Height Images Research Articles

High-Density Polyethylene–Polypropylene Blends: Examining the Relationship Between Nano/Microscale Phase Separation and Thermomechanical Properties

The phase separation of high-density polyethylene (HDPE)–polypropylene (PP) blends was studied using atomic force microscopy in tapping mode to obtain height and phase images. The results are compared with those from scanning electron microscopy imaging and are connected to the thermomechanical properties of the blends, characterised through differential scanning calorimetry, dynamic mechanical analysis (DMA), and tensile testing. Pure PP, as well as 10:90 and 20:80 weight ratio HDPE–PP blends, showed a homogeneous morphology, but the 25:75 HDPE–PP blends exhibited a sub-micrometre droplet-matrix structure, and the 50:50 HDPE–PP blends displayed a more complex co-continuous nano/microphase-separated structure. These complex phase separation morphologies correlate with the increased loss modulus (viscous properties) of the corresponding blends as measured by DMA, demonstrating the potential for the creation of strong and simultaneously tough, energy-absorbing materials for numerous applications.

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases.

Assessing the potential of hand grip strength as an indicator of spinal muscle size

In order to assess how well hand grip strength can predict spinal muscle size and to determine if scaling improves prediction Biobank data was acquired consisting of hand grip strength, age, height, body mass and abdominal magnetic resonance images for 150 age-matched male and female participants. The cross-sectional area of the multifidus and erector spinae was measured from the images at the L3/L4 level. Correlation strength and prediction errors were quantified for muscle size predicted from hand grip strength, age, height, and body mass. The effect of scaling muscle area by height and height squared was also determined. All variables correlated significantly with spine muscle size. The strongest correlator was hand grip strength (r = 0.61, p < 0.05) with a prediction error of 678 mm2. The strength of the correlations was reduced when muscle areas were scaled. Hand grip strength can predict spine muscle size in male and female participants; however, the confidence intervals on the predicted values are larger than would be expected from measuring muscle size directly using imaging technologies. Scaling by height or height squared does not improve the ability of hand grip strength to predict muscle size.

Trace_y: Software algorithms for structural analysis of individual helical filaments by three-dimensional contact point reconstruction atomic force microscopy.

Atomic force microscopy (AFM) is a powerful and increasingly accessible technology that has a wide range of bio-imaging applications. AFM is capable of producing detailed three-dimensional topographical images with high signal-to-noise ratio, which enables the structural features of individual molecules to be studied without the need for ensemble averaging. Here, a software tool Trace_y, designed to reconstruct the three-dimensional surface envelopes of individual helical filament structures from topographical AFM images, is presented. Workflow using Trace_y is demonstrated on the structural analysis of individual helical amyloid protein fibrils where the assembly mechanism of heterogeneous, complex and diverse fibril populations due to structural polymorphism is not understood. The algorithms presented here allow structural information encoded in topographical AFM height images to be extracted and understood as three-dimensional (3D) contact point clouds. This approach will facilitate the use of AFM in structural biology to understand molecular structures and behaviors at individual molecule level.

A clinician approach to assess the accuracy of cone beam computerized tomography in dental implant placement

Dental radiography is widely used by clinicians in dentistry, whereas in radiology continuously and rapidly developing devices have become integral to dental practices. The conventional dental x-rays have limited diagnostic value as being two-dimensional (2D) depictions of three dimensional (3D) oral cavity. The Cone Beam Computed Tomography (CBCT) currently in practice, is considered to be gold standard for diagnostic assessment in orofacial dentistry. CBCT enables a precise 3D image of the orofacial region and provides more accuracy in evaluating the architecture, contouring and density of the bone and soft tissues. It has wide implementation in implant dentistry. This research article evaluates the accuracy of the diagnostic imaging technique CBCT for postoperative accuracy. In 22 patients, 136 implants were replaced for missing teeth with equal males to female ratio. 56 implants were placed in maxilla and 80 in mandible. The age of the group was between 45 to 60 yrs. Variety of implant systems with endosseous implants were replaced to accommodate and facilitate the missing teeth. All patients underwent CBCT Scan before and also within 06 months of implant placement. Standard torque of 25 Newton and 35 Newton were applied to stabilize the implants in the osteotomy cut in maxilla and mandible respectively. The patients were given prosthetic part after securing interim period of osseointigration with prefabricated available abutments. The results were evaluated preoperatively and post operatively on CBCT image for measurements of bone height and width at different level along with implant size variations in CBCT images. It has been significant dimensional changes observed in bone height measuring by Shapiro-Wilk test for normality (P = 0.0033, two-tailed), indicating a small but meaningful change in bone height following surgery. In coronal width using a paired t-test, no statistically significant difference pre- and postoperative measurements (P = 0.9232) was noted likewise in apical width 2 (P = 0.9232) indicating stability after surgery. The implant height and width post CBCT images measured comparative to actual implant size, they are statistically significant (P < 0.0001) in both directions, indicating a slight overestimation in both height and width by CBCT, underscoring the need to account for these variations in clinical practice.

Producing annual Australia-wide vegetation height images from GEDI and Landsat data

ABSTRACT Vegetation height, and its spatial and temporal changes, is an important environmental parameter required for understanding natural habitats, estimating carbon stores and monitoring forestry activities. Recent satellite LiDAR altimetry sensors have discontinuous spatial coverage but can be combined with spatially complete remote sensing data to extrapolate to large regions. Earlier studies have focused on producing a single spatially continuous vegetation height product. This research builds on past studies, using Landsat (annual surface reflectance and fractional cover products) and the Global Ecosystem Dynamics Investigation (GEDI) data to generate annual vegetation height layers from 1988 to 2022. GEDI data for 2019 were used to train and validate the model, resulting in a root mean square error (RMSE) of 5.45 m, mean absolute error (MAE) of 3.82 m, and coefficient of determination (R2) of 0.63. This accuracy reduces when the modelled height for 2020, 2021, and 2022 is compared to GEDI data for the same years (RMSE = 6.08–6.29 m, MAE = 4.36–4.73 m, and R2 = 0.48–0.54). Validation with independent field measurements across Australia from 2011 to 2021 shows an RMSE, MAE, and R2 of 8.2 m, 5.2 m, and 0.48, respectively. One source of error is the saturation of the Landsat signal in tall, closed canopy vegetation. While model accuracy is correlated with plot-based vegetation height measurements, results indicate that accuracy reduces for the years outside of the model calibration year (i.e. 2019). When compared to other vegetation height products (also produced using GEDI and spatial remote sensing data) from three independent published studies (one for 2009, one for 2019, and one for 2020), the model developed here tends to estimate 2–4 m taller than the first two studies and around 5 m shorter when compared to the third study. This investigation demonstrates the potential to produce multiyear vegetation height at a continental scale but also highlights the large uncertainty in modelled estimates especially when extrapolating to years other than the model calibration year.

Thiol Coordination Softens Liquid Metal Particles To Improve On-Demand Conductivity.

Achieving tunable rupturing of eutectic gallium indium (EGaIn) particles holds great significance in flexible electronic applications, particularly pressure sensors. We tune the mechanosensitivity of EGaIn particles by preparing them in toluene with thiol surfactants and demonstrate an improvement over typical preparations in ethanol. We observe, across multiple length scales, that thiol surfactants and the nonpolar solvent synergistically reduce the applied stress requirements for electromechanical actuation. At the nanoscale, dodecanethiol and propanethiol in toluene suppress gallium oxide growth, as characterized by transmission electron microscopy and X-ray photoelectron spectroscopy. Quantitative AFM imaging produces force-indentation curves and height images, while conductive AFM measures current while probing individual EGaIn particles. As the applied force increases, thiolated particles demonstrate intensified softening, rupturing, and stress-induced electrical activation at forces 40% lower than those for bare particles in ethanol. To confirm that thiolation facilitates rupturing at the macroscale, a laser is used to ablate samples of EGaIn particles. Scanning electron microscopy and resistance measurements across macroscopic samples confirm that thiolated EGaIn particles coalesce to exhibit electrical activation at 0.1 W. Particles prepared in ethanol, however, require 3 times higher laser power to demonstrate a similar behavior. This unique collection of advanced techniques demonstrates that our particle synthesis conditions can facilitate on-demand functionality to benefit electronic applications.

Differences in the body image based on physical parameters among young women from the Czech Republic and Slovakia.

The increased prevalence of overweight and obesity in the Czech Republic and Slovakia has led to heightened emphasis on weight control, particularly among women. Our aim is to explore body image perceptions among women in both countries and compare their attitudes, focusing on the relationship between body image and body mass index (BMI), height, weight, body fat and the weight control. The cross-sectional study involved 358 female students from the University of Pardubice and Constantine the Philosopher University in Nitra, with equal representation from the Czech Republic and Slovakia. Body parameters were assessed using anthropometric methods and the InBody 230 diagnostic device, while participants' perceptions of their bodies were evaluated using the Body Shape Questionnaire. The results confirmed that BMI did not significantly influence self-perception among Slovak students, while Czech participants with increasing obesity tended to perceive themselves more negatively. Significantly higher median values for BMI (p = 0.0509), weight (p = 0.0507), height (p = 0.05) and body image (p = 0.002) were observed in the Czech Republic compared with Slovakia. No significant difference was found in weight control and fat between participants from both countries. Although obesity was demonstrated in both nations, body satisfaction was different for participants from the Czech Republic and Slovakia.

UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning

Water stress is a significant challenge for global food production. Rainfall pattern is becoming unpredictable due to climate change that causes unprecedent water stress conditions in cereals production including wheat which is one of the important staple food crops. To sustain wheat production under water limiting conditions, there is an urgent need to develop drought-tolerant wheat varieties. For this, screening large numbers of wheat genotype for traits related to growth and yield under water stressed conditions is crucial. In this study, we deployed high-throughput phenotyping approaches, including uncrewed aerial vehicle (UAV)-based multispectral imaging, advanced machine and deep learning regression models. Two separate field experiments, irrigated and rainfed, were conducted comprising 553 wheat genotypes, and collected dataset for traits such as plant height, phenology, grain yield, and timeseries multispectral imaging. UAV-multispectral imagery derived plant height measurements showed a high correlation (R2=0.75) with manual measurements. Vegetation indices derived from multispectral data differentiated growth pattern of genotypes under rainfed and irrigated conditions and were used in yield prediction modeling. Wheat genotypes were effectively ranked, and their response differentiated for water stress tolerance based on yield index, stress susceptibility index, and yield loss%. Importantly, yield prediction in genotypes was computed using four machine learning regression algorithms i.e., linear regression, support vector machine, random forest, and deep learning H2O-3, where H2O-3 was the most accurate model with R2=0.80. Results show that multispectral-driven traits combined with machine learning models effectively phenotyped large wheat population and such approaches can be integrated in crop breeding program to develop varieties tolerant to water stress.

Atomic force microscopy 3D structural reconstruction of individual particles in the study of amyloid protein assemblies.

Recent developments in atomic force microscopy (AFM) image analysis have made three-dimensional (3D) structural reconstruction of individual particles observed on 2D AFM height images a reality. Here, we review the emerging contact point reconstruction AFM (CPR-AFM) methodology and its application in 3D reconstruction of individual helical amyloid filaments in the context of the challenges presented by the structural analysis of highly polymorphous and heterogeneous amyloid protein structures. How individual particle-level structural analysis can contribute to resolving the amyloid polymorph structure-function relationships, the environmental triggers leading to protein misfolding and aggregation into amyloid species, the influences by the conditions or minor fluctuations in the initial monomeric protein structure on the speed of amyloid fibril formation, and the extent of the different types of amyloid species that can be formed, are discussed. Future perspectives in the capabilities of AFM-based 3D structural reconstruction methodology exploiting synergies with other recent AFM technology advances are also discussed to highlight the potential of AFM as an emergent general, accessible and multimodal structural biology tool for the analysis of individual biomolecules.

Chiroptical and morphological investigation of achiral PF6BT blended with chiral polymers: Chirality induction and visualization of topological structures

In this contribution, we explore the chiroptical properties of achiral PF6BT blended with three chiral polyfluorene derivatives using the same 1:9 chiral/achiral ratio. Circular dichroism spectroscopy (CD) revealed that the sergeants and soldiers principle was operating in annealed thin films, reaching a CD intensity of 1400 mdeg, signal unequivocally associated to the PF6BT backbone. Dissymmetry factor obtained in two blends were superior to that of pristine chiral polymers. Atomic force and confocal microscopies were employed to explore the morphological properties. Confocal analysis results showed that different types of chiral:achiral aggregations were evidenced in the blends. AFM height images revealed several self-assembled helical fibers and nanotoroids, and a rare supramolecular topological morphology, a Möbius strip.

Vertical control framework with levelling method as the based for detailed mapping in UNS Campus of Pabelan

Detail maps are one of the outputs created by measuring soil and mapping the topography of the earth’s surface on a huge scale. Preliminary steps must be completed, including the creation of fundamental mapping framework points that are evenly scattered across the area to be mapped. The goal of this study is to construct a vertical control framework and determine the accuracy of vertical control frameworks basic outline points in the form of polygons, measuring, processing data, displaying in the form of different height images and verifying their accuracy, and assessing the findings comprise the implementation technique. Vertical Control Framework measurement employs the chain levelling method and a levelling measuring tool. The results of the data analysis obtained a high difference of 8 mm in the results of the point before the correction, so the calculation of the height of the point after correction so that the height of the point equals zero in order to meet the feasibility as the basic framework of detail mapping and can be used for further measurements. The implications of the vertical control framework are utilized as a guide to generate a detail map.

Real-Time Face Mask Detection in Mass Gathering to reduce COVID-19 Spread

The outbreak of Covid 19 (coronavirus) pandemic has become one of the lethal health crises worldwide. So far no effective and completely curable vaccine or any alternative has been provided to stop this transmission. This virus gets transmitted from a person when they sneeze or when they speak also in the form of respiratory droplets. According to leading and well known scientists, wearing face masks and maintaining six feet of social distance are the most substantial protections to limit the virus’s spread. In the proposed model we have used the Convolutional Neural Network(CNN) algorithm of Deep Learning(DL) to ensure efficient real-time mask detection. We have divided the system in two parts – 1. Train Face Mask Detector 2. Apply Face Mask Detector for better understanding. This is a real time application which is used to discover or detect the person who is wearing a mask at proper position or not. The system has achieved an accuracy of 99% after being trained with the dataset, which contains around 2500 images of width and height 224×224. This type of model just needs to be integrated with a camera and the rest of the detection will be performed, like at airports, offices, railway stations, malls etc., to check whether a government guidelines are properly being followed or not.

Atomic force microscopy and scanning electron microscopy characterization of the controlling of surface morphology of epoxy‐amine‐cured spin‐coated films

AbstractAtomic force microscopy (AFM) was successfully used to study spin‐coated, amine‐cured epoxy film microstructure and morphology. The air‐epoxy and epoxy‐substrate interfaces were examined using tapping‐mode height and phase imaging AFM. The impact of relative humidity on the morphology and microstructure of the surfaces was determined. AFM was able to elucidate the changes on the surface as relative humidity during processing increased. It was observed that large nodular formations formed on the epoxy surface expose to the air but not epoxy surface formed on the substrate in addition to varying regions of more or less compliant structures, which was attributed to carbamate formation caused by the amine curing agent reaction with atmospheric CO2. Scanning electron microscopy (SEM) was used to further elucidate interface and interphase morphology of spin‐coated epoxies. Experimentation also demonstrated that post‐curing above the glass transition did not change the morphology structure, suggesting surface structures are “locked‐in.” SEM was used to further elucidate how the interface and interphase change with changing environmental conditions at both the air‐epoxy and epoxy‐substrate interface/interphases, including the impact of atmospheric CO2 on Marangoni cell formation.

Electrical characterization of Al/SnO2/PbO/Si double layer MOS under the moderate radiation effect

The metal-oxide semiconductor-based structures are among the most prominent optoelectronic devices that can be used in several application fields. The performance of these structures is strongly dependent on the local radiation dose. In this study, we examined the sample structures with and without radiation. The electrical parameters of the studied samples such as ideality factor, series resistance, saturation current, barrier height, and zero-bias barrier height were obtained by performing the forward bias I–V data analyses. The values of n indicate that the irradiation emission mechanism is dominated. Also, this value depends on the evaluation method of the device. According to the Cheung method, while the ideality factor was found to be 5.72 before irradiation, it was found to be 7.04 after irradiation with 30 kGy. According to the TE method, on the other hand, the value of the ideality factor was found to be 6.337 before irradiation while it was found to be 5.016 after irradiation. The high figures of n were considered to be the result of the interface states of the metal/oxide/semiconductor, i.e. the device, and the barrier height's image force lowering. The obtained dielectric parameters do not have to be absolutely correct, especially when there is radiation on the device. It was found that the value of Rs extremely depended on the particular current transport mechanism's contributions to the total current. The non-ideality factor n increases with the 30 kGy irradiation according to the Cheung method while it does not according to the TE theory. At the same time, the forward bias I–V characteristics and their logarithmic plots reveal two distinct linear sections in the entire linearly increasing voltage.

6.83 Height and Subjective Body Image are Associated With Suicide Ideation Among Korean Adolescents

6.83 Height and Subjective Body Image are Associated With Suicide Ideation Among Korean Adolescents

Study of Early-Age Hydration, Mechanical Properties Development, and Microstructure Evolution of Manufactured Sand Concrete Mixed with Granite Stone Powder.

This study explored the potential of granite stone powder (GSP) as a supplementary cementitious material (SCM). The 72 h early hydration process stages of GSP-mixed slurry were analyzed in depth, and the mechanical properties of manufactured sand concrete (MSC) mixed with GSP were investigated. Physical phase types, morphological characteristics, and pore structure evolution were investigated using an X-ray diffractometer, scanning electron microscope, and mercury intrusion approach (MIP). Atomic force microscopy was used to show the interface transition zone between aggregate and slurry in phase images, height images, and 3D images, allowing quantification of ITZ and slurry by calculating the roughness. Gray entropy analysis was used to evaluate the significance of the effect of pore size distribution parameters on mechanical strength, and the GSP-content-mechanical-strength gray model GM (1, 1) was established to predict mechanical strength. The results indicate that, compared with the reference group, the GSP cement slurry system exhibited a delayed hydration process acceleration rate, with a 1.04% increase in cumulative heat of hydration observed in the 5% test group and an 11.05% decrease in the 15% test group. Incorporating GSP in MSC led to decreased mechanical properties at all ages, with significant decay observed when incorporation ranged from 10% to 15%. Although the type of hydration products remained unchanged, there was a decrease in the number of C-S-H gels and gel pores, while large pores increased, resulting in increased porosity and roughness of the interface transition zone and slurry. Large pores (>1000 nm) were found to have the greatest influence on mechanical strength, with gray correlation above 0.86. The GM (1, 1) model yielded accurate predictions, showing good agreement with measured data and thus it can be identified as belonging to a high-precision prediction model category. These findings provide theoretical support and a reference for applying GSP as an SCM, laying the groundwork for data-based specification development.

Height and subjective body image are associated with suicide ideation among Korean adolescents.

Suicide is the leading cause of death among Korean adolescents. Suicide has been found to be associated with body mass index (BMI), height, and subjective body image among adults, but investigations of these associations among adolescents are limited. Thus, we aimed to examine to what extent suicide ideation is associated with height, BMI, and subjective body image among Korean adolescents. This study examined the data of 6,261 adolescents, selected from a nationally representative survey. The participants were divided into subgroups by sex, suicide ideation, and subjective body image. Logistic regression analyses were performed to examine the association of suicide ideation with height, BMI, and subjective body image. The proportion of perceived obesity was high in the total sample; the height Z-score was lower for the group with suicide ideation than the group without suicide ideation; the height Z-scores were also lower for female participants with suicide ideation than those female participants without suicide ideation. The proportions of depressed mood, suicide ideation, and suicide attempts were higher among the total sample and female participants with perceived obesity than among those with a normal body image. On logistic regression, perceived obesity was positively associated with suicide ideation even after adjusting for age, height Z-score, weight Z-score, and depressed mood, whereas height Z-score was negatively associated with suicide ideation. These relationships were more prominent among female participants than among male participants. Low height and perceived obesity, not real obesity, are associated with suicide ideation among Korean adolescents. These findings indicate that the need for an integrated approach to growth, body image, and suicide in adolescents is warranted.

Knowledge-Guided Convolutional Neural Network Model for Similar Three-Dimensional Wear Debris Identification With Small Number of Samples

Abstract Wear debris analysis (WDA) enables the provision of essential information towards the monitoring of machine fault diagnosis and the analysis of wear mechanism. However, this experience-based technology has not yet been automated for the identification of similar particle types due to the small number of samples and highly dispersed features. To address this problem, a knowledge-guided convolutional neural network model is developed to focus on two representative severe wear particles: fatigue and severe sliding particles that have highly similar contours but weakly discriminative surfaces. The height images of particle surfaces are adopted as the initial objective. Characterized by typical particle features, the empirical WDA knowledge is represented into the feature-marked images, and further automatically learned by a U-Net-based knowledge extraction network. By weighting with the U-Net output, a knowledge-guided particle classification network is constructed to identify similar particles under a small number of samples. With this methodology, the empirical WDA knowledge is transferred to guide the classification network for locating the discriminative features in particle height images. Thirty sets of fatigue and severe sliding particles are acquired from wear tests as the training and testing samples. For verification, the network kernel is visualized to trace the particle feature propagation in the classification. Experimental results reveal that the proposed method can accurately identify fault particles that are acquired from wear tests.

Molecularly Imprinted Nanoparticle Ensembles for Rapidly Identifying S. epidermidis

Staphylococcus epidermidis (S. epidermidis) belongs to methicillin-resistant bacteria strains that cause severe disease in humans. Herein, molecularly imprinted polymer (MIP) nanoparticles resulting from solid-phase synthesis on entire cells were employed as a sensing material to identify the species. MIP nanoparticles revealed spherical shapes with diameters of approximately 70 nm to 200 nm in scanning electron microscopy (SEM), which atomic force microscopy (AFM) confirmed. The interaction between nanoparticles and bacteria was assessed using height image analysis in AFM. Selective binding between MIP nanoparticles and S. epidermidis leads to uneven surfaces on bacteria. The surface roughness of S. epidermidis cells was increased to approximately 6.3 ± 1.2 nm after binding to MIP nanoparticles from around 1 nm in the case of native cells. This binding behavior is selective: when exposing Escherichia coli and Bacillus subtilis to the same MIP nanoparticle solutions, one cannot observe binding. Fluorescence microscopy confirms both sensitivity and selectivity. Hence, the developed MIP nanoparticles are a promising approach to identify (pathogenic) bacteria species.