in real time. The episodes detected were associated with different prevailing meteorological regimes, suggesting that different point sources were involved. In the first episode, propene/cyclopropane, butenes, benzene, toluene and ethylbenzene/xylenes were associated with north and northwesterly weak winds. Complaints associated with hydrocarbon odor were reported. The pollution originated from industrial and petrochemical units located to the north of Quintero, which transport and store natural gas, liquified petroleum gas and oil. The second episode was linked to an oil refinery located south of our measurement site. In this case, high levels of phenol, furan and cresols occurred under strong southwesterly winds. During this event, headaches and dizziness were reported. By contrast, the levels of other aromatic compounds (benzene, toluene, ethylbenzene/xylenes) were lower than in the first air pollution episode.