Subject: Computer Science And Mathematics, Information Systems Keywords: defect management; defect detection; defect categorization; defect prioritization; defect removal defect veriﬁcation
Online: 9 December 2019 (07:45:05 CET)
The success of a software system is highly dependent upon its quality which is a very critical aspect and is the ultimate goal to be achieved. Defect Management is the most important process in ensuring software quality, it involves defect detection, defect categorization, defect prioritization, defect removal and defect veriﬁcation. The main purpose of this process is to develop and produce defect free or least defected software systems of high quality. It plays a vital role in gaining customer conﬁdence and satisfaction which is essential for the reputation of the organization developing the system. The central objective of this study is to explain the importance of defect management and its impact on the quality of the software along with various defect management techniques to support the objective.
ARTICLE | doi:10.20944/preprints202305.2107.v1
Subject: Biology And Life Sciences, Food Science And Technology Keywords: Serratia marcescens; cheese, blue-veined cheese; technological defect; colour defect; traditional cheeses
Online: 30 May 2023 (10:15:27 CEST)
Technological defects in the organoleptic characteristics of cheese (odour, colour, texture and flavour) reduce quality and consumer acceptance. A red colour defect in Cabrales cheese (a traditional, blue-veined, Spanish cheese made from raw milk) occurs infrequently but can have a notable economic impact on family-owned, artisanal cheesemaking businesses. This work reports the culture-based determination of Serratia marcescens strain R01 as the microbe involved in the appearance of red spots on the surface and nearby inner areas of such cheese. Sequencing and analysis of its genome revealed a cluster of 16 genes involved in the production of prodigiosin, a tripyrrole red pigment. HPLC analysis confirmed the presence of prodigiosin in methanol extracts of S. marcescens RO1 cultures. The same was also observed in extracts from red areas of affected cheeses. The strain showed low survival rates under acidic conditions, but was not affected by concentrations of up to 5% NaCl (the usual value for blue cheese). The optimal conditions for prodigiosin production by S. marscescens RO1 on agar plates were 32 ºC and aerobic conditions. Prodigiosin has been reported to possess antimicrobial activity, which agrees with the here-observed inhibitory effect of RO1 supernatants on different bacteria, its inhibition of Enterobacteriaceae, and the delayed development of Penicillium roqueforti during cheesemaking. The association between S. marcescens and the red colour defect was strengthened by recreating the fault in experimental cheeses inoculated with RO1. The data gathered in this study points towards the starting milk to be the origin of this bacterium in cheese. These findings should help in the development of strategies that minimize the incidence of pigmenting S. marcescens in milk, the red defect the bacterium causes in cheese, and its associated economic losses.
REVIEW | doi:10.20944/preprints202311.0142.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: pericyte; osteogenesis; bone defect; regeneration
Online: 2 November 2023 (09:55:56 CET)
Pericytes, as perivascular cells, are present in all vascularized organs and tissues, and they actively interact with endothelial cells in capillaries and microvessels. Their involvement includes functions like blood pressure regulation, tissue regeneration, and scarring. Studies have confirmed that pericytes play a crucial role in bone tissue regeneration through direct osteodifferentiation processes, paracrine actions, and vascularization. Recent pre-clinical and clinical experiments have shown that combining perivascular cells with osteogenic factors and tissue-engineered scaffolds can be therapeutically effective in restoring bone defects. This approach holds promise for addressing bone-related medical conditions. In this review, we have emphasized the characteristics of pericytes and their involvement in angiogenesis and osteogenesis. Furthermore, we have explored recent advancements in the use of pericytes in preclinical and clinical investigations, indicating their potential as a therapeutic resource in clinical applications.
REVIEW | doi:10.20944/preprints202305.0574.v1
Subject: Chemistry And Materials Science, Metals, Alloys And Metallurgy Keywords: SLM; Research status; Application field; Defect analysis
Online: 9 May 2023 (05:23:24 CEST)
With the development of industrialization, traditional manufacturing technologies are no longer able to meet the production needs of modern society. Additive manufacturing has successfully solved the problems existing in traditional manufacturing technology. Selective laser melting (SLM) is a powder bed melting technology that produces metal parts by selectively melting metal powders on a platform using a laser beam. It is widely used in additive manufacturing. This article introduces the characteristics and current development status of SLM technology, summarizes the main application fields and common defects of SLM, and finally explores and prospects the future development trend of SLM.
ARTICLE | doi:10.20944/preprints202207.0297.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Wharton's Jelly; Minimal Manipulation; Structural Tissue Defect
Online: 20 July 2022 (07:39:43 CEST)
One in four adults in the US suffer from cartilage degeneration of the Intervertebral Disc (DDD) or load bearing joints (DJD). Combined DDD and DJD leads to billions of dollars in surgical health care costs annually. Since cartilage is avascular, it has a limited regenerative capacity. Conventional non-surgical treatment modalities provide brief symptomatic relief, have sided effects, and do not address the actual structural tissue defect in the cartilage itself. As such, new alternatives are needed. Perinatal tissue allografts have emerged as a novel frontier for bio-mechanical cartilage engineering research. Birth product-specific therapeutic roles and clinical outcomes are actively being investigated. The tissues of interest include umbilical cord-derived Wharton’s Jelly (WJ). This study assessed WJ tissue samples via ZEISS Supra 55VP Field-Emission Scanning Electron Microscope (SEM) at 100 and 300nm resolution scales. The captured images of pre and post-processed structural tissue matrices in WJ allografts were analyzed against themselves and peer-reviewed SEM images of articular cartilage, intervertebral disc cartilage, and muscle fascia. SEM images of post-processed WJ structural tissue matrices were analogous to structural tissue matrices in human articular cartilage, intervertebral disc cartilage, and muscle fascia. Relevant characteristics of pre- and post-processed structural tissue matrices in WJ allografts were comparable. This is the first study, that we are aware of, to utilize SEM to compare the pre-and post-processing relevant structural characteristics of WJ allografts and additionally demonstrate that structural collagen matrices in post-processed WJ allografts are analogous in structure to the cartilage in articular joints, intervertebral discs, and muscle fascia.
ARTICLE | doi:10.20944/preprints202010.0258.v2
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: point defect; crystal lattice; interstitial; austenitic; stabilization; superalloys
Online: 11 January 2023 (09:00:32 CET)
Bubble (point defect) – a precursor of fuzz or under dense nanostructure formation is crystal lattice defect. Suitable selection of crystal lattice which inhibit Frenkel pair generation and intrinsically promotes self- interstitial solid solution strengthening contributes effectively towards making plasma facing material. For this, interstitial sites, their size, amount / fraction, positions, tendency of occupation and diffusion parameters (e.g. activation energies (Q), activation volumes) are determined. Fcc iron carbon alloys (austenitic stainless steels AISI / SAE 321, fcc structure, Pearson code cF4, space group Fm3̅m) are proposed as suitable candidates. Along with their room temperature fcc structure having 12 interstitial positions (4 octahedral, 6 coordination sites and 8 tetrahedral, 4 coordination sites / unit cell) to allow insertion of self (iron) atoms, they have excellent corrosion resistance, thermal conductivity, and non- magnetic properties. After their melting, casting, and machining to required dimensions and geometry, stabilizing heat treatment is applied to precipitate all carbon as TiC and prevent formation of Cr23C6 (sensitization). This resist heat and surface degradation and yield excellent architecture which not onlyinhibit Frankel pair generation but will also allow bulk assimilation or surface annihilation (loop punching) of this lattice point defect. A superior thermal, fluid, and structural design augment above. A second choice is presented as Co base superalloys owing to same fcc crystal structure and excellent properties (such as strength, dimensional stability, oxidation resistance) at high temperature.
ARTICLE | doi:10.20944/preprints201806.0472.v1
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: SiC-polycrystalline fiber; defect; strength; surface roughness
Online: 28 June 2018 (12:49:39 CEST)
Polymer-derived SiC-polycrystalline fiber (Tyranno SA) shows excellent heat-resistance up to 2000oC, and relatively high strength. Up to now, through our research, the relationship between the strength and residual defects of the fiber, which were formed during the production processes (degradation and sintering), has been clarified. In this paper, we addressed the relationship between the production condition and the surface roughness of the obtained SiC-polycrystalline fiber, using three different raw fibers (Elementary ratio: Si1Al0.01C1.5O0.4~0.5) and three different types of reactor (Open system, Partially-open system, and Closed system). With increase in the oxygen content in the raw fiber, the degradation during the production process easily proceeded. In this case, the degradation reactions (SiO+2C=SiC+CO and SiO2+3C=SiC+2CO) in the inside of each filament become faster, and then the CO partial pressure on the surface of each filament is considered to be increased. In consequence, according to Le Chatelier’s principle, the surface degradation reaction and grain growth of formed SiC crystals would be considered to become slower. That is to say, using the raw fiber with higher oxygen content and closed system (highest CO content in the reactor), much smoother surface of the SiC-polycrystalline fiber could be achieved.
ARTICLE | doi:10.20944/preprints202311.1447.v1
Subject: Engineering, Mechanical Engineering Keywords: Mask R-CNN; generative adversarial network; defect detection
Online: 23 November 2023 (04:57:34 CET)
When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to the imbalance in samples dataset, as well as the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network(GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to generate masks for the defect images automatically. Experimental results shows the effectiveness of our proposed method, as it allows for the simultaneous generation of LCD image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.
ARTICLE | doi:10.20944/preprints202309.1666.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: texture; defect detection; anomaly detection; Fourier transform; reconstruction
Online: 25 September 2023 (11:23:30 CEST)
Texture is essential information for image representation, capturing patterns, and structures. Consequently, texture plays a crucial role in the manufacturing industry and has been extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade the image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform were employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, analyzing the frequency domain enables effective defect detection because texture defects often exhibit characteristic changes in specific frequency ranges. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with those of existing approaches.
ARTICLE | doi:10.20944/preprints202308.2150.v1
Subject: Biology And Life Sciences, Forestry Keywords: particleboard; super-resolution reconstruction; generate adversarial networks; defect
Online: 31 August 2023 (09:58:34 CEST)
As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.
REVIEW | doi:10.20944/preprints202308.1283.v1
Subject: Chemistry And Materials Science, Electrochemistry Keywords: catalytic mechanism; edge engineering; defect engineering; phase engineering
Online: 17 August 2023 (11:48:21 CEST)
MoS2 has long been considered as a promising catalyst for hydrogen production. At present, there are many strategies to further improve its catalytic performance, such as edge engineering, defect engineering, phase engineering and so on. However, at present, there is still a great deal of controversy about the mechanism of MoS2 catalytic hydrogen production. For example, it is generally believed that the base plane of MoS2 is inert, but it has been reported that the inert base plane can undergo a transient phase transition in the catalytic process to play the catalytic role, which is contrary to the common understanding that the catalytic activity is only at the edge. Therefore, it is necessary to further understand the mechanism of MoS2 catalytic hydrogen production. In this article, we summarized the latest research progress on the catalytic hydrogen production of MoS2, which is of great significance for revisited the mechanism of MoS2 catalytic hydrogen production.
ARTICLE | doi:10.20944/preprints202301.0483.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Pulsed thermography; Deep learning; Defect detection; Nondestructive evaluation
Online: 26 January 2023 (17:11:04 CET)
Pulsed thermography is a vital technique in the nondestructive evaluation field. However, its data analysis can be complex and requires skilled experts. Advances in deep learning have yielded exceptional results, including image segmentation. Therefore, many efforts have been made to apply deep learning methods to data processing for nondestructive evaluation. Despite this, there is currently no public Pulsed thermographic dataset available for evaluating various spatial-temporal deep methods of segmenting pulsed thermographic data. This article aims to provide such a dataset and assess the performance of commonly used deep learning-based instance segmentation models on it. Additionally, the impact of the number of frames and data transformations on model performance is examined. The findings suggest that suitable preprocessing methods can effectively reduce the data size without compromising the deep models’ performance.
ARTICLE | doi:10.20944/preprints201905.0232.v1
Subject: Chemistry And Materials Science, Polymers And Plastics Keywords: modelling; carbon fiber composite; experimental mechanics; multiscale; defect
Online: 20 May 2019 (08:55:18 CEST)
A multiscale modelling approach was developed in order to estimate the effect of defects on the strength of unidirectional carbon fiber composites. The work encompasses a micromechanics approach, where the known reinforcement and matrix properties are experimentally verified and a 3D finite element model is meshed directly from micrographs. Boundary conditions for loading the micromechanical model are derived from macroscale finite element simulations of the component in question. Using a microscale model based on the actual microstructure, material parameters and load case allows realistic estimation of the effect of a defect. The modelling approach was tested with a unidirectional carbon fiber composite beam, from which the micromechanical model was created and experimentally validated. The effect of porosity was simulated using a resin-rich area in the microstructure and the results were compared to experimental work on samples containing pores.
ARTICLE | doi:10.20944/preprints202310.0356.v1
Subject: Engineering, Industrial And Manufacturing Engineering Keywords: eddy current; steel filament; surface defect; longitudinal scratch; inclusion
Online: 6 October 2023 (15:35:08 CEST)
In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generates a magnetic field with high frequency surrounding a coil of sensor positioned at the filament's output end. The sensor's capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide.
ARTICLE | doi:10.20944/preprints202204.0209.v1
Subject: Physical Sciences, Condensed Matter Physics Keywords: defect-induced superconductivity; graphite; stacking faults; magnetic force microscopy
Online: 22 April 2022 (03:43:45 CEST)
Granular superconductivity at high temperatures in graphite can emerge at certain two-dimensional (2D) stacking faults (SFs) between regions with twisted (around the c-axis) or untwisted crystalline regions with Bernal (ABA...) and/or rhombohedral (ABCABCA...) stacking order. One way to observe experimentally such 2D superconductivity is to measure the frozen magnetic flux produced by a permanent current loop that remains after removing an external magnetic field applied normal to the SFs. Magnetic force microscopy was used to localize and characterize such a permanent current path found in one natural graphite sample out of ∼50 measured graphite samples of different origins. The position of the current path drifts with time and roughly follows a logarithmic time dependence similar to the one for flux creep in type II superconductors. We demonstrate that a ≃10nm deep scratch on the sample surface at the position of the current path causes a change in its location. A further scratch was enough to irreversibly destroy the remanent state of the sample at room temperature. Our studies clarify some of the reasons for the difficulties of finding a trapped flux in remanent state at room temperature in graphite samples with SFs.
Subject: Computer Science And Mathematics, Software Keywords: laser powder bed fusion; process monitoring; defect detection; coaxial
Online: 19 April 2019 (11:18:13 CEST)
This paper describes a multi-channel in-situ monitoring system developed to better understand defect formation signatures in metal additive manufacturing. Three high-speed imaging modes coupled with an image computer capable of processing and storing these data streams allowed an examination of defect formations signatures and mechanisms. It was found that defects later detected in X-ray computed tomography (CT) scans were related to regions with anomalous heat signatures and powder bed morphology. Automated defect detection algorithms based on these defect signatures captured 80% of defects greater than 300 µm.
ARTICLE | doi:10.20944/preprints202306.0344.v1
Subject: Engineering, Energy And Fuel Technology Keywords: defect density; capture cross-section; perovskite solar cell; SCAPS, interfaces.
Online: 5 June 2023 (16:06:02 CEST)
This paper focuses on the impact of defects density and carrier capture cross-section area in the electron transport material (ETM), hole transport material (HTM), and absorber layers on the performance of perovskite solar cells and quantum efficiency (QE). Furthermore, the impact of defects density at the interface between ETM/absorber and absorber/HTM is also studied. SCAPS-1D software is used in the current study in determining solar cell performance. The proposed perovskite solar cell structure is a planar FTO/TiO2/ CH3NH3PbI3/ Cu2O. The results indicated that increasing the defect density in the absorber layer significantly affects cell performance, while in ETM and HTM layers, the cell parameters remain unaffected. It is also found that the defect capture cross-section has a similar behavior to the defect density in the main layers (ETM, absorber, and HTM). In addition, it is observed that by increasing the defects density in the ETM/absorber and absorber/HTM interfaces layer, the cell parameters FF, Jsc, and PCE have been slightly decreased, with no effect on Voc. Moreover, it is also noted that the quantum efficiency QE is sharply reduced. Finally, this paper introduced the correlation between the defect density and the capture cross-section, which is the first attempt to find such a relationship in perovskite solar cells to the knowledge of the authors.
ARTICLE | doi:10.20944/preprints202210.0355.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: Auto encoder; surface defects; abnormal defects; visual inspection; unsupervised defect
Online: 24 October 2022 (07:56:04 CEST)
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and L1 loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.
ARTICLE | doi:10.20944/preprints201912.0295.v3
Subject: Chemistry And Materials Science, Polymers And Plastics Keywords: damage and defect assessment; magnetic resonance imaging; polymer matrix composite
Online: 4 November 2020 (10:17:19 CET)
Defectively manufactured and deliberately damaged composite laminates fabricated with different continuous reinforcing fibres (respectively, carbon and glass) and polymer matrices (respectively, thermoset and thermoplastic) were inspected in magnetic resonance imaging equipment. Two pulse sequences were evaluated during non-destructive examination conducted in saline solution-immersed samples to simulate load-bearing orthopaedic implants permanently in contact with biofluids. The orientation, positioning, shape, and especially the size of translaminar and delamination fractures were determined according to stringent structural assessment criteria. The spatial distribution, shape, and contours of water-filled voids were sufficiently delineated to infer the amount of absorbed water if thinner image slices than this study were used. The surface texture of composite specimens featuring roughness, waviness, indentation, crushing, and scratches was outlined, with fortuitous artefacts not impairing the image quality and interpretation. Low electromagnetic shielding glass fibres delivered the highest, while electrically conductive carbon fibres produced the poorest quality images, particularly when blended with thermoplastic polymer, though reliable image interpretation was still attainable.
Subject: Engineering, Automotive Engineering Keywords: software defect prediction; machine learning approach; integrated approach; Deep Forest
Online: 6 December 2019 (04:25:21 CET)
Accurate prediction of defects in software components plays a vital role in administrating the quality of the quality and efficiency of the system to be developed. So we have written a systematic literature review in order to evaluate the four main defect prediction techniques. Defect prediction paves way for the testers to find bugs and modify them in order to achieve input to output conformance. In this paper we have discussed the open issues in predicting software defects and have provided with a detailed analyzation of different methods including Machine Learning, Integrated Approach, Cross-Project and Deep Forest algorithm in order to prevent these flaws. However, it is almost impossible to rule which method is better than the other so every technique can be analyzed separately and the best technique according to the problem at hand can be used or can be altered to create hybrid technique suitable for the cause.
COMMUNICATION | doi:10.20944/preprints202307.0963.v1
Subject: Chemistry And Materials Science, Metals, Alloys And Metallurgy Keywords: Al-Si alloys; cast defect; Sn; Sn oxides; FIB; HADDF-STEM
Online: 14 July 2023 (12:46:57 CEST)
The cast defects strongly degrade the mechanical properties in cast alloys. The effect of Sn addition on Al-Si alloys was investigated by 3D computed tomography, SEM and TEM. Amorphous Sn oxides are found near the alumina film, cause more shrinkage pores, initiate cracks and deteriorate mechanical properties. This work suggests not adding Sn in various Al alloys used in cast state.
ARTICLE | doi:10.20944/preprints202307.0989.v1
Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: DCR-YOLO; Defect detection; Printed circuit board; SDDT-FPN; PCR; C5ECA
Online: 14 July 2023 (11:07:25 CEST)
Abstract: For the problem of small target of printed circuit board surface defects and low detection accuracy, the printed circuit board surface defect detection network DCR-YOLO is designed to meet the premise of real-time detection speed and effectively improve the detection accuracy. Firstly, the backbone feature extraction network DCR-backbone, consisting of two CR residual blocks and one common residual block, is used for small target defect extraction on printed circuit boards. Secondly, the SDDT-FPN feature fusion module is responsible for the fusion of high level features to low level features, while enhancing feature fusion for the feature fusion layer where the small target prediction head YOLO Head-P3 is located to further enhance the low level feature representation. the PCR module enhances the feature fusion mechanism between the backbone feature extraction network and the SDDT-FPN feature fusion module at different scales of feature layers. the C5ECA module is responsible for adaptive adjustment of feature weights and adaptive attention to the requirements of small target defect information, further enhancing the adaptive feature extraction capability of the feature fusion module. Finally, three YOLO-Heads are responsible for predicting small target defects for different scales. Experiments show that the DCR-YOLO network model detection Map reaches 98.58%, the model size is 7.73MB, which meets the lightweight requirement, and the detection speed reaches 103.15fps, which meets the application requirements for real-time detection of small target defects.
ARTICLE | doi:10.20944/preprints202306.1069.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: wheel surface defect detection; deep learning; YOLO; object detection; machine vision
Online: 15 June 2023 (07:20:42 CEST)
Surface defect detection is a crucial step in the process of automotive wheel production. However, the task possesses challenges due to complex background and a wide range of defect types. In order to detect the defects on the wheel surface accurately and quickly, this paper proposes a YOLOv5-based algorithm for automotive wheel surface defect detection. The algorithm trains and tests the YOLOv5s model using the self-created automotive wheel surface defect dataset, which contains four kinds of defects: linear, dotted, sludge, pinhole. The extensive experimental results demonstrate that the deep learning network trained by our method can achieve an average accuracy of 71.7% and 57.14 FPS. Our findings prove that this detection algorithm performs better than other common target detection algorithms and meets the real-time requirements of industrial applications.
ARTICLE | doi:10.20944/preprints202304.0813.v1
Subject: Engineering, Transportation Science And Technology Keywords: shield subway tunnel; surface defects; 3D laser scanning; defect association analysis
Online: 24 April 2023 (03:53:13 CEST)
The surface defects of shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise the operation safety. To effectively detect the multiple surface defects, this research employs a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting the inspection of the shield tunnel on a metro line section, various surface defects are identified by the TIT, including water leakage defect, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, the association rules between different defects are calculated via an improved Apriori algorithm. Results show that: i) there are significant differences in different association rules of various surface defects of the shield tunnel; ii) the average confidence of the association rule “dislocation & spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; iii) the weakest rule appears at “water leakage → spalling”, with the average confidence of 13%. The association analysis can be used in predicting the critical defects influencing the structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for the shield subway tunnel.
ARTICLE | doi:10.20944/preprints202303.0362.v1
Subject: Physical Sciences, Optics And Photonics Keywords: single-photon emitters; atom defect; first principle calculations; telecommunication band; stress
Online: 21 March 2023 (01:44:51 CET)
Point defect-based single-photon emitters (SPEs) in GaN have aroused a great deal of interest due to their room-temperature operation, narrow line width and high emission rate. The room-temperature SPEs at the telecommunication bands have also been realized recently by localized defects in GaN in experiments, which are highly desired for the practical applications of SPEs in quantum communication with fiber compatibility. However, the origin and underlying mechanism of the SPEs remain unclear to date. Herein, our first-principle calculations predict and identify an intrinsic point defect NGa in GaN that owns a zero-phonon line (ZPL) at telecommunication windows. By tuning the triaxial compressive strain of the crystal structure, the ZPL of NGa can be modulated from 0.849 eV to 0.984 eV, covering the fiber telecommunication windows from the O band to the E band. Besides the ZPL, the formation energy, band structure, transition process and lifetime of the SPEs under different strains are investigated systematically. Our work gives insight into the emission mechanism of the defect SPEs in GaN and also provides effective guidance for achieving wavelength-tunable SPEs working in fiber telecommunication windows.
REVIEW | doi:10.20944/preprints202111.0035.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Fruits & Vegetables; Classification; Convolutional Neural Network; Support Vector Machine; Defect detection
Online: 2 November 2021 (10:31:56 CET)
Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.
ARTICLE | doi:10.20944/preprints202011.0340.v1
Subject: Chemistry And Materials Science, Biomaterials Keywords: drop-weight impact; unidirectional carbon composites; orientation angle; internal defect; impact
Online: 12 November 2020 (10:16:23 CET)
.With the increasing use of carbon fiber reinforced plastics in various area, carbon fiber composites based on prepregs have attracted attention in industries and academia research. However, prepreg manufacturing processes are costly, and the strength of structures varies depending on the orientation and defects (pores and delamination). For non-contact evaluation of internal defects, we proposed lock-in infrared thermography to investigate orientation angles after a compression test. We also conducted a drop-weight impact test to study the behaviour of the composites after impact according the fibers orientation for composite fabricated using unidirectional carbon fiber prepregs. Using CAI tests, we determined the residual compressive strength and confirmed the damage modes using a thermal camera. The results of the drop weight impact tests show that the specimen laminated at 0° suffered the largest damage because of susceptibility of the resin to impact. In contrast, the specimens oriented in of 0°/90° and +45°/–45° directions transferred more than 90% of the impact energy back to the impactor because of the lamination of fibers in the orthogonal directions. Furthermore, the specimens that underwent complete damage in the impact tests were subjected to the lock-in method and showed internal delamination and cut fibers. With the finite elements analysis, the damage of each ply could be observed. Moreover, the temperature differences in the residual compression tests were not significant.
ARTICLE | doi:10.20944/preprints201801.0149.v1
Subject: Chemistry And Materials Science, Biomaterials Keywords: porous scaffold; collagen coating; bioactive peptide; skull defect repair; tissue engineering
Online: 17 January 2018 (06:48:17 CET)
The treatment of large-area bone defects remains a challenge; however, various strategies have been developed to improve the performances of scaffolds in bone tissue engineering. In this study, poly(lactide-co-glycolide)/hydroxyapatite (PLGA/HA) scaffold was coated with Asp-Gly-Glu-Ala (DGEA)-incorporated collagen for the repair of rat skull defect. Our results indicated that the mechanical strength and hydrophilicity of PLGA/HA scaffold were clearly improved and conducive to cell adhesion and proliferation. The collagen-coated scaffold with DGEA significantly promoted the repair of skull defect. These findings indicated that a combination of collagen coating and DGEA improved scaffold properties for bone regeneration, thereby providing a new potential strategy for scaffold design.
ARTICLE | doi:10.20944/preprints202311.0871.v1
Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: animal study; bone healing; histology; lateral augmentation; bone transplantation; biomaterial; bone defect
Online: 14 November 2023 (11:16:45 CET)
Background: Xenogenous bone has been proposed as an alternative to overcome the disadvantages of autogenous grafting. The aim of the present study was to study bone dynamics at inlay and onlay xenografts used for bone augmentation applying a ring technique. Methods: The bone at the lateral surface of the mandibular angle of 12 adult male New Zealand White rabbits was exposed bilaterally. The cortical layer received multiple perforations at one side of the mandible and a xenograft block of collagenated cancellous equine bone, 7 mm in diameter and 3 mm in width, was fixed on the prepared surface using an implant (onlay group). On the opposite side, a defect 7 mm in diameter and 3 mm in depth was prepared, the xenograft block was adapted to the defect and fixed with an implant (inlay group). Results: After ten weeks of healing, in the onlay grafts, new bone was mainly formed on the trabeculae surface, reaching in some specimens the most coronal regions of the block. In the inlay grafts, new bone was found arranged on the trabecular surfaces but also occupying the spaces among the trabeculae. The entrance of the defect was often found close at the top of the block by newly formed bone. A higher percentage of new bone was found in the inlay (19.0 ±9.3%) compared to the onlay (10.4 ±7.4%) groups (p=0.031). The mean gain in osseointegration at the implant in relation to the base of the original 3 mm deep defect was 0.95 ±1.05% at the onlay group and 0.78 ±0.71% at the inlay group (p=0.603). Conclusion: The inlay grafts exhibited a higher new bone percentage than the onlay grafts possibly due to the defect conformation that presented more sources for bone formation. The trabecular conformation and the composition of the grafts made possible the expression of the osteoconductive properties of the material used. This resulted, in several specimens, in growth of bone on the graft trabeculae toward the most superior regions in both groups, and in the closure of the coronal entrance of the defects in the inlay group.
ARTICLE | doi:10.20944/preprints202308.1032.v2
Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: experimental critical size bone defect; maxillofacial area; chitosan; bone formation; morphometry; rats
Online: 6 October 2023 (08:32:17 CEST)
Abstract: A biomaterial is proposed for closing extensive bone defects in the maxillofacial region. The composition of the biomaterial includes high-molecular chitosan, chondroitin sulfate, hyaluronate, heparin, alginate and inorganic nanostructured hydroxyapatite. The purpose of the study is to demonstrate morphological and histological early signs of reconstruction of a bone cavity of critical size. The studies were carried out on 84 white female rats weighing 200-250 g. The study group consisted of 84 subjects in total, 40 in the experimental group and 44 in the control group. In all animals, three-walled bone defects measuring 0.5 x 04 x 05 cm were applied subperiosteally in the region of the angle of the lower jaw and filled in experimental group using lyophilized gel mass of chitosan-alginate-hydroxyapatite (CH-SA-HA). In control animals, the bone cavities were filled with an auto-blood clot after bone trepanation and bleeding. The observation periods are 3.5.7 days, 184.108.40.206.8 and 10 weeks. The control of bone regeneration was carried out using multiple morphological and histological analyses. Results showed that following implantation the chitosan construct actively replaced early-stage defects with the formation of a full-fledged new bone tissue as compared to the control group. Already, by the 7th day morphological analysis showed that formation of spongy bone tissue could be seen. After 2 weeks there was a pronounced increase in bone volume (P<0.01), and at 6 weeks after surgical intervention the closure of the defect was 70-80%, after 8 weeks - 100% without violation of bone morphology with a high degree of mineralization. Thus, the use of modified chitosan after filling eliminates bone defects of a critical size in the maxillofacial region, reveals early signs of bone regeneration, and serves as a promising material in reconstructive dentistry.
ARTICLE | doi:10.20944/preprints202309.0756.v1
Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: animal study; bone healing; histology; lateral augmentation; bone transplantation; biomaterial; bone defect
Online: 13 September 2023 (05:48:08 CEST)
Background: The conformation of the recipient site for an inlay graft presents an increased contact with the parent bone compared to the onlay graft. This might favor bone growth within the inlay compared to the onlay grafts. Hence, the objective of this study was to compare bone incorporation and remodeling process of xenogeneic en bloc grafts, placed using two bone grafting techniques, i.e., onlay vs. inlay. Methods: In this prospective, randomized, split-mouth study (test and control sides in the same animal), two bone grafting techniques were comparatively evaluated on the lateral aspect of the rabbit mandibles. One side was prepared with perforations (onlay site), whereas the other side was prepared with trephines and drills to obtain a wide (7 mm) standardized recipient site (inlay site). A xenogeneic bone block was fixed in the center with a titanium screw in both sides of the mandible and covered with a collagen membrane. Two healing periods were applied in the study: 2 and 10 weeks of healing. Results: after 2 weeks of healing, the mean percentage of new bone was 10.4% and 23.3% at the onlay and inlay grafts, respectively (p=0.022). After 10 weeks of healing, new bone increased to 13.2% and 25.4%, respectively (p=0.080). In this period of healing, the inlay grafts presented new bone percentage >20% in all regions examined while the onlay graft presented a lower percentage in the most external regions of the graft. Conclusion: The percentage of new bone increased faster and was higher in the inlay compared to the onlay grafts. The composition of the grafts allowed new bone to reach the most peripheral regions in both graft groups, even though it was higher in the inlay group. A marginal closure of the defects by newly formed bone was observed in the inlay group.
ARTICLE | doi:10.20944/preprints202308.1559.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: convolutional neural networks; defect recognition; partial discharge measurement durations; epoxy resin; PRPD.
Online: 23 August 2023 (07:33:51 CEST)
This study focuses on the impact of weak partial discharges (PDs) on defect recognition accuracy in epoxy resin through phase-resolved partial discharge (PRPD) analysis. Two measurement conditions are compared until PRPD pattern saturation: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the PD inception voltage (PDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques.
ARTICLE | doi:10.20944/preprints201808.0517.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: fractal dimension; surface defect identification; adaptive fractal filtering; edge extraction; image denoising
Online: 30 August 2018 (05:53:25 CEST)
In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection.
ARTICLE | doi:10.20944/preprints201804.0040.v1
Subject: Medicine And Pharmacology, Ophthalmology Keywords: Methanol exposure; toxic effects; subcontractor manufacturing; dispatched workers; visual defect; neurobehavioral function
Online: 3 April 2018 (16:11:15 CEST)
An outbreak of occupational methanol poisoning occurred in small-scale 3rd tier factories of large-scale smartphone manufacturer, in the Republic of Korea, in 2016. To investigate the working environment and the health effect of the methanol exposure among co-workers of the methanol poisoning cases, we performed a cross sectional study on 155 workers at the five aluminum CNC cutting factories. Air and urinary methanol concentration were measured by gas chromatography, and health examination included symptoms, ophthalmological examinations and neurobehavioral tests. Multiple logistic regression analyses controlled for age and sex were conducted for revealing association of employment duration with symptoms. Air concentrations of methanol in factory A and E were ranged from 228.5 to 2220.0 ppm. Mean urinary methanol concentrations of the workers in each factory were from 3.5 mg/L up to 91.2 mg/L. The odds ratios for symptom of deteriorating vision and CNS increased, according to the employment duration, after adjusting for age and sex. Four cases with injured optic nerve and two cases with decreased neurobehavioral function were founded among co-workers of the victims. This study showed that the methanol exposure under poor environmental control not only produce eye and CNS symptoms but also affect neurobehavioral function and optic nerve.
ARTICLE | doi:10.20944/preprints202210.0251.v1
Subject: Medicine And Pharmacology, Pediatrics, Perinatology And Child Health Keywords: Pregnancy; Birth-defect; National; PRAMS (Pregnancy Risk Assessment Monitoring System); Smoking; Diabetes; Depression
Online: 18 October 2022 (05:48:22 CEST)
Abstract: Objective: To assess both individual and interactive effects of prenatal medical conditions depression and Diabetes, and health behaviors including smoking during pregnancy on infant birth defects. Methods: The data for this research study were collected by the Pregnancy Risk Assessment Monitoring System (PRAMS) in 2018. Birth certificate records were used in each participating jurisdiction to select a sample representative of all women who delivered a live-born infant. Complex sampling weights were used to analyze the data with a weighted sample size of 4,536,867. Descriptive statistics were performed to explore frequencies of the independent and dependent variables. Bivariate and multivariable analyses were conducted to examine associations among the independent and dependent variables. Results: The results indicate and significant interaction between the variables smoking and Depression and Depression and Diabetes (OR= 3.17; p-value <0.001 and OR= 3.13; p-value <0.001 respectively). Depression during pregnancy was found to be strongly associated with delivering an infant with a birth defect (OR= 1.31, P-value < 0.001). Conclusion: Depression during pregnancy and its interaction with smoking and Diabetes are vital in determining birth defects in infants. The results indicate that birth defects in the United States can be lowering Depression in pregnant women.
ARTICLE | doi:10.20944/preprints202205.0282.v1
Subject: Biology And Life Sciences, Agricultural Science And Agronomy Keywords: cyclone; defect; hurricane; likelihood of failure; storm damage; typhoon; urban ecology; urban forestry
Online: 21 May 2022 (11:03:18 CEST)
Urban trees are often more sun- and wind-exposed than their forest-grown counterparts. These environmental differences can impact how many species grow – impacting trunk taper, crown spread, branch architecture, and other aspects of tree form. Given these differences, windthrow models derived from traditional forest production data sources may not be appropriate for urban forest management. Additionally, visual abnormalities historically labeled as “defects” in timber production may not have a significant impact on tree failure potential. In this study, we look at urban tree failures associated with Hurricane Irma in Tampa, Florida, USA. We used spatial analysis to determine if patterns of failure existed among our inventoried trees. We also looked at risk assessment data to determine which visual defects were the most common and the most likely to be associated with branch or whole-tree failure. Results indicate that there was no spatial pattern associated with the observed tree failures – trees failed or withstood the storm as individuals. While some defects like decay and dead wood were associated with increased tree failure, other defects like weak branch unions and poor branch architecture were less problematic.
ARTICLE | doi:10.20944/preprints202102.0443.v1
Subject: Medicine And Pharmacology, Orthopedics And Sports Medicine Keywords: critical sized bone defect; bone tissue regeneration; nano-gelatin/ hydroxyapatite fiber (NGF); metformin.
Online: 19 February 2021 (14:35:11 CET)
Tissue engineering and regenerative medicine has gradually evolved as a promising therapeutic strategy to the modern healthcare of the aging and diseased population. In this study, we developed a novel nano-fibrous scaffold and verified its application in the critical bone defect regeneration. The metformin-incorporated nano-gelatin/hydroxyapatite fibers (NGF) was produced by electrospinning, cross-linked, and then characterized by XRD and FTIR. Cytotoxicity, cells adhesion, cell differentiation, and quantitative osteogenic gene and protein expression were analyzed by bone marrow stem cells from rat. Rat forearm critical bone defect model was performed for the in vivo study. The nano-gelatin/hydroxyapatite fibers (NGF) were characterized by their porous structures with proper interconnectivity without significant cytotoxic effects; the adhesion of bone marrow stem cells on the nano-gelatin/hydroxyapatite fibers (NGF) could be enhanced. The osteogenic gene and protein expression were upregulated. Post implantation, the new regenerated bone in bone defect was well demonstrated in the NGF samples. We demonstrated that the metformin-incorporated nano-gelatin-hydroxyapatite fibers greatly improved healing potential on the critical sized bone defect. Although metformin-incorporated nano-gelatin/hydroxyapatite fibers had advantageous effectiveness during bone regeneration, further validation is required before it can be applied to clinical applications.
ARTICLE | doi:10.20944/preprints202012.0217.v1
Subject: Engineering, Automotive Engineering Keywords: Eddy current sensor; defect orientation; angled crack, thin-skin regime; non-destructive testing.
Online: 9 December 2020 (10:57:15 CET)
Electromagnetic sensors have been used for inspecting small surface defects of metals. Based on the eddy-current thin-skin regime, a revised algorithm is proposed for a triple-coil drive-pickup eddy-current sensor scanning over long surface crack slots (10 mm) with different rotary angles. The method is validated by the voltage measurement of the designed EC sensor scanning over a benchmark (ferromagnetic) steel with surface defects of different depths and rotary angles. With an additional sensing coil for the designed EC sensor, the defect angle (or orientation) can be measured without spatially and coaxially rotating the excitation coil. By referring to the voltage change (due to the defect) diagram (voltage sum versus voltage different) of two sensing pairs, the rotary angle of the surface crack is retrieved with a maximum residual deviation of 3.5 %.
ARTICLE | doi:10.20944/preprints202310.0313.v1
Subject: Chemistry And Materials Science, Electronic, Optical And Magnetic Materials Keywords: defect-adamantine; quaternary chalcogenide; crystal growth; single crystals; structure; cation distribution; band gap energy
Online: 6 October 2023 (06:20:25 CEST)
Single crystals of quaternary adamantine type Cu•GaGeS4 were grown by chemical vapor transport technique using iodine as transport agent. Dark red transparent crystals grew in a temperature gradient of dT = 900–750 °C. Chemical characterization by X-ray fluorescence showed an off-stoichiometric composition of the Cu•GaGeS4 crystals, in particular a slight Ge-deficit. By X-ray diffraction, Cu•GaGeS4 was found to adopt the chalcopyrite-type structure with the space group I-42d. The cation distribution in this structure was analyzed by multiple energy anomalous synchrotron X-ray diffraction and it was found that Cu and vacancies occupy the 4a site, whereas Ga and Ge occupy the 4b site. The band gap energies of several off-stoichiometric Cu•GaGeS4 crystals were determined by UV-Vis spectroscopy and are in the range of 2.1 to 2.4 eV. A non-linear correlation of the band gap energy with the Ge-content of the compound follows the usual bowing behaviour of semiconductor alloys with a bowing parameter of b = 1.45(0.08).
ARTICLE | doi:10.20944/preprints202308.1566.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: immunohistochemistry; foreign body reaction; bone defect; scaffold-guided bone regeneration; polycaprolactone; in vivo; sheep
Online: 23 August 2023 (05:29:15 CEST)
Large volume bone defect regeneration is complex and demands time to complete. Several regeneration phases with unique characteristics including immune responses follow, overlap, and interdepend on each other and, if successful, lead to the regeneration of the organ bone's form and function. However, during traumatic, infectious, or neoplastic clinical cases, the intrinsic bone regeneration capacity may exceed, and surgical intervention is indicated. Scaffold-guided bone regeneration (SGBR) has recently shown efficacy in preclinical and clinical studies. To investigate different SGBR strategies over periods of up to 3 years we have established a well characterized large segmental tibial bone defect ovine model, for which we have developed and optimized immunohistochemistry (IHC) protocols. We present an overview of the immunohistochemical characterization of different experimental groups in which all ovine segmental defects were treated with a bone grafting technique combined with a three-dimensionally printed medical-grade polycaprolactone-tricalcium phosphate (mPCL-TCP) scaffold. The qualitative data set is based on osteoimmunological findings gained from IHC analyses of over >350 sheep surgeries over the past two decades. Our systematic and standardized IHC protocols enabled us to gain further insight into the complex and long-drawn-out bone regeneration processes, which ultimately proved to be a critical element for successful translational research.
ARTICLE | doi:10.20944/preprints202210.0467.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: abnormality detection; surface defect detection; feature extraction; gray level co-occurrence matrix; energy variations
Online: 31 October 2022 (06:26:06 CET)
Surface defect detection is one of the most widely used research areas in the field of image processing and machine vision. Detection of surface defects is used in visual inspection systems and medical image analysis. In this manuscript, an innovative method for detecting surface defects based on energy changes in co-occurrence matrices is presented in several directions. The method presented in this manuscript includes two stages of learning and testing. In the learning phase, to extract texture features, the gray level co-occurrence matrix operator is applied on the healthy image of the desired level. Then the energy value of the output matrix is calculated. In the following, changes in the amount of energy are considered as statistical characteristics that are a good representative of the image of a healthy surface. Finally, with its help, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective windows that have suffered from non-normality are distinguished from the healthy surface sections. In the results section, the efficiency of the mentioned method has been measured on medical images and stone and ceramic images, and its detection accuracy has been compared with some previous effective methods. The advantages of the presented method include high accuracy, low calculations and compatibility with all types of levels due to the use of the learning stage. The proposed approach can be used in medical applications to diagnose abnormalities such as diseases. All extracted features are statistical, so its detection speed is higher than deep neural networks.
ARTICLE | doi:10.20944/preprints202104.0249.v1
Subject: Engineering, Automotive Engineering Keywords: conjugate heat transfer; convection-radiation; Rosseland approximation; P1 approximation; finite difference; defect correction - multigrid.
Online: 8 April 2021 (17:57:29 CEST)
The effect of thermal radiation on the two – dimensional, steady-state, conjugate heat transfer from a circular cylinder with an internal heat source in steady laminar crossflow is investigated in this work. P0 (Rosseland) and P1 approximations were used to model the radiative transfer. The mathematical model equations were solved numerically. Qualitatively, P0 and P1 approximations show the same effect of thermal radiation on conjugate heat transfer; the increase in the radiation – conduction parameter decreases the cylinder surface temperature and increases the heat transfer rate. Quantitatively, there are significant differences between the results provided by the two approximations.
REVIEW | doi:10.20944/preprints201912.0062.v1
Subject: Business, Economics And Management, Business And Management Keywords: six sigma; lean six sigma; DMAIC; DMADV; agile; defect per million opportunities(DPMO); SPC
Online: 5 December 2019 (04:22:42 CET)
The main purpose of this research is to use “DMAIC” and “DMADV” framework of six sigma to reduce cost of projects, increase yields, improve performance and reduce defects. This study conclude that the sigma level of cement bag production in four production lines is “4.7 DPMO” values of 710 and the possibility of defects per unit of 11 possibilities this situation was handled by using six sigma. Any business or industry run only to satisfy customer and increase their profits, this can be attained as development of quality product..For the ranking of newly established universities the certification of those institutes is very important and also a critical process. For this process we used Lean six sigma approach that can identify the wastes that affect this process.Six sigma can be applied to any work field such as education, power optimization and other types of industries Six sigma “DMAIC” approach is also used for the testing of EDA tools that occurs due to the complex coding or configurations, flows and platforms they support. To improve the process quality of SDLC it’s necessary to remove the defects of system in advance along with thorough valuation of size and it also makes project accordant with real time environment
ARTICLE | doi:10.20944/preprints201811.0461.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: Software quality; cross-project defect prediction; multi-source; dissimilarity space; arc-cosine kernel function
Online: 19 November 2018 (11:48:50 CET)
Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods using feature attributes to represent samples, which can not avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space ( DM-CPDP). This method first uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to form the dissimilarity space, and in this space the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. Finally, we use TrAdaBoost method to establish the prediction model. The experimental results show that our approach has better performance than other traditional CPDP methods.
ARTICLE | doi:10.20944/preprints201701.0066.v1
Subject: Engineering, Mechanical Engineering Keywords: nuclear facility; ultrasonic interface wave; defect detection; nondestructive testing; finite element method; inaccessible nozzle
Online: 13 January 2017 (10:01:23 CET)
An effective method to inspect inaccessible nuclear power facility by interface wave which propagate along the shrink fit boundary of reactor head is proposed in this study. Reactor head is relatively thick to inspect from the outside of reactor by conventional ultrasonic testing. The proposed interface wave can propagate a long distance from the fixed transducer position. The inside of nuclear reactor is limited to access due to the high radiation, so transducers are located at outside of nuclear facility and interface wave propagates into the nuclear reactor for defect detection. The numerical simulation and experiments were carried out to validate the effectiveness of the proposed interface wave inspection method. Various defect cases simulating field failures are also presented with satisfactory detectability by the proposed technique with the features for defect classification.
ARTICLE | doi:10.20944/preprints202308.0532.v1
Subject: Engineering, Civil Engineering Keywords: Concrete-filled steel tubular; Travel time tomography; Piezoelectric lead zirconate titanate; Defect imaging; Parameter analysis
Online: 8 August 2023 (03:31:38 CEST)
Concrete-filled steel tube (CFST) members have been widely used in the field of civil engineering due to their advanced superior mechanical properties. However, internal defects such as concrete core voids and interface debonding are likely to weaken the load-carrying capacity and stiffness of these members, which affects safety and serviceability of CFST structures. Visualizing the inner defects of concrete core in CFST members have been a critical need in civil engineering construction, a travel time tomography (TTT) is introduced to quantitatively identify and visualize the sizes and positions of CFST members in this paper. Moreover, a parameter analysis is performed to investigate the relationship between TTT imaging qualities and influence factors, e.g. inversion parameters, defect sizes and positions. The effectiveness and accuracy of the TTT algorithm are verified by several numerical examples and the results demonstrate that TTT can identify the sizes and positions of concrete core void defects in CFST members efficiently and several inversion parameters including model weighting matrix and inversion grid size really pose a significant impact on the imaging results of CFST members. In addition, several optimum parameters are recommended to benefit the future study of the promising TTT approach for CFST members.
ARTICLE | doi:10.20944/preprints202304.0022.v1
Subject: Engineering, Aerospace Engineering Keywords: non-destructive testing; deep learning; automated defect recognition (ADR); semantic segmentation; digital X-ray radiography
Online: 3 April 2023 (10:25:31 CEST)
In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.
ARTICLE | doi:10.20944/preprints202011.0465.v1
Subject: Engineering, Industrial And Manufacturing Engineering Keywords: coating defect; electrolyte layer; temperature; thermal deformation; roll-to-roll slot-die coating systems; wrinkle
Online: 18 November 2020 (10:43:39 CET)
In roll-to-roll (R2R) processing, uniformity of the web is a crucial factor that can guarantee high coating quality. To understand web defects due to thermal deformation, we analyzed the effects of web unevenness on the coating quality of an yttria-stabilized zirconia (YSZ) layer, a brittle electrolyte of solid oxide fuel cells (SOFCs). We used finite element analysis to analyze the thermal and mechanical deformations at different drying temperatures. A YSZ layer was also coated using R2R slot-die coating to observe effects of web unevenness on the coating quality. It was seen that web unevenness was generated by thermal deformation due the conduction and convection heat from the dryer. Owing to varying web unevenness with time, the YSZ layer developed cracks. At higher drying temperatures, more coating defects having larger widths were generated. Results indicated that web unevenness at the coating section led to coating defects, which could damage the SOFC and decrease its yield in the R2R process. From this study, we suggest that coating defects, generated by the web unevenness owing to the convection and conduction heat, should be considered for the high-volume production of brittle electrolytes using the R2R process.
ARTICLE | doi:10.20944/preprints201904.0322.v1
Subject: Engineering, Mechanical Engineering Keywords: aluminum profile surface defects; multiscale defect detection network; deep learning; average precision(AP); saliency maps
Online: 29 April 2019 (09:37:07 CEST)
Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.
REVIEW | doi:10.20944/preprints202308.0283.v1
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: carbon nanostructure; defect; heteroatom doping; catalysis; electrocatalysis; electrochemistry; supercapacitor; microwave irradiation; microwave-assisted synthesis; inorganic nanoparticle
Online: 3 August 2023 (05:33:53 CEST)
In this review, we focus on a small section of the extensive literature that deals with the materials containing pristine defective CNs and those incorporated in the hybrid materials. We will discuss only those topics that focus on structural defects related to the introduction of perturbation into the surface topology of a nanostructure. We focus mainly on the method using microwave (MW) irradiation, which is a powerful tool for synthesizing and modifying carbon-based solid materials. In addition, the simplicity of the technique, economy, and the possibility of conducting the reaction in solvents and solid phase, in the presence of components of different chemical nature, allows use in various combinations. In this review, we will emphasize the advantages of synthesis using MW-assisted heating and indicate the influence of the structure of the obtained materials on their physical and chemical properties. We will also highlight the role of the occurrence of defects in the carbon material and the implication in designing their properties and applications.
REVIEW | doi:10.20944/preprints202012.0322.v1
Subject: Medicine And Pharmacology, Neuroscience And Neurology Keywords: Cerebrospinal fluid; real-time MRI; hydrocephalus; space flight disease; aquaporin; spontaneous intracranial hypotension; neural tube defect
Online: 14 December 2020 (10:21:21 CET)
New experimental and clinical findings question the historic view of hydrocephalus and its 100-year-old classification. In particular, real-time MRI evaluation of CSF flow and detailed insights into brain water regulation on the molecular scale indicate the existence of at least three main mechanisms that determine the dynamics of neurofluids. (i) Inspiration is a major driving force (ii) Adequate filling of brain ventricles by balanced cerebrospinal fluid upsurge is sensed by cilia (iii) The perivascular glial network connects the ependymal surface to the pericapillary Virchow-Robin spaces. Hitherto, these aspects have not been considered a common physiologic framework improving knowledge and therapy for severe disorders of normal-pressure and post-haemorrhagic hydrocephalus, spontaneous intracranial hypotension and spaceflight disease.
ARTICLE | doi:10.20944/preprints202309.1044.v1
Subject: Engineering, Other Keywords: Ceramic capacitors; Donor-acceptor complex; Defect dipole engineering; Dielectric and ferroelectric properties; Energy storage density and efficiency
Online: 15 September 2023 (07:10:40 CEST)
In this paper, we investigate the structural, microstructural, dielectric, and energy storage properties of Nd and Mn co-doped Ba0.7Sr0.3TiO3 [(Ba0.7Sr0.3)1-xNdxTi1-yMnyO3 (BSNTM) ceramics (x = 0, 0.005, and y = 0, 0.0025, 0.005, and 0.01)] via a defect dipole engineering method. The complex defect dipoles (MnTi"-VO∙∙)∙ and (MnTi"-VO∙∙) between acceptor ions and oxygen vacancies capture electrons, enhancing the breakdown electric field and energy storage performances. XRD, Raman spectroscopy, and microscopic investigations of BSNTM ceramics revealed the formation of a tetragonal phase, increased oxygen vacancies, and reduced grain size with Mn dopant, respectively. The BSNTM ceramics with x=0.005 and y=0 exhibit a high dielectric constant of 2058 and a dielectric loss of 0.026 at 1 kHz. These values gradually decreased to 1876 and 0.019 for x=0.005 and y=0.01 due to the Mn2+ ions at Ti4+-site, which facilitates the formation of oxygen vacancies, and prevents the decrease of Ti4+. In addition, the defect dipoles act as a driving force for depolarization to tailor the domain formation energy and domain wall energy, which provides a high difference between the maximum polarization of Pmax and remnant polarization of Pr (ΔP=10.39 µC/cm2). Moreover, the complex defect dipoles with optimum oxygen vacancies in BSNTM ceramics can provide not only a high ΔP but also reduce grain size, which together improve the breakdown strength from 60.4 to 110.6 kV/cm, giving rise to a high energy storage density of 0.41 J/cm3 and high efficiency of 84.6% for x=0.005 and y=0.01. These findings demonstrate that defect dipoles engineering is an effective method to enhance the energy storage performance of dielectrics for capacitor applications.
ARTICLE | doi:10.20944/preprints202305.0796.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: keyword light-weight; insulator and defect detection; YOLOv5; Ghost module; convolutional block attention module; unmanned aerial vehicles
Online: 11 May 2023 (05:21:17 CEST)
Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art network of object detection, YOLOv5, has been widely used on insulator and defect detection. However, YOLOv5 network has some limitations like poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced Ghost module into YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance in unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention module (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) 0.5 and the mAP0.5:0.95 of our model can reach 99.4% and 91.7%, the parameters and model weight are reduced to 3807372 and 8.79M, which can easily deploy to embedded devices like UAVs. And the speed of detection can reach 10.9ms/image, which can meet the real-time detection requirement.
REVIEW | doi:10.20944/preprints202304.0652.v1
Subject: Medicine And Pharmacology, Pediatrics, Perinatology And Child Health Keywords: 22q11.2 deletion; microdeletion; DiGeorge syndrome; velocardiofacial syndrome; dysmorphism; inborn errors of immunity; thymus; congenital heart defect; hypocalcemia
Online: 20 April 2023 (10:41:14 CEST)
The 22q11.2 deletion syndrome is a multisystemic disorder characterized by a marked variability of phenotypic features making the diagnosis challenging for clinicians. The wide spectrum of clinical manifestations includes congenital heart defects, most frequently conotruncal cardiac anomalies, thymic hypoplasia and predominating cellular immune deficiency, laryngeal developmental defects, midline anomalies with cleft palate and velar insufficiency, structural airway defects, facial dysmorphism, parathyroid and thyroid gland hormonal dysfunctions, speech delay, developmental delay, neurocognitive and psychiatric disorders. Significant progress has been made in understanding the complex molecular genetic etiology of the 22q11.2 deletion syndrome underpinning the heterogeneity of clinical manifestations. The deletion is caused by chromosomal rearrangements in meiosis and is mediated by non-allelic homologous recombination events between low copy repeats or segmental duplications in the 22q11.2 region. A range of genetic modifiers, environmental factors as well as the impact of hemizygosity on the remaining allele contribute to the intricate genotype-phenotype relationships. This comprehensive review has been aimed at highlighting the molecular genetic background of 22q11.2 deletion syndrome in correlation with a clinical multidisciplinary approach.
REVIEW | doi:10.20944/preprints202107.0384.v2
Subject: Medicine And Pharmacology, Cardiac And Cardiovascular Systems Keywords: miRNAs; valvular heart diseases; aortic stenosis; calcification; mitral valve prolapse, aortic valve defect; vectors; delivery systems; nanoparticles
Online: 8 November 2021 (14:25:40 CET)
miRNAs have recently attracted investigators' interest as regulators of valvular diseases pathogenesis, diagnostic biomarkers, and therapeutical targets. Evidence from in-vivo and in-vitro studies demonstrated stimulatory or inhibitory roles in mitral valve prolapse development, aortic leaflet fusion, and calcification pathways, specifically osteoblastic differentiation and transcription factors modulation. Tissue expression assessment and comparison between physiological and pathological phenotypes of different disease entities, including mitral valve prolapse and mitral chordae tendineae rupture, emerged as the best strategies to address miRNAs over or under-representation and thus, their impact on pathogeneses. In this review, we discuss the fundamental intra- and intercellular signals regulated by miRNAs leading to defects in mitral and aortic valves, congenital heart diseases, and the possible therapeutic strategies targeting them. These miRNAs inhibitors comprise of antisense oligonucleotides and sponge vectors. The miRNA mimics, miRNA expression vectors, and small molecules are instead possible practical strategies to increase specific miRNA activity. Advantages and technical limitations of these new drugs, including instability and complex pharmacokinetics, are also presented. Novel delivery strategies, such as nanoparticles and liposomes, are described to improve knowledge on future personalized treatment directions.
ARTICLE | doi:10.20944/preprints201811.0212.v1
Subject: Biology And Life Sciences, Biology And Biotechnology Keywords: plant polyphenol; EGCG; gelatin; bone formation; congenital bone defect; dedifferentiated fat cell; adipose-derived stem cell; scaffold
Online: 8 November 2018 (11:34:11 CET)
Cost-effective and functionalized scaffolds are in high demand for stem-cell-based regenerative medicine to treat refractory bone defects in craniofacial abnormalities and injuries. One potential strategy is to utilize pharmacological and cost-effective plant polyphenols and biocompatible proteins, such as gelatin. Nevertheless, the use of chemically modified proteins with plant polyphenols in this strategy has not been standardized. Here, we demonstrated that gelatin chemically modified with epigallocatechin gallate (EGCG), the major catechin isolated from green tea, can be a useful material for dedifferentiated fat cells and adipose-derived stem cells and can induce bone regeneration in a rat congenial cleft-jaw model in vivo. Vacuum-heated gelatin sponge modified with EGCG (vhEGCG-GS) induced superior osteogenesis from these two cell types compared with vacuum-heated gelatin sponge (vhGS). The EGCG-modification converted the water wettability of vhGS to a hydrophilic property (contact angle: 110° to 3.8°) and the zeta potential to a negative surface charge; the modification enhanced the cell adhesion property and promoted calcium phosphate precipitation. These results suggest that the EGCG-modification with chemical synthesis can be a useful platform to modify the physicochemical property of gelatin. This alteration is likely to provide a preferable microenvironment for multipotent progenitor cells, inducing superior bone formation in vivo.
ARTICLE | doi:10.20944/preprints202011.0527.v1
Subject: Engineering, Aerospace Engineering Keywords: Aircraft Maintenance Inspection; Anomaly Detection; Defect Inspection; Convolutional Neural Networks; Mask R-CNN; Generative Adversarial Networks; Image Augmentation
Online: 20 November 2020 (09:16:13 CET)
Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Through supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35% respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approaches uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approache combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%)
ARTICLE | doi:10.20944/preprints201709.0054.v2
Subject: Engineering, Control And Systems Engineering Keywords: product design; design defect; robust statistics; nonparametric statistics; model uncertainty; optimization; liability; tortious product liability; strict product liability
Online: 17 December 2017 (08:48:29 CET)
Statistical modeling lies at the heart of product design and development throughout numerous engineering disciplines, especially since processing large amounts of data has become increasingly ubiquitous. While mathematical statistics provide elegant guidance pertaining to the question of whether or not some particular underlying modeling assumptions are justified and appropriate, when pursuing a more comprehensive assessment of product design and development other considerations often increase in significance. Therefore, we will examine and analyze the tedious interactions and implications of statistical modeling choices and product liability exposure. To the best of our knowledge, this paper is the first to draw attention to and explore some often overlooked or oversimplified dangers and pitfalls that enter the equation when product design heavily relies on statistical modeling. In particular, through a diligent analysis of both statistical and legal aspects we will explore how statistically optimal procedures may yield far from optimal outcomes in terms of product liability when applied to actual real life problems and why suboptimal nonparametric or robust approaches may constitute better alternatives.
ARTICLE | doi:10.20944/preprints202307.1095.v1
Subject: Engineering, Metallurgy And Metallurgical Engineering Keywords: high Mg content Al-Mg alloy; Finite Element Analysis (FEA); electron backscattered diffraction (EBSD); cross-rolling; defect; Goss texture
Online: 17 July 2023 (10:56:33 CEST)
This study investigated defect formation and strain distribution in high Mg content Al-Mg alloys during normal rolling and cross-rolling processes. The finite element analysis (FEA) revealed the presence of wave defects and strain localization-induced zipper cracks in normal cold rolling, which were confirmed by experimental results. The concentration of shear strain played a significant role in crack formation and propagation. However, the influence of wave defects was minimal in the cross-rolling process, which exhibited a relatively uniform strain distribution. Nonetheless, strain concentration at the edge and center regions led to the formation of zipper cracks and edge cracks, with more pronounced propagation observed in the experiments compared to FEA predictions. Furthermore, texture evolution was found to be a crucial factor affecting crack propagation, particularly with the development of the Goss texture component which is observed by electron backscattered diffraction analysis at bending points. The Goss texture hindered crack propagation, while the Brass texture allowed cracks to pass through. This phenomenon was consistent with both FEA and experimental observations. To mitigate edge crack formation and propagation, potential strategies involve promoting the formation of the Goss texture at the edge through alloy and process conditions, as well as implementing intermediate annealing to alleviate stress accumulation. These measures can enhance the overall quality and reliability of Al-Mg alloys during cross-rolling processes. In summary, understanding the mechanisms of defect formation and strain distribution in Al-Mg alloys during rolling processes is crucial for optimizing their mechanical properties. The findings of this study provide insights into the challenges associated with wave defects, strain localization, and crack propagation. Future research and optimization efforts should focus on implementing strategies to minimize defects and improve the overall quality of Al-Mg alloys in industrial applications.
Subject: Computer Science And Mathematics, Mathematics Keywords: harmony search; meta-heuristic; parameter optimization; software defect prediction; just-in-time prediction; software quality assurance; maintenance; maritime transportation
Online: 31 December 2020 (09:27:46 CET)
Software is playing the most important role in recent vehicle innovation, and consequently the amount of software has been rapidly growing last decades. Safety-critical nature of ships, one sort of vehicles, makes Software Quality Assurance (SQA) has gotten to be a fundamental prerequisite. Just-In-Time Software Defect Prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in maritime domain reported feasible prediction performance. However, we still consider that the prediction model has still rooms for improvement since the parameters of the model are not optimized yet. Harmony Search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce the better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond baseline of previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite we considered balance as performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with high class imbalance ratio. Finally, we expect that our research can be extended to improve performance of JIT-SDP not only in maritime domain software but also in open source software.
ARTICLE | doi:10.20944/preprints202211.0395.v1
Subject: Chemistry And Materials Science, Polymers And Plastics Keywords: polymeric composite drilling; GFRP reinforced with Ti interlayers; hole drilling quality; delaminations; defect severity prediction; ANN based prognosis of the quality; tool geometry and machining conditions.
Online: 22 November 2022 (02:32:05 CET)
The main purpose of this study was to develop a model for predicting the quality of holes drilled in the root part of a spar of helicopter main rotor blades made of the Glass Fiber Reinforced Plastic (GFRP)-Ti multilayer polymer composite. As the main quality criterion, delaminations at the entry and exit of the drill from the hole were taken. In the experimental study a conventional drill and two modified geometry drills: a double-point angle drill and a dagger drill were used. Preliminary experiments showed the best hole quality when using modified drills, which allowed further detailed study only with both modified drills at different drilling speeds and feed rates. Its results in the form of training sets were used to build the Artificial Neural Networks (ANNs) to predict delamination at the entry and exit of drilled holes. The analysis of the fitted response functions, presented as 3D surfaces plots and superimposed contour plots, made it possible to choose the better tool - a double-point angle drill and determine the optimal area for drilling speed and feed rates, confirming that the prediction of the quality and productivity of machining composites based on ANN is an effective tool to search and quantify the quality criteria of such technologies.
ARTICLE | doi:10.20944/preprints202210.0311.v1
Subject: Medicine And Pharmacology, Dentistry And Oral Surgery Keywords: Animal study; beagle dog; β-tricalcium phosphate (TCP); immunohistochemistry; micro computed tomography (CT); periodontal tissue engineering; periostin; recombinant human collagen peptide (RCP); scaffold material; 3-wall intrabony defect
Online: 20 October 2022 (12:24:49 CEST)
Recombinant human collagen peptide (RCP) is a recombinantly created xeno-free biomaterial enriched in RGD (arginine-glycine-aspartic acid) sequences, with good processability that is being investigated for regenerative medicine applications. Recently, the biocompatibility and osteogenic ability of β-TCP/RCP (RCP granules combined with β-tricalcium phosphate (TCP) submicron particles) were demonstrated. In the present study, β-TCP/RCP was implanted into experimental periodontal tissue defects (three-walled bone defect) created in beagle dogs to investigate tissue responses and subsequent regenerative effects. Micro computed tomography image analysis at 8 weeks postoperatively showed that the amount of new bone after β-TCP/RCP graft was significantly greater (2.2 fold, P<0.05) than that of the control (no graft) group. Histological findings showed that the transplanted β-TCP/RCP induced active bone-like tissue formation including TRAP-positive and OCN-positive cells as well as bioabsorbability. Ankylosis did not occur, and periostin-positive periodontal ligament-like tissue formation was observed. Histological measurements revealed that β-TCP/RCP implantation formed 1.7-fold more bone-like tissue and 2.1-fold more periodontal ligament-like tissue than the control, and significantly suppressed gingival recession and epithelial downgrowth (P<0.05). These results suggest that β-TCP/RCP is effective as a periodontal tissue regenerative material.