Technical note: A procedure to clean, decompose and aggregate time series

Errors, gaps and outliers complicate and sometimes invalidate the analysis of time series. While most fields have developed their own strategy to clean the raw data, no generic procedure has been promoted to standardize the pre-processing. This lack of harmonization makes the inter-comparison of studies difficult, and leads to screening methods that are usually ambiguous or case-specific. This study provides a generic pre-processing procedure (called past, implemented in R) dedicated to any univariate time series. Past is based on data binning and decomposes the time series into a long-term trend and a cyclic component (quantified by a new metric, the Stacked Cycles Index) to finally aggregate the data. Outliers are flagged with an enhanced Boxplot rule called Logbox. Three different Earth Science datasets (contaminated with gaps and outliers) are successfully cleaned and aggregated with past. This illustrates the robustness of this procedure that can be valuable to any discipline.

Laser Scanning Ship Hulls to Support Hydrodynamic Simulations

Terrestrial laser scanning is an effective technology to capture high density and accurate point clouds about objects with complex geometry. Ship industry requires 3D hull models for multiple reverse engineering purposes; renovation, as-built analysis, simulations etc. The paper discusses how terrestrial laser scanning can be applied to capture ship hull geometry to support hydrodynamic simulations. It presents recommendations of survey geometry and methods considering scanner locations, reflectivity issues. Hydrodynamic simulations require specific types of surface models as inputs; data processing procedure is discussed how the point clouds are effectively transformed to models to be applied. Resource analysis is also included, such as duration of survey and processing, equipment to be used.

A novel parameter derived from post-processing procedure of dual energy CT for identification of gout

ROI analysis is frequently used for obtaining acid content on rapid-kV-switching dual energy CT (DECT), providing inadequate accuracy. A new parameter derived from post-processing procedure, maximum lower limit with stain visible (MLLSV), was used by us to diagnose gout. 30 gout patients and 20 healthy volunteers were analyzed by using MLLSV. MLLSV was defined as the maximum lower limit of display window allowing only one stained site visible. Radiologists were asked to continuously increase the lower limit of display window of uric acid to decrease number of stained sites until the last stained site disappeared. MLLSV obtained by this way was compared between gout patients and volunteers. Receiver operating characteristic (ROC) curve was used to determine the performance. MLLSV of gout patients was significantly higher than that of volunteers (1373.3 ± 23.0 mg/cm3 vs. 1315.4 ± 20.7 mg/cm3, p = 0.000). The area under ROC curve of MLLSV was 0.993 in identifying gout. When using the optimal cutoff of 1342 mg/cm3, the sensitivity and specificity of MLLSV in identification of gout were 96.7% and 95% respectively. MLLSV derived from post-processing procedure of DECT is useful in discriminating gout patients from healthy people.

Separation of 44Sc from 44Ti in the Context of A Generator System for Radiopharmaceutical Purposes with the Example of [44Sc]Sc-PSMA-617 and [44Sc]Sc-PSMA-I&T Synthesis

Today, 44Sc is an attractive radionuclide for molecular imaging with PET. In this work, we evaluated a 44Ti/44Sc radionuclide generator based on TEVA resin as a source of 44Sc. The generator prototype (5 MBq) exhibits high 44Ti retention and stable yield of 44Sc (91 ± 6 %) in 1 mL of eluate (20 bed volumes, eluent—0.1 M oxalic acid/0.2 M HCl) during one year of monitoring (more than 120 elutions). The breakthrough of 44Ti did not exceed 1.5 × 10−5% (average value was 6.5 × 10−6%). Post-processing of the eluate for further use in radiopharmaceutical synthesis was proposed. The post-processing procedure using a combination of Presep® PolyChelate and TK221 resins made it possible to obtain 44Sc-radioconjugates with high labeling yield (≥95%) while using small precursor amounts (5 nmol). The proposed method takes no more than 15 min and provides ≥90% yield relative to the 44Sc activity eluted from the generator. The labeling efficiency was demonstrated on the example of [44Sc]Sc-PSMA-617 and [44Sc]Sc-PSMA-I&T synthesis. Some superiority of PSMA-I&T over PSMA-617 in terms of 44Sc labeling efficiency was demonstrated (likely due to presence of DOTAGA chelator in the precursor structure). It was also shown that microwave heating of the reaction mixture considerably shortened the reaction time and improved radiolabeling yield and reproducibility of [44Sc]Sc-PSMA-617 and [44Sc]Sc-PSMA-I&T synthesis.

Kenaf plant pest and disease detection using faster regional based convolutional neural network

<span>Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligence-based method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection. </span>

An Image Pre-Processing Improvement Procedure for Standardizing Multiple Colour Variations in the Astrocytoma Immunohistochemical Staining Images

Colour variations in the histopathological images can occur when the acquired images came from different laboratories. This issue happens may be due to different protocols between each laboratory. Besides that, the usage of dye or staining is also different since it arrived from different manufacturers. Hence, it will affect the performance of a system, especially for an automated image diagnosis system. If this issue is taken lightly, the whole process of image analysis can produce a false diagnosis outcome, and thus, it will affect the patient’s treatment options and endanger the patient’s life. This paper proposed an automated image pre-processing procedure for standardizing the colour variations in immunohistochemical staining images. The proposed procedure was done by modifying the reference image, which will be used later for the stain standardization process. Based on 50 astrocytoma histopathological images, the proposed procedure showed a promising result, where the quality of the output images has been enhanced and standardized.

Lunar regolith and substructure at Chang’E-5 landing site in the northern Oceanus Procellarum

The Lunar Regolith Penetrating Radar (LRPR) on the Chang’E-5 (CE-5) lander was deployed to investigate structures of the regolith. The migration and ridge detection methods were used to process the radar data, and the results indicate a 4.5 m regolith thickness that contains four units at the landing site, which is characterized by different internal reflections that point to their various compositions, mainly comprise protolith and admixed ejecta from the Harpalus, Copernicus, and Aristarchus. High-resolution processing for the LRPR data indicates a few rocks or slates with depth from ~ 0.2 m to over 1 m in the subsurface at the landing site, which was validated by the force analysis during the drilling of the regolith into ~ 1 m depth. The processing procedure proposed in this study is capable of producing reliable and precise images of the lunar regolith substructure, which provides important geological context on the returned drilling samples.

A Novel Nondestructive Procedure for Tire Tread Viscoelastic Characterization

ABSTRACT Vehicle dynamics is largely influenced by the phenomena occurring in the tire-road interface, and a great portion of these phenomena is mainly conditioned by the viscoelastic properties of the tire tread compound. It is not surprising that the possibility of obtaining the viscoelastic response of a compound by means of a nondestructive procedure is a growing research topic that affects application fields ranging from monitoring of the material performance during its entire life cycle to the quantitative analysis of product quality and repeatability of production processes. In this article, a novel nondestructive procedure for the viscoelastic characterization of tire tread compound is proposed. A portable instrument, based on instrumented indentation, was designed and prototyped with the aim to allow a real-time assessment of moduli directly on site. The testing procedure adopted to perform the test on three different compounds was described. A signal-processing procedure was developed for the identification of compound stiffness and damping parameters from which viscoelastic moduli were estimated. The results were also compared with the DMA characterization showing the same relative ranking between the compounds with a different trend in temperature due to the amount of the tests' indentation depth.

An Advanced Sensor for Particles in Gases Using Dynamic Light Scattering in Air as Solvent

Dynamic Light Scattering is a technique currently used to assess the particle size and size distribution by processing the scattered light intensity. Typically, the particles to be investigated are suspended in a liquid solvent. An analysis of the particular conditions required to perform a light scattering experiment on particles in air is presented in detail, together with a simple experimental setup and the data processing procedure. The results reveal that such an experiment is possible and using the setup and the procedure, both simplified to extreme, enables the design of an advanced sensor for particles and fumes that can output the average size of the particles in air.

Towards Drug Delivery Control Using Iron Oxide Nanoparticles in Three-Dimensional Magnetic Resonance Imaging

The purpose of this paper was to detect and separate the cluster intensity provided by Iron oxide nanoparticles (IO-NPs), in the MRI images, to investigate the drug delivery effectiveness. IO-NPs were attached to the macrophages and inserted into the eye of the inflamed mouse’s calf. The low resolution of MRI and the tiny dimension of the IO-NPs made the situation challenging. IO-NPs serve as a marker, due to their strong intensity in the MRI, enabling us to follow the track of the macrophages. An image processing procedure was developed to estimate the position and the amount of IO-NPs spreading inside the inflamed mouse leg. A fuzzy Clustering algorithm was adopted to select the region of interest (ROI). A 3D model of the femoral region was used for the detection and then the extraction IO-NPs in the MRI images. The results achieved prove the effectiveness of the proposed method to improve the control process of targeted drug delivered. It helps in optimizing the treatment and opens a promising novel research axis for nanomedicine applications.