Search Results (252)

The surface texture parameter is a real reflection of the surface topography, which is closely related to the tribological properties of the surface, and the study of the correlation between the surface texture parameter and wear resistance is of great significance in revealing the tribological influence mechanism of the surface and realising the functional manufacturing of the surface. This paper takes the ball-end milling surface as the research object, establishes the three-dimensional simulation model of the surface topography, and analyses the surface topography and the surface texture parameter change rule. Based on the improved correlation analysis model, the correlation characteristics between the surface texture parameters, and between the surface texture parameters and the relative wear rate of per unit sliding distance K_V, were investigated, and the prediction model of K_V was established based on the surface texture parameters Sku, Sa, Sxp, Sp, and Ssk, and the correctness of the model was verified by experiments. The study in this paper provides a new idea to further reveal the relationship between surface topographical features and wear resistance and to guide the functional manufacturing of surfaces. Full article

The increasing demand for effective hair care products has highlighted the necessity for rigorous claims substantiation methods, particularly for products that target specific hair types. This is essential because the effectiveness of a product can vary significantly based on the hair’s condition and characteristics. A well-defined bleaching protocol is crucial for creating a standardized method to assess product efficacy, especially for products designed to repair damaged hair. The objective of this study was to create a practical bleaching protocol that mimics real-world consumer experiences, ensuring that hair samples exhibit sufficient damage for testing. This approach allows for a reliable assessment of how well various products can repair hair. The protocol serves as a framework for evaluating hair properties and the specific effects of each product on hair structure. Color, brightness, lightness, morphology, and topography were primarily used to understand the big differences in the hair fiber when treated with two repair benchmark products, K18^® and Olaplex^®, in relation to the Bleached hair. The devised bleaching protocol proved to be a fitting framework for assessing the properties of hair and the unique characteristics of each tested product within the hair fiber. This protocol offers valuable insights and tools for substantiating consumer claims, with morphological and mechanical methods serving as indispensable tools for recognizing and validating claims related to hair. The addition of K18^® and Olaplex^® demonstrated an increase in hair brightness (Y) and lightness (L* and a*) in relation to the Bleached samples, which were considered relevant characteristics for consumers. Olaplex^®’s water-based nature creates a visible inner sheet, effectively filling empty spaces and improving the disulfide linkage network. This enhancement was corroborated by the increased number of disulfide bonds and evident changes in the FTIR profile. In contrast, K18^®, owing to the lipophilic nature of its constituents, resulted in the formation of an external layer above the fiber. The composition of each of the products had a discrete impact on the fiber distribution, which was an outcome relevant to the determination of spreadability by consumers. Full article

In recent decades, the creation and availability of Voluntary Geographic Information (VGI) have changed the paradigm associated with the production of Geospatial Information (GI), since, due to its free access, citizens can view, analyse, process, and validate this type of data. One of the most popular examples of VGI is the collaborative OpenStreetMap (OSM) project which covers a wide range of themes or characteristics associated with the real world. One of these themes is the feature “waterway” that represents watercourses. The quality of OSM data characteristics is a topic that has been published by many authors in recent years, particularly on the analysis of the completeness indicator. However, few references are found in the literature about studies that analyse the completeness of OSM watercourses or even watercourses obtained by other sources. All this motivated the authors to develop a study that aims to analyse the completeness of these specific lines that have so much relevance to hydrologists. The study presents an analysis of the variation over time in completeness/coverage of the OSM “waterway” feature in the period between 2014 and 2023 in a mountainous region included in the Mondego River basin, located in the Inland of Portugal. The methodology applied is supported by classical methods of measuring the completeness of lines that may be found in the literature. The total length of the watercourses was calculated and compared in percentage terms with the total length of the reference watercourses for dates under analysis. The watercourses of the military official hydrography of the 1/25,000 scale were used as a reference. The relation of the OSM completeness with some indicators related to terrain surface (altitude, slope, and location/proximity settlements) was also analysed. The choice of these indicators was motivated by the fact that the study area has strong mountain characteristics and is crossed by the main Portuguese river. The analysis was performed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data and satellite image of Geographic Information System software. The results show that the completeness of this OSM feature (waterway) has a slight increase, considering the amplitude of the studied period (nine years) and the fact that, nowadays, digital mobile devices enable easy access to satellite images, allowing the digitalization of geographic entities or objects of the real world remotely. Regarding the indicator altitude, slope, and location/proximity of the settlements, we believe that there is no influence of these indicators on the evolution of the completeness of the OSM waterways in the study area. Full article

Turbulence is the most important, ubiquitous, and difficult problem of classical physics. Feynman viewed it as essentially unsolved, without a rigorous mathematical basis to describe the statistical dynamics of this most complex of fluid motion. However, the paradigm shift came in 1959, with the formulation of the Eulerian direct interaction approximation (DIA) closure by Kraichnan. It was based on renormalized perturbation theory, like quantum electrodynamics, and is a bare vertex theory that is manifestly realizable. Here, we review some of the subsequent exciting achievements in closure theory and subgrid modelling. We also document in some detail the progress that has been made in extending statistical dynamical turbulence theory to the real world of interactions with mean flows, waves and inhomogeneities such as topography. This includes numerically efficient inhomogeneous closures, like the realizable quasi-diagonal direct interaction approximation (QDIA), and even more efficient Markovian Inhomogeneous Closures (MICs). Recent developments include the formulation and testing of an eddy-damped Markovian anisotropic closure (EDMAC) that is realizable in interactions with transient waves but is as efficient as the eddy-damped quasi-normal Markovian (EDQNM). As well, a similarly efficient closure, the realizable eddy-damped Markovian inhomogeneous closure (EDMIC) has been developed. Moreover, we present subgrid models that cater for the complex interactions that occur in geophysical flows. Recent progress includes the determination of complete sets of subgrid terms for skilful large-eddy simulations of baroclinic inhomogeneous turbulent atmospheric and oceanic flows interacting with Rossby waves and topography. The success of these inhomogeneous closures has also led to further applications in data assimilation and ensemble prediction and generalization to quantum fields. Full article

Accurate tropospheric delays are of great importance for both Global Navigation Satellite System (GNSS)-based positioning and precipitable water vapor monitoring. The gridded tropospheric delay products, including zenith hydrostatic delays (ZHD) and zenith wet delays (ZWD), are the most ideal method for accessing accurate tropospheric delays. The vertical adjustment method is critical for implementing the gridded tropospheric products. In this work, we consider the different models used for grid products and assess their performance over Yunnan mountains with complex topography. We summarize the main results as follows: (1) The products can provide accurate ZHD with mean biases of −2.6 mm and mean Standard Deviation (STD) of 1.5 mm while the ZWD results from grid products show a performance with biases of −0.4 mm and STD of 1.3 cm over the Yunnan area. (2) The $T_v$-based model shows a better performance than the $T_0$-based model and IGPZWD in rugged areas with large height differences. The grid products can provide hourly ZHD with biases of 3 mm and wet delay with mean biases of within 2 cm and mean STD of below 3 cm in the Yunnan mountains, which exhibit a large height difference of around 1.5 km. (3) The radiosondes results confirm that the $T_v$-based model has an obvious advantage in calculating ZHD height corrections for differences within 2 km while the $T_0$-model suffers from a loss in accuracy in the case of large height differences. If the site is located more than 1 km below the reference height, the IGPZWD model can provide a better ZWD with a mean bias of 1.5 cm and a mean STD of 1.7 cm. With vertical reduction models, the grid products can provide accurate ZHD and ZWD in real time, even if in complex area. Full article

While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions. Recent advances in machine learning applied to semantic scene understanding and environment representations, coupled with modern embedded computational means and sensors hold promising potential in this matter. This paper therefore introduces the combination of semantic understanding, continuous implicit environment representation and smooth informed path-planning in a new method named COSMAu-Nav. It is specifically dedicated to autonomous ground robot navigation in unstructured environments and adaptable for embedded, real-time usage without requiring any form of telecommunication. Data clustering and Gaussian processes are employed to perform online regression of the environment topography, occupancy and terrain traversability from 3D semantic point clouds while providing an uncertainty modeling. The continuous and differentiable properties of Gaussian processes allow gradient based optimisation to be used for smooth local path-planning with respect to the terrain properties. The proposed pipeline has been evaluated and compared with two reference 3D semantic mapping methods in terms of quality of representation under localisation and semantic segmentation uncertainty using a Gazebo simulation, derived from the 3DRMS dataset. Its computational requirements have been evaluated using the Rellis-3D real world dataset. It has been implemented on a real ground robot and successfully employed for its autonomous navigation in a previously unknown outdoor environment. Full article

(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter’s reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful. Full article

The default mode network (DMN) is a complex network that plays a significant and active role during naturalistic stimulation. Previous studies that have used naturalistic stimuli, such as real-life stories or silent or sonorous films, have found that the information processing involved a complex hierarchical set of brain regions, including the DMN nodes. The DMN is not involved in low-level features and is only associated with high-level content-related incoming information. The human sexual experience involves a complex set of processes related to both external context and inner processes. Since the DMN plays an active role in the integration of naturalistic stimuli and aesthetic perception with beliefs, thoughts, and episodic autobiographical memories, we aimed at quantifying the involvement of the nodes of the DMN during visual sexual stimulation. After a systematic search in the principal electronic databases, we selected 83 fMRI studies, and an ALE meta-analysis was calculated. We performed conjunction analyses to assess differences in the DMN related to stimulus modalities, sex differences, and sexual orientation. The results show that sexual stimulation alters the topography of the DMN and highlights the DMN’s active role in the integration of sexual stimuli with sexual schemas and beliefs. Full article

Large-scale diffraction gratings were fabricated in surface relief on azobenzene thin films and transferred to flexible PDMS substrates using soft lift-off lithography. The PDMS gratings were strained along the grating vector axis and the resulting surface topography was analyzed using diffraction angle measurements, AFM imagery and surface plasmon resonance (SPR) spectra. All measurement methods exhibited a linear response in strain indicating the useability of these sensors in real-world applications. For SPR-based strain sensing, an increasing pitch and a decreasing modulation depth were observed with increasing strain. The SPR peak shifted by ~1.0 nm wavelength and the SPR intensity decreased by ~0.3 a.u. per percentage of applied strain. The tested PDMS samples retained their integrity even after multiple cycles of stretching and relaxation, making them a suitable strain sensor. Full article

The surface topography of substrates is a crucial factor that determines the interaction with biological materials in bioengineering research. Therefore, it is important to appropriately modify the surface topography according to the research purpose. Surface topography can be fabricated in various forms, such as wrinkles, creases, and ridges using surface deformation techniques, which can contribute to the performance enhancement of cell chips, organ chips, and biosensors. This review provides a comprehensive overview of the characteristics of soft, hard, and hybrid substrates used in the bioengineering field and the surface deformation techniques applied to the substrates. Furthermore, this review summarizes the cases of cell-based research and other applications, such as biosensor research, that utilize surface deformation techniques. In cell-based research, various studies have reported optimized cell behavior and differentiation through surface deformation, while, in the biosensor and biofilm fields, performance improvement cases due to surface deformation have been reported. Through these studies, we confirm the contribution of surface deformation techniques to the advancement of the bioengineering field. In the future, it is expected that the application of surface deformation techniques to the real-time interaction analysis between biological materials and dynamically deformable substrates will increase the utilization and importance of these techniques in various fields, including cell research and biosensors. Full article

Topographic scale characteristics contain valuable information for interpreting landform structures, which is crucial for understanding the spatial differentiation of landforms across large areas. However, the absence of parameters that specifically describe the topographic scale characteristics hinders the quantitative representation of regional topography from the perspective of spatial scales. In this study, false-color composite images were generated using normalized topographic relief data, showing a type of scale-oriented terrain pattern. Subsequent analysis indicated a direct correlation between the luminance of the patterns and the normalized topographic relief. Additionally, a linear correlation exists between the color of the patterns and the change rate in normalized topographic relief. Based on the analysis results, the issue of characterizing topographic scale effects was transformed into a problem of interpreting terrain patterns. The introduction of two parameters, flux and curl of topographic field, allowed for the interpretation of the terrain patterns. The assessment indicated that the calculated values of topographic field flux are equivalent to the luminance of the terrain patterns and the variations in the topographic field curl correspond with the spatial differentiation of colors in the terrain patterns. This study introduced a new approach to analyzing topographic scale characteristics, providing a pathway for quantitatively describing scale effects and automatically classifying landforms at a regional scale. Through exploratory analysis on artificially constructed simple DEMs and verification in four typical geomorphological regions of real terrain, it was shown that the terrain pattern method has better intuitiveness than the scale signature approach. It can reflect the scale characteristics of terrain in continuous space. Compared to the MTPCC image, the terrain parameters derived from the terrain pattern method further quantitatively describe the scale effects of the terrain. Full article

Thanks to the recent development of innovative instruments and software with high accuracy and resolution, 3D modelling provides useful insights in several sectors (from industrial metrology to cultural heritage). Moreover, the 3D reconstruction of objects of artistic interest is becoming mandatory, not only because of the risks to which works of art are increasingly exposed (e.g., wars and climatic disasters) but also because of the leading role that the virtual fruition of art is taking. In this work, we compared the performance of four 3D instruments based on different working principles and techniques (laser micro-profilometry, structured-light topography and the phase-shifting method) by measuring four samples of different sizes, dimensions and surface characteristics. We aimed to assess the capabilities and limitations of these instruments to verify their accuracy and the technical specifications given in the suppliers’ data sheets. To this end, we calculated the point densities and extracted several profiles from the models to evaluate both their lateral (XY) and axial (Z) resolution. A comparison between the nominal resolution values and those calculated on samples representative of cultural artefacts was used to predict the performance of the instruments in real case studies. Overall, the purpose of this comparison is to provide a quantitative assessment of the performance of the instruments that allows for their correct application to works of art according to their specific characteristics. Full article

Although the main purpose of conventional geographical positioning systems (GPSs) is to determine either the fastest path or the shortest distance to a destination, this function may not be enough for electric vehicles (EVs). This is simply because the fastest/shortest path may consume relatively higher energy when compared to other paths depending on the nature, speed limit, and topography of the road. This means that the driving range of the EV per charge decreases dramatically. This paper aims to develop a new algorithm and model dedicated for EV GPS which not only selects shortest/fastest routes, but also focuses on the most energy efficient route. This is achieved by considering many factors including aerodynamics, wind speed, topology of roads, with a clear objective of reducing the energy consumed from the battery. A MATLAB Simulink model is developed and validated with real-life case studies to ensure the results are realistic and accurate. Full article

The progressive substitution of traditional buses with electric ones in urban public transport constitutes a fundamental challenge towards sustainable mobility. This paper presents a methodological approach to assess energy consumption in urban bus networks, focusing on a city with varied topography and examining alternative bus lines for similar trips. Utilizing a quasi-static longitudinal model, real GPS data, and open access terrain models, the analysis aims to provide a nuanced understanding of energy performance, considering factors such as stop characteristics, gradients, and driving styles. The influence of driving style on commercial speed is observed to be modest, yet significant, in terms of energy efficiency. This research identifies the most advantageous line for transitioning from Internal Combustion Engine (ICE) to Electric Motor (EM) powertrains, resulting in a significant 68% reduction in energy consumption. Beyond specific line details, this methodology offers insights applicable to medium-sized cities, emphasizing efficient route prioritization and providing enhanced user information for informed decisions in the context of sustainable transportation solutions. Full article

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires. Full article