Types Of Surface Materials Research Articles

Weather effects on natural surface trail use in an urban wilderness multi-use trail system

Previous research has concluded weather conditions can influence physical activity (PA) on trails. However, these studies focused primarily on mixed-use paved surface trails. Many recreational areas use natural surface trails for mountain biking, trail running, and walking. The purpose of this study was to analyze the association between weather conditions and the use of natural surface trails for PA. In addition, this investigation sought to determine if the type of trail surface material, gravel vs. dirt, impacted PA differently by weather factors. Infrared trail counters were used to measure hourly trail use within Knoxville's Urban Wilderness in Knoxville, TN. An onsite weather station was used to obtain hourly weather variables. Negative binomial regression models were used to analyze the relationship between trail use and weather conditions. Both trail locations experienced bi-modal peaks in usage with Spring and Fall months showing the highest usage. As temperature increased, so did trail use (β = 0.115, p = 0.00); however, gravel trail use began to decrease at temperatures above 86 °F. Precipitation negatively impacted trail use on both gravel (β = −3.114, p = 0.00) and dirt (β = −2.281, p = 0.00) trails. Relative humidity and had a negative relationship with trail use on both trails. UVRI (β = −0.036) was only significantly associated with dirt trail use. The direction of the effect of weather measures on natural surface trail use is similar to previous studies on paved trails; however, the magnitude of the effect varies. Gravel trail use was more sensitive to temperature and precipitation, while relative humidity, absolute pressure, and UVRI had a greater impact on dirt trail use. The variations in the magnitude of the effect may be a function of the activity performed on the different trail surfaces. Trail surface material should be considered to fully understand the impact weather has on trail use. Management implicationsBy understanding the behaviors of natural surface trail users in response to various weather conditions, managers can adapt their trail planning (i.e., type of trail, surface material), maintenance, and funding for various projects. Managers could collect weather-related information in conjunction with trail surface conditions to inform users of more user-friendly trails during adverse weather events. This could decrease trail maintenance by shifting users to trails that can tolerate more use in poor weather. Additionally, this information would enable managers to develop programs more effectively (e.g., special events, learning clinics) and thus engage more people in outdoor recreation.

Modelling, optimization, and testing of novel cuboidal spherical plate lattice structures

ABSTRACT This study investigates the mechanical behavior of a novel set of Cuboidal Spherical Plate Lattice (CSPL) materials. The procedure of constrained-domain topology optimization is implemented with the aim of enhancing stiffness. The micromechanical finite element homogenization approach is used to evaluate the effective elastic-plastic properties of the CSPLs and their topologically optimized counterparts, TOCSPLs (Topologically Optimized Cuboidal Spherical Plate Lattices). The TOCSPLs demonstrate higher uniaxial, shear, and bulk moduli compared to the CSPLs, with an increase of 31%, 14%, and 36% respectively. Moreover, there is an increase in the yield strengths under uniaxial, shear, and hydrostatic loading conditions, with enhancements of 103%, 55%, and 62%, respectively. The topologies are additively manufactured through Fused Deposition Modeling (FDM) out of ABS thermoplastic material. The quasi-static compression experiments demonstrate the superiority of TOCSPL 111+100 over the other topologies in terms of uniaxial modulus. The suffix 111+100 denotes the crystallographic planar orientations in which the solid plate-like disks were formed within a cubic system. The topologies proposed herein outperform certain types of Triply Periodic Minimal Surface, honeycomb, truss-, and plate-based lattice materials. The proposed topologies offer a compelling justification for their utilization in applications that require load-bearing and impact absorption capabilities.

Robotic Kinesthesia: Estimating Object Geometry and Material With Robot's Haptic Senses.

Humans excel at determining the shape and material of objects through touch. Drawing inspiration from this ability, we propose a robotic system that incorporates haptic sensing capability into its artificial recognition system to jointly learn the shape and material types of an object. To achieve this, we employ a serially connected robotic arm and develop a supervised learning task that learns and classifies target surface geometry and material types using multivariate time-series data from joint torque sensors. Additionally, we propose a joint torque-to-position generation task to derive a one-dimensional surface profile based on torque measurements. Experimental results successfully validate the proposed torque-based classification and regression tasks, suggesting that a robotic system can employ haptic sensing (i.e., perceived force) from each joint to recognize material types and geometry, akin to human abilities.

Recovery and Visualisation of Methamphetamine-Contaminated Fingermarks from Non-Porous Surfaces

Introduction: Fingermarks left at a crime scene can indicate the presence of an individual and his/her involvement in the crime. Fingermarks, usually invisible, can appear on any surface and may be contaminated by any exogenous substances, including drug substance. Recovery of fingermarks contaminated by drug substance is crucial to link an individual with the drug-related crimes. Hence, this study was aimed to investigate the recovery and visualisation of methamphetamine-contaminated fingermarks from various non-porous surface materials. Methods: In this study, fingermarks were deposited on 11 types of surface materials varied by the presence of methamphetamine contamination, immediacy of deposition, and their concentration levels. Each fingermark was then developed using white and black fingerprint powders, graded, and compared based on the different settings. Results: Application of fingerprint powder was good in developing fingermarks; however, its suitability depends on the nature of the surface materials. Black fingerprint powder produced better visualisation where the fingermarks on all the 11 surface materials tested in this study were successfully recovered compared to white fingerprint powders. Methamphetamine-contaminated fingermarks could still be recovered using the fingerprint powder dusting method, but the fingermark grade was reduced due to the presence of exogenous substance. Conclusion: To conclude, the recovery and visualisation of methamphetamine-contaminated fingermarks on non-porous surfaces were successfully carried out through the application of fingerprint powder. A more severe contamination might lead to lower fingermark grade showing lesser ridge details.

Green infrastructure modelling for UHI control to urban thermal comfort: a case study of Temanggung urban area

ABSTRACT The Temanggung urban area in Central Java (Indonesia) already has considerable urban Green Infrastructure (GI), but the phenomenon of Urban Heat Island (UHI) has expanded to 268% in the last seven years. This study explores the theoretical gaps related to urban thermal comfort by combining tree management modelling as the independent variable and existing urban morphology as the dependent variable to identify the right place for the right tree using ENVI-met v5 simulations. We use the Universal Thermal Climate Index (UTCI) to determine urban thermal comfort, and our results showed a temperature decrease from 37°C (strong heat stress) to 32.5°C–35.5°C (moderate heat stress) and an average decrease of 1°C–3.5°C. These results were obtained by simulating the placement pattern of substations, land use zoning, tree size composition, paying attention to the type of road surface material, wind direction and speed and direction of road orientation, average air temperature, and location of building masses. This study provides a basis for consideration to assist local governments and researchers in managing GI in Temanggung Urban Area to create comfortable urban thermal comfort for their residents.

Evaluation of a tabletop UV-LED device for disinfection of smart devices and tools

Smart devices and tools, which are transferred into clean rooms, have a risk of surface contamination. Chemical disinfection can harm such devices and the process is prone to error due to the manual procedure. Therefore, a UVC-LED based disinfection device was developed, which can inactivate almost all kind of pathogens. The disinfection efficiency was investigated on 3 test surfaces (stainless steel, plastic, glass), each with 5 different types of pathogens (S. aureus, P. aeruginosa, A. brasiliensis-spores, B. subtilis-spores, Pae. glucanolyticus-spores). The tabletop device is capable of significant inactivation rates between 2 and 8 log10 depending on the type of pathogen in less than 10 min. For the first time, we report that Pae. glucanolyticus-spores can be inactivated with UV-LEDs. Furthermore, we report that the type of surface material does have an impact on the disinfection performance. We assume that it is related to the surface roughness of the different materials combined in conjunction with the size of the microorganisms. The novel UV-LED disinfection device has the potential to become a routine device for bringing tablets and other electronical devices into cleanrooms.

A Comparison of Acoustical Performance Between Traditional and Modern Church

Due to various worship approaches, churches' plans and designs have evolved over time.Intraditional churches, the chancel is where the preacher and choir are while the nave is where the audience is seated. The modern churches on the other hand usually follow the auditorium layout, where the chancel is replaced by a stage. The stage serves asa space for the musical worship and performances, other than preaching by the pastors. In acoustical terms, the chancel and the stage are known as the sound source and the audience is the sound receiver. It is essential to understand the relationship between the differences in the characteristics of traditional and modern churches and the impact on their acoustical performance.Two churches with different layout (cruciform and rectangular) from traditional and modern churches in Malaysia were selected to run a computer simulation using ODEON Room Acoustic Software. The results were then reviewed and analyzedin acoustical parameters: (i) Reverberation Time (RT) and (ii) Speech Transmission Index (STI). Smaller church scored better in STI thus it is better for speech driven worship service as while bigger church scored better in RT thus it is more ideal for musical worship. In addition, the existing materials found in modern churches improved the overall acoustical performances. In conclusion, the church acoustical performance is affected by factors like the volume, distance between the sound origin, receivers and the surrounding walls, total absorption area and types of surface material and its absorption coefficient.

Tunable nanochannel resistive pulse sensing device using a novel multi-module self-assembly

Nanochannel-based resistive pulse sensing (nano-RPS) system is widely used for the high-sensitive measurement and characterization of nanoscale biological particles and biomolecules due to its high surface to volume ratio. However, the geometric dimensions and surface properties of nanochannel are usually fixed, which limit the detections within particular ranges or types of nanoparticles. In order to improve the flexibility of nano-RPS system, it is of great significance to develop nanochannels with tunable dimensions and surface properties. In this work, we proposed a novel multi-module self-assembly (MS) strategy which allows to shrink the geometric dimensions and tune surface properties of the nanochannels simultaneously. The MS-tuned nano-RPS device exhibits an enhanced signal-to-noise ratio (SNR) for nanoparticle detections after shrunk the geometric dimensions by MS strategy. Meanwhile, by tuning the surface charge, an enhanced resolution for viral particles detection was achieved with the MS-tuned nano-RPS devices by analyzing the variation of pulse width due the tuned surface charge. The proposed MS strategy is versatile for various types of surface materials and can be potentially applied for nanoscale surface reconfiguration in various nanofluidic devices.

Arena surface vertical impact forces vary with surface compaction

Mechanical properties of arena surfaces are extrinsic factors for musculoskeletal injury. Vertical impact forces of harrowed and compacted cushion were measured at five locations on 12 arena surfaces (five dirt, seven synthetic [dirt and fiber]). Eight variables related to impact force, displacement, and acceleration were calculated. Surface temperature, cushion depth and moisture content were also measured. The effects of surface material type (dirt/synthetic) and cushion compaction (harrowed/compacted) on vertical impact properties were assessed using an analysis of variance. Relationships of manageable surface properties with vertical impact forces were examined through correlations.Compacted cushion exhibited markedly higher vertical impact force and deceleration with lower vertical displacement than harrowed cushion (P < 0.001), and the effect was greater on dirt than synthetic surfaces (P = 0.039). Vertical displacement (P = 0.021) and soil rebound (P = 0.005) were the only variables affected by surface type. Surface compaction (harrowed, compacted) had a significantly greater effect on vertical impact forces than surface type (dirt, synthetic). By reducing surface compaction through harrowing, extrinsic factors related to musculoskeletal injury risk are reduced. These benefits were more pronounced on dirt than synthetic surfaces. These results indicate that arena owners should regularly harrow surfaces, particularly dirt surfaces.

Mechanism of laser-inducing hierarchical surface structures improving bonding strength of metal-plastic hybrids

The mechanism of laser-inducing hierarchical metal surface structures was investigated both experimentally and numerically, providing insights into improving bonding strength of metal-plastic hybrids (MPHs). For this purpose, metal sheets were first treated with the nanosecond pulsed laser to induce surface microstructures, followed by being put into the mold cavity, and jointed with plastic via the injection molding process. Both aluminum and titanium alloys were adopted as the metals and the glass fiber reinforced polyphenylene sulfide was chosen as the plastic material for MPHs. Specifically, laser parameters including the scanning speed, laser frequency, scan repetition, and modulation switch pulse-width were varied to investigate their impact on metal surface microstructure morphology and thus the MPH bonding strength. It is found that although having no clear trend while changing the laser parameters, the MPH bonding strength is strongly dependent on both surface morphology and material type of the metals. Finally, the process was optimized based on observations concerning the laser-inducing mechanism. The titanium alloy surfaces with bumpy morphology facilitated forming microscale mechanical interlocking between the metal and the plastic, leading to enhanced MPH bonding strength.

Benchmarking the Photocatalytic Self-Cleaning Activity of Industrial and Experimental Materials with ISO 27448:2009.

Various industrial surface materials are tested for their photocatalytic self-cleaning activity by performing the ISO 27448:2009 method. The samples are pre-activated by UV irradiation, fouled with oleic acid and irradiated by UV light. The degradation of oleic acid over time is monitored by taking water contact angle measurements using a contact angle goniometer. The foulant, oleic acid, is an organic acid that makes the surface more hydrophobic. The water contact angle will thus decrease over time as the photocatalytic material degrades the oleic acid. In this study, we argue that the use of this method is strongly limited to specific types of surface materials, i.e., only those that are hydrophilic and smooth in nature. For more hydrophobic materials, the difference in the water contact angles of a clean surface and a fouled surface is not measurable. Therefore, the photocatalytic self-cleaning activity cannot be established experimentally. Another type of material that cannot be tested by this standard are rough surfaces. For rough surfaces, the water contact angle cannot be measured accurately using a contact angle goniometer as prescribed by the standard. Because of these limitations, many potentially interesting industrial substrates cannot be evaluated. Smooth samples that were treated with an in-house developed hydrophilic titania thin film (PCT/EP2018/079983) showed a great photocatalytic self-cleaning performance according to the ISO standard. Apart from discussing the pros and cons of the current ISO standard, we also stress how to carefully interpret the results and suggest alternative testing solutions.

Electronic transport on the two state “ON–OFF” of 1,3,3-trimethylindolino-6′-nitrobenzopyrylospiran as a light-driven molecular optical switch: A first-principle study

Here, the electronic transport characteristics of the 1,3,3-trimethylindolino-6′-nitrobenzopyrylospiran were studied by using NEGF and DFT methods. By being exposed to UV or visible light, this molecule can change from its MC to SP states. The titled structure was optimized at B3LYP/6–311++G(d,p) level of theory. To obtain conductivity results, the electrons of metal and organic atoms were approached by the single-plus polarization (SZP) for single-zeta and double-plus polarization (DZP) for double zeta basis sets, respectively. We examined some factors in this study, including various molecule geometries, the type of surface materials (Platinum, gold, and silver), the switching ratio, the gap between HOMO and LUMO levels, and the transmission spectra, at different bias voltages. The outcomes show that conductivity transforms from an off-state (high resistance) to an on-state as the molecule switches from MC to SP form (low resistance).

A benchmark dataset for binary segmentation and quantification of dust emissions from unsealed roads

The generation of reference data for machine learning models is challenging for dust emissions due to perpetually dynamic environmental conditions. We generated a new vision dataset with the goal of advancing semantic segmentation to identify and quantify vehicle-induced dust clouds from images. We conducted field experiments on 10 unsealed road segments with different types of road surface materials in varying climatic conditions to capture vehicle-induced road dust. A direct single-lens reflex (DSLR) camera was used to capture the dust clouds generated due to a utility vehicle travelling at different speeds. A research-grade dust monitor was used to measure the dust emissions due to traffic. A total of ~210,000 images were photographed and refined to obtain ~7,000 images. These images were manually annotated to generate masks for dust segmentation. The baseline performance of a truncated sample of ~900 images from the dataset is evaluated for U-Net architecture.

Recognition of whole-landscape changes due to extreme rain events in a hyperarid desert

Infrequent extreme rain events in extraordinarily dry environments can dramatically shape the landscape. Here, a change-detection method is demonstrated that uses Landsat 8 OLI scenes to document land surface modification across thousands of square kilometers due to two extreme rain events which impacted the Atacama Desert. Images for 4 dates, in 3 date-pairs, are analyzed using a Change Vector Analysis (CVA) technique which delivers a measure of the intensity of change (Intensity-CVA). In 2015, an extreme rain event affected the southern Atacama and one in 2019 affected the northern Atacama. In this work, we show with the 2015 event the efficacy of CVA to identify areas of mass transference by favorable comparison to published Interferometric Synthetic Aperture Radar (InSAR) permanent coherence loss for areas located in nearly flat terrains. The efficacy of Intensity-CVA was also demonstrated for the 2019 rain event, where it identified mass transference and mineral transformations. To clarify the physical processes behind the Intensity-CVA response, for specific areas with known types of surface materials we conducted Principal Component Analysis (PCA) on quadratic difference of Surface Reflection (SR) values of Landsat 8 OLI scenes for multiple dates and also analyzed Intensity-CVA trends. These two analyses reveal that, among locations with strong Intensity-CVA change, remotely identified changes are dominated by soil moisture variations associated with active alluvial deposits, playa lakes and evaporitic units verified by PCA even 60 Km away from the focus of the rain. Secondarily we detected salt crystals growth several months after the rain. This approach, validated in the Atacama, successfully identified the consequences of extreme rain events at the scale of the entire landscape. This result opens the possibility to use a long-lived sensor, such as Landsat, to examine geomorphological surface changes through time in arid environments in a context of climate change.

Electromagnetic particle simulation of secondary electron multipactor characteristics in inner surface of 110 GHz microwave output window

The secondary electron multipactor on the inner surface of the output window is one of the main factors limiting the power capacity of high power microwave. Therefore, it is of great significance to carry out relevant research. In this work, the process of secondary electron multipactor and the resulting loss of power are numerically simulated by using the electromagnetic particle model with one-dimensional spatial distribution and three-dimensional velocity distribution at the microwave frequency of 110 GHz. The influences of microwave electric field at the surface and dielectric material type on the power loss are studied. The simulation results show that the electron number density is higher than the critical cut-off number density after the secondary electron multipactor has reached the steady state, but the microwave electric field does not show obvious change. This is because the electrons in a very high electrostatic field are mainly concentrated in the domain of several micrometers near the dielectric surface, which is far less than the corresponding skin depth. The electron number density in the multipactor steady state increases with the microwave electric field increasing, but the ratio of the power loss to the microwave power at the surface increases slowly. After the multipactor reaches the steady state, the number density of electrons near the sapphire surface is the highest, followed by the number density near the crystal quartz surface, and the number density near the fused quartz surface is the lowest, so the corresponding power loss decreases successively. In order to verify the accuracy of the model, the simulated value of the multipactor threshold is compared with the experimental data, and the difference between them is discussed.

Automatic Segmentation of Bulk Material Heaps Using Color, Texture, and Topography from Aerial Data and Deep Learning-Based Computer Vision

This article proposes a novel approach to segment instances of bulk material heaps in aerial data using deep learning-based computer vision and transfer learning to automate material inventory procedures in the construction-, mining-, and material-handling industry. The proposed method uses information about color, texture, and surface topography as input features for a supervised computer vision algorithm. The approach neither relies on hand-crafted assumptions on the general shape of heaps, nor does it solely rely on surface material type recognition. Therefore, the method is able to (1) segment heaps with “atypical” shapes, (2) segment heaps that stand on a surface made of the same material as the heap itself, (3) segment individual heaps of the same material type that border each other, and (4) differentiate between artificial heaps and other objects of similar shape like natural hills. To utilize well-established segmentation algorithms for raster-grid-based data structures, this study proposes a pre-processing step to remove all overhanging occlusions from a 3D surface scan and convert it into a 2.5D raster format. Preliminary results demonstrate the general feasibility of the approach. The average F1 score computed on the test set was 0.70 regarding object detection and 0.90 regarding the pixelwise segmentation.

Recognition of Underwater Materials of Bionic and Natural Fishes Based on Blue-Green Light Reflection.

Thanks to the advantages of low disturbance, good concealment and high mobility, bionic fishes have been developed by many countries as equipment for underwater observation and data collection. However, differentiating between true and bionic fishes has become a challenging task. Commonly used acoustic and optical technologies have difficulty in differentiating bionic fishes from real ones due to their high similarity in shape, size, and camouflage ability. To solve this problem, this paper proposes a novel idea for bionic fish recognition based on blue-green light reflection, which is a powerful observation technique for underwater object detection. Blue-green light has good penetration under water and thus can be used as a signal carrier to recognize bionic fishes of different surface materials. Three types of surface materials representing bionic fishes, namely titanium alloy, carbon fiber, and nylon, are investigated in this paper. We collected 1620 groups of blue-green light reflection data of these three kinds of materials and for two real fishes. Following this, three machine learning algorithms were utilized for recognition among them. The recognition accuracy can reach up to about 92.22%, which demonstrates the satisfactory performance of our method. To the best of our knowledge, this is the first work to investigate bionic fish recognition from the perspective of surface material difference using blue-green light reflection.

Towards optimal foreign object debris detection in an airport environment

Foreign object debris (FOD) can critically damage aircraft engines as well as injure personnel in an airport environment. Airfield inspectors routinely inspect runways for the presence of FOD items, which differ in material, shape, and color, using conventional and automated methods. The major shortcoming of the current methods is their inability to detect all types of foreign objects in an accurate and timely manner for removal from the airport runways. In this study, we address this shortcoming, i.e., the lack of accuracy and timeliness in detection, by developing an object detection framework to detect FOD for quick removal from the airfields. Our proposed FOD detection framework consists of (i) unmanned aerial system (UAS) for inspecting and collecting data from the airfields, (ii) data preprocessing and augmentation techniques to counter the issue of learning on limited types of foreign objects, weather conditions, and airport surface materials that are present in the data sets, and (iii) a computer vision-based object detection model to attain high accuracy and faster inference time for deployment in a real-world airport environment. We generated the training data with the UAS at an air force range and developed various models, including the You Only Look Once (YOLO) object detector family of models (one-stage object detectors) in our framework. Our models are evaluated on previously unseen data collected by the UAS and a publicly available data set of FOD images. The experiment results demonstrate that our proposed approach with a version of the YOLO model (YOLOv4) with transfer learning provides a faster inference time for FOD detection and outperforms the other models in the precision and recall metric values.

Hot Surface Ignition of Liquid Fuels Under Ventilation.

Mine equipment fires remain as one of the most concerning safety issues in the mining industry, and most equipment fires were caused by hot surface ignitions. Detailed experimental investigations were conducted at the NIOSH Pittsburgh Mining Research Division on hot surface ignition of liquid fuels under ventilation in a mining environment. Three types of metal surface materials (stainless steel, cast iron, carbon steel), three types of liquids (diesel fuel, hydraulic fluid, engine oil), four air ventilation speeds (0, 0.5, 1.5, 3 m/s) were used to study the hot surface ignition probability under these conditions. Visual observation and thermocouples attached on the metal surface were used to indicate the hot surface ignition from the measured temperatures. Results show that the type of metal has a noticeable effect on the hot surface ignition, while ventilation speed has a mixed influence on ignition. Different types of liquid fuels also show different ranges of ignition temperatures. Results from this work can be used to help understand equipment mine fires and develop mitigation strategies.

Skin-inspired textile-based tactile sensors enable multifunctional sensing of wearables and soft robots

Multifunctional tactile sensors that can mimic the sensory capabilities of human skin to perceive various external static and dynamic stimuli are essential to interact with the environment and humans for wearable electronics and soft intelligent robotics. Here, inspired by human skin, we report a textile-based tactile sensor capable of multifunctional sensing for personalized healthcare monitoring and soft robotic control. The tactile sensor consists of a triboelectric nanogenerator sensing layer to mimic the function of fast adapting (FA) mechanoreceptors and a piezoresistive sensing layer to achieve the functionality of slow adapting (SA) mechanoreceptors. The tactile sensor has been demonstrated to be able to recognize voice and monitor physiological signals and human motions in a real-time manner. Combined with a machine learning framework, the tactile sensor is able to percept surface textures and material types with high accuracy. It is also demonstrated as an effective human-machine interface for the control of assistive robotics.