Post-processing Methods Research Articles

Breadfruit (Artocarpus altilis): Processing, nutritional quality, and food applications.

Breadfruit is an underutilized but highly nutritive crop containing complex carbohydrates while being low in fat. It is also a good source of essential amino acids (leucine, isoleucine, and valine). With a better understanding of breadfruit's morphology, its potential as a global solution to food security has been gaining popularity. Breadfruit has been forecasted to have a larger amount of suitable cultivable land area compared to major crops such as rice and wheat, making its cultivation more desirable. Due to its highly perishable nature, good post-harvesting and post-processing practices are essential to extend the shelf life of breadfruit for global transportation and consumption. This paper aims to provide a comprehensive review on various processing methods of flour and starch, nutritional significance and new food applications of this novel food staple. In this review, the effects of the different processing and post-processing methods of breadfruit flour and starch have been described, and the nutritional composition and application of breadfruit flour as an ingredient replacer in various food applications have been discussed. It is vital to understand the processing and post-processing methods of breadfruit flour to enhance its shelf-life, physicochemical and functional properties. Furthermore, a compilation of novel food applications has been done to promote its use in the food industry. In conclusion, breadfruit flour and starch are highly versatile for use in numerous food products with added health benefits.

Intensified Partial Least Squares Fault Diagnosis Method based on CARS

Partial Least Squares (PLS) is a fault diagnosis model used in statistical process monitoring. PLS decomposes the data space into a quality correlated subspace and an uncorrelated subspace, and monitors the state of both subspaces by setting the appropriate statistics. However, post-processing methods such as modified-PLS (MPLS) and efficient projection to latent structures (EPLS) gradually lose their ability to detect faults when low-intensity faults that do not affect quality. In order to eliminate the drawbacks of post-processing methods and improve the stability of algorithms under different conditions, this paper introduces the competitive and adaptive reweighted sampling method (CARS) into the field of fault diagnosis and proposes a fault filtering method combining CARS and orthogonal signal correction algorithm (OSC). First, an initial screening of the variable space was completed using CARS method. OSC algorithm is then used to remove quality- uncorrelated components. Next, the variable space is completely decomposed into a quality-correlated and an irrelevant subspace using intensified partial least squares method (IPLS). Finally, the validity of the model was verified using the simulations.

TempNet – temporal super-resolution of radar rainfall products with residual CNNs

AbstractThe temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. While TempNet achieves a mean absolute error of 0.332 mm/h, comparison methods achieve 0.35 and 0.341, respectively. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies.

Longitudinal assessment of cervical spinal cord compartments in multiple sclerosis.

Although cervical spinal cord (cSC) area is an established biomarker in MS, there is currently a lack of longitudinal assessments of cSC gray and white matter areas. We conducted an explorative analysis of longitudinal changes of cSC gray and white matter areas in MS patients. 65 MS patients (33 relapsing-remitting; 20 secondary progressive and 12 primary progressive) and 20 healthy controls (HC) received clinical and upper cSC MRI assessments over 1.10±0.28 years. cSC compartments were quantified on MRI using the novel averaged magnetization inversion recovery acquisitions sequence (in-plane resolution=0.67×0.67mm2), and in-house developed post-processing methods. Patients were stratified regarding clinical progression. Patients with clinical progression showed faster reduction of cSC areas over time at the level of cSC enlargement (approximate vertebral level C4-C5) compared to stable patients (p<0.05). In addition, when compared to the rostral-cSC (approximate vertebral level C2-C3), a preferential reduction of cSC and white matter areas over time at the level of cSC enlargement (p<0.05 and p<0.01, respectively) was demonstrated only in patients with clinical progression, but not in stable MS patients and HC. Compared to HC, MS patients showed comparable changes over time in all cSC compartments. MS patients with clinical disease progression demonstrate subtle signs of a more pronounced tissue loss at the level of cSC enlargement. Future studies should consider larger sample sizes and more extended observation periods.

Effect of post-processing methods on the surface quality of Ti6Al4V fabricated by laser powder bed fusion

Ti6Al4V is widely used in aerospace and medical applications, where high demands on dimensional accuracy and surface quality require the application of post-processing to achieve optimal performance. However, the surface quality of parts fabricated by LPBF is inferior due to the inherent defects of LPBF. Therefore, it is important to investigate the effect of post-processing on the surface quality of Ti6A14V parts fabricated by LPBF. In this work, the effect of post-processing methods (i.e., sandblasting, electrolytic polishing, chemical polishing, and abrasive flow polishing) on the surface quality of Ti6Al4V fabricated by laser powder bed fusion (LPBF) additive manufacturing was investigated. The changes in surface roughness and morphology of the 45° inclined square and curved pipe Ti6Al4V samples processed with post-processing were observed, and the weight and elemental changes of the parts were also analyzed. The result reveals that sandblasting, electrolytic polishing, chemical polishing, and abrasive flow polishing are all effective in improving the surface quality of Ti6Al4V parts fabricated by LPBF. The effect of sandblasting is mainly caused by sharp-edged grit driven by high-speed airflow, resulting in the lowest surface roughness and the least influence on the weight, but may contaminate the surface with residual brown corundum. Electrolytic polishing and chemical polishing achieve surface quality improvement through different corrosion patterns without changing the surface composition. The surface smoothness of parts processed with chemical polishing is the best, while the weight loss rate of the sample processed with electrolytic polishing is the most at about 7.47%. Abrasive flow polishing presents a remarkable effect on polishing the internal surface of the Ti6Al4V sample by the extrusion scratching, extrusion deformation, and micro-cutting effects of abrasive on the surface. The findings can provide important engineering references for the post-processing of precision Ti6Al4V parts fabricated by LPBF and further promote the engineering applications of Ti6Al4V parts.

Lighting Up the Heritage Sciences: The Past and Future of Laser-Induced Fluorescence Spectroscopy in the Field of Cultural Goods

With the rapid scientific and technological changes that occur every day, a new kind of necessity, real-time, rapid, and accurate detection methods, preferably also non- or minimally invasive and non-destructive, has emerged. One such method is laser-induced fluorescence spectroscopy (LIF), applied in various fields of activity in recent decades, ranging from industry and biochemistry to medicine and even heritage sciences. Fluorescence-based spectroscopic methods have all of the above-mentioned characteristics, and their functionality has been proven in many studies. Yet, they have not known great success as other molecular techniques. This paper is a short synthesis of the role of the laser-induced fluorescence spectroscopy technique in heritage sciences, the main applications in this field, along with its advantages and limitations. The article focuses on the most common types of lasers used, the merging of two or more methods into hybrid techniques, the enhancement of the analytical capabilities of LIF and post-processing methods, and also explores some future development possibilities of LIF.

POSYDON: A General-purpose Population Synthesis Code with Detailed Binary-evolution Simulations

Most massive stars are members of a binary or a higher-order stellar system, where the presence of a binary companion can decisively alter their evolution via binary interactions. Interacting binaries are also important astrophysical laboratories for the study of compact objects. Binary population synthesis studies have been used extensively over the last two decades to interpret observations of compact-object binaries and to decipher the physical processes that lead to their formation. Here, we present POSYDON, a novel, publicly available, binary population synthesis code that incorporates full stellar structure and binary-evolution modeling, using the MESA code, throughout the whole evolution of the binaries. The use of POSYDON enables the self-consistent treatment of physical processes in stellar and binary evolution, including: realistic mass-transfer calculations and assessment of stability, internal angular-momentum transport and tides, stellar core sizes, mass-transfer rates, and orbital periods. This paper describes the detailed methodology and implementation of POSYDON, including the assumed physics of stellar and binary evolution, the extensive grids of detailed single- and binary-star models, the postprocessing, classification, and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on precalculated grids. The first version of POSYDON targets binaries with massive primary stars (potential progenitors of neutron stars or black holes) at solar metallicity.

Rapid flavonoid-focused sub-chemome characterization of Draconis Sanguis using UPLC-Q-TOF-MS in combination with molecular weight imprinting and mass defect filtering

Draconis Sanguis is a precious Chinese medicinal material for activating blood and resolving stasis, and its effective components are flavonoids. However, the structural diversity of flavonoids in Draconis Sanguis brings great challenges to the in-depth chara-cterization of its chemical composition profiles. To clarify the substance basis of Draconis Sanguis, ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was used in this study to acquire MS data of Draconis Sanguis. The molecular weight imprinting(MWI) and mass defect filtering(MDF) were developed for rapid screening of flavonoids in Draconis Sanguis. Full-scan MS and MS~2 were recorded within the mass range m/z 100-1 000 in positive ion mode. Accor-ding to previous literature, MWI was employed to hunt for reported flavonoids in Draconis Sanguis, and the mass tolerance range of [M+H]~+ was set as ±10×10~(-3). A five-point MDF screening frame was further constructed to narrow the screening range of flavonoids from Draconis Sanguis. Combined with diagnostic fragment ions(DFI) and neutral loss(NL) as well as mass fragmentation pathways, 70 compounds were preliminarily identified from the extract of Draconis Sanguis, including 5 flavan oxidized congeners, 12 flavans, 1 dihydrochalcones, 49 flavonoids dimers, 1 flavonoids trimer and 2 flavonoid derivatives. This study clarified the chemical composition of flavonoids in Draconis Sanguis. Moreover, it also showed that high-resolution MS combined with data post-processing methods such as MWI and MDF could achieve rapid characterization of the chemical composition in Chinese medicinal materials.

Systematic and benchmarking studies of pipelines for mammal WGBS data in the novel NGS platform

BackgroundWhole genome bisulfite sequencing (WGBS), possesses the aptitude to dissect methylation status at the nucleotide-level resolution of 5-methylcytosine (5-mC) on a genome-wide scale. It is a powerful technique for epigenome in various cell types, and tissues. As a recently established next-generation sequencing (NGS) platform, GenoLab M is a promising alternative platform. However, its comprehensive evaluation for WGBS has not been reported. We sequenced two bisulfite-converted mammal DNA in this research using our GenoLab M and NovaSeq 6000, respectively. Then, we systematically compared those data via four widely used WGBS tools (BSMAP, Bismark, BatMeth2, BS-Seeker2) and a new bisulfite-seq tool (BSBolt). We interrogated their computational time, genome depth and coverage, and evaluated their percentage of methylated Cs.ResultHere, benchmarking a combination of pre- and post-processing methods, we found that trimming improved the performance of mapping efficiency in eight datasets. The data from two platforms uncovered ~ 80% of CpG sites genome-wide in the human cell line. Those data sequenced by GenoLab M achieved a far lower proportion of duplicates (~ 5.5%). Among pipelines, BSMAP provided an intriguing representation of 5-mC distribution at CpG sites with 5-mC levels > ~ 78% in datasets from human cell lines, especially in the GenoLab M. BSMAP performed more advantages in running time, uniquely mapped reads percentages, genomic coverage, and quantitative accuracy. Finally, compared with the previous methylation pattern of human cell line and mouse tissue, we confirmed that the data from GenoLab M performed similar consistency and accuracy in methylation levels of CpG sites with that from NovaSeq 6000.ConclusionTogether we confirmed that GenoLab M was a qualified NGS platform for WGBS with high performance. Our results showed that BSMAP was the suitable pipeline that allowed for WGBS studies on the GenoLab M platform.

Fabrication and Testing of a Vapor Polishing Device for ABS 3D-Printed Parts

Post-processing plays a significant role in improving the surface and mechanical properties of 3D-printed parts. One of these post-processing methods is vapor polishing which utilizes acetone to polish 3D-printed Acrylonitrile Butadiene Styrene (ABS) specimen. This process is usually done using an improvised vapor polishing setup to achieve the desired surface finish of parts. Hence, in order to accomplish a uniform and standard polishing procedure for laboratory use, a vapor polishing device has been developed in this study. To assess the efficiency of the said device, the resulting surface roughness, dimensional accuracy, and tensile strength of ABS 3D-printed polished specimens have been evaluated and compared to unpolished specimen. The surface roughness of the cube specimen was captured using a Trinocular Microscope and was uploaded to the Mountains9 Topography software. Further, the dimensional accuracy of both polished and unpolished specimen has been measured using a digital Vernier Caliper. The data demonstrated that the polished specimen’s surface was enhanced while its shape, geometry, and dimension were preserved. Tensile tests on two (2) sample sets of polished and unpolished specimens revealed that polishing with acetone vapor using this developed device could improve the specimen’s tensile strength.

Post-processing of additively manufactured IN625 thin-walled structures using laser remelting in directed energy deposition

Blown-powder directed energy deposition (DED) is especially useful for depositing on an existing part for the purpose of repair or adding features. Thin substrate deposition, wherein the width of the substrate is smaller than the laser diameter, is an important configuration for building and repairing complex components in various geometries. The present study seeks to understand the efficacy of in-line post-processing methods based on laser remelting (LR) for reducing internal (e.g., porosity) and external (e.g., surface roughness) for such thin-walled repair configurations. In this work, different levels of LR energy density and substrate width were evaluated using the LR process. Post processing outcomes in terms of deposit geometry as well as spatial distribution of porosity, pore density, pore volume fraction, pore diameter, and surface roughness were evaluated using X-ray microcomputed tomography and optical profilometry. The results showed that LR reduced pore density up to 88 % and reduced areal surface roughness to 4.159 μm. At some threshold, increasing LR energy density increased surface waviness and modified deposition geometry, this indicative of limits to the post processing method. Strategies for using LR to reduce total porosity, near-surface porosity, and improving surface roughness are briefly discussed.

Development of integration mode proton imaging with a single CMOS detector for a small animal irradiation platform

A novel irradiation platform for preclinical proton therapy studies foresees proton imaging for accurate setup and treatment planning. Imaging at modern synchrocyclotron-based proton therapy centers with high instantaneous particle flux is possible with an integration mode setup. The aim of this work is to determine an object’s water-equivalent thickness (WET) with a commercially available large-area CMOS sensor. Image contrast is achieved by recording the proton energy deposition in detector pixels for several incoming beam energies (here, called probing energies) and applying a signal decomposition method that retrieves the water-equivalent thickness. A single planar 114 mm × 65 mm CMOS sensor (49.5 µm pixel pitch) was used for this study, aimed at small-animal imaging. In experimental campaigns, at two isochronous cyclotron-based facilities, probing energies suitable for small-animal-sized objects were produced once with built-in energy layer switching and the other time, using a custom degrader wheel. To assess water-equivalent thickness accuracy, a micro-CT calibration phantom with 10 inserts of tissue-mimicking materials was imaged at three phantom-to-detector distances: 3 mm, 13 mm, and 33 mm. For 3 mm and 13 mm phantom-to-detector distance, the average water-equivalent thickness error compared to the ground truth was about 1% and the spatial resolution was 0.16(3) mm and 0.47(2) mm, respectively. For the largest separation distance of 33 mm air gap, proton scattering had considerable impact and the water-equivalent thickness relative error increased to 30%, and the spatial resolution was larger than 1.75 mm. We conclude that a pixelated CMOS detector with dedicated post-processing methods can enable fast proton radiographic imaging in a simple and compact setup for small-animal-sized objects with high water-equivalent thickness accuracy and spatial resolution for reasonable phantom-to-detector distances.

Drift analysis and sectional post-processing of indoor simultaneous localization and mapping (SLAM)-based laser scanning data

Even though the SLAM laser scanners (LSs) have the required resolution for construction analysis, a drift error has been detected in georeferencing phase of the data processing. The reduction of drift error in SLAM LSs with third-party software has not been studied. This paper demonstrates the drift error behavior of commercial SLAM LSs and their correction with sectional post-processing methods, and shows that the drift error is mainly in a vertical direction, and it is largest in the middle of the trajectory. The drift error was reduced from 10.6 cm to 0.2 cm. Circa 30 s length sections have better results than larger sections with non-rigid transformation algorithm. The method has shown potential to improve the quality and cost-effectiveness of multitemporal construction documentation. In addition, locations of additional control points were analyzed to prevent drift error, with adjustments to the walking path.

Transition of catalytic recombination pathways on silica-based thermal protection materials at different temperatures using reactive molecular dynamics method

Silica-based ceramic material is one of the most competitive options of thermal protection material. However, catalytic reaction mechanism of gaseous atoms on it is complex and confusing. To model catalytic recombination of dissociated oxygen atoms accurately in prediction of chemical non-equilibrium flow and aerodynamic heating, the reactive molecular dynamics method was adopted to simulate gas–surface interaction on the interface, and a series of post-processing methods were constructed to analyze recombination pathways of atomic oxygen on α-quartz. It was found that there are four types of adsorbates on α-quartz surface and five pathways to produce recombined oxygen molecules. Recombination pathways would change from Eley–Rideal recombination-dominated to molecule desorption-dominated with increase in temperature. Information extracted by current post-processing methods explains how and why the recombination coefficient changes with temperature. The post-processing methods can be further applied in analysis of catalytic recombination on other thermal protection materials.

DeepErythema: A Study on the Consistent Evaluation Method of UV SPF Index Through Deep Learning

This study presents a deep learning-based approach for consistent Sun Protection Factor (SPF) grading by quantifying skin erythema. Despite strict international standards, the current human-based Minimum Erythema Dose (MED) determination method for SPF evaluation suffers from inconsistency due to subjective criteria and the visual characteristics of erythema’s large variance. We propose DeepErythema, a novel method that uses erythema quantification for MED determination. The proposed method comprises pre-processing methods, a deep learning segmentation model, and post-processing methods. The UV irradiation area pointing is a pre-processing method that accurately detects the inspection area as a UV irradiated port, while Median Gradation Reduction eliminates the gradation that arises due to the digital image collection environment. The deep learning segmentation model parts introduce a methodology for improving performance using various feature extractors and methodologies like SeLu, Reverse Attention Gate, and MixedLoss. For post-processing, Perspective Transform Rectangle Restoration restores a distorted inspection area, and Relative Density Evaluation calculates a density score to consider skin characteristics and tone. We verified that utilizing the DeepErythema score in the UV SPF MED Evaluation (USME) dataset reduced the distribution of human-based MED decisions from 36 to 9 in the experimental results. This reduction in MED decision scope leads to improved consistency in SPF Index grading. Overall, the study contributes to the development of an objective and consistent evaluation method for SPF grading.

Inverted Non-Maximum Suppression for More Accurate and Neater Face Detection

CNN-based face detection methods have achieved significant progress in recent years. In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection. In general, the face detection method predicts several candidate bounding- boxes for one face. NMS is used to filter out inaccurate candidate boxes to get the most accurate box. The principle of NMS is to select the box with a higher score as the basic box and then delete the box which has a large overlapping area with the basic box but has a lower score. However, the current NMS method and its improved versions do not perform well when face image quality is poor or faces are in a cluster. In these situations, even after NMS filtering, there is often a face corresponding to multiple predicted boxes. To reduce this kind of negative result, in this paper, we propose a new NMS method that operates in the reverse order of other NMS methods. Our method performs well on low-quality and tiny face samples. Experiments demonstrate that our method is effective as a post-processor for different face detection methods. The source code has been released on https://github.com/.

Early surge detection on a turbocharger used to pressurize a SOFC plant emulator

High-speed centrifugal compressors are commonly exploited to pressurize fuel cell-based hybrid energy systems. In such complex plants, because of significant interposed volumes due to fuel cells, dynamic compressor response can induce severe vibrations caused by low mass flow rates instability. In particular, surge strongly limits centrifugal compressors stable working region when moving towards low mass flow rate due to a change in system operating point. Consequently, a complete system identification is performed in order to adequately characterize compressor dynamic response for early surge detection. To this goal, a tailored experimental activity has been carried out at the Thermochemical Power Group of the University of Genoa on a vaneless diffuser compressor turbocharger used for the pressurization of an innovative solid oxide fuel cell (SOFC) emulator plant. Several post-processing methods have been performed on system vibro-acoustic responses to better predict and classify compressor status as stable or unstable. The obtained results provide original diagnostic insights for monitoring systems capable of preventing surge and other low mass flow unstable phenomena, such as rotating stall cells inception. Low mass flow rate fluid-dynamic instabilities prevention can extend compressor operating range, performance, and reliability to allow better integration with other plant components.

Evaluation of Two Post-Processing Analysis Methods of Proton Magnetic Resonance Spectroscopy in Glioma Tumors.

Magnetic resonance spectroscopy (MRS) is a non-invasive diagnostic and the neuroimaging method of choice for the noninvasive monitoring of brain metabolism in patients with glioma tumors. 1H-MRS is a reliable and non-invasive tool used to study glioma. However, the metabolite spectra obtained by 1H-MRS requires a specific quantification procedure for post-processing. According to our knowledge, no comparisons have yet been made between spectrum analysis software for quantification of gliomas metabolites. Current study aims to evaluate the difference between this two common software in quantifying cerebral metabolites. In this analytical study, we evaluate two post-processing software packages, java-based graphical for MR user interface packages (jMRUI) and totally automatic robust quantitation in NMR (TARQUIN) software. 1H-MRS spectrum from the brain of patients with gliomas tumors was collected for post-processing. AMARES algorithms were conducted to metabolite qualification on jMRUI software, and TARQUIN software were implemented with automated quantification algorithms. The study included a total of 30 subjects. For quantification, subjects were divided into a normal group (n=15) and group of gliomas (n=15). When calculated by TARQUIN, the mean metabolites ratio was typically lower than by jMRUI. While, the mean ratio of metabolites varied when quantified by jMRUI vs. TARQUIN, both methods apparent clinical associations. TARQUIN and jMRUI are feasible choices for the post-processing of cerebral MRS data obtained from glioma tumors.

400 Gb/s physical random number generation based on deformed square self-chaotic lasers

A circular-sided square microcavity laser etched a central hole has achieved chaos operation with a bandwidth of 20.8 GHz without external optical feedback or injection, in which the intensity probability distribution of a chaotic signal with a two-peak pattern was observed. Based on the self-chaotic microlaser, physical random numbers at 400 Gb/s were generated by extracting the four least significant bits without other complex post-processing methods. The solitary chaos laser and minimal post-processing have predicted a simpler and low-cost on-chip random number generator in the future.

Imitation System of Humanoid Robots and Its Applications

In this paper, we propose an imitation system that imitates human motions in videos to plan robot actions that are similar to human motions, with the aim of the complicated whole-body action planning of humanoid robots. Additionally, we created an interaction system that will enable basic human-robot interaction for our humanoid robot. To obtain the 3D coordinates of the key points on the human body, we used the 3D pose estimation model. The key points were then transformed into various trajectory files needed by the robot to complete the motion, using the mapping method proposed in this research, which refers to the control strategy and stability of the robot. In addition, we proposed some post-processing methods to post-process the trajectories. In the interaction system, we created a speech and vision system so that the robot could detect human gestures or postures and converse with people. It also has a music rhythm recognition system developed by seniors that enables the robot to dance to the beats of the song. Finally, through this system, we completed several human-robot interaction scenarios, which proved the convenience, and effectiveness of motion planning with an imitation system, and the completeness of the interaction system.