Synthetic Surfaces Research Articles

The Effect of a Synthetic-Grass Sport Surface on Physiology and Perception During Intermittent Exercise in Hot Conditions.

The current study aimed to determine the effect of a synthetic-grass sport surface on core body temperature, skin temperature, heart rate, thermal sensation, thermal comfort, and rating of perceived exertion (RPE)during intermittent exercise in hot conditions. Using a randomized crossover design, 13 trained/developmental team-sport athletes completed two 50-minute standardized intermittent running protocols on a synthetic and anatural-grass surface, on separate days (control-condition air temperature 32.6 °C [1.3 °C], relative humidity 43.2% [5.3%]). Final skin temperature was significantly higher on synthetic compared with natural grass at the calf (40.1 °C [2.5 °C] vs 33.4 °C [0.6 °C]; P < .001), shoulder (36.6 °C [1.7 °C] vs 33.7 °C [0.7 °C]; P < .001), and chest (33.2 °C [1.1 °C] vs 31.8 °C [1.2 °C]; P = .02). Thermal sensation (median: 2.3; interquartile range [0.5] vs 2.2 [0.5], P = .03) and sweat rate (1.5 [0.4]L·h-1 vs 1.2 [0.3]L·h-1; P = .02) were also significantly higher on synthetic grass. While final core body temperature was significantly higher on the natural than synthetic grass (38.4 °C [0.3 °C] vs 38.2 °C [0.4 °C]),there were no significant differences in delta core temperature, as well as heart rate, thermal comfort, or RPE. Higher skin temperatures, thermal sensation, and sweat rates suggest that exercising on synthetic grass in hot conditions may increase some markers of heat strain during exercise. However, delta core body temperature, heart rate, thermal comfort, and RPE remained unaffected.

A Robust Method for Selecting a High‐Quality Interferogram Subset in InSAR Surface Deformation Analysis

AbstractThe accuracy of Interferometric Synthetic Aperture Radar surface deformation solutions depends on the quality of the chosen interferogram subset. We present a method to select interferogram subsets based on unwrapping errors rather than temporal baseline thresholds. Using Sentinel‐1 interferograms over the Tulare Basin, California, we show that subtle phase noise can lead to up to 31.5 mm/yr line‐of‐sight (LOS) errors in short temporal baseline subset solutions, while decorrelation leads to a systematic underestimation of LOS rates (up to 92.3 mm/yr) in long temporal baseline subset solutions. Our new workflow better mitigates these noise sources at the same time. In the Eagle Ford region, Texas, our strategy better reconstructed up to 11.9 cm of cumulative LOS deformation between 2017 and 2021 over a 900 region. This deformation feature can be linked to the total volume of produced oil and water through a linear relationship.

Synthesis of engineering surfaces using representative elementary patterns of roughness

Synthesis of surface roughness is a long-standing problem that has many practical applications. Here novel algorithms for the synthesis of rough surfaces at nano/micro scales are proposed. The algorithms are based on introduction and development of two new concepts, namely the representative elementary pattern of roughness (REPR) and the statistically representative pattern of surface roughness (SRPSR). From the statistical point of view, the REPR is the smallest interval (or area) over which a measurement can be made that represents statistically the whole surface. However, synthesis of surfaces by the direct use of the REPR may cause some artificial singularities. To avoid this drawback and to incorporate the synthetic surface in a numerical scheme of the contact solver, one needs to extend the REPR to a non-singular SRPSR that satisfies additional conditions of the scheme used. Our findings indicate that specific time-series analysis techniques, such as the moving window approach, can be effectively utilized to extract the REPR from experimental data. The representativeness may be justified by the use of the Kolmogorov–Smirnov statistic. Extraction of REPRs of surfaces and constructions of appropriate SRPSRs are demonstrated on experimental data obtained by stylus and atomic-force microscopy at micro and atomic/nano scales, respectively.

Optimization of Machining Parameters for Nimonic PE16 Using Machine Learning Models

Machining high-temperature alloys such as Nimonic PE16 demands precise control of machining parameters to achieve desired outcomes while minimizing tool wear and optimizing surface finish. In this study, we propose using machine learning regression models combined with synthetic data and response surface methodology strategies to optimize machining parameters for PE16. We aim to develop a predictive model that accurately estimates optimal cutting speeds and feed rates based on key output parameters, including cutting forces and surface roughness. Our methodology involves collecting experimental data from controlled machining tests conducted on PE16 samples under varying conditions. We used the datasets to train and validate regression models to establish correlations between input parameters and machining outcomes. The performance of each model is evaluated based on metrics such as mean absolute error and coefficient of determination. These metrics show relationships within the data and can determine a model’s success. The proposed machine learning framework offers a data-driven approach to optimize machining processes for PE16, facilitating enhanced efficiency, productivity, and quality in nuclear and other high-performance applications. Our findings contribute to understanding machining dynamics in challenging materials and provide valuable insights for intelligent machining systems.

Legendre Polynomial Fitting-Based Permutation Entropy Offers New Insights into the Influence of Fatigue on Surface Electromyography (sEMG) Signal Complexity.

In a recently published work, we introduced local Legendre polynomial fitting-based permutation entropy (LPPE) as a new complexity measure for quantifying disorder or randomness in time series. LPPE benefits from the ordinal pattern (OP) concept and incorporates a natural, aliasing-free multiscaling effect by design. The current work extends our previous study by investigating LPPE's capability to assess fatigue levels using both synthetic and real surface electromyography (sEMG) signals. Real sEMG signals were recorded during biceps brachii fatiguing exercise maintained at 70% of maximal voluntary contraction (MVC) until exhaustion and were divided into four consecutive temporal segments reflecting sequential stages of exhaustion. As fatigue levels rise, LPPE values can increase or decrease significantly depending on the selection of embedding dimensions. Our analysis reveals two key insights. First, using LPPE with limited embedding dimensions shows consistency with the literature. Specifically, fatigue induces a decrease in sEMG complexity measures. This observation is supported by a comparison with the existing multiscale permutation entropy (MPE) variant, that is, the refined composite downsampling (rcDPE). Second, given a fixed OP length, higher embedding dimensions increase LPPE's sensitivity to low-frequency components, which are notably present under fatigue conditions. Consequently, specific higher embedding dimensions appear to enhance the discrimination of fatigue levels. Thus, LPPE, as the only MPE variant that allows a practical exploration of higher embedding dimensions, offers a new perspective on fatigue's impact on sEMG complexity, complementing existing MPE approaches.

Surface pressure reconstruction from LPT data with boundary conforming grids

Abstract The coupling between fluid-structure interactions is governed by the pressure distribution over the interaction surface between the fluid and solid domains. The capabilities of non-intrusive optical techniques, such as particle image velocimetry and Lagrangian particle tracking (LPT), have been proven to provide accurate velocity and acceleration information within the flow field while simultaneously tracking the corresponding structural deformations. However, scattered data from LPT measurements are typically mapped onto Cartesian grids, independently of the shape of the solid objects in the measurement domain. The use of Cartesian grids poses challenges for the determination of the surface pressure because the velocity gradients close to the object’s surface are not captured accurately. Therefore, an alternative surface pressure reconstruction scheme utilizing LPT data based on the arbitrary Lagrangian–Eulerian approach is proposed to mitigate the error propagation associated with the use of uniform grids. The introduced method provides an exact surface conformation utilizing boundary fitted coordinate systems and radial basis function based mesh deformations, which eliminates the need to use extrapolations to obtain surface pressure distributions. The introduced approach is assessed by means of a synthetic hill surface probing a three-dimensional analytical flow field; its practical applicability is demonstrated through an experimental characterization of turbulent boundary layer interactions with a steadily and unsteadily deforming elastic membrane.

Assessment of suitability of the Dniester River's water for drinking water supply

The Dniester is a transboundary river between Ukraine and the Republic of Moldova. It is the second longest river in Ukraine and the ninth in Europe. From its source to a town of Staryi Sambir, the Dniester flows through the Carpathian Mountains, then across the flat territory of Ukraine and Moldova. The Dniester Basin extends across seven regions in southwestern Ukraine (Lviv, Ivano-Frankivsk, Ternopil, Khmelnytskyi, Vinnytsia, Chernivtsi and Odesa Regions). A small area of the river in its upper reaches is located in Poland. The basin's shape is a very elongated oval that is curved in the middle, about 700 km long with an average width of 120 km. As it flows into the Black Sea, the Dniester River forms the Dniester estuary in Odesa region (between Ovidiopol and Bilhorod-Dnistrovskyi districts). The estuary area of the Dniester River is a complex landscape system at the sea-river-land contact. Drinking water is an important factor determining a person's health. Due to the intensive development of industry, agriculture and utility services, the number of pollutants that enter the environment and worsen the quality of drinking water in Ukraine keeps increasing. This increases the risk of toxic and infectious diseases spread among the population that consumes such low-quality water. That is why solving the problem of drinking water monitoring and assessment of its quality is a pressing task requiring urgent action. The article examines the quality of water for drinking needs at the water intake site of the Dniester River, a town of Biliaivka, for 2018-2022. Water quality assessment was carried out according to DSTU 4808: 2007 based on average and worst values. The methodology covers 80 indicators and, in accordance with sanitary legislation, is divided into seven separate groups (blocks). According to hygienic standards, the quality of river water is considered suitable for use if it belongs to quality classes 1-3. In this research, the analysis was carried out according to 23 indicators included in three groups, so the assessment is considered as tentative: Group I - organoleptic indicators (chromaticity, turbidity); Group II - general sanitary indicators (dry residue (mineralization), sulfates, chlorides, magnesium, alkalinity, hydrogen index, ammonium nitrogen, nitrite nitrogen, nitrate nitrogen, phosphate phosphorus, dissolved oxygen, bichromate oxidation (COD), BSK5); Group VII – toxicological parameters (total iron, manganese, chromium (VI), zinc, petroleum products, synthetic surface active agents (surfactants), volatile phenols). It was determined that, according to the average values, the water quality of the Dniester at the water intake site is characterized as "good", which means it has clean water of acceptable quality. As for the worst indicators, the water was assessed as transitional in quality from "good" (clean) to "satisfactory" (slightly polluted).

Stochastic geometry models for texture synthesis of machined metallic surfaces: sandblasting and milling

Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.

Electrofiltration-Enabled Nitrate Reduction Reactivity of Carbon Nanotube Electrified Membrane for Drinking Water Treatment

Electrocatalytic nitrate reduction could enable nitrate removal in drinking water without generating concentrated waste streams. However, current processes heavily rely upon metal catalysts with high activity to achieve sufficient nitrate removal, which increases treatment costs and can inevitably lead to leaching of metals into treated water. In this study, we elucidate how electrified filtration enables sufficient nitrate conversion to meet drinking water standards in metal-free defective CNTs by tunable matching of mass transport and reaction timescales. The metal-free carbon nanotube (CNT)-based electrified membrane (EM) was employed as a porous flow-through cathode to decrease the diffusion boundary layer. Computational fluid dynamics simulations revealed that the flow-through mode mitigates diffusion limitations to enhance overall reaction activity, as compared to the conventional flow-by mode at the same flow rate. Additionally, the nitrate reduction rate could be tuned by controlling the nitrate advection rate using permeate flux (0 to 60 L h-1 m-2). A maximum removal efficiency of 86.9% was reached when the mass transport rate matched the reaction rate across a range of applied potentials. Furthermore, defects in CNTs were identified as the catalytic active sites. We employed density functional theory and molecular dynamics simulations to gain insight into CNT defect-catalyzed nitrate reduction under electrofiltration. Finally, the long-term stability, tolerance of environmental interferences, and sufficient nitrate removal and N2 selectivity to meet drinking water standards were demonstrated in synthetic surface water, suggesting that the CNT-EM and flow-through reactor may provide a promising solution for decentralized nitrate destruction in drinking water.

Durable, Photostable Omniphobic Synthetic Leather Surfaces with Anti-Biofouling Properties for Hygienic Applications.

Globally, the public health domain is increasingly emphasizing the need for surfaces that can resist bacterial contamination, as the consumption of bacteria-infected substance may cause illnesses. Thus, this study aimed to modify polyurethane (PU) synthetic leather surfaces by coating their upper layer with fluorine-functionalized nano-silica particles (FNPs). This simple modification imparted omniphobic characteristics, realizing anti-biofouling and self-cleaning properties. The effectiveness in preventing bacterial adhesion was confirmed by the dip-inoculation method using Escherichia coli O157:H7 and Staphylococcus epidermidis. Bacterial adhesion was evaluated based on bacterial counts using the pour plate method and by directly enumerating from scanning electron microscopy images. The attachment of bacteria to the modified omniphobic FNPs-coated PU leather surface decreased by over 98.2% compared to that on the bare surface. We expect that the method developed in this study will significantly reduce or even eliminate the potential risks associated with various biological cross-contamination scenarios, thereby enhancing hygiene standards.

Antimicrobial Polymer Surfaces Containing Quaternary Ammonium Centers (QACs): Synthesis and Mechanism of Action.

Synthetic polymer surfaces provide an excellent opportunity for developing materials with inherent antimicrobial and/or biocidal activity, therefore representing an answer to the increasing demand for antimicrobial active medical devices. So far, biologists and material scientists have identified a few features of bacterial cells that can be strategically exploited to make polymers inherently antimicrobial. One of these is represented by the introduction of cationic charges that act by killing or deactivating bacteria by interaction with the negatively charged parts of their cell envelope (lipopolysaccharides, peptidoglycan, and membrane lipids). Among the possible cationic functionalities, the antimicrobial activity of polymers with quaternary ammonium centers (QACs) has been widely used for both soluble macromolecules and non-soluble materials. Unfortunately, most information is still unknown on the biological mechanism of action of QACs, a fundamental requirement for designing polymers with higher antimicrobial efficiency and possibly very low toxicity. This mini-review focuses on surfaces based on synthetic polymers with inherently antimicrobial activity due to QACs. It will discuss their synthesis, their antimicrobial activity, and studies carried out so far on their mechanism of action.

Accuracy compensation method with the combined dissimilar measurement devices for enhanced measurement quality: a digital aircraft assembly technology

PurposeThe pivotal aspect of aircraft assembly lies in precise measurement accuracy. While a solitary digital measuring tool suffices for analytical and small surfaces, it falls short for extensive synthetic surfaces like aircraft fuselage panels and wing spars. The purpose of this study is to develop a “combined measurement method” (CMM) that enhances measurement quality and expands the evaluative scope, addressing the limitations posed by singular digital devices in meeting measurement requirements across various aircraft components.Design/methodology/approachThe study illustrated the utilization of the CMM by combining a laser tracker and a portable arm-measuring machine. This innovative approach is tailored to address the intricate nature and substantial dimensions of aircraft fuselage panels. The portable arm-measuring machine performs precise scans of panel components, while common points recorded by the laser tracker undergo coordinate conversion to reconstruct the fuselage panel’s shape. The research outlines the CMM’s measurement procedure and scrutinizes the data processing technique. Ultimately, the investigation yields a deviation vector matrix and chromatogram deviation distribution, pivotal in achieving enhanced measurement precision for the novel CMM device.FindingsThe use of CMM noticeably enhances fuselage panel assembly accuracy, concurrently reducing assembly time and enhancing efficiency compared to conventional measurement systems.Practical implicationsThe research’s practical implication lies in revolutionizing aircraft assembly by mitigating accuracy issues through the innovative digital CMM for aircraft synthetic structure type product (aircraft fuselage panel). This ensures safer flights, reduces rework and enhances overall efficiency in the aerospace industry.Originality/valueIntroducing a new aircraft assembly accuracy compensation method through digital combined measurement, pioneering improved assembly precision. Also, it enhances aerospace assembly quality, safety and efficiency, offering innovative insights for optimized aviation manufacturing processes.

Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico

AbstractThe detection and quantification of strong sea surface winds, reaching up to 25 m·s−1, whether associated with deep convection aloft or not, have been extensively discussed in previous studies. This method involves the combined observation of the same event from both low‐orbit altitude and geostationary (GEO) satellites. Strong surface winds observed by the Sentinel‐1 C‐band synthetic aperture radar (SAR) are robustly associated with deep convective clouds detected by GEO infrared sensors. The current paper aims to generalize the previous assessment of several convective wind events by collecting a larger dataset and comparing these data to in‐situ wind observations. To achieve this, we evaluated wind speeds retrieved from Sentinel‐1 SAR images against corresponding in‐situ wind measurements from all active buoys/stations in the Gulf of Mexico. Significant agreement between satellite‐based winds and in‐situ data was achieved, particularly for wind speeds exceeding 3 m·s−1, with even better agreement for wind speeds over 10 m·s−1. From this dataset, three specific convective cases were extracted to illustrate various stages of convective squall events: before, during, and after the occurrence of a squall peak. In each case, comparison with in‐situ measurements showed that SAR‐estimated wind speeds closely matched observed speeds, including the peak convective winds, which were estimated at 18.90 m·s−1 and measured at 20.69 m·s−1. Furthermore, combining these findings with GOES‐16 sequential images illustrates the temporal and spatial similarity between deep convection areas, estimated strong sea surface wind patterns, and measured wind speeds.

The Effect of Fibrillation, Semi-Dry Pressing, and Surface Treatment on the Barrier Properties of Water Molecules and Oxygen on Food Packaging Paper.

The characteristics of fiber morphology and paper structure are critical to the barrier properties of food packaging paper. Herein, this study aimed to use pulp fibrillation, paper semi-dry pressing and carboxymethyl starch (CMS) coating to flatten the fibers, which were formed on the paper surface with good barrier properties due to the tight bond between fibers. The results showed that the permeability of paper was reduced by 87.56%, from 81.44 μm/Pa·s to 10.13 μm/Pa·s after the pulp fibrillation treatment (60 °SR). Moreover, semi-dry pressing treatment contributed to decreasing the water vapor transmission coefficient (WVP) by 50.98% to 2.74 × 10-10 g/m·s·Pa, and the oxygen permeation coefficient (OP) decreased by 98.04% to 1.93 × 10-14 cm3·cm/cm2·s·Pa. After coating the paper surface with titanium dioxide (TiO2) and CMS, the WVP of the paper was further reduced to 1.55 × 10-10 g/m·s·Pa, and OP was reduced to 0.19 × 10-14 cm3·cm/cm2·s·Pa. These values were 72.27% and 99.8% lower than those of the original paper, respectively. Therefore, through pulp fibrillation, semi-dry pressing of paper, TiO2 filling, and surface coating with CMS, there is no need to use synthetic polymer surface film-forming agents to achieve the high barrier properties that are required for low water and oxygen molecules permeation in food packaging paper.

Playing surface and sport contact status modulate time to lower extremity musculoskeletal injury at a greater level than concussion history among collegiate student-athletes

ObjectivesWhile previous investigations have demonstrated a positive correlation between previous concussion and risk of subsequent lower extremity musculoskeletal injury (LEMSKI), the effect of sport- and patient-specific factors on time to injury has not been thoroughly described. This study's main objective was to evaluate the relationship between prior concussion and time to LEMSKI among a population of collegiate student-athletes. Secondary objectives were to evaluate the relationship between playing surface, sport contact status, and biologic sex on time to LEMSKI. We hypothesized that those with previous concussions, those competing on synthetic surfaces, and those competing in collision sports would experience decreased latency to LEMSKI overall. MethodsA retrospective observational analysis of National Collegiate Athletic Association (NCAA) Division I student-athletes was conducted utilizing a mixed linear model analysis with contrasts. Inclusion criteria included participation in the Pac-12 Health Analytics Program with a documented LEMSKI between 2017 and 2020. Exclusion criteria included concurrent concussion and LEMSKI, injury resulting in serious morbidity or mortality, and incomplete medical record. Participants were classified by whether they sustained a concussion prior to LEMSKI in each athletic season. ResultsOf 1179 athletes included, 1140 had no previous concussion and 37 had a previous concussion. There was no observed effect of previous concussion (F ​= ​0.038; p ​= ​0.846) on time to LEMSKI overall. Student-athletes competing on constructed surfaces sustained a subsequent LEMSKI 14.5 days sooner (SE ​= ​5.255; p ​= ​0.045), and those competing on organic surfaces sustained a subsequent LEMSKI 23.5 days sooner (SE ​= ​4.018; p ​< ​0.001) in the season than those competing on synthetic surfaces. Contact sport student-athletes sustained a subsequent LEMSKI 52.1 days sooner than collision sport student-athletes (SE ​= ​5.248; p ​< ​0.001), and limited contact sport student-athletes sustained a subsequent LEMSKI 42.29 days sooner than collision sport student-athletes (SE ​= ​4.463; p ​< ​0.001). There was no observed effect of biologic sex (F ​= ​0.602; p ​= ​0.438) on time to LEMSKI overall. ConclusionThere was no observed impact of concussion on time on LEMSKI overall in this collegiate athletic population. Contact sports were associated with decreased time to LEMSK, while synthetic surfaces were associated with increased time to LEMSKI in this population. There was no observed impact of biologic sex on time to LEMSKI. Level of evidenceCase-control, level of evidence III.

Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net

In this paper a deep learning model is used to fully reconstruct the 3D distribution of arbitrarily shaped subsurface fatigue damages in a fiber/epoxy composite from synthetic thermal surface images. Synthetic thermal surface images (TIs) of self-heating damage hotspots are produced by thermal finite element analysis which are consequently used to train a Residual U-Net based on recent architectures designed for image segmentation. Different augmentation techniques are employed to mitigate the computational cost of generating training data through thermal finite element analysis. The Residual U-Net model accurately reconstructed – layer by layer – the ground truths and thereby enabled the quantitative assessment of location, size, shape, depth and gradient of an internal fatigue damage distribution. Moreover, the Residual U-Net achieved good predictions for comparatively small training set sizes.

Reel-to-Reel Coating of a Conductive Polymer on Synthetic Textile Yarns in a Semi-closed Batch Oxidative CVD System

In this manuscript, we demonstrate the ability to use a reel-to-reel processing technology for conductive surface functionalization of textile yarns using oxidative chemical vapor deposition in a continuous manner. We designed and built a vacuum deposition system, which allows the winding of yarns into the oCVD reactor by unreeling from the outside atmosphere, where the yarn is pre-treated with oxidant solution. Iron(III)chloride (FeCl3) and 3,4-ethylenedioxythiophene were used as the oxidant and monomer, respectively, to deposit thin films of poly(3,4-ethylenedioxythiophene) (PEDOT) thin films on the synthetic PET yarn surfaces. FTIR and XPS analyses were carried out to verify the chemical structure of as-deposited PEDOT films. Effects of temperature, oxidant concentration, and winding speed on the electrical conductivities of the yarns after oCVD were studied. All yarns exhibited non-zero conductivity values independent of the deposition conditions studied. Very high conductivity uniformities were observed along the longitudinal direction of the yarns even at the highest studied winding speed of 24 cm/min.

Antifouling nanoplatform for controlled attachment of E. coli

Biofouling is the most common cause of bacterial contamination in implanted materials/devices resulting in severe inflammation, implant mobilization, and eventual failure. Since bacterial attachment represents the initial step toward biofouling, developing synthetic surfaces that prevent bacterial adhesion is of keen interest in biomaterials research. In this study, we develop antifouling nanoplatforms that effectively impede bacterial adhesion and the consequent biofilm formation. We synthesize the antifouling nanoplatform by introducing silicon (Si)/silica nanoassemblies to the surface through ultrafast ionization of Si substrates. We assess the effectiveness of these nanoplatforms in inhibiting Escherichia coli (E. coli) adhesion. The findings reveal a significant reduction in bacterial attachment on the nanoplatform compared to untreated silicon, with bacteria forming smaller colonies. By manipulating physicochemical characteristics such as nanoassembly size/concentration and nanovoid size, we further control bacterial attachment. These findings suggest the potential of our synthesized nanoplatform in developing biomedical implants/devices with improved antifouling properties.

Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis

AbstractEnsemble data assimilation methods, such as the Ensemble Kalman Filter (EnKF), are well suited for climate reanalysis because they feature flow‐dependent covariance. However, because Earth System Models are heavy computationally, the method uses a few tens of members. Sampling error in the covariance matrix can introduce biases in the deep ocean, which may cause a drift in the reanalysis and in the predictions. Here, we assess the potential of the hybrid covariance approach (EnKF‐OI) to counteract sampling error. The EnKF‐OI combines the flow‐dependent covariance computed from a dynamical ensemble with another covariance matrix that is static but less prone to sampling error. We test the method within the Norwegian Climate Prediction Model, which combines the Norwegian Earth System Model and the EnKF. We test the performance of the reanalyzes in an idealized twin experiment, where we assimilate synthetic sea surface temperature observations monthly over 1980–2010. The dynamical and static ensembles consist respectively of 30 members and 315 seasonal members sampled from a pre‐industrial run. We compare the performance of the EnKF to an EnKF‐OI with a global hybrid coefficient, referred to as standard hybrid, and an EnKF‐OI with adaptive hybrid coefficients estimated in space and time. Both hybrid covariance methods cure the bias introduced by the EnKF at intermediate and deep water. The adaptive EnKF‐OI performs best overall by addressing sampling noise and rank deficiencies issues and can sustain low analysis errors by doing smaller updates than the standard hybrid version.

Prediction of surface reflectance using a deep learning model trained on synthetic surface images

Surface reflectance primarily depends on geometric properties. Although obtaining precise geometric details from real surfaces is challenging, deep learning models trained on synthetic surfaces with varying roughness can provide valuable insights. Such models, although they do not currently exist, might have the potential to accurately predict reflectance from real surface images that include roughness information. In this article, we propose a deep learning model based on Residual Network 34 to predict the reflectance of rough surfaces using confocal laser scanning microscopy (CLSM) surface images as the inputs. The model was trained using only synthetic surface images constructed by combining two basic patterns of inverted pyramids and cones. The ground-truth reflectance of the synthetic surfaces was obtained using ray tracing simulations combined with angle-dependent Fresnel reflection. The optimal synthetic surfaces were observed to have a pattern array size of 28 × 28 and an optimal pattern mixing ratio of 5:5. Thirty-two stainless steel surfaces with a wide reflectance range of 8.54–55.40% were fabricated by forming laser-induced periodic surface structures with a femtosecond laser. Three were used for validation, and the remaining 29 were used as the test dataset. When three actual surface CLSM images were used for validation, the R2 prediction accuracy of the model was 92.40%, and the mean absolute error of 29 tests was 2.98%. This study demonstrates that a model trained with synthetic surfaces can be used to predict the reflectance of real surfaces.