Quantitative Inspection Research Articles

The impact of second language teacher education on the development of pre-service teachers’ pedagogical beliefs

The current study explores the impact of second language teacher education (SLTE) on the development of the pedagogic beliefs held by fifty student-teachers enrolled in a public university in central Mexico. Specifically, the research investigates the extent to which conceptions of effective teaching changed, or didn’t, over the course of a four-year BA. Repertory grid interviews were used as the primary method of data collection. The repertory grid technique is the best known of several data solicitation instruments associated with the field of personal construct psychology. Data was subjected to both qualitative and quantitative inspection, including an assessment of cognitive complexity and principal component analyses. Results suggest that the participants who were SLTE students did not appear to significantly change their pedagogic beliefs over the course of the educational intervention. However, graduates of the SLTE program were found to hold significantly different kinds of pedagogical beliefs than the participants still enrolled in the BA, particularly in terms of their beliefs about reflection, student autonomy, and student-centeredness. This suggests that with time, internalized learnings from the program may have an opportunity to come to the fore and positively impact teacher practice.

Ultrasonic TFM imaging inspection of large complex rings using defect quantitative evaluation mappings

ABSTRACT Large complex rings are widely used in wind power energy, aerospace, large construction machinery. There are different scales of defects in the manufacturing process for large complex rings. Because of the complex cross-section and large thickness, it is difficult to quantitative evaluation of defects by conventional ultrasonic technology. Therefore, the ultrasonic total focusing method (TFM) defect quantitative inspection for large complex rings is proposed in this paper. First, the TFM inspection technology of large complex rings for simultaneous scanning of the end-face and side-face is designed. Then, the maximum amplitudes of defects are collected at each grid point of the imaging area by the TFM inspection of calibration specimens. The Φ1.5mm defect quantitative evaluation mappings are obtained by the interpolation fitting algorithm. Finally, the ultrasonic TFM and B-scan inspection experiments of the Φ3440mm large ring are carried out. The results show that the TFM quantitative evaluation mapping has higher accuracy compared to the conventional B-scan method. Especially for the Φ1.5mm defects, the average error of TFM quantitative evaluation is 2.92%. The proposed method can effectively identify defects of other sizes that exceed the calibration mapping. This work has industrial application value for developing ultrasonic TFM inspection of large complex components.

Evaluation of Saffron Quality Using Rapid Quantitative Inspection Technology with Near-Infrared Spectroscopy.

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky-Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R2 values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system.

Influence of the System Downtime Cost Rate on the Performance and Robustness of Periodic Inspection and Replacement and Quantitative Inspection and Replacement Maintenance Strategies Using Monte Carlo Method

Abstract This essay analyzes the crucial role of maintenance in production and service-oriented enterprises, highlighting the necessity for integrated viewpoints in contemporary corporate management. Focusing on time-based maintenance (TBM) and condition-based maintenance (CBM) techniques, the study provides a novel criteria and applies Monte Carlo simulation for assessment. The purpose is to discover ideal values for the system downtime cost rate, establishing a balance between performance and resilience. The organized parts include deterioration and failure models, maintenance assumptions, and cost models, finishing with a summary of noteworthy results and recommendations for further investigations. The study brings significant insights into the subject of maintenance strategies and analyzes the influence of the system downtime cost rate on the performance and robustness of condition-based maintenance strategies for a system with more or less stable behavior.

Assessing Nonoverlap in Single-Case Data: Strengths, Challenges, and Recommendations

AbstractOverlap is one of the data aspects that are expected to be assessed when visually inspecting single-case experimental designs (SCED) data. A frequently used quantification of overlap is the Nonoverlap of All Pairs (NAP). The current article reviews the main strengths and challenges when using this index, as compared to other nonoverlap indices such as Tau and the Percentage of data points exceeding the median. Four challenges are reviewed: the difficulty in representing NAP graphically, the presence of a ceiling effect, the disregard of trend, and the limitations in using p-values associated with NAP. Given the importance of complementing quantitative analysis and visual inspection of graphed data, straightforward quantifications and new graphical elements for the time-series plot are proposed as options for addressing the first three challenges. The suggestions for graphical representations (representing within-phase monotonic trend and across-phases overlaps) and additional numerical summaries (quantifying the degree of separation in case of complete nonoverlap or the proportion of data points in the overlap zone) are illustrated with two multiple-baseline data sets. To make it easier to obtain the plots and quantifications, the recommendations are implemented in a freely available user-friendly website. Educational researchers can use this article to inform their use and application of NAP to meaningfully interpret this quantification in the context of SCEDs.

Natural rail fracture defect calibration using infrared thermography and edge detection

Rail breakage can cause train derailment that leads to catastrophic consequences, prevention of which is worthy of a significant amount of investment in finance and time. Non-destructive evaluation can provide qualitative and quantitative inspection of rail tracks in-situ to detect dangerous faults, even though these defects are primarily invisible or hardly noticed from the surface of the rail. To make it efficient and convenient, infrared thermography technique offers a remote operation without interfering with rail transportation. The image processing for edge detection can further quantify the defect deterioration by depicting the contour of the defect. Moreover, the optimal parameters for various edge operators provide reliable detection for natural fracture defects.

A novel stress concentration inspection method for marine oil and gas pipeline based on UNSM

Stress concentration of marine oil and gas pipelines can lead to deformation and cracks, laying out great hidden dangers for safe operation. This paper proposes a novel in-line inspection method for stress concentration based on UNSM (unsaturated magnetization) for oil and gas pipelines. Firstly, the stress concentration inspection mechanism with UNSM is analyzed, and the equivalent magnetic circuit model of the inspection probe is derived. Secondly, the response signal features of axial, radial and complex stresses were investigated parametrically by numerical simulation. Then, quantitative tensile stress inspection was carried out with the UNSM inspection probe. Finally, the pipeline stress state is continuously scanned by the self-developed defect dynamic scanning experimental system. The results show that the UNSM inspection probe can effectively detect the abnormal stress of the pipeline. Meanwhile, the axial tensile, radial compressive stress and complex stress increase the axial permeability of the pipeline, resulting in a negative correlation of characteristic signals. Besides, the signal feature shows the "basin" distribution and decreases gradually with the increase of the lift-off. The above research provides a reference for developing in-line inspection equipment for the abnormal stress of marine pipelines.

Non-destructively qualitative and quantitative inspection methods based on THz spectroscopy and imaging

Non-destructively qualitative and quantitative inspection methods based on THz spectroscopy and imaging

A Comprehensive Assessment of the Pansharpening of the Nighttime Light Imagery of the Glimmer Imager of the Sustainable Development Science Satellite 1

The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the “traces of human activities” for the implementation of the United Union’s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection.

Documenting psychosocial attitudes of persons with aphasia following semantic-based therapy: an exploratory study

Quality of life (QOL) for individuals with aphasia is very individualistic and subjective as the condition presents multifaceted impairments in communication skills. Most intervention strategies for persons with aphasia (PWA) emphasise improving communication and insignificantly focus on a person's social and emotional well-being, which are vital for effective social communication. The present study aims to analyse the effects of a semantic-based treatment approach on the QOL of PWA. The current study inculcated a “modified pre-test post-test design” to study the impact of semantic cueing for verbs and its thematic role (SCVTr) therapy among eleven Kannada-speaking PWAs. The Stroke and Aphasia Quality of Life-39 (SAQOL-39) Kannada version assessed QOL among PWAs. SAQOL-39 includes 39 physical, psychosocial, communication, and energy questions. The questionnaire was administered in pre-, mid-, post-, and follow-up phases after SCVTr therapy. The study deployed qualitative, quantitative, and visual inspection methods. The qualitative and quantitative scores for QOL measures showed pronounced effects among most participants across pre- to mid- to post-treatment phases. In addition, the results of maintenance of the QOL were observed among most participants post-SCVTr therapy. The uptrend in QOL among PWAs is attributed to the semantic activation enhanced through SCVTr protocol, near normal physical mobility, adequate social network, and family support, with a barrier-free environment. The study's findings pave the way for speech-language pathologists (SLPs) on the importance of aphasia intervention and its effect on QOL. Indeed, SLPs must observe every PWA beyond linguistic deficits and attempt to notice the psychosocial level, which may invariably vary with severity and other individualistic factors.

Acoustic impedance measurement method using spherical waves

The mechanical properties and composition of materials can be better understood through ultrasonic non-destructive testing. Ultrasound is a valuable tool for characterizing elastic moduli, material microstructure, morphological conditions, and correlated acoustical/mechanical properties. Although ultrasound measurement techniques like transmission and pulse-echo are well-established, there is a growing demand for new technologies to facilitate automated inspection and enhance the widespread application of this method. This paper presents a novel approach to evaluate the characteristic acoustic impedance of a sample using measurements of reflected spherical waves. Employing a spherical sound source makes the energy distribution more uniform in all directions, minimizing measurement errors caused by system alignment. Additionally, that configuration aids in determining the experiment's geometry. The impedance was evaluated by analysis of the spherically reflected Gaussian-modulated sinusoidal pulses, which are less susceptible to noise interference. The acquired signal is fitted into a mathematical model to calculate the acoustic impedance, considering the decrease in sound pressure amplitude at the spherical wavefront according to the inverse distance law. Materials with a wide range of impedances (2 MRayl < Z < 46 MRayl) were measured to validate the method. The experimental impedances exhibited statistically significant agreement with values obtained via through-transmission impedance evaluation. The linear regression indicates a bias of 0.02 MRayls and an overall relative error of 1.7 % between the methods. This experimental setup holds promise for accurately assessing submerged structures, offering potential advantages for quantitative inspection.

Boosting Semantic Segmentation of Remote Sensing Images by Introducing Edge Extraction Network and Spectral Indices

Deep convolutional neural networks have greatly enhanced the semantic segmentation of remote sensing images. However, most networks are primarily designed to process imagery with red, green, and blue bands. Although it is feasible to directly utilize established networks and pre-trained models for remotely sensed images, they suffer from imprecise land object contour localization and unsatisfactory segmentation results. These networks still need to explore the domain knowledge embedded in images. Therefore, we boost the segmentation performance of remote sensing images by augmenting the network input with multiple nonlinear spectral indices, such as vegetation and water indices, and introducing a novel holistic attention edge detection network (HAE-RNet). Experiments were conducted on the GID and Vaihingen datasets. The results showed that the NIR-NDWI/DSM-GNDVI-R-G-B (6C-2) band combination produced the best segmentation results for both datasets. The edge extraction block benefits better contour localization. The proposed network achieved a state-of-the-art performance in both the quantitative evaluation and visual inspection.

Quantitative detection and evaluation of Rayleigh ultrasonic wave for fatigue crack on turbine blade surface

In this paper, different characteristics of micro-cracks on the surface of turbine blades have been successfully evaluated based on the detection of Rayleigh ultrasonic wave by numerical simulation and experimental study. The quantitative relationship between the maximum amplitude of Rayleigh ultrasonic wave and the features of cracks including width, depth, excitation position, and inclination angle was established. Numerical calculations of different curvature models show that curvature has no significant effect on the propagation of Rayleigh ultrasonic wave. According to the developed multi-functional ultrasonic equipment, cracks on complex surfaces can be detected by clamping two probes on the arc guide rail. The experimental results verify the validity of the numerical simulations. It gives an excellent basis for the effective automatic detection of cracks on the surface of turbine blades. This research provides a suitable method and new equipment for rapid quantitative inspection of turbine blades.

Towards safe shearography inspection of thick composites with controlled surface temperature heating

Thick glass fiber-reinforced polymer (GFRP) composites, e.g. thickness of more than 50 mm, are increasingly used in a wide variety of industries, particularly in the marine and wind energy sectors. Defect detection and characterisation in these composites remain appealing challenges due to the material complexity and the presence of various manufacturing and in-service defects. In this study, we propose a novel shearography method with controlled surface temperature (CST) heating for deep defect detection (i.e., 15 mm depth and more) in thick GFRP laminates. The proposed CST heating has been developed based on analytical solutions to control the maximum surface temperature of a test object during shearography inspection. Numerical and experimental studies have been performed to analyse the defect behaviour and defect detection under various heating scenarios, a topic which is rarely reported for thick composites with shearography. Compared with conventional shearography, the CST shearography method maximises heating energy input with a controlled and stable maximum surface temperature for deep defect detection. Results indicate an enhancement of about 27% in defect signal for the defect at 15 mm depth in comparison to conventional heating. The results also provide insight for implementing an efficient inspection in terms of the inspection duration and the number of datasets. This study makes a step towards safe, quantitative and predictable inspection of deep defects in thick composites.

Solidarity in Prophet Muhammad’s Safe Conduct to Non-muslims: A Positive Discourse Analysis

This paper studies solidarity through thematic analysis within the positive framework of discourse analysis. Despite the fact that there are countless traces of solidarity in Prophet Muhammad's (Peace Be Upon Him and His Family) (henceforth PBUH) tradition, the author discovers a severe deficiency in researches investigating Prophet Muhammad's (PBUH) safe conducts in the field of positive discourse analysis. In this line, it is decided to scrutinize solidarity in Prophet Muhammad's (PBUH) safe conduct to non-Muslims. Firstly, a brief introduction that embraces the question, the aim and the hypothesis understudy is engaged. Secondly, positive discourse analysis is introduced according to several sources all of which approve that not all discourses are negative. Thirdly, the concept of solidarity and the criteria of thematic analysis are conducted and then the qualitative thematic analysis is followed by the quantitative inspection. Finally, results that signify traces of solidarity in the discourse understudy are qualitatively illustrated and quantitatively presented. The table demonstrates statistical result more evidently. Key word: positive discourse analysis, solidarity, thematic analysis, Prophet Muhammad (PBUH), promise

Quantitative assessment of heavy-duty anticorrosion coating thickness via near-infrared measurements

The application of heavy-duty anticorrosion coatings is a typical method for preventing corrosion in steel bridges. However, thus far, no efficient and quantitative inspection technique has been established that can remotely and non-destructively evaluate the remaining thickness of the top coat in anticorrosion coatings. This study proposes a new inspection method called the "active self-reference lock-in near-infrared measurement method", which can accurately and quantitatively assess the remaining thickness of the top coat, irrespective of the lighting conditions. Experimental studies conducted on specimens and field studies carried out on on-site bridge measurements have confirmed the efficacy of the proposed method.

Synthetic data for X-ray CT of healthy and disordered pear fruit using deep learning

Over the last years, deep learning (DL) models have led to an enormous breakthrough in a wide range of computer vision tasks, including the classification and quantification of internal defects in fruit using X-ray CT images. However, developing these models typically requires large annotated datasets. Obtaining such datasets faces multiple challenges as the availability of defect fruit is unpredictable and it is unclear whether the disordered fruit in the dataset are representative of all possible defects. This work proposes a method to circumvent these problems by generating synthetic CT images using a conditional Generative Adversarial Network (cGAN) that learns the underlying distribution of gray values for different classes (“healthy tissue”, “internal browning”, “background, core and cavities”) in manually annotated images of pear fruit. The performance of the predictor was evaluated by quantitative metrics and visual inspection. To generate new synthetic data, operations are suggested and tested on annotated data to create as much variation as possible in the generated pears. The Fréchet Inception Distance (FID) and visual observations showed that the cGAN was effective in image-to-image translation, generating CT images of both healthy and disordered fruit based on their annotations. The best performing model obtained a low FID score of 10.2 and produced detailed visual features in the synthetic CT data, such as vascular bundles. The high potential and broad applicability was further demonstrated on augmented image annotations for generating new healthy and disordered pears with different appearances of defects. The proposed method can be used to produce additional training data when datasets are limited and laborious to manually annotate. Furthermore, the robustness of other DL models might increase by including new synthetic samples with a high variety of disorders during training.

Incorporating vertical dimensionality improves biological interpretation of hidden Markov model outputs

Quantifying animal movement is a central component of ecological inquiry. Movement patterns provide insights into how animals make habitat decisions in pursuit of their life‐history requirements. Within this context, animals are expected to modulate their movement when navigating landscape complexities like steep or uneven slopes. However, the analytical tendency to predict animal movement as a function of bivariate (x, y) telemetry data (i.e. 2D methods) excludes such complexities and presumes that the landscapes over which this movement occurs are completely flat. Failure to consider vertical dimensionality may inhibit quantification and interpretation of animal behaviors, such as outputs of hidden Markov models (HMMs) built upon geometric measurements of animal movement like step length and turning angle. To explore the analytical consequences of this assumption, we utilized a dataset of GPS collared pumas Puma concolor in the Santa Cruz mountains of central California. We fit HMMs using traditional 2D step lengths and turning angles and compared them to HMMs built upon movement geometries in which we incorporated vertical dimensionality (i.e. 2D+). We then used a combination of quantitative inspection of model outputs and visual evaluation in 3D rendering software to understand what new states and biological interpretations can be facilitated by using 2D+ data. We found that 2D+ HMMs outperformed 2D HMMs in their ability to explain variation in vertical dimensionality. Furthermore, 2D+ models were able to isolate distinctive behavioral states associated with vertical dimensionality, such as movements on and off ridgelines. Our results show that 2D+ techniques enable researchers to directly investigate variation in animal movement and behavioral states across complex landscapes. We discuss the implications of our results for future study of animal behavior and energetics as well as illustrate how our methods can be tractably incorporated into HMMs to enable researchers to gain greater insights into animal movement ecology.

Quantitatively evaluate the cylindricity of Large size pipe fitting via laser displacement sensor and Digital twin technology

The rapid development of intelligent detection technology guarantees accuracy in the assembly process. Aiming to solve the quantitative inspection of the cylindricity of Large size pipe fitting, this paper presents an online method technology using a laser sensor and Digital twin technology. The detection system comprises visual positioning, servo drive, and laser displacement sensor information acquisition systems. After positioning by the machine vision camera system, the servo system and laser displacement sensor scan the large-size pipe fitting layer by layer and establish the information interaction between physical space and virtual space through digital twinning technology. The digital twin scatters information model was found to calculate the maximum tangent circle and minimum peripheral circle, fitting the curve area of the closed point cloud layer by layer. And the least square method was used to calculate the center of the layered circle. The radial deviation of pipe fitting was evaluated comprehensively. The system makes a linear fitting of the center of the most miniature square circle of each layer, compares its deflection Angle with the solid center line, and calculates the center line deviation of the hole. Finally, the precision of the proposed method was validated by varying experiments. The cylindricity measurement of pipe fittings with an inner diameter of 300mm and height of 270mm was carried out by a pair of C-shaped semicircles fixed by the radial assembly. The cylindricity of the pipe fitting is 0.2372mm.

Digital representation of the wear behaviour of Ti-6Al-4V and Ti-6Al-7Nb biomaterial alloys: A fractal analysis of the worn surface

Currently, Ti-6Al-4V alloy is one of the most used materials in biomedical devices, namely in orthopedic prothesis, thanks to its outstanding properties. However, its composition contains vanadium ions, considered toxic to the human body. Since the 1980s, there has been a growing effort to replace this alloy with less toxic alternatives. A promising candidate is the Ti-6Al-7Nb alloy. There are relatively few studies on the surface properties of this alloy, on the other hand, the knowledge of its tribological performance is of great importance for the use of this alloy in biomedical applications. Digital tribology has great potential in a quantitative inspection of wear in materials engineering. Methodologies of surface inspection, such as fractal description, can explain the relationship between microstructural, tribological parameters, and surface roughness. In this work, the inspection of the fractal behavior of worn surfaces of both Ti-6Al-4V and Ti-6Al-7Nb samples was carried out. Tribological tests were performed in a block-on-disk tribometer, using AISI 1045 steel as antagonist material, under different conditions. The fractal values were correlated to tribological parameters and the evolution of morphological aspects along the surface. Elevation maps of the sliding surfaces were built from pictures obtained from optical microscopy (OM). The “Extended depth from focus”, (EDF) reconstruction method, allowed the ordering of stacks of images to conduct a quantitative investigation of the behavior of surfaces.