Present Analytical Method Research Articles

Design and Manufacturing a Portable Smart Sole for Measuring the Ground Reaction Force

The systematic study of human locomotion known as gait analysis can be applied in various contexts, including athletics, rehabilitation, and clinical diagnoses. However, the present gait analysis methods have several limitations that make them inappropriate for individual use, such as the fact that they are expensive, non-portable, need a significant amount of time to set up, and require additional time for post-processing. This study aims to investigate and develop a gait analysis system to measure the vertical ground reaction forces. The measurement instrument qualities of being flexible, portable, and comfortable are essential to the design of wearable sensors. The device was calibrated using a universal testing machine (Force plate device). In addition, this study used flexible force sensors for detecting vGRF. The result shows the device works with high efficiency and accuracy in measurement when calculating the values of ground reaction force compared with the values ​​ of reaction force measured by the university testing machines. Keywords: Gait cycle, Ground reaction force, Arduino, Force plate, Smart sole

Integrating the effect of abutments in estimating the average vertical stress of elastic hard rock pillars by combining numerical modelling and artificial neural networks

Estimating average vertical pillar stresses is a critical step in designing room-and-pillar mines. Several analytical methods can be used to estimate the vertical stresses acting on the pillars. However, the present analytical methods fail to adequately account for the influence of abutments on the distribution of vertical stresses, especially when applied to narrow panel widths and pillar layouts comprising evenly spaced barriers. In this study, a multi-layer perceptron neural network (MLPNN) was applied to predict the vertical loads of regular pillars more accurately. Hundreds of room-and-pillar mine layouts were modeled using a displacement discontinuity method (DDM), and a database of 2355 sampled pillar cases was compiled. The MLPNN was trained based on this database, and its prediction capabilities were further validated using simulations by a finite difference code (i.e., FLAC3D). The model predictions and the FLAC3D simulations reasonably agreed with a regression coefficient of 0.99. The model was also adapted for mine cases with evenly spaced barrier pillars, and its application to a real case study mine has shown to provide accurate pillar stress estimations; hence, this model is suitable for practical use at mines. Even though the MLPNN model cannot be applied universally to all mine situations, it seems as a significant improvement over existing analytical techniques in terms of accounting for the influence of abutments on pillar stresses.

Parametric resonance of multi-frequency excited MEMS based on homotopy analysis method

The nonlinear dynamics and control of micro electromechanical systems (MEMS) is an important topic at present and homotopy analysis method (HAM) is an effective semi-numerical and semi-analytical method for solving strongly nonlinear problems. In this paper, the parametric resonance of MEMS with multi-frequency excitation is studied by HAM. Firstly, the differential equation of electrostatically driven microbeam is processed by Taylor expansion, and it is transformed into the parametric motion model with multi-frequency excitation. Then, the approximate solution and amplitude–frequency response equation of the system are obtained by HAM, and compared with the numerical solution. Finally, the influence of direct current (DC) and alternating current (AC) on principal parametric resonance and superharmonic resonance is discussed. The results show that HAM is an effective method to analyze the parametric vibration of multi-frequency excitation system, and the amplitude–frequency response curve of microbeam about parametric motion depends on the time scale in high DC and high AC state. This study effectively extends the application of HAM in parametric resonance, which is of great significance to the study of nonlinear vibration of MEMS.

Quantitative 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy.

2616 Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 µm section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and failure to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire, intact lung tissue samples. Herein, we present analysis methods for comprehensive 3D assessment of intact NSCLC tissue. Methods: Human NSCLC tissue, stored frozen in optimal cutting temperature (OCT) compound, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Twenty tissue samples measuring 1-5 mm3 in volume were imaged at 2 µm/pixel resolution with a hybrid open-top light-sheet microscope. Smaller regions of interest (ROIs) with interesting pathologic features were re-imaged at higher resolution, 0.17 µm/pixel. Visualization and analysis was performed using Aivia software, custom spatial analysis software and python scripts. Results: All NSCLC tissue samples were successfully imaged in 3D at both low and high resolution. Computational approaches, such as machine learning, were used to segment and classify tumor cells and lymphocytes within the high resolution ROIs. In one example we quantified a 0.0045 mm3 ROI containing 1x109 voxels. We segmented 1,474 unique objects, and classified 683 (46%) of the objects as lymphocytes and 309 (20%) of the cells as tumor cells. We found that on average the lymphocytes were within 12 µm of the nearest tumor cell. Additionally the tissue was broken into separate lymphocyte dense (58% of volume) and tumor dense regions (42% of volume). Further analysis is in progress to quantify more features, such as tertiary lymphoid structures, stromal cells, and fibrosis over the full 20 sample dataset. Conclusions: We successfully implemented 3D machine learning analysis pipelines using images from intact NSCLC tissue samples measuring up to 5 mm3. The imaging was performed using our custom hybrid open-top light-sheet microscope. This novel method enables us to identify and quantify key histological features such as tumor cells and lymphocytes within the tumor microenvironment. In addition, machine learning driven algorithms clustered cells into distinct tissue regions in 3D which enables a more precise assessment of disease state and heterogeneity. Further investigation is ongoing to link these results to patient outcomes.

Modification of Ballast Filtering Device on John Lie Training Ship to Prevent Marine Pollution

This study aims to present appropriate selection methods for evaluation, analysis, and comparison between different ballast water treatment technology systems in order to make good decisions in selecting the most optimal treatment system. This research designed a ballast water filter device on the John Lie training ship to prevent marine pollution due to species movement through the ballast water transfer process by referring to the D2 standard of the IMO Ballast Water Management Convention. During the ballasting process, ballast water contains thousands of species of marine animals and plants that are carried in ballast tanks, causing problems for the marine environment and human health. The modification of the filtration tool designed by the researcher used a 0,1 mm and 0,01 mm net tied to the ballast tank inlet pipe using a clamp on the ballast pipe of the John Lie training ship. This can fully be used during the process of filling ballast water from port seawater to enter the ballast tank through this filtration system. The results shown after passing the filter with a total plate count of 36 microorganisms indicate that the filtration system designed by the researcher can meet the D2 standard of the IMO BWM Convention Regulation.

A comparison of past and present computational methods for shape analysis of double-shell x-ray radiographs.

Implosion symmetry is a key requirement in achieving a robust burning plasma in inertial confinement fusion experiments. In double-shell capsule implosions, we are interested in the shape of the inner shell as it pushes on the fuel. Shape analysis is a popular technique for studying said symmetry during implosion. Combinations of filtering and contour-finding algorithms are studied for their promise in reliably recovering Legendre shape coefficients from synthetic radiographs of double-shell capsules with applied levels of noise. A radial lineout max(slope) method when used on an image pre-filtered with non-local means and a variant of the marching squares algorithm are able to recover p0, p2, and p4 maxslope Legendre shape coefficients with mean pixel discrepancy errors of 2.81 and 3.06, respectively, for the noisy synthetic radiographs we consider. This improves upon prior radial lineout methods paired with Gaussian filtering, which we show to be unreliable and whose performance is dependent on input parameters that are difficult to estimate.

Data‐based analysis of iterative learning control for MIMO nonaffine nonlinear systems with multiple nonrepetitive uncertainties

AbstractTwo challenging problems are addressed in this work for the convergence analysis of iterative learning control (ILC), that is, the rigorous assumption on repetitive conditions and the severe dependence on linear or nonlinear parametric models. Consequently, a data‐based analysis method of ILC is presented for a general multi‐input multi‐output nonaffine nonlinear system in the existence of multiple nonrepetitive uncertainties in initial states, external disturbances, reference trajectories, and plant models. An extended iterative dynamic relationship is constructed to extract the linear relationship of I/O dynamics for a nonaffine nonlinear system between two different iterations. The data‐based double dynamics analysis method is developed for analyzing that the tracking error is convergent and the I/O sequence is bounded. The tracking error is shown to converge to a finite bound related to nonrepetitive uncertainties and achieve a perfect convergence if nonrepetitive uncertainties are also iteratively convergent to zero. Notably, the presented analysis method merely uses the I/O data without relying on the plant model. Simulations validate the effectiveness of our theoretical results.

Vibration characteristics of a functionally graded polymer nanocomposite shaft-disk rotor reinforced with graphene nanoplatelets

In this paper, a rotating functionally graded (FG) polymer nanocomposite shaft-disk assembly reinforced with graphene nanoplatelets (GPLs) resting on elastic supports is modelled and its vibration behaviours are analysed. The effective material properties of the shaft and disk are assumed to vary along their radius directions and determined via the Halpin–Tsai model together with the rule of mixture. Different non-uniform and uniform distributions of GPLs in the rotating assembly are taken into account. In accordance with the finite element (FE) method, the modelling and free vibration analysis of the nanocomposite shaft-disk rotor system is conducted. To verify the present analysis, both the theoretical and experimental methods are employed. A comprehensive parametric study on the effects of the graphene nanoplatelets (GPL) weight fraction, GPL distribution pattern, length-to-thickness ratio and length-to-width ratio of GPLs, shaft length, elastic support stiffness and rotating speed on the free vibration results are investigated, which gives effective ways to achieve improved mechanical performance of a rotating nanocomposite disk-shaft assembly.

Thermoacoustic response of polymethyl methacrylate composite shells reinforced by carbon nanotube resting on Winkler–Pasternak elastic foundation

This paper presented an analytical method for investigating the vibroacoustic response of polymethyl methacrylate composite shells reinforced by CNTs resting on an elastic foundation under thermal loading. This structure is submerged in a moving fluid and excited by an acoustics plane wave. The CNTs are assumed to have a graded distribution in the polymethyl methacrylate matrix. The properties of the CNTs are assumed to be temperature-dependent. Different types of CNTs distributions such as UD, X, V, and O are considered. The Winkler–Pasternak model is applied to describe the vibrational behavior of the elastic foundation. The equations of motion of the structure are based on Hamilton’s principle using the TSDT, considering the effects of shear and rotation. To validate the present analytical method, the obtained results are compared with those of other researchers. Next, the effects of various significant parameters such as elastic foundation, temperature gradient, CNTs distributions, and Mach number on the TL are studied. The results show increasing the temperature reduces the TL because the stiffness bending of the structure decreases. It can be seen that the addition of CNTs to the matrix, with an appropriate volume fraction, improves the sound transmission loss due to the enhancement of stiffness.

How our understanding of memory replay evolves.

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

A hybrid analytical method for predicting vibrations in vacuum and water of the sandwich plate with z-direction reinforcements and cavities

This paper presents a multi-functional sandwich plate with z-direction reinforcements and cavities that has the potential to meet the pressure resistance and the acoustic stealth requirement of the underwater equipment in deep sea. In order to predict its free and forced vibrations in both vacuum and water, a hybrid analytical method is proposed by combining a multi-level homogenization model and a layerwise model. The core with z-direction reinforcements and cavities is homogenized and effective elastic constants are calculated by the Mori-Tanaka formula. Vibration equations of the sandwich plate are derived utilizing the layerwise model based on the first-order shear deformation theory. The water effect is taken into account by the Euler's equation. Vibration results are solved under simply supported boundaries. Validation studies and parameter analyses are then conducted. It is found that different from the existing theoretical approach based on the equivalent single-layer model that is only applicable for the sandwich with a hard core material, the present hybrid analytical method is well suitable for vibration predictions of the sandwich plate with z-direction reinforcements and cavities, no matter the hard or soft core. Besides, due to the homogenization of the three-phase core and the neglecting of local vibrations in the layerwise model, the present analytical method only predicts accurate free vibrations when the half wave numbers are less than numbers of cavities in x and y directions, and also just shows the high precision in force vibration calculations as the maximum analysis frequency is less than the natural frequency of the mode corresponding to numbers of cavities.

Machine Learning for Brain Imaging Genomics Methods: A Review

In the past decade, multimodal neuroimaging and genomic techniques have been increasingly developed. As an interdisciplinary topic, brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotypic measures derived from structural and functional brain imaging. This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans. It is well known that machine learning is a powerful tool in the data-driven association studies, which can fully utilize priori knowledge (intercorrelated structure information among imaging and genetic data) for association modelling. In addition, the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is explored. In this paper, the related background and fundamental work in imaging genomics are first reviewed. Then, we show the univariate learning approaches for association analysis, summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning, and present methods for joint association analysis and outcome prediction. Finally, this paper discusses some prospects for future work.

Thermodynamic and kinetic analysis on oligomeric protein dissociation using high-pressure native PAGE velocity method

High pressure is known to dissociate several oligomeric proteins, and regarded as an important tool to shift the oligomerization equilibrium. Native polyacrylamide gel electrophoresis (native PAGE) at high pressure can characterize the dissociates and clearly discriminate the aggregates. However, a band smearing of migration profiles often hinders more detailed analyses (Miwa et al., High Pressure Res. (2019) 39, 218–224). In this paper, we focused on the band smearing dependent on the migration velocity so as to extract both thermodynamic and kinetic parameters. We systematically perturbed the migration velocity by changing the gel concentration and carried out numerical analysis for a series of the migration profiles based on a simple dissociation reaction scheme with limited thermodynamic and kinetic parameters. Then, complete volumetric properties on oligomerization process can be available. We term the present analysis method as a high-pressure native PAGE velocity method. We also report the application of this method to revisit the pressure dissociation of tetrameric lactate dehydrogenase (LDH) from pig heart.

Double Legendre polynomial quadrature-free method for axisymmetric vibration of functionally graded piezoelectric circular plates

A double Legendre polynomial quadrature-free method was presented to analyze axisymmetric free vibration problem for axially polarized Functionally Graded Piezoelectric circular plates. The present analytical method directly incorporated the boundary conditions into the vibration control equation. At the same time, the mechanical displacements and the electrical potential were defined as the form of double Legendre polynomials series. Then, the solution of eigenvalue was used to obtain the resonant/anti-resonant frequencies. In view of including lots of time-consuming integration calculations, the quadrature-free process was deduced to improve the computational efficiency. The valid of the presented method was verified by the comparison with the existing literature and the commercial software COMSOL. Furthermore, the effects of the material gradient index, radius-thickness ratio on the frequencies, and electro-mechanical coupling efficient were studied. The results showed that the frequency-radius product tends to be stable with the increase of the radius-thickness ratio, and the increase of gradient index led to the increase of the frequency-radius product.

Interference-free quantitation of aromatic amino acids in two complex systems by three-way calibration with ultraviolet–visible spectrophotometer: Exploration of trilinear decomposition of spectrum-pH data

Aromatic amino acids play an extremely important role in life activities and participate in many biological processes. Their concentration levels are associated with a variety of diseases, such as phenylketonuria and colorectal cancer. Therefore, the quantification of aromatic amino acids is an important task. In the present work, a novel and rapid three-way analytical method was proposed to detect the levels of aromatic amino acids in prostate cancer cells (PC3 cells) and Dulbecco's modified minimal essential medium (DMEM cell culture), by using the affordable ultraviolet–visible spectrophotometer. First, spectrum-pH second-order data were designed per sample; Second, properties of the resulted spectrum-pH-sample three-way data were investigated by utilizing the parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), and constrained alternating trilinear decomposition (CATLD) algorithms, and a flexible scanning approach for determining the constraint parameters of CATLD was proposed; Third, a three-way calibration method based on the CATLD algorithm with the proposed scanning approach was developed for interference-free quantification of aromatic amino acids in these systems. The average relative predictive errors of validation (ARPEV) for phenylalanine, tyrosine, and tryptophan were 1.4%, 3.0%, and 0.7% in prostate cancer cells, and ARPEV for phenylalanine, tyrosine, and tryptophan were 4.1%, 1.2%, and 0.7% in DMEM cell culture. The predicted contents of tyrosine and tryptophan in DMEM cell culture were 64.2 ± 2.9 μg mL−1, 5.6 ± 0.3 μg mL−1, there are no significant differences in the concentrations between the developed analytical method and high performance liquid chromatography method. The proposed spectrum-pH-sample three-way calibration method based on CATLD algorithm can provide an interesting analytical strategy with high selectivity and accuracy for ultraviolet–visible spectrophotometer.

Real-time selective pesticide detection using catalytic behavior of zwitterionic conjugated polymer

We focus on the catalytic behavior of zwitterionic conjugated polymer (ZIPF) as a novel sensing platform for a toxic pesticide, namely diquat (DQ). The synthesized ZIPF with the oxidative polymerization method exhibits high photoluminescence stability in water over the pH range (1–13). We show that the ZIPF rapidly degrades the toxic DQ into the highly fluorescent intermediate diquat monopyridone (DMP) at pH 13 without any pretreatment, leading to real-time, highly sensitive, and selective detection. The present analytical method responses within few seconds and can detect up to excellent nanomolar level of DQ (3.48 nM) in water. The sensing mechanism with the catalytic behavior of ZIPF is carefully investigated.

On-chip microfluidic dual detection of amino acid metabolism disorders using cell-free protein synthesis

Various metabolic diseases are associated with the accumulation of specific amino acids due to abnormal metabolic pathways, and thus can be diagnosed by measuring the level of amino acids in body fluids. However, present methods for amino acid analysis are not readily accessible because they require a complex experimental setup, expensive equipment, and a long processing time. Here, we present a dual sensing microfluidic device that enables fast, portable, and quantitative analysis of target amino acids, harnessing the biological mechanism of protein synthesis. In this device, the working principle of a finger-actuated pumping unit is applied, and the microchannels are designed to perform cell-free synthesis of a reporter protein in response to the target amino acids in the assay samples. Multiple steps required for the translational assay are controlled by the simple operation of two pushbuttons on the device. It is demonstrated that the developed microfluidic device provides precise quantification of two amino acids (methionine and phenylalanine) within 30 min at room temperature. We expect that the application of the presented device can be readily extended to the point-of-care testing of other metabolic compounds.

Occlusion detection of traffic signs by voxel-based ray tracing using highly detailed models and MLS point clouds of vegetation

Visibility analysis plays a vital role in the design and placing of traffic signs in the urban street environment. This work investigates the occlusion detection of traffic lights and traffic signs caused by vegetation. The presented analysis method is built upon the inputs from the expected situation reflected by a highly detailed 3D city model and the as-is situation captured by 3D Mobile Laser Scanning (MLS). The model contains the location and orientation of streets, traffic lights, and traffic signs; the measurements add detail on irregular-shaped and morphing objects such as vegetation, respectively. The analysis covers the visibility of traffic lights and traffic signs by ray-tracing in an occupancy grid that is generated by the voxelization of the space. The voxels facilitate the distinction between occupied and empty space. The identification of unknown volumes is added and considered in the decision process, to cope with the regions invisible to the sensor. As output, we provide a visibility metric and detailed 3D space descriptions on different levels of granularity, including the knowledge of the semantic classes of traversed voxels. During the whole process, the awareness of unknown volumes is added to an otherwise binary decision between visible and invisible targets. Experiments are conducted on the TUM-MLS-2016 dataset. Results demonstrate that the proposed method is feasible for the detection of occlusions by vegetation in the street scenario, and reveal that the identification of unknown volumes proves necessary for a reliable interpretation of the measurements. • Agent-based visibility analysis of traffic installations. • Combination of a street environment model with actual MLS measurements of vegetation. • Hence solving that realistic volumetric modeling of trees is often not present in city models. • Inclusion of information on volumes not covered by measurements. • The analysis gains an additional level of confidence depending on lines of sight traveling through space of unknown occupation.

Experimental design of non-ionic hydrophobic DES-DLLME coupled with injector port silylation-GC–MS/MS for the quantitative determination of 13 bisphenols in food samples

Non-ionic hydrophobic deep eutectic solvent based DLLME method with thymol and camphor (1:1 mole ratio) as extraction solvent and acetonitrile as disperser solvent was developed for the determination of 13 bisphenols (BPs) in food commodities by auto IPS-GC–MS/MS. Further, the influential parameters pH, vortex time, and volume of NIHDES for extraction of 13 BPs were optimized by employing the design of experiment. The LOQ and recoveries were observed to be 0.1 ng mL−1, and 77.8–109.2%, respectively. The expanded uncertainty was found to be 4.8–18.0% for the developed and validated method, which was applied to food commodities to determine 13 BPs and their levels were in the range of 0.01–1.24 ng mL−1 in liquid foods (milk, water, and beverages) and 0.29–3.25 ng g−1 in meat samples (goat, chicken, and fish). The present green analytical method is highly suitable for the rapid determination of 13 BPs in food commodities for routine analysis.

Temperature and size modulation of the lattice thermal expansion on transition metallic nanostructures

By combining the bond-order-length-strength theory, local bond average approach and core–shell model, a new analytical method for temperature- and size-dependent thermal expansion coefficient (TEC) of transition metallic nanostructures has been established. It shows that both cohesive energy and cubic Debye temperature are inversely proportional to the TEC in the crystalline materials, that both cohesive energy and Debye temperature decrease with the reduction in size, which leads to the size effect of TEC, and the bond nature parameter modulate the intensity of size-dependent TEC. In agreement with the measurements, the Debye temperature, rigidity factor and bond nature parameter for 12 kinds of transition metals were quantitatively obtained. The present new analytical method for thermal properties of transition metals is simple and accurate, which is beyond the scope of existing methods in thermal-modulation and -management of nano-photoelectronic devices.