Axis Length Research Articles

Experimental characterization of the size, shape and ash composition of solid particles from different biomasses and separated by elutriation

Biomass particles have a wide variety of size and shape, and their characterization is fundamental for energy generation design, production of biofuels, simulation, and sizing of processes involving two-phase flow, such as fluidized bed, fixed bed, and aerodynamic transport. In order to find design data for projects involving biomass, this study characterized sugarcane bagasse, sugarcane straw, bamboo, and cassava plant branches regarding size and shape, using two methods of particle separation: sieving and elutriation, in addition to image analysis. An experimental bench was built for the separation of biomass by elutriation. Each biomass was separated by sieving in the range 388 to 2360μm openings and drag velocities from 0.6 to 6 m/s. The size distribution (mass, major and minor axis length, area and volume) and the calculation of sphericity resulted in different patterns according to the method used: sieving or elutriation. Biomass portions of smaller physical size, or smaller aerodynamic size, concentrate a higher ash content than larger portions. However, some minerals, such as Fe, Si, and Cl, were more concentrated in larger particles, characteristic details that may be useful for controlling the composition of biomass ash to prevent corrosion or incrustation in thermal process equipment.

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes

Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology. We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization. In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p= 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p<0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity. Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy. This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.

Posterior corneal elevation changes during 12 month of overnight orthokeratology

Aimsthe aim of this study to investigate the elevation changes in posterior corneal surface after 12 months of orthokeratology (ortho-k) treatment. MethodsIn this retrospective chart review, medical records of 37 Chinese children who wore ortho-k lenses over 12 months were reviewed. The data of only right eye were analyzed. Variables including the flat and steep keratometry of anterior and posterior corneal principal meridians, central corneal thickness (CCT), posterior thinnest elevation of cornea (PTE), posterior central elevation of cornea (PCE) and posterior mean elevation of cornea (PME) were measured by Pentacam. Variables including anterior chamber depth (ACD), lens thickness (CLT) and ocular axis length (AL) were measured by optical biometry. All variables differences between baseline and 12 months after ortho-k treatment were assessed by statistical analyses. ResultsThe average age of all subjects was 10.70 ± 1.75 years (range 8–15 years old). The baseline spherical equivalent (SE) was −3.26 ± 1.52 D (−0.50D to −5.00D). Both flat and steep keratometry of anterior corneal surface and CCT were significantly decreased after 12 month follow up during ortho-k treatment (both P < 0.000). Both flat and steep keratometry of posterior corneal surface were not significantly different after 12 month follow up compared with that of baseline (P = 0.426, 0.134 respectively). PCE, PTE and PME were not significantly changed over 12 months of ortho-k treatment (P = 0.051, 0.952 and 0.197 respectively). The ACD was significantly decreased in 12 month follow up during ortho-k treatment (P = 0.001). The CLT and the AL were significantly increased during this period (both P < 0.000). ConclusionAlthough the anterior corneal surface was significantly changed by ortho-k lens, the posterior corneal surface did not show any changes during 12 months follow up. Simultaneously, The ACD, CLT and AL were significantly changed during this period.

Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer.

The aim of this study is to investigate the role of [68Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (PSISUP) grade in primary prostate cancer (PCa). This retrospective study included 47 PCa patients who underwent [68Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of PSISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal-Wallis and Mann-Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models' performance. The predictions of the best performing model were compared against ISUP grade at biopsy. ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM-Zone Entropy and Shape-Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann-Whitney p > 0.05). These findings support the role of [68Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PSISUP grade.

Study on Shear Behavior and Failure Characteristics of Bolted Anisotropic Rock Joints

Anisotropic discontinuity exists widely in rock masses of mines, tunnels, slopes, water conservancy and hydropower projects. The shear characteristics of bolted anisotropic rock joints are extremely important for the stability design of engineering rock mass. However, few scholars have studied the bolted anisotropic rock joint. The different rock properties on both sides of the rock joint, especially the different rock strengths, will greatly affect the deformation characteristics and failure mode of the rock mass. Based on this, a series of shear tests were carried out on the bolted anisotropic rock joint under different normal stresses, and the characteristics of shear stress-shear displacement curve, shear strength, failure characteristics of the rock joint and deformation characteristics of the bolt are discussed. λ is defined as the strength ratio of upper and lower rock on the structural surface. The results show that the effect of λ on the shear stress-shear displacement curve is not obvious at the pre-fracture stage. The shear stress-shear displacement curve at the pre-breaking stage of the bolt presents a softening stage when the normal stress is equal to 0.5 MPa, tends to be horizontal when the normal stress is equal to 1 MPa and presents a hardening stage when the normal stress is greater than 1 MPa. After the bolt is broken, the shear stress-shear displacement curve presents a stepped-down descent. With the increase in λ, the breaking shear stress of the bolt increases. Elliptic failure occurs on the surface of the bolted anisotropic rock joint, and the length of the major axis of the ellipse decreases with the increase in λ value and normal stress. The bolts with different λ values of anisotropic rock joint show "Z-shaped" tensile bending deformation characteristics after shear fracture, and the horizontal and vertical components of the bolt deformation decrease with the increase in λ value and normal stress. The fracture shear displacement of the bolt increases with the increase in normal stress and decreases with the increase in λ value. The research results are helpful to further understand the shear mechanical characteristics and differences of bolted rock joints and provide a reference for solving the engineering problems of the composite layered rock mass.

Experimental investigation on rock microfabrical effects on dynamic hardness behaviour

Rocks' physicomechanical properties are influenced by their textural qualities. Rock mass stability is largely governed by these variables. Many geoengineering facilities depend on the capability to assess both short and long-term rock behaviors based on the interaction between different textural characteristics and physicomechanical properties. Rock hardness is frequently used in excavation engineering and is measured by several methods with different mechanisms. The Leeb dynamic hardness method has been recently applied in rock engineering applications as a new, inexpensive, non-destructive, and portable technique. This paper aims to show the interaction between the quantitative microfabrical features and the Leeb hardness of the rocks. For this purpose, 33 various rock types were chosen by considering their physicomechanical properties and microfabrical characteristics. Thin sections were prepared and their microphotographs were analyzed by ImageJ software. Four basic characteristics, including grain perimeter and area and length of minor and major axes, were measured. Then, seven textural quantities, including equivalent diameter (Dequi), shape factor (SF), aspect ratio (AR), grain compactness (C), texture coefficient (TC), interlocking index (g), and grain size homogeneity (t), were calculated based on those basic parameters and statistically analyzed. The findings indicate that the Leeb hardness increases with increasing the grain area and perimeter, equivalent diameter, grain compactness, homogeneity of grain size, grain interlocking, and TC. In contrast, Leeb's hardness decreases with increasing the AR and SF. The texture coefficient reveals the most meaningful relationship (R2 > 0.8) with the Leeb dynamic hardness test among the studied microfabrical characteristics in sedimentary and igneous rock samples.

SELF-ASSEMBLY AND PHOTOINDUCED DEFORMATION OF MICROSPHERES OF AN AZO MOLECULE CONTAINING A 1,4,3,6-DIANHYDROSORBITOL CORE AND CINNAMATE PERIPHERAL GROUPS

This study investigated the controllable fabrication of anisotropic particles with different sizes and morphologies via self-assembly and photoinduced deformation of azo colloidal microspheres, which were formed from a typical azo molecular material (IAC-4) that contains a 1,4,3,6-dianhydrosorbitol core and the cinnamate peripheral groups. Firstly, azo colloidal microspheres were obtained by self-assembly via gradually adding deionized water into IAC-4 solution of tetrahydrofuran. For precisely controlling the length of short axis of anisotropic particles, IAC-4 colloidal microspheres with different and uniform sizes were obtained by adjusting the IAC-4 concentration, adding rate of water and stirring rate. The size of colloidal microspheres becomes smaller with the increase in IAC-4 concentration, the adding rate of water and the stirring rate. Finally, IAC-4 colloidal microspheres in the solid state, perpendicularly irradiated by the linearly polarized laser beam (λ = 488 nm), were irreversibly and controllably stretched into anisotropic particles with the different morphologies, length of long axis and axial ratio via adjusting the irradiation time and the power of laser beam.

Quantitative analysis of retinal microvascular structure and correlation with ocular parameters: A normative database of pediatric OCT angiography.

To analyze the vascular structure of the healthy pediatric population using optical coherence tomography angiography (OCTA), and to investigate its relationship with ocular parameters. This cross-sectional, prospective study included 142 eyes of 71 healthy subjects aged 8-18 years. All patients underwent a complete ocular assessment, biometric evaluation and OCTA analysis. Anterior chamber depth, axial length, corneal thickness values were also reported and their relationship with OCTA measurements was evaluated. There was no statistical difference between girls and boys in terms of demographic characteristics (p>0.05). Neither vascular density in the deep and superficial capillary plexus nor FAZ area was significantly related to the ocular parameters or age of any patient in the study. Despite the relatively small number of participants, this study may represent normative data for the Turkish pediatric population. It was also shown that vascular density and FAZ area in childhood are not affected by age, axis length, anterior chamber depth, cornea and foveal thickness.

Abstract P4-09-08: AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer

Abstract Background: Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and cancer nuclear morphology has been linked to genomic alterations. Quantitative analyses of the nuclear features of cancer cells and other tumor-resident cell types, such as cancer-associated fibroblasts (CAFs), may reveal novel biomarkers for prognosis and treatment response. Here, we applied a pan-cancer nucleus detection and segmentation algorithm and a cell classification model to hematoxylin and eosin (H&amp;E)-stained whole slide images (WSIs) of breast cancer specimens, enabling the measurement of morphological features of nuclei of multiple cell types within a tumor. Methods: Convolutional Neural Network models for 1) nucleus detection and segmentation and 2) cell classification were deployed on H&amp;E-stained WSIs from The Cancer Genome Atlas (TCGA) breast cancer dataset (primary surgical resections; N=890). Separate models were trained to segment regions of stromal subtypes, such as inflamed and fibroblastic stroma. Nuclear features (area, axis length, eccentricity, color, and texture) were computed and aggregated across each slide to summarize slide-level nuclear morphology for each cell type. Next-generation sequencing-based metrics of genomic instability (N=774) and gene expression (N=868) were acquired and paired with TCGA WSIs. Gene set enrichment analysis was performed using the Molecular Signatures Database. Spearman correlation compared nuclear features to genomic instability metrics. Linear regression was used to assess the relationship between nuclear features and bulk gene expression. Multivariable Cox regression with age and ordinal tumor stage as covariates was used to find association between overall survival (OS) and nuclear features. All reported results were significant (p&amp;lt; 0.05) when adjusted for false discovery rate via the Benjamini-Hochberg procedure. Results: Variation in cancer cell nuclear area, a quantitative metric related to pathologist-assessed nuclear pleomorphism, was calculated by the standard deviation of the nuclear area of cancer cells across a WSI. This feature was associated with genomic instability, as measured by aneuploidy score (r=0.448) and homologous recombination deficiency score (r=0.382), and reduced OS. In contrast, the variability in fibroblast and lymphocyte nuclear areas did not correlate with either metric of genomic instability (all r&amp;lt; 0.1, p&amp;gt;0.05). Furthermore, an association between variation in cancer cell nuclear area with the expression of cell cycle and proliferation pathway genes was observed, suggesting that increased nuclear size heterogeneity may indicate a more aggressive cancer phenotype. Features quantifying CAF nuclear morphology were also assessed, revealing that CAF nucleus shape (larger minor axis length) was associated with lower OS, as well as the expression of gene sets involved in extracellular matrix remodeling and degradation. Conclusions: The nuclear morphologies of breast cancer cells and CAFs reflect underlying genomic and transcriptomic properties of the tumor and correlates with patient outcome. The application of digital pathology analysis of breast cancer histopathology slides enables the integrative study of genomics, transcriptomics, tumor morphology, and overall survival to support research into disease biology research and biomarker discovery. Citation Format: John Abel, Christian Kirkup, Filip Kos, Ylaine Gerardin, Sandhya Srinivasan, Jacqueline Brosnan-Cashman, Ken Leidal, Sanjana Vasudevan, Deepta Rajan, Suyog Jain, Aaditya Prakash, Harshith Padigela, Jake Conway, Neel Patel, Benjamin Trotter, Limin Yu, Amaro Taylor-Weiner, Emma L. Krause, Matthew Bronnimann, Laura Chambre, Ben Glass, Chintan Parmar, Stephanie Hennek, Archit Khosla, Murray Resnick, Andrew H. Beck, Michael Montalto, Fedaa Najdawi, Michael G. Drage, Ilan Wapinski. AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-08.

A Low-Frequency Unidirectional Underwater Acoustic Transducer

A unidirectional underwater acoustic transducer has the advantage of narrower beam width. In the low-frequency field, a single transducer is usually omnidirectional because its dimension is much smaller than the wavelength of the sound waves. Here, a low-frequency unidirectional underwater acoustic transducer, the Single-Sided Radiation Flextensional Transducer (SSR FT), which is driven by a stack with the longitudinal vibration, was designed by the finite-element method. The vibration mode of the transducer was first studied and analyzed. Then, an underwater harmonic response analysis of the transducer was conducted. We found that the second resonance, corresponding to the fourth vibration mode, could be unidirectional based on a specific s br, which is the ratio of r b (the radius of the tail mass) to l maj (the inner length of the semi-major axis of the radiation surface). Finally, the effect of s br on the vibration velocity and directivity of the SSR FT was investigated. We established that the vibration velocity amplitude and phase of the shell were different at the same vibration mode but at different s br, which resulted in different directivity.

Gust Response Analysis Using the Kriging Method for Laminar Flow

This paper presents an input–output reduced-order aerodynamic model employing the kriging interpolation for laminar flow. The kriging interpolation is a regression method, particularly useful for large motions. In the targeted flow regime, the response includes unsteady vortex shedding at high angles of attack. The reduced-order model (ROM) predicts the aerodynamic response of a rigid airfoil to a () gust under a wide range of angles of attack, gust lengths, and gust intensities. We study two algorithms for sampling training simulations, equally spaced and Latin hypercube methods. Both methods result in a ROM with an accurate prediction of aerodynamic forces and moments, given sufficient training points, particularly along the gust length axis. We use this ROM in coupling with the structural dynamics equations to study the aeroelastic response of a one-degree-of-freedom elastic airfoil. The aeroelastic gust response predictions are accurate for moderately large pitch angle deformation, using only the ROM created from computational fluid dynamics simulation of the rigid airfoil.

Development of Potato Mass Estimation System Based on Deep Learning

Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle.

Terahertz all-dielectric metasurface sensor based on quasi-bound states in the continuum.

We proposed a quasi-bound states in the continuum (QBICs) metasurface to realize sensing in the terahertz band. It consists of silicon split ellipse cylinders with different short-long axes and a quartz substrate. By introducing two asymmetric split ellipse cylinders unit cells, magnetic dipole and electric quadrupole resonances of the proposed structure are investigated by multiple Pole theory. This shows that the continuum bound states are transformed into quasi-BICs by tuning the length of the ellipse long axis, and so a high-quality factor can be obtained. The Q value of the proposed structure is 3205, and the figure of merit is 469.64. It has potential applications in gas, liquid, and biomaterial sensing.

A radiomic biomarker for prognosis of resected colorectal cancer liver metastases generalizes across MRI contrast agents.

Contrast-enhanced MRI is routinely performed as part of preoperative work-up for patients with Colorectal Cancer Liver Metastases (CRLM). Radiomic biomarkers depicting the characteristics of CRLMs in MRI have been associated with overall survival (OS) of patients, but the reproducibility and clinical applicability of these biomarkers are limited due to the variations in MRI protocols between hospitals. In this work, we propose a generalizable radiomic model for predicting OS of CRLM patients who received preoperative chemotherapy and delayed-phase contrast enhanced (DPCE) MRIs prior to hepatic resection. This retrospective two-center study included three DPCE MRI cohorts (n=221) collected between January 2006 and December 2012. A 10-minute delayed Gd-DO3A-butrol enhanced MRI discovery cohort was used to select features based on robustness across contrast agents, correlation with OS and pairwise Pearson correlation, and to train a logistic regression model that predicts 3-year OS. The model was evaluated on a 10-minute delayed Gd-DO3A-butrol enhanced MRI validation cohort (n=121), a 20-minute delayed Gd-EOB-DTPA (n=72) cohort from the same institute, and a 5-minute delayed Gd-DTPA cohort (n=28) from an independent institute. Two features were selected: minor axis length and dependence variance. The radiomic signature model stratified high-risk and low-risk CRLM groups in the Gd-DO3Abutrol (HR = 6.29, p = .007), Gd-EOB-DTPA (HR = 3.54, p = .003) and Gd-DTPA (HR = 3.16, p = .04) validation cohorts. While most existing MRI findings focus on a specific contrast agent, our study shows the potential of MRI features to be generalizable across main-stream contrast agents at delayed phase.

On aerodynamic characteristics of rarefied hypersonic flows over variable eccentricity elliptical cavities

A comprehensive numerical study is performed to reveal the pressure on and heat flux to elliptical cavity surfaces using the direct simulation Monte Carlo method with the influences of elliptical cavity eccentricity, freestream altitude, freestream Mach number, and upstream wall curvature analyzed in detail. The obtained results show that both the high-pressure and high heat flux areas are located around the top lip of the downstream sidewall of an elliptical cavity, and whose peak pressure and peak heat flux are usually tens of times larger than those on the cavity floor; the depth of the high-pressure and high heat flux areas moving to the cavity floor is determined by the half axis length in streamwise, while the width of the two areas is controlled by that in spanwise. Compared with the cylindrical cavity, the elliptical cavity has slightly smaller peak pressure and peak heat flux on the downstream sidewall but has much smaller values on the cavity floor. For two elliptical cavities with the same eccentricity, the one that has a shorter length in streamwise is overall better in reducing the surface pressure and heat flux. The freestream altitude and Mach number also play an important role in affecting the surface pressure and heat flux. In comparison with the horizontal upstream wall, the convex upstream wall reduces the peak pressure and peak heat flux on the downstream sidewall but greatly raises them on the cavity floor, and the concave upstream wall makes them drop sharply in the form of a cliff no matter on the downstream sidewall or on the cavity floor. The physical mechanism behind the wall curvature effect can be explained as follows: surface pressure and heat flux on the downstream sidewall are originated from the violent impact of the external high-speed airstream on the sidewall, while those on the cavity floor are derived from the friction between the rotating vortices inside the cavity and the cavity floor.

Grounding fault location method of overhead line based on dual-axis magnetic field trajectory

To address the challenges in fault location in distribution networks, the distribution of magnetic field under overhead line and its relationship with three-phase currents are explored in this paper. At the same time, considering the influence of sensor installation position, line sag and galloping on magnetic field, a grounding fault location method of an overhead line based on dual-axis magnetic field trajectory is proposed. The analytical expressions of the magnetic field on the x-axis and y-axis under the overhead line are obtained by least squares fitting. The Lissajous figure synthesized by dual-axis is then compared with the general equation of an ellipse, and the characteristic quantity expression characterizing the magnetic field trajectory structure is obtained. Finally, a fault location criterion is constructed using the difference of the characteristic quantities of the ellipses synthesized by x-axis and y-axis magnetic fields upstream and downstream of the fault point, i.e., the difference of the length of the major axis and the minor axis, and the sign for the ratio of the cosine value of the inclination angle. Compared with other location methods based on electrical quantity, the principle of this method is simpler and it can locate faults more quickly and accurately. A large number of simulation results show that the proposed method is suitable for different types of fault conditions.

An axial length measurement system based on the VIPA spectrometer

In the ophthalmology field, the length of the eye axis is an effective way to determine whether the eye is healthy or not. Thus, the accurate measurement of the axial length becomes very important. In this paper, a measurement system for axial length based on a virtual image phase array spectrometer is planned. This system has many advantages, such as short acquisition time, wide spectrum measurement variability, high spectral resolution, etc. With the advantages of it being non-invasive and the fact that it can be used in real time, the optical coherence tomography system can be used to enhance the speed of the interference image spectrum of the cornea and retina. By verifying the effectiveness of the system, eye samples with an axis length of 18–32 mm are measured accurately with a measurement error of ±0.07 mm and a minimum measurement length of around 0.55 mm. The optical power of the incident light entering the sample at a wavelength of 810 nm is measured by an optical power meter to be less than 710 μm, and the power of the reflected light from the sample is 95nW. Based on the data above, the measurement system in this paper is identified as a biometric instrument that complies with internationally accepted safety standards.

Development and principles of intramedullary and extramedullary segmental bone transport: overview and clinical results

Segmental bone transport using intramedullary or extramedullary techniques is one of the most commonly used procedures for bone defect management in the diaphyseal region of long bones. The purpose of this work is to provide an overview of the historical development and general principles as well as the current status of extramedullary and intramedullary techniques of bone segment transport. In addition, own results on internal segmental bone transport are presented. Aretrospective database analysis included all patients between May 2019 and January 2021 who underwent segmental bone transport via afully implantable intramedullary segment transport nail system. In addition to the radiological outcome, the clinical outcome was assessed using the lower extremity functional scale (LEFS). A total of 9 patients (8male, 1female; age 41.7 ± 3.9years) could be included. The cause of the segmental bone defect was chronic fracture-related infection in all cases. The segmental defect size was 62 ± 10 mm. Sufficient bridging of the bone defect was achieved in all patients, with amean distraction rate of 0.9 ± 0.1 mm per day. Bony consolidation of the regeneration was achieved after 292 ± 57days, and final bony consolidation of the docking zone was achieved after 469 ± 116 days. At 22 ± 3months after implantation of the segmental transport nail, the LEFS demonstrated aresult of 59 ± 4points. No recurrence of infection or regeneration failure occurred. The aim of treating segmental bone defects, in addition to reconstructing aload-bearing bone, is to restore correct joint angles, leg length, and leg axis without torsional deviation and can be achieved via classical external segmental bone transport. In recent years, internal segmental bone transport has been developed as an alternative. It remains to be seen whether the preliminary and reproducibly good clinical and radiological results of segmental transport nails can be confirmed for widespread use in the future.

Comparison of multiclass classification techniques using dry bean dataset

BackgroundThe application of classsification methods through multivariate and machine learning techniques has enormous significance in agricultural sector. It is vital to classify various types of seeds as well as identify the quality of seeds which has a great impact on the production of crops. There is a wide range of genetic variations in dry beans all over the world. Many studies have been conducted previously on various dataset to indentify the sorts of dry beans, however most of them focused on machine learning techniques with binary classification. ObjectiveThe aim of this study is to investigate a reliable classifier which has the lowest noise implications and establish an algorithm for dry bean classification effectively. This paper focuses on outlier removals, oversampling with Adaptive Synthetic (ADASYN) algorithm and finding the best classifier to guarantee the highest possible accuracy. MethodsThe raw dataset for this study was accessed from UCI Machine Learning Repository. The dataset contained grains having 16 features, 12 dimensions, and 4 distinct shapes. For the purpose of eliminating missing values from the dataset, interquartile range (IQR) with python programming was utilized. Eight most popular classifiers were used in this study which are Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perception (MLP) with balanced and imbalanced classes. The authors utilized frequency tables, bar diagrams, boxplots, analysis of variance for descriptive analysis as well as data preprocessing. ResultsThe XGB classifier preferably outperformed than other classifiers with balanced and imbalanced distribution of dry beans within each class. It has acquired accuracy (ACC) 93.0% and 95.4% in imbalanced and balanced classes respectively. In case of balanced dataset, after application of ADASYN algorithm both KNN and RF techniques also performed well regarding the Classification Accuracy (ACC), Sensitivity (SE), Specificity (SP) and Cohen's kappa coefficient (Kappa) etc. The most important attributes for classifying the dry beans were found ShapeFactor2, Minor Axis Length, and ShapeFactor1 along with EquivDiameter, Roundness and ConvexArea. ConclusionsFor classification of dry seeds, the XGB classifier had performed well when the dataset contained both balanced and imbalanced distribution in classes. Also, it is the primary approach of identifying the classes of seeds/beans with balanced or not. If the classes of the target variable are balanced well, then the KNN and RF algorithms may be applied along with XGB technique for more accurate classification.

3D Imaging of Roadside and Underground Railway Particulate Matter Structures

Air pollution in cities is associated with adverse health conditions; however, the underlying reasons are poorly understood. Previous particulate matter (PM) imaging studies have mostly used 2D microscopic techniques. In this study, the 3D appearances of roadside and underground railway station airborne PMs and the elemental characteristics of underground station PM were examined. Roadside and underground station PMs were collected using a cyclone particulate matter collection system. The 3D structures of the PMs were examined using an Olympus LEXT OLS4100 confocal microscope. Elemental characteristics of underground railway station PM were evaluated with a Zeiss EVO 50 scanning electron microscope (SEM) in backscattered electron (BSE) mode. Confocal microscopy of both PM sources indicated some particles can have sharp appearing edges. The longest axis length of roadside particles had a median (range) of 4.0 (1.3 to 6.6) μm and height of 1.4 (0.3 to 2.1) μm. SEM imaging of underground railway station PM was consistent in appearance and size with confocal imaging; BSE mode indicated compositions primarily of iron, calcium, and silicon. In conclusion, we observed that some roadside and underground railway station PMs can have sharp appearing surfaces from 3D confocal microscopy. SEM imaging of underground railway station PM was consistent with confocal microscopy and enabled elemental analysis.