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Single-particle inductively coupled plasma mass spectrometry (spICP-MS) has been used for particle size measurement of diverse types of individual nanoparticles and micrometer-sized carbon-based particles such as microplastics. However, its applicability to the measurement of micrometer-sized non-carbon-based particles such as silica (SiO2) particles is unclear. In this study, the applicability of spICP-MS to particle size measurement of non-porous/mesoporous SiO2 microspheres with a nominal diameter of 5.0 µm or smaller was investigated. Particle sizes of these microspheres were measured using both spICP-MS based on a conventional calibration approach using an ion standard solution and scanning electron microscopy as a reference technique, and the results were compared. The particle size distributions obtained using both techniques were in agreement within analytical uncertainty. The applicability of this technique to the detection of metal-containing protein-binding mesoporous SiO2 microspheres was also investigated. Bound iron (Fe)-containing proteins (i.e., lactoferrin and transferrin) of mesoporous SiO2 microspheres were detected using Fe as a presence marker for the proteins. Thus, spICP-MS is applicable to the particle size measurement of large-sized and non-porous/mesoporous SiO2 microspheres. It has considerable potential for element-based detection and qualification of bound proteins of mesoporous SiO2 microspheres in a variety of applications. [ABSTRACT FROM AUTHOR]

Simple Summary: Intraductal papillary mucinous neoplasms are non-invasive precursor lesions for pancreatic cancer that are often surgically resected when certain diagnostic criteria are met. While multiple cyst features play a role in determining their resectability, cyst size is an important factor. The aim of this study is to compare the cyst size on seven commonly-obtained MRI sequences with surgically-resected specimen pathology size in a retrospective cohort of 52 patients. The maximum MRI size across the sequences correlated well but tended to overestimate length compared to pathology measurement. BACKGROUND: While multiple cyst features are evaluated for stratifying pancreatic intraductal papillary mucinous neoplasms (IPMN), cyst size is an important factor that can influence treatment strategies. When magnetic resonance imaging (MRI) is used to evaluate IPMNs, no universally accepted sequence provides optimal size measurements. T2-weighted coronal/axial have been suggested as primary measurement sequences; however, it remains unknown how well these and maximum all-sequence diameter measurements correlate with pathology size. This study aims to compare agreement and bias between IPMN long-axis measurements on seven commonly obtained MRI sequences with pathologic size measurements. METHODS: This retrospective cohort included surgically resected IPMN cases with preoperative MRI exams. Long-axis diameter tumor measurements and the presence of worrisome features and/orhigh-risk stigmata were noted on all seven MRI sequences. MRI size and pathology agreement and MRI inter-observer agreement involved concordance correlation coefficient (CCC) and intraclass correlation coefficient (ICC), respectively. The presence of worrisome features and high-risk stigmata were compared to the tumor grade using kappa analysis. The Bland-Altman analysis assessed the systematic bias between MRI-size and pathology. RESULTS: In 52 patients (age 68 ± 13 years, 22 males), MRI sequences produced mean long-axis tumor measurements from 2.45–2.65 cm. The maximum MRI lesion size had a strong agreement with pathology (CCC = 0.82 (95% CI: 0.71–0.89)). The maximum IPMN size was typically observed on the axial T1 arterial post-contrast and MRCP coronal series and overestimated size versus pathology with bias +0.34 cm. The radiologist interobserver agreement reached ICCs 0.74 to 0.91 on the MRI sequences. CONCLUSION: The maximum MRI IPMN size strongly correlated with but tended to overestimate the length compared to the pathology, potentially related to formalin tissue shrinkage during tissue processing. [ABSTRACT FROM AUTHOR]

Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing method and intelligently adjusting the operating parameters of the harvesting device are the foundation of high-quality harvesting for lettuces. The overlapped leaves of adjacent hydroponic lettuces cause difficulties in measuring lettuce size, especially the leaves expansion size. Therefore, we proposed an image processing method for measuring lettuce height and leaves expansion size according to the upper contour feature of lettuces and an image included three lettuces. Firstly, the upper contours of the lettuces were extracted and segmented via image preprocessing. Secondly, lettuce height was measured according to the maximum ordinate of the contour. Lastly, the lettuce's upper contour was fitted to a function to measure the leaves expansion size. The measurement results showed that the maximal relative error of the lettuce height measurements was 5.58%, and the average was 2.14%. The effect of the quadratic function in fitting the upper contour was the best compared with the cubic function and sine function. The maximal relative error of the leaves expansion size measurements was 8.59%, and the average was 4.03%. According to the results of the lettuce height and leaves expansion size measurements, the grabbing parameters of each lettuce were intelligently adjusted to verify the harvesting effect. The harvesting success rates of lettuces was above 90%, and the injured leaves areas of the left, middle, and right lettuces in each image were 192.6 mm2, 228.1 mm2, and 205.6 mm2, respectively. This paper provides a reference for the design and improvement of intelligent harvesters for hydroponic lettuces. [ABSTRACT FROM AUTHOR]

Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision ( m A P ) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection. [ABSTRACT FROM AUTHOR]

Flow cytometry is a widespread and powerful technique whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. Herein, we propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel–Gaussian beam array, the light sheet is 12 μ m wide, 12 μ m high, and has a thickness of ~0.8 μ m . The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O (100 nm ) to 10 μ m (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz , which provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds. [ABSTRACT FROM AUTHOR]

In this paper, an image measurement method using a high-speed imaging system is proposed for the evolution of crystal population sizes during cooling crystallization processes. Firstly, to resist the negative effect from solution stirring and particle motion during crystallization, a U-net network-based image processing method is established to efficiently detect sufficiently clear crystals from the online captured microscopic images. Accordingly, the crystal size distribution model is analyzed in terms of the counted probability densities of these crystal images. Subsequently, a measurement method of size growth rate based on crystal population distribution is proposed to estimate the growth condition. An experimental case on a crystallization process of β-form LGA is used to show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this work, we investigated a platform for real-time emulsion droplet detection and size measurement in optofluidic platforms. An 8.2 µm core diameter input optical fiber and a multi-mode Gradient Refractive Index (GRIN) output fiber were integrated into an acrylic microfluidic channel platform consisting of three layers. Water-in-oil emulsions were investigated, since relevant applications have emerged in the recent past for these types of emulsions, such as drug encapsulation as well as droplet-based Polymerase Chain Reaction (PCR) amplification of DNA, among others. The main contribution of this work is in understanding the main physical phenomena (i.e., total internal reflection, refraction, and interference) behind the complex transmittance pattern obtained for these droplets. For this purpose, a frequency domain electromagnetic wave propagation modelling of the structure using the Finite Element Method (FEM) was used along with experimental measurements. [ABSTRACT FROM AUTHOR]

The deposited particulate material within a fibrous filter affects the pressure drop which develops through three different stages. The implementation of a time-adjustable matrix is intended to cause detachment of particle structures from fibers within the upstream layers at low flow velocities. The deposited particle structures are transported further within the filter and clear up void space for an extension of filter service life. As in previous studies observed fiber stretching initiate cracks and following detachment of particle structures with a simultaneously applied airflow. For complete detachment of the particle structure, five consecutive stretching cycles are performed in this study. The elongation velocity, the flow velocity and the particle loading are varied. Using an image analysis technique and a laser-light measurement technique simultaneously, the cumulative fraction of detached particle structures and the size of detached particle structures are determined. A high initial particle loading on the fiber induces early detachment of larger structures from the fiber. The size of detached structures is increased by the increase of the elongation velocity. The mean value remains almost constant whether the elongation velocity or superficial velocity are increased. For small initial structures on the fiber, a decrease in superficial velocity causes detachment of larger particle fragments. [ABSTRACT FROM AUTHOR]