Deep Surface Research Articles

Monitoring upwelling regions in major coastal zones using deep learning and sea surface temperature images

ABSTRACT The coastal region of northwest Africa experiences a persistent and variable upwelling phenomenon throughout most of the year, contributing to the presence of one of the world’s largest and most productive fishing ports. In this study, we introduce Deep Coas t up -Net a convolutional neural network architecture designed for identifying and extracting upwelling regions from satellite sea surface temperature (SST) images. Our model is trained on a dataset of SST images captured along the Moroccan Atlantic coast, utilizing essential input parameters, including SST, latitudinal position ( LA T pos ), and distance from the coastline ( D coast ). By incorporating these physical parameters as input features and training the model with corresponding masks for the Atlantic coast of Morocco, the Deep Coas t up -Net network learns to accurately segment upwelling regions in major coastal zones and demonstrates its generalization capability. We validate our methodology using a quantitative index. The results of this validation showcase the effectiveness of our approach. Subsequently, we explore seasonal and interannual upwelling trends within a 21-year time series, spanning from 2000 to 2020.

Fatigue strength improvement of aluminum alloy with surface defect by ball burnishing

AbstractBall burnishing (BB) is a surface finishing process that involves moving the tool while pressing it against the workpiece under rolling contact. We investigated the effects of BB on the fatigue strength of an aluminum alloy with a semicircular slit simulating an initial surface defect. After performing BB under a typical condition, compressive residual stress was induced to a depth of 0.4 mm, with a reduction in surface roughness by 81%. Consequently, the fatigue strength increased by 22%. BB prevented the fatigue strength reduction caused by the 0.1 mm deep slit. The result indicated that BB could render the below 0.1 mm deep surface defect harmless in terms of fatigue strength. The maximum depth of surface defect that can be rendered harmless is 0.18 mm according to fracture mechanics, which is in agreement with the experimental results.

Enhancing subgingival margin restoration: a comprehensive review and meta-analysis of deep margin elevation's impact on microleakage.

Restorative dentistry faces complex challenges with deep proximal surface destruction, requiring novel approaches like DME (Deep Margin Elevation). In order to achieve the best results while treating severe tooth damage, this study examines the advantages, disadvantages, and possible collaborations of different treatments. This systematic review investigates the efficacy of DME as an adjunctive procedure in restorative dentistry, specifically focusing on its impact on microleakage. The study adheres to PRISMA guidelines and employs the PICOS framework for eligibility criteria. 394 potentially qualifying studies were discovered andthorough literature search was carried out via databases. After applying inclusion criteria, 7 studies were included in the analysis. Articles were selected based on criteria that included indirect restoration and performing DME and were compared with indirect restorations without DME. Composite resin was used for DME. Other materials for DME performing, including GI and composite flow, were systematically reviewed. Data analysis was done by biostat software (α = 0.05). The meta-analysis of selected studies reveals a statistically significant positive effect of DME on reduction of microleakage (p = 0.001). The results of this study underscore the potential of DME in addressing subgingival margin challenges and provide valuable insights for restorative dentistry practices.

Finite Element Analysis based Stress Intensity Factor Solutions for Surface Cracks

The stress intensity concept is important in termsof crack extension as critical values of the stress intensityfactors govern crack initiation. Therefore, the present workdetermines stress intensity factors for semielliptical shallowand deep surface cracks as a function of parametric angle,crack depth, and aspect ratio for tension and bending loads.The stress intensity factors are obtained from a threedimensional finite-element analysis of semielliptical surfacecracks in finite plates subjected independently to tensionand bending loads under elastic conditions. The obtainedstress intensity factor is used to predict the crack growthunder linear elastic conditions. Results show that the stressintensity factor varies along the crack front for shallow anddeep cracks. At the deepest point in bending and tension thestress intensity factor increases as the ratio of the crackdepth to the crack length at surface decreases. However, atthe free surface the stress intensity factor becomesmaximum when the crack depth equals crack length. Asurface crack in tension loading is predicted to break thewall thickness with a relatively small amount of crackgrowth at surface. While in bending the crack breaksthrough with large amount of crack growth in the widthdirection.

A CNN-based spline active surface method with an after-balancing step for 3D medical image segmentation

Over the past few years, tasks such as automatic information extraction and object surface detection from 3D medical images have become increasingly popular, providing clinicians with numerous new opportunities for exploration and diagnostic assistance. Nonetheless, the segmentation process can present various challenges. This paper introduces an automatic 3D deep parametric active surface model (3D B-snake) approximated by bivariate Unified and Extended spline (UE-spline) functions with a parameter controlling the shape of the snake. We introduce an energy term that enables to separate the different textures present in an image using a Convolutional Neural Network (CNN) learning model, namely U-Net. After balancing our snake surface to align it with the complex parts of the object to be segmented, an object snake alignment strategy is proposed using an interpolation scheme that exploits some geometric properties of the object. Experiments performed on the BraTS21 dataset show that our model is very powerful to deal with various difficulties encountered in medical image segmentation.

A new online detection method of tobacco impurities for tobacco robot.

In the tobacco industry, impurity detection is an important prerequisite for ensuring the quality of tobacco. However, in the actual production process, the complex background environment and the variability of impurity shapes can affect the accuracy of impurity detection by tobacco robots, which leads to a decrease in product quality and an increase in health risks. To address this problem, we propose a new online detection method of tobacco impurities for tobacco robot. Firstly, a BCFormer attention mechanism module is designed to effectively mitigate the interference of irrelevant information in the image and improve the network's ability to identify regions of interest. Secondly, a Dual Feature Aggregation (DFA) module is designed and added to Neck to improve the accuracy of tobacco impurities detection by augmenting the fused feature maps with deep semantic and surface location data. Finally, to address the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes, this paper proposes an optimized loss function to more accurately assess the quality of the bounding boxes. To evaluate the effectiveness of the algorithm, this paper creates a dataset specifically designed to detect tobacco impurities. Experimental results show that the algorithm performs well in identifying tobacco impurities. Our algorithm improved the mAP value by about 3.01% compared to the traditional YOLOX method. The real-time processing efficiency of the model is as high as 41 frames per second, which makes it ideal for automated inspection of tobacco production lines and effectively solves the problem of tobacco impurity detection.

Biomechanics of Bruxism Potentially Determine the Sites of Severe TMJ Osteoarthritis

The objective of this study was to assess the osteoarthritis (OA) disease severity in 47 temporomandibular joints (TMJs) using a validated scale for gross signs of OA while noting the specific sites for profound disease on the donor condyle and fossa. A disease severity score of Grade 0–4, representing absent to severe disease, was awarded to each specimen’s condyle and fossa by two blinded investigators who have demonstrated interrater reliability. The mandibular fossa was more pathological compared to the mandibular condyle (* p = 0.001). When the deepest focal lesions were qualitatively assessed, it was demonstrated that the mandibular fossa was more severely degenerated than the articular eminence in 58% of donors. In this subpopulation, 74% of the severe mandibular fossa pathology was seen on the deep articular surface. When the articular eminence was the most severely degenerated region of the fossa, it was equivalently likely to see severe focal lesions on the lateral eminence (35%) or equally distributed across the entire eminence (35%). The greatest disease severity was discovered in sites of overloading, which may be associated with paranormal mandibular movements and potentially bruxism. Patients with bruxism produce significant translational movements (grinding) in the upper joint compartment and heavy vertical loading (clenching). Theoretically, this amplifies pressure and inflammation on the lateral articular surfaces and in the deep fossa.

Accuracy of Smartphone-Based Three-Dimensional Facial Scanning System: A Systematic Review.

Recently, the integration of 3D face scanning into smartphones has raised vast interest in plastic surgery. With the release of smartphones featuring 3D face scanning technology, users now can capture detailed 3D models of their faces using their smartphones. However, trueness and precision of this system is less well established. PubMed, Cochrane Library, Embase, ScienceDirect, Scopus, and Web of Science databases were searched for studies evaluating 3D scanning of smartphone devices and conventional 3D imaging systems from January 1, 2017, to June 1, 2023. A qualitative systematic review was conducted by two review authors after independently selecting studies, extracting data, and assessing the risk of bias of included studies. A total of 11 studies were included, all focusing on the accuracy of smartphone 3D facial scanning. The results show that although smartphones perform poorly on deep and irregular surfaces, they are accurate enough for clinical applications and have the advantage of being economical and portable. Smartphone-based 3D facial scanning has been basically validated for clinical application, showing broad clinical application prospects in plastic surgery. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

Research on bearing capacity and stability of deep-water novel combined wellhead based on experimental methods

The combined wellhead has the advantage of high bearing capacity and broad prospects for application in the construction of deep water surface wells; therefore, it is important to conduct relevant research to guide the construction of combined wellheads. The aim of this paper is to study the mechanical characteristics of the combined wellhead in the longitudinal direction and establish the method for real-time calculation of the bearing capacity of the combined wellhead taking into account the effects of installation. The variations of external wall pressure and internal soil pressure during installation and the waiting process of the combined wellhead were investigated by indoor experiments. The installation efficiency of the combined wellhead at different negative pressure conditions was also compared. The experimental results show that the pressure on the outer wall of the combined wellhead facility gradually increases as the depth of the combined wellhead facility increases, and the closer it is to the bottom, the greater the soil pressure. The pressure on the inner soil will increase rapidly during the installation process to almost the same level, while the pressure on the outer soil will increase slightly. During the waiting process of the combined wellhead, as the waiting time increases, the soil damaged during the installation process gradually recovers, and the external wall pressure gradually rises. Moreover, the pressure on the outer wall gradually increases, the pressure on the inner soil decreases slightly leveling off within about 24 h. An appropriate increase in suction pressure facilitates the installation of the combined wellhead, and approximately 2.0 L/min is the optimal option for installation. The computational model developed in this paper and the experimental measurements corroborate each other. These combined wellheads significantly improve the stability of deep water soft-soil well construction and provide a theoretical benchmark for developing oil, gas and hydration in deep water.

Deep learning of left atrial structure and function provides link to atrial fibrillation risk

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.

Current Advances in Artificial Intelligence in the Field of Aesthetic Surgery and Breast Augmentation: Short Review

Study design: This review is structured as a systematic analysis of the literature to evaluate the impacts of artificial intelligence (AI) on the field of plastic surgery, with a focus on breast augmentation and aesthetic surgical procedures. Key areas of exploration include advancements in machine-learning techniques relevant to plastic surgery, the integration of AI into preoperative planning processes, and the historical evolution of AI in aesthetic surgery. The review also systematically assesses current AI tools specifically developed for breast augmentation, such as 3D imaging and predictive analytics, to understand their efficacy and role in clinical practice. Objective: This article explores the current state and future prospects of AI in plastic surgery, with a focus on breast augmentation and aesthetic procedures. Emphasis is placed on the benefits, challenges, and the imperative for collaborative efforts in the integration of AI technologies. Methods: Advancements in machine-learning algorithms and AI technologies are examined for their potential in automating the assessment and enhancement of surgical skills. The role of AI in facilitating objective evaluations in aesthetic surgery is discussed, addressing challenges such as the lack of standardized training datasets and integration issues. The importance of mitigating potential biases introduced by AI to ensure objectivity in patient assessments is highlighted. Results: The article discusses the historical evolution of AI, from Alan Turing’s conceptualization to contemporary applications in aesthetic surgery. Artificial intelligence’s ability to analyze vast patient datasets is explored, showcasing its potential for offering personalized treatment recommendations and improving accuracy over time. Specific AI tools for breast augmentation, including Canfield Mirror, QuantifiCare LifeViz Infinity Pro, Crisalix, BreastGAN, Arbrea Breast Software (ABS), and Deep Surface AI, are examined in detail, emphasizing their advantages and drawbacks. Conclusions: Evaluation of clinical photography techniques, relying on specific hardware, is presented, with consideration given to the potential of AI-based illumination systems to enhance consistency in preoperative images. The review concludes by envisioning the transformative future of AI in aesthetic surgery, considering its untapped potential in diagnostic imaging, personalized treatment plans, and enhanced surgical precision through integration with virtual and augmented reality. Despite challenges, the promises of AI in personalized treatments, precise patient care, and improved surgical assistance suggest a transformative future for plastic surgery, contingent on addressing current concerns. Collaborative efforts are deemed essential for the successful implementation of AI technologies in the field.

Michel Serres’s Sensorial Philosophy and the Importance of Skin in Francis Bacon’s Nudes

Prompted by the exhibition Francis Bacon: Man and Beast (January–April 2022), this article takes a cultural studies and historical poetics approach to the significance of skin in Bacon’s nudes. Drawing upon Michel Serres’s philosophy and the work of prominent figures within skin studies, this article argues that Bacon’s paintings intuitively embody the significance of skin as a dynamic site/sight of sensory, lived, experience shaped by socio-historical context. Bacon’s nudes are considered in relation to the dermalogical turn; the topological body; Didier Anzieu’s skin ego and skin as our ‘deepest surface’; and the importance of movement in Bacon’s imagery, which correlates with Serres’s notion that learning occurs through bodily motion. Serres recognises that cultural works – including painting and other creative practices – can convey complex multiplicities of knowledge synthesised into new and distinct forms.

First identification of Mid-Miocene north–south trending dikes in the eastern Qiangtang terrane, eastern Tibet: Mantle melting and implications for plateau uplift

The uplift of the Tibetan Plateau during the Miocene is crucial to understanding continental deformation processes and global climatic events. However, the eastern Tibetan Plateau remains poorly investigated. Mantle-derived magmatism provides crucial insights into the deep dynamic processes and surface uplift of the plateau. In this paper we report the first discovery of north–south trending lamprophyre dikes from the Aduo Basin in the eastern Qiangtang terrane, eastern Tibet. Our new age data show that these lamprophyre dikes were generated in Mid-Miocene (15–13 Ma). This new discovery has bridged the Mid-Miocene mantle-derived magmatic gap in the Qiangtang terrane. Trace element and Nd–Sr isotopic data indicate that they were derived by partial melting of enriched subcontinental lithospheric mantle. Our study implies that the eastern Tibetan Plateau had its attained near-maximum elevation by the Mid-Miocene. Combined with previous research results, we propose that the entire plateau almost simultaneously reached its near-maximum elevation by the Mid-Miocene.

DL-RSM: Deep learning-integrated Response Surface Methodology for positive and negative-ideal environmental conditions estimation for crop yield

Understanding the most suitable and unsuitable environmental scenarios for crop production is critical to ensuring food security and sustainability. In this direction, the presented work proposes a novel approach, DL-RSM, to identify the optimized levels of environmental factors for wheat yield by integrating the Deep Learning technique with Response Surface Methodology (RSM). The uniqueness of this approach lies in its utilization of a 1-Dimensional Convolutional Neural Network model for response surface modelling. Additionally, the study has investigated the combination of numerical methods-based partial derivative calculation with a deep learning model. Specifically, our approach incorporates a symmetric difference quotient-based method for calculating partial derivatives, ultimately leading to the determination of optimal parameter values. Besides, multiple positive and negative ideal solutions are obtained from the modelled response surface using a modified gradient-based method. The effectiveness of our approach is illustrated through a case study on finding the optimized parameter values for the district-wise wheat yields of the Indo-Gangetic plains of India. The study results demonstrate the combinations of monthly rainfall levels and min. and max. temperature variations correspond to maximizing and minimizing wheat yield in the target region. The presented results provide significant insights into the relationship between wheat yields and the considered environmental factors that have not been sufficiently covered in the existing literature. The proposed methodology can benefit agricultural scientists, policymakers and farmers by offering an innovative approach to maximize crop yield and minimize resource utilization. Moreover, the study findings provide novel insights into the development of similar optimization strategies for other crops and systems.

Strain-Based damage model for ductile tearing simulation under combined tensile and shear modes

This paper proposes a strain-based damage model to simulate ductile tearing under combined tensile and shear modes. For tensile mode, the multiaxial fracture strain model depending only on the stress triaxiality is used, determined by analysing smooth bar tensile test and fracture toughness test data. For shear mode, a simple constant fracture strain model is used, determined by analysing smooth bar torsion test and torsion fracture test using a deep surface cracked cylinder specimen. The proposed damage model is applied to simulate circumferential surface cracked pipe made of SUS 304 under combined bending and torsion. Comparison with full-scale surface cracked pipe test data suggests that the use of the combined tensile and shear damage model with the linear interaction rule gives good and conservative prediction results.

Revising palaeoelevation estimates for the Tibetan Plateau by integrating latitude, paleogeographic and paleotemperature data

Quantifying the palaeoelevation of the Tibetan Plateau (TP) is important for investigations of deep earth dynamics, surface uplift, and climate evolution. In particular, high precision palaeogeographic data can provide a more comprehensive understanding of the processes and mechanisms of TP uplift and can help refine palaeoclimate simulations. As a whole, there are complex physical and chemical interactions between the six concentric layers of the Earth. In this study, we used published palaeogeographic and palaeotemperature data to revise the evolution of the TP elevation for six time intervals of the Cenozoic according to the time nodes of key changes in each layer. We then revised the palaeoelevations using palaeotemperature data and linear and polynomial regression models. Our revised palaeoelevation results solve the problems of the low resolution of palaeotemperature data and the low accuracy of palaeogeographic data, and they enable us to fine tune the palaeoelevation data for the studied intervals. Our findings contribute to reconstructions of the palaeogeomorphology and palaeoelevation of the TP and provide supporting data for future studies of the TP system on long timescales.

Advancement of Sea Surface Convective Wind Gust Observation by Different Satellite Sensors and Assessment with In Situ Measurements

This paper shows the observation and estimation of convective wind gusts by different satellite sensors at the C-band (Sentinel-1 SAR) and L-band (ALOS-1 SAR and SMAP radiometer) over Lake Victoria, the Gulf of Guinea, and the Gulf of Mexico. These areas are significantly impacted by deep convection associated with strong surface winds and heavy rainfall. In particular, the collocation of Sentinel-1 and SMAP images enables the observation of the movement of surface wind gusts corresponding to that of deep convective clouds. The convective wind intensity estimated from Sentinel-1 and SMAP data varies from 10 to 25 m/s. Additionally, we present an agreement in the observation of deep convective clouds, dynamics, and strong surface winds by different satellite sensors, including Meteosat geostationary (GEO), Aeolus Lidar, and Sentinel-1 SAR, respectively. We also evaluate the estimated convective wind gusts by comparison with the in situ measurements of the weather stations installed in the Gulf of Mexico, where deep convection occurs regularly. The result shows an agreement between the two wind sources estimated and measured. Likewise, the peaks of the measured wind gusts correspond to the occurrence of deep convective clouds observed by the GOES-16 GEO satellite.

In vitro and in vivo evaluation of periosteum-derived cells and iPSC-derived chondrocytes encapsulated in GelMA for osteochondral tissue engineering.

Osteochondral defects are deep joint surface lesions that affect the articular cartilage and the underlying subchondral bone. In the current study, a tissue engineering approach encompassing individual cells encapsulated in a biocompatible hydrogel is explored in vitro and in vivo. Cell-laden hydrogels containing either human periosteum-derived progenitor cells (PDCs) or human induced pluripotent stem cell (iPSC)-derived chondrocytes encapsulated in gelatin methacryloyl (GelMA) were evaluated for their potential to regenerate the subchondral mineralized bone and the articular cartilage on the joint surface, respectively. PDCs are easily isolated and expanded progenitor cells that are capable of generating mineralized cartilage and bone tissue in vivo via endochondral ossification. iPSC-derived chondrocytes are an unlimited source of stable and highly metabolically active chondrocytes. Cell-laden hydrogel constructs were cultured for up to 28 days in a serum-free chemically defined chondrogenic medium. On day 1 and day 21 of the differentiation period, the cell-laden constructs were implanted subcutaneously in nude mice to evaluate ectopic tissue formation 4 weeks post-implantation. Taken together, the data suggest that iPSC-derived chondrocytes encapsulated in GelMA can generate hyaline cartilage-like tissue constructs with different levels of maturity, while using periosteum-derived cells in the same construct type generates mineralized tissue and cortical bone in vivo. Therefore, the aforementioned cell-laden hydrogels can be an important part of a multi-component strategy for the manufacturing of an osteochondral implant.

Robust sulfur and oxygen isotope evidence for a highly anoxic paleoenvironment in Late Devonian seawater: Insights from marine anhydrites in the Zaige Formation, South China

A global-scale mass extinction event occurred in the Late Devonian, and its triggering mechanisms have remained a subject of controversy. The key to resolving this controversy lies in elucidating paleoenvironmental characteristics and their coupling relationship with the mass extinction. Marine evaporites serve as sensitive indicators that can be used for reconstructing the geochemical history of ancient oceans. In this study, we present a dual sulfur and oxygen isotopic dataset of marine anhydrites in the Lunatian gypsum deposit (Yunnan, China), and investigate its paleoenvironmental significance within the framework of the Late Devonian mass extinction. These anhydrites originate in arid and hot continental margin seas near convergent plate boundaries, forming as a result of the dehydration of marine gypsums during burial processes. Anhydrites exhibit a conspicuous positive anomaly in δ34S values (23.6–25.1‰, averaging 24.3‰), primarily attributed to bacteria-mediated sulfate reduction. The δ18O values of the Lunatian anhydrites (12.2–15.2‰, averaging 14.0‰) indicate a slightly higher oxygen isotope composition for Late Devonian seawater (8.7–11.7‰, averaging 10.5‰) compared to those of contemporary seawater (9.5–10.1‰). Such an oxygen isotopic characteristic for the Lunatian anhydrites is intricately associated with the chemical processes that occur during the diffusion of sulfides into the upper seawater. The anhydrite-forming evaporating system has a noticeable brine layering where the surface seawater is less dense than the deeper brine, and bacterial processes gradually increase δ34S in the deep brine. When the deep brine and surface seawater rapidly mix, it leads to a significant positive shift in the sulfur isotope composition of the resulting evaporites. Our sulfur and oxygen isotope data from Lunatian anhydrites validate the existence of an exceedingly anoxic marine environment during the Late Devonian, potentially serving as a pivotal factor in the mass extinction event.