Height Distribution Research Articles

Rate of Weak Convergence of Random Walk with a Generalized Reflecting Barrier

In this study, a random walk process with generalized reflecting barrier is considered and an inequality for rate of weak convergence of the stationary distribution of the process of interest is propounded. Under some conditions, the weak convergence theorem for the stationary distribution of the considered process is obtained in literature, however, the rate of the convergence is not studied sufficiently. Nonetheless, one of the most crucial issues in probability theory is the convergence rate in limit theorems, as it affects the precision and effectiveness of using these theorems in practice. Therefore, for the rate of convergence for the examined process, comparatively simple inequality is represented. The obtained inequality demonstrates that the rate of convergence is correlated with the tail of the distribution of ladder heights of the random walk.

Enhancing residential heating load prediction with advanced machine learning and optimization techniques

The effective prediction of heating load in residential buildings is crucial for optimizing energy consumption and enhancing thermal comfort. This study investigates three prominent regression techniques Extra Tree Regression, Decision Tree Regression, and Support Vector Regression utilized for this purpose. To improve their predictive performance, a novel metaheuristic optimization algorithm, the Tyrannosaurus Rex Optimization Algorithm, is employed. The dataset used includes features such as Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, and Glazing Area Distribution. Each machine learning model (Extra Tree Regression, Decision Tree Regression, Support Vector Regression) is trained on historical heating load data and optimized using the Tyrannosaurus Rex Optimization Algorithm to adjust internal parameters. Comparative analysis demonstrates that the proposed Extra Tree + Tyrannosaurus Rex Optimization Algorithm model achieves superior performance with an R-squared value of 0.987 and a low Root Mean Square Error of 1.167. By leveraging the strengths of different machine learning algorithms, this research presents a reliable approach for accurately predicting heating load in residential buildings. This method holds promise for improving energy efficiency, enhancing thermal comfort, and promoting sustainable building design.

Cloud Height Distributions and the Role of Vertical Mixing in the Tropical Cyclone Eye Derived From Compact Raman Lidar Observations

AbstractThe distribution of tropical cyclone (TC) eye cloud heights is documented for the first time using compact Raman lidar (CRL) measurements with high spatial resolution. These cloud heights act as tracers for low‐level vertical mixing in the eye region. Cloud height distributions using all available data from nine Atlantic TCs in 2021 and 2022 show significant vertical variance, dispelling the notion of a flat stratiform eye cloud deck. Eye cloud widths are multiscale, with shallow convective clouds dominating CRL returns. Data from Hurricane Sam (2021) highlight the evolution of shallow convective clouds in the TC eye and their associated temperature inversions. The frequent appearance of convective eye clouds, along with observed vertical wind fluctuations, suggests that vertical mixing from the boundary layer frequently occurs in the TC eye, even beneath strong inversions. This strong vertical mixing should be accurately portrayed by TC simulations and forecasts.

Tsunami hazard assessment in the South China Sea based on geodetic locking of the Manila subduction zone

Abstract. This study provides a dataset and shows the spatial distribution of tsunami hazard in the South China Sea sourced from the Manila subduction zone. The plate motion data around the Manila subduction zone are used to invert the geodetic locking of the Manila subduction zone, further used to estimate the maximum possible magnitude and applied to obtain a more reliable tsunami hazard assessment. The spatial distribution of tsunami wave height with a 1000-year return period is shown, and several high-hazard areas in the South China Sea are pointed out. Uncertainties in the seismic source are explored, including the slip heterogeneity, the upper limit of seismic magnitude and segmentation. The impact of the locking distribution and randomness of slip on tsunami hazard assessment demonstrates that the traditional uniform slip assumption significantly underestimates the tsunami hazard. Moreover, the assessment results involving the effect of the locking distribution should be more realistic and show a larger tsunami height than when only considering the stochastic slip in most areas, which should prompt coastal management agencies to enhance tsunami prevention awareness.

Reparation with moving least squares sampling and extraction of body sizes of beef cattle from unilateral point clouds

Body measurements of beef cattle at various growth stages are crucial for assessing breeding quality. To decrease the stress responses of live cattle during measurement, noncontact body size measurement using a deep sensor is conducted. After directly obtaining the point cloud data (PCD) of a unilateral surface, novel point cloud repair and body measurement are proposed. The PCD of beef cattle are first preprocessed using Euclidean clustering, conditional filtering, and Random Sample Consensus. To merge the unilateral PCD into the entire contour, a Euclidean transformation matrix and moving least squares are applied. To address holes caused by stitching due to camera angle restrictions, various upsampling techniques based on least squares deformation are investigated to repair holes in different surfaces with varying growth stages. Finally, polynomial fitting is chosen to be in accordance with the feature point distribution of the body height (BH), chest girth (CG), back height (AH), waist height (WH), ischial tuberosity, and shoulder end of beef cattle. Using the formulas of Euclidean distance and cubic B–spline curve fitting, the values of BH, AH, WH, CG, and body length are obtained and then compared with manual measurement data. The predicted values of the five body sizes of 100 beef cattle had a mean absolute error and mean absolute percentage error of 7.77 cm and 5.42 %, respectively. The largest absolute error was 12.05 cm, and the smallest absolute error was 4.12 cm. A series of PCD processing and repair methods provides a new method for the noncontact automated measurement of beef cattle. This approach promotes more effective practices in livestock welfare and trait assessment compared with traditional management.

Seismic response of irregular RC buildings designed for gravity and seismic loads

Irregular reinforced concrete framed buildings are peculiar and their seismic response is difficult to predict using simplified approaches. The irregularity in structural configuration is characterized by cross-sectional area reduction of the columns along the height, in-elevation and in-plan irregular distribution of the masses, complex floor geometry or floor geometry variation along the height. This study analyses the seismic response of several four-storey buildings with different types of irregularities, namely in-elevation floor height and floor geometry variation. Additionally, responses of both seismically designed and gravity load designed structures are compared for each geometry considered. A numerical model accounting for non-linear flexural and shear response of the structure is developed, aimed at conducting non-linear incremental dynamic analyses. The results are discussed in terms of inter-storey drift, floor acceleration profiles, fragility functions and floor response spectra. A significant influence of the irregularity on floor accelerations and displacements was observed, as well as on the spectral acceleration at collapse, mainly caused by mass and stiffness variation along the height. On the other hand, no significant influence was detected on failure modes.

Multiphysics machine learning framework for on-demand multi-functional nano pattern design by light-controlled capillary force lithography

Nature finds ways to realize multi-functional surfaces by modulating nano-scale patterns on their surfaces, enjoying transparent, bactericidal, and/or anti-fogging features. Therein height distributions of nanopatterns play a key role. Recent advancements in nanotechnologies can reach that ability via chemical, mechanical, or optical fabrications. However, they require laborious complex procedures, prohibiting fast mass manufacturing. This paper presents a computational framework to help design multi-functional nano patterns by light. The framework behaves as a surrogate model for the inverse design of nano distributions. The framework’s hybrid (i.e., human and artificial) intelligence-based approach helps learn plausible rules of multi-physics processes behind the UV-controlled nano patterning and enriches training data sets. Then the framework’s inverse machine learning (ML) model can describe the required UV doses for the target heights of liquid in nano templates. Thereby, the framework can realize multiple functionalities including the desired nano-scale color, frictions, and bactericidal properties. Feasibility test results demonstrate the promising capability of the framework to realize the desired height distributions that can potentially enable multi-functional nano-scale surface properties. This computational framework will serve as a multi-physics surrogate model to help accelerate fast fabrications of nanopatterns with light and ML.

ON THE PECULIARITIES OF REFLECTION OF SHEAR WAVES WITH LINEAR POLARIZATION FROM THE ROUGH SURFACE OF A CRACK

The reflection coefficient of shear waves with SV- and SH-linear polarization from a statistically inhomogeneous surface consisting of scattering linear elements with a feature of their height distribution according to the normal law in a two-dimensional approximation is estimated. The estimation of the reflection coefficients in the reverse and forward directions is based on the calculation of the reflected wave energy flow propagating from an inhomogeneous surface averaged over the possible values of the height of the reflecting elements. It is theoretically shown and confirmed experimentally that the ratio of the coefficients of the reverse reflection of SH- and SV-polarization waves is significantly greater than 1, which provides better detection of cracks by SH-waves. The peculiarity of the behavior of the dependence of the coefficients of the reverse and direct reflected signal of the SV-polarization waves on the mean square deviation corresponds to the results of theoretical estimates. The coefficient of variation of the ratio of reflection coefficients shows the statistical stability of the direct reflected signal, which determines the feasibility of using such a signal to measure the equivalent area of the reflector.

Individual Tree Identification and Segmentation in Pinus spp. Stands through Portable LiDAR

Forest inventories are essential for sustainable forest management. In inventories at the tree level, all the information is linked to individuals: species, diameter, height, or spatial distribution, for example. Currently, the implementation of Portable LiDAR (PLS) is being studied, aiming to digitalize forest environments and increase the reliability of forest observations. Performing automatic individual tree identification (ITD) and segmentation (ITS) is essential for the operational implementation of PLS in forestry. Multiple algorithms have been developed for performing these tasks in LiDAR point clouds. Their performance varies according to the LiDAR system and the characteristics of the stand. In this study, the performance of several ITD and ITS algorithms is analyzed in very high-density PLS point clouds in Pinus species stands with a varying presence of understory, shrubs, and branches. The results showed that ITD methods based on finding trunks are more suitable for tree identification in regular stands with no understory. In the ITS process, the methods evaluated are highly conditioned by the presence of understory and branches. The results of this comparison help to identify the most suitable algorithm to be applied to these types of stands, and hence, they might enhance the operability of PLS systems.

Guidline to Asses Geometrical Intolerance of Thin-Walled Blanks After Burnishing Process

AbstractApplication of lightweight material like aluminum alloy is increasing its importance in various industries due to effective reduction of structure weight and sequential advantages like reduction of greenhouse gas emission and carbon footprint. However, deflection of aluminum thin-walled blank during production by machining is a challenge that merits further studies. Burnishing as a non-metal removal finish-machining process is usually used as a final treatment in the production chain of samples. However, in burnishing of thin-walled structure, machining-induced residual stress causes dimensional and geometrical distortion followed by problems in manufacturing accuracy and mismatch in assembly. Therefore, to minimize the consequence of the abovementioned errors, the source of the distortion should be identified and minimized during machining since usually no further operation is placed in the production chain after burnishing. To effectively tackle this challenge, in the present study an analytical model is developed to find how the burnishing process factors i.e. pass number and static force together with initial blank size impact the distortion of thin-walled 6061-T6 plates. The curvatures which were derived from analytical model were compared to those of burnished samples measured by coordinate measuring machine. It was found from the results that the burnishing pass number because of its impact on work hardening and regeneration of stress together with blank size play crucial role on determining the sample’s distortion. It was obtained that with 2 pass burnishing results in minimizing the distortion of material. Moreover, the blank’s length to width ratio due to its impact on material stiffness in corresponding direction significantly impacts the deformation after unclamping. The results which were derived from analytical model were compatible well with experimental values in term of final distribution of residual stress and maximum height of distorted parts.

Granular Scaling Approach to Landslide Runout.

A main objective in landslide research is to predict how far they will travel. Landslides are complex, and a complete understanding in principle requires accounting for numerous parameters. Here we engender a simplification by investigating the maximum landslide runout using granular laboratory experiments and a scaling analysis. We find that correctly accounting for the fall height and grain size distribution not only yields an improved correlation of normalized runout, but also quantitatively unites laboratory and field data. In particular, we find that the mobility of landslides increases with the square root of the fall height and with the skewness of the grain size distribution.

Characterization of microstructural evolution of asphalt due to water damage using atomic force microscopy

Water erosion significantly impacts the microstructure of asphalt, which in turn affects the macroscopic performance of asphalt mixtures, such as reducing the adhesion between asphalt and aggregates as well as diminishing water stability. This study utilizes atomic force microscopy (AFM) to investigate the micromorphology, surface height distribution, nanosurface roughness, and microsurface energy of asphalt under different water immersion durations. Furthermore, it considers the influence of asphalt's penetration grade, oil source, and modifiers on the evolution of its microstructure. AFM analysis reveals that, with the exception of crumb rubber modified asphalt (PJ90/CR), the quantity and area of beelike structures in the asphalt decrease with increasing water immersion time. The surface height distribution of asphalt exhibits characteristics of an approximate normal distribution, with an increase in nanoscale heights over time due to the dissolution of surface components by water and physical erosion effects. Notably, the surface height of PJ90/CR and SBS modified asphalt (PJ90/SBS) is significantly lower than that of PJ90 before and after water immersion. Water damage causes asphalt surface particles to dissolve and re-aggregate, forming distinct nano-protrusions and pits, thereby increasing the asphalt's roughness. Among them, the roughness of SK90 are generally higher than those of SK70, with the average roughness (Ra) and peak-to-valley roughness (Rt) increasing by 28.93 % and 60.56 %, respectively, before immersion. Moreover, water damage leads to a decrease in the surface energy of asphalt to varying degrees, indicating that water more easily penetrates the asphalt film through adsorption and diffusion actions, altering its microstructure and, consequently, reducing its surface energy. The variations in the microstructure of SK90, Shell90, and PJ90 under different water immersion durations highlight the significance of oil source on asphalt performance.

Fractional Brownian motion as a rough surface

In this study we consider two dimensional fractional Brownian motion (fBM) as a rough surface, tuned by the Hurst exponent H, with a focus on the scaling relations. While this system fulfills partially the requirements for self-similar Gaussian surfaces, (like Gaussian height distribution), it shows deviations for the global features. A thorough examination of the Kondev’s hyperscaling relations is presented. The global features associated with level lines which define contour loop ensemble (CLE) are studied in terms of H. We show that in the thermodynamic limit the fractal dimension of loops (Df), the critical exponent of the distribution of loop length (τl), and the gyration radius (τr) vary linearly with H. The hyperscaling relations between these quantities however challenge this hypothesis. While Df, τl and τr fulfill partially the Kondev relations, the loop correlation exponent xl depends weakly on H, i.e. shows deviations from 12 which is hypothesized by Kondev to be super-universal for the Gaussian surfaces.

Assessment on the heterogeneity and roughness of fracture surface of steel fiber reinforced concrete

The characterization of fracture morphology in steel fiber reinforced concrete (SFRC) remains an area of limited study. This research investigates the influence of heterogeneity on fracture surfaces in SFRC across various surface sizes, sampling intervals (δ), and directions. Utilizing point-cloud matching technique, fracture morphologies are characterized to analyze damages in the heterogeneous fracture process zone. Four roughness parameters, i.e., root mean square deviation (Sq), arithmetic average height (Sa) distribution, surface area to projective area (RS) and root mean square of directional derivatives (Z2–3D), are used to quantify morphology. Results reveal that as surface size arises, Sq and Sa increase with their ratio (Sq/Sa) approaching 1.25, while the ratios of Sq and Sa to size length decrease following a double-exponential decay model, indicating a size scale dependence. Meantime, roughness anisotropy, characterized by coefficient of variations in directional Z2–3D values, declines. RS and Z2–3D exhibit higher sensitivity to textural features than surface size, showing a δ-dependence. As δ rises, RS and Z2–3D sharply decrease, whereas Sq, Sa and Sq/Sa stabilize. This analysis highlights the significance of Z2–3D in providing insights into fracture morphological features and heterogeneity of SFRC.

Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling

Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.

Wave height distribution for plunging breakers induced by air bubbles

This study highlights the wave height distributions based on plunging breaking waves and air bubble effects in the surf zone. Wave height distributions predicted previously by different models have been reformulated in terms of air bubble effects. Some of the most commonly applied wave height distributions are judged using laboratory observations in a custom-built wave flume. Results have revealed a notable deviation from the Rayleigh distribution, with the proposed model demonstrating a closer match to experimental data, particularly for larger wave heights. Probability densities of larger wave heights are reduced significantly by entrained air bubbles, leading to measured wave heights below Rayleigh distribution predictions. In addition, the different wave height parameters derived from the proposed model exhibited good agreement with laboratory data compared to the Rayleigh distribution. Furthermore, theoretical analysis shows the scale parameter's dependence on the decay coefficient, aligning well with measurements and simplifying the proposed distribution to a one-parameter model. Again, based on the error analysis, the proposed model's results show good performance compared to others.

Improvement in optical nonlinearity of spiral square Mn-based nanostructures with increasing the number of square sides

Spiral square Mn-based nanostructures with different numbers of square sides were grown on glass substrate using the glancing angle deposition method. The crystallinity of the products was determined by X-ray diffraction. The surface properties of the synthesized nanostructures were analyzed by field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM). 2D AFM images have been used for more detail on grain growth evolution and height distribution. The band gaps of the nanostructures were determined using the Tauc formula in the range of 2.72–3.06 eV. It was observed a decrease in band gap energy of spiral square Mn-based nanostructures with increasing the number of square sides. Nonlinear optical measurements of synthesized samples were performed using a Z-scan setup under a CW laser irradiated at 532 nm. Values of the order of 10−4 cm W−1 and 10−7 cm2 W−1 were calculated for nonlinear absorption coefficients and refraction indices of spiral square nanostructures, respectively. This investigation revealed an increase of about 1.7 times for nonlinear optical parameters of Mn-based nanostructures with increasing the number of square sides from 8 up to 10 that is significant increment. These results suggest spiral square Mn-based nanostructures as a potential material for application in optics.

Present and new operational quantities evaluated from photon spectrum measurements at workplaces in the research reactor and accelerator facility at the JAEA

The International Commission on Radiation Units and Measurements (ICRU) has proposed to change the definitions of the operational quantities used for the area and individual monitoring of external exposure in ICRU Report 95. Since introducing new operational quantities into the radiation monitoring may affect the dose assessment using the present personal dosimeters, precise assessments of the influence on monitoring are necessary by characterizing the energy spectrum in the workplace and the energy dependency of the dosimeters to be used. This study measured photon spectra using a NaI(Tl) scintillation detector or a LaBr3(Ce) scintillation detector at the Japan Research Reactor No. 3 (JRR-3) and Japan Proton Accelerator Research Complex (J-PARC) workplaces at the Japan Atomic Energy Agency (JAEA). The photon energy spectra were obtained by unfolding the pulse height distributions measured at each workplace. The present and new operational quantities were then evaluated and compared using the spectra measured at the workplaces.In JRR-3, the photon energy spectra were measured at the workplaces in the reactor room and thermal neutron beam hall while the reactor was operated. The ambient dose equivalent rates H˙*(10) at the workplaces were 0.2 μSv·h−1 – 13 μSv·h−1. High energy gamma-rays of 6–7 MeV from 16N were observed near the fuel failure detection system in the reactor room. Those of 7–8 MeV produced by the thermal neutron capture of iron were observed in the reactor room and the beam hall. In J-PARC, the photon energy spectra were measured at the workplaces in the beam tunnel of the 3 GeV synchrotron beam ring five days after the operation. The ambient dose equivalent rates H˙*(10) at the workplaces were 35 μSv·h−1 –430 μSv·h−1.It was found that the new personal doses, Hp, were 10%–20% smaller than the present personal dose equivalents, Hp(10), at the workplaces.

Interfacial adhesion features of high-viscosity asphalt and aggregate under water conditioning: Micro perspective and molecular simulation

Interface properties between asphalt and aggregate intimately affect the service performance and long-term durability of asphalt pavement. This study seeks to explore the interfacial adhesion features of high-viscosity asphalt (HVA)-aggregate under water conditioning to provide the understanding for the moisture damage of HVA-aggregate interface and asphalt pavement. To this end, the HVA was initially suffered from different durations of water conditioning, and atomic force microscopy (AFM) test was employed to gather the microscopic phase structure and adhesion force of water-conditioned HVA and aggregate, to analyze the adhesion variations of HVA-aggregate interface. Further, molecular dynamics method was used to simulate the changing process and influencing mechanism of adhesion effect at the HVA-aggregate interface under water immersion. The findings indicated that with the increasing water immersion, Catana phase of HVAs vanished, and concave and convex structures appeared on the surface, along with pit groove dispersion phase structures. Longer immersion time resulted in augmented surface roughness of HVA and a more dispersed height distribution in the area domain, most notably during the initial stages of water immersion. Aging increased sensitivity to immersion duration in terms of surface roughness, mechanical properties and adhesion ability of HVAs. Water immersion had a diminishing effect on the mechanical properties of HVA at different aging degrees, although it increased the micro-mechanical modulus of HVAs and impaired the interface adhesion with aggregates. Under water immersion, the phase structure and mechanical properties of HVAs and the interface adhesion indexes all distinguished a prominent negative Pearson correlation (correlation coefficient |R| > 0.7000), and these factors were critical for HVA-aggregate interface adhesion, as alterations in surface phase structure and micro-mechanical traits of HVAs triggered variations in the HVA-aggregate interface adhesion. In addition, water content in the interface layer, simulation temperature and mineral anisotropy significantly affected the interfacial adhesion of HVA and aggregate in water environment. The introduction of water and the rise of temperature considerably weakened the interfacial adhesion effect of asphalt-aggregate system, and the interface adhesion failure occurred once the water layer thickness surpassed 5 Å. Comparatively, the HVA-calcite system demonstrated better interfacial adhesion competence than the HVA-α-quartz system, and the HVA-calcite {1 0 4} system showed the optimal interfacial adhesion.

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.