Types Of Defects Research Articles

471P Interfering with CUG toxic repeats using AAV.U7snRNAs rescue myotonia and splicing defects in myotonic dystrophy type 1

471P Interfering with CUG toxic repeats using AAV.U7snRNAs rescue myotonia and splicing defects in myotonic dystrophy type 1

Green buffer areas and green roofs: performance design to improve the urban water cycle

Abstract While in all Europe the land use does not decrease – contrary to EU soil strategy that would actualize the zero net land use by 2050 – cities are plagued by problems such as air pollution, urban heat island effect and uncontrolled run-off following intense rain events, which degrade the environment threatening the health and the safety of inhabitants. The impermeable materials and are responsible for the worsening of the urban water cycle and, at the same time, for the increase in summer air temperatures: both those effects can be mitigated by nature-based and draining solutions. The paper explains the hydrological phenomena of the urban impervious surfaces, highlighting the typical functional defects, providing then a list of various green technologies suitable to overcome the common issues of city centers like high density / reduced spaces, irreversible impervious surfaces, undersized drainage, presence of sub-structures / sub-systems, conservation of architectural heritage. The technical green solutions on the ground (bio-retention systems, rain gardens, trees, pervious pavements…) and on the roofs, specifically designed, behave 1) as a buffer for the stormwater and superficial run-off, being able to collect water, 2) as providers of side effects that can affect water quality, biodiversity, amenity in cities. Experimental results from a rain simulation are presented in order to argue potentials and limitations of these solutions, often narrated as salvific, or, on the contrary, underestimated in their technological specificities.

Research of Converter Station Intelligent Inspection System Algorithm

Abstract This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

PE Gas Pipeline Defect Detection Algorithm based on Improved YOLO v5

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects,improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%,which can achieve the detection and identification of defect types of effects in PE gas pipes.

A Surface Defect Detection Method for Cold-rolled Strip Steel Based on Improved YOLOv8

Cold-rolled strip steel has excellent mechanical properties, and it is widely used in the fields such as automotive manufacturing, construction industry, aircraft manufacturing and electronic product manufacturing. However, many different processing defects occur in the manufacturing process of cold-rolled strip steel, and this article proposes a visual detection method based on YOLOv8 to identify the surface defects on cold-rolled strip steel more accurately. Firstly, several common types of defects in cold-rolled strip steel were analyzed. Then, in order to enrich the dataset for subsequent detection, wavelet transform was used for filtering, denoising, and canny edge detection in the image processing stage for obtaining the clearer input model image with the more prominent graphics. Lastly, the CA attention mechanism was added into the YOLOv8 which is useful to improve the features recognition ability of the model, at the same time, the original standard convolution was replaced by DCS convolution and the loss function was optimized which is useful to get the lightweight models. The improved YOLOv8 model was validated on the NEU-DET dataset, the overall detection performance of the model such as the recall, the accuracy, and the average precision has significantly improved.

Issues of classification and examination of lemons in the course of forensic commodity expertise

Identification and examination of export-import goods, in particular lemons, plays a significant role in facilitating the detection of customs violations and crimes, as well as in ensuring preventive measures to eliminate them. The article analyses the peculiarities of examination of citrus fruits, in particular lemons, with a view to their classification under classifier of goods Commodity Nomenclature of Foreign Economic Activity. The methodological approaches to the classification of citrus fruits within the framework of commodity expertise are considered. The main diagnostic tasks to be solved by a forensic expert in the course of lemon examination are identified: determination of product quality, chemical analysis, phytopathological analysis, toxicological analysis, organoleptic analysis, microbiological analysis, identification of origin, assessment of storage and transportation conditions. Modern analytical methods allow to conduct a comprehensive study of lemons at various levels – molecular, chemical and morphological – to obtain objective data on their quality, safety and other important characteristics. This data can be useful in legal proceedings to determine the origin, compliance with quality standards and dispute resolution. The article summarises information on the typical defects of lemons, namely: mechanical damage, diseases and lesions, growth and development defects, skin defects, parasites and pests, physiological defects, chemical defects (these defects are of natural origin). Defects inherent in low-quality products with signs of deterioration include mechanical damage, rot and mould, discolouration, wrinkling, shape defects, pests, disease damage, additional chemical treatment, loss of aroma and taste, improper storage and transportation, black spots, and skin tears. The mechanism for conducting a forensic examination of lemons is described in detail.

Efficient Network-based Fault Detection in Elevator Vibration Signals: A Weighted Fusion Approach of Displacement and Acceleration Data

This research addresses human vibration analysis, rule-based lift fault detection algorithm noise interference, and early fault diagnosis. A new network-based defect diagnosis system uses lift sensor acceleration and displacement data. Convolutional Neural Networks CNNs automatically extract features like motor current (average, maximum, lowest values during a recording session) from this fused data stream and sensor data. CNNs learn key features from pooled data to reduce noise and detect faults early. To build a baseline for normal operation, 132 vibration signal characteristics (RMS, peak-to-peak, mean, standard deviation), lift ID, timestamp, operating mode (Up/Down/Idle), and load condition (Empty/Low/Medium/High) data points are used. Regular operational data and 201 gearbox fault data points are collected. The problem affects motor behaviour via vibration signals, timestamps, lift data, and motor current values. Learning to distinguish this defect type from normal operational data allows the network-based technique identify it. Research shows that network-based strategies outperform traditional methods. It finds weaknesses better than before researches. This method automatically extracts features from the fused data stream to detect defects in real time. Displacement and acceleration data, motor current readings, and CNNs improve noise resistance and failure signal detection. Network-based lift failure detection is advanced in real time problem identification. Precision, early detection, noise resistance, and real-time defect categorization are its strengths. Study affects lift maintenance and find defects early to keep lifts functioning. Real-time defect classification eliminates laborious analysis, simplifying maintenance in which enhancements improve lift performance and reliability.

Understanding intra-pore electrolyte resistances and distributed capacitances in carbon electrodes used in supercapacitors using a robust modeling process to separate the charge-storage occurring into macro-, meso-, and micro-pore structures

In this study, a Modified Generic Electrochemical (MGE) model is introduced for studying rough/porous carbon-based electrode materials employed in electrical double-layer capacitors (EDLC) which contain different types of surface defects. Instead of adopting the common theoretical approach involving the use of transmission line models or ad hoc equivalent circuits, a rational equivalent circuit model has been devised, which permitted us to derive the different equations for each electrochemical technique. The MGE representing the overall electrochemical behavior of EDLCs obtained using the different techniques (e.g., cyclic voltammetry, chronoamperometry, chronopotentiometry, and impedance) is formally derived based on a simple premise considering that the different inhomogeneous surface defects, which are geometrically arranged under equipotential conditions, are not electrochemically equivalent. In this sense, the presence of hierarchically distributed charge-storage processes with different time constants, accounting for the ionic accumulation at the electrode/solution interface, can be ascertained. We verified that is imperative to avoid the determination of the specific capacitances and resistances in EDLCs using a single electrochemical technique or several techniques whose model equations are based on different theoretical premises. The use of the MGE model unequivocally revealed that isolated measurements of the specific capacitance in the absence of the associated intra-pore resistances are not sufficient to ensure the presence of rapid charge storage allied with high-power characteristics. With the appropriate choice of carbon-based materials with very low intra-pore resistances, substantial progress can be made in ushering in the next generation of EDCLs with very high-power characteristics.

A Modelling Framework for Rapid Evaluation of Speed Limitations during Extrusion of Aluminium Profiles

Hot tearing is a well-known limitation when trying to maximize the throughput rate in aluminium extrusion. In the present work an analytical modelling framework is presented which can be used to predict the maximum extrusion speed that can be applied in production without formation of this type of surface defect. The modelling framework allows almost instantaneous estimates on the resulting productivity in terms of maximum extrusion speed. This is obtained by developing an analytical model for the maximum temperature at the die exit which incorporate the effect of alloy composition and billet processing. The results are consolidated into extrusion limit diagrams, mapping the maximum allowable extrusion speed as a function of billet pre-heat temperature, alloy composition, and homogenisation heat treatment. The calculated temperatures and extrusion limit diagrams obtained from the analytical model are compared with measured temperatures and critical extrusion speeds from extrusion tests of various 6xxx series alloys for a simple rod-shaped geometry. The comparisons indicate that the presented modelling approach gives sufficiently accurate predictions for future application in optimisation of alloy composition and process parameters in extrusion of profiles.

A highly transferable and efficient machine learning interatomic potentials study of [formula omitted]-Fe–C binary system

Machine learning interatomic potentials (MLIPs) for α-iron and carbon binary system have been constructed aiming for understanding the mechanical behavior of Fe–C steel and carbides. The MLIPs were trained using an extensive reference database produced by spin polarized density functional theory (DFT) calculations. The MLIPs reach the DFT accuracies in many important properties which are frequently engaged in Fe and Fe–C studies, including kinetics and thermodynamics of C in α-Fe with vacancy, grain boundary, and screw dislocation, and basic properties of cementite and cementite–ferrite interfaces. In conjunction with these MLIPs, the impact of C atoms on the mobility of screw dislocation at finite temperature, and the C-decorated core configuration of screw dislocation were investigated, and a uniaxial tensile test on a model with multiple types of defects was conducted.

Adjusting the energy band of Cu2O by changing the deposition conditions and its effect on photoelectrochemistry performance of Cu2O/TiO2 heterojunction

The band structure of heterojunction plays an important role in the performance of actual application and can be adjusted by the change of preparation condition. Herein, Cu2O nanofilms with different band structures were prepared via electrodeposition method by modifying the solution environment. With the value of pH increased from 7 to 12, the conductivity of the Cu₂O nanofilms shifted from n-type to p-type. Combined the obtained Cu2O film with TiO2 nanoarray, the Cu2O/TiO2 heterojunction were prepared. The photoelectrochemical performance of the Cu2O/TiO2 heterojunction was affected by the interfacial band structure. The T7 sample has higher photocurrent (450 μA) caused by the unique S-scheme band structure, the T11 sample with a p-n junction structure takes second place (225 μA). The n-n type II heterojunction has the lowest photocurrent (20 μA). The Fermi level of Cu2O nanofilm was affected by the type of defects, such as oxygen and copper vacancies, leading to changes of band structure at the interface between Cu2O and TiO2. The obtained result further illustrates the importance of energy band engineering, and provides route and sight for the development and application of Cu2O/TiO2 heterojunction.

The connection between the accumulation of structural defects caused by proton irradiation and the destruction of the near-surface layer of Li4SiO4 – Li2TiO3 ceramics

The key problem of using lithium-containing ceramics as materials of breeders for the propagation of tritium in thermonuclear reactors is phase stability, as well as the preservation of strength and thermophysical parameters of ceramics during their operation, which is accompanied by the accumulation of fission products in the near-surface layers, alongside mechanical influences from the outside. Moreover, in contrast to other types of ceramics, the presence of lithium in the composition of the samples under study leads to limitations in the use of classical analysis methods (scanning electron microscopy, energy dispersive analysis or optical spectroscopy) of structural changes caused by the accumulation of radiation damage, which requires the use of more complex methods for the assessment of the defect concentration in the structure, as well as establishing their relationship with the deterioration of strength and thermophysical parameters, playing a key role in determining the stability mechanisms and further exploitation of ceramics for tritium production. In this regard, the aim of the study is to determine the kinetics of changes in the near-surface layer of two-phase Li4SiO4 – Li2TiO3 ceramics associated with the accumulation of structural distortions caused by irradiation, as well as their relationship with strain embrittlement and disorder. During the studies, it was found that the accumulation of implanted hydrogen in the near-surface layer under high-dose irradiation initiates deformation distortion processes, the intensity of which depends on the ratio of components in the ceramics, according to which the optimal compositions of two-phase ceramics are ratios of components from 0.3 to 0.6. Determination of the type of defects in the composition of the damaged layer, as well as their concentration, was carried out using the electron spin resonance (ESP) method. During the studies, it was found that at low irradiation fluences, the dominant role in the accumulation of structural defects is played by vacancy defects associated with E′-centers, the formation of which is associated with structural distortions, while as the fluence grows, the structure is dominated by deformation disordering caused by the accumulation of Ti3+ defects and HC2 centers (SiO43-), the concentrations of which have a clear dependence on the phase composition of the ceramics.

Prevalence of Congenital Heart Defects and their Non-Inherited Risk Factors among Children through the Period 2019-2023: A Retrospective Comparative Study

Background: Congenital heart defects (CHD) are the most common types of congenital disabilities. The frequency of CHD among their patient populations also remains the leading cause of their increased mortality, accounting for an estimated 50% of all causes of death from abnormalities. Objectives: The study aimed to identify the prevalence of non-inherited risk factors among children with cardiac defects and compare the prevalence of congenital heart defects among children in different governorates in Iraq related to non-inherited risk factors. Materials and Methods: A retrospective study design was used to study congenital heart defects in children in Babylon Centre for Catheterization and Cardiac Surgery in Al-Imam Sadiq Hospital Babylon province, Iraq, from 15/10/2023 to 13/6/2024. A nonprobability convenience sample of 542 patients aged 0-18 years with CHD was collected by administering a constructed tool for the study. The data were collected from all Iraqi governorates, including the north, center, and south, as well as east and west countries. Result: The study shows the mean age of children was 2.2 ±9.2 years. The majority of children included were within infant age groups, almost equal in their sexes, giving girl to boy, 1.04:1. The age at screening and diagnosis of Echo was more than half in the first month, and the type of defect was Ventricular-septal-defect (VSD) and Tetralogy of Fallot (TOF). The study findings show that there is a statistically significant relationship between risk factors related to maternal medication history, including Folic acid Supplements. Maternal diseases such as Rubella, with CHD, and the highest recorded governorates for congenital heart defects are Baghdad, Mosul, and Diyala. Conclusions: The study concludes that most children included were within infant age groups, almost equal in their sexes, and the highest recorded governorates for congenital heart defects are Baghdad, Mosul, and Diyala. Also, the most frequent CHD is VSD and TOF, the history of maternal medication use, including folic acid supplements, and maternal diseases such as Rubella were significant factors in assessing the risk or outcome.

ECM-YOLO: a real-time detection method of steel surface defects based on multiscale convolution

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network’s robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e., multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.

New evaluation method for defects propagation in toroidal roller bearings using dominant frequency of acoustic emission

ABSTRACT The increased use of roller bearings in recent years has made it vital to develop techniques for Condition Monitoring (CM) of these valuable assets. Toroidal Roller Bearings (TRB) are one of the recent inventions that has revolutionised some industries because of its unique design. In this study, newly produced TRBs were tested using Acoustic Emission (AE), vibration and temperature, on a specially designed test rig. The AE signal was recorded, processed, and analysed using FFT-based methods to transform from the time domain to the frequency domain, and to specify the Dominant Frequency (DF) of each hit in the signal. The result of the analysis was a layout on the time domain of the DFs of all AE hits along with RMSAE and other parameters, this layout was called DF map. The test was divided into three phases, and the DF bands were divided horizontally into three band groups. The aim is to establish a correlation between the phases and DF bands. Knowing the type of defects that are typical for each phase in the test provided insights to what are the DFs of that type. Promising results can be achieved using this measuring method on other types of bearings.

RESULTS OF THE TECHNICAL INSPECTION OF A MULTI-STORY RESIDENTIAL BUILDING DAMAGED AS A RESULT OF A ROCKET IMPACT

The article presents the results of the examination of the technical condition of part of the construction structures of the building damaged as a result of a missile strike, the determination of the characteristics and classification of their defects and damage, the provision of conclusions and recommendations regarding the correction of the life cycle and further safe and reliable operation of the building. As part of the study, the object of the survey was a monolithic 22-story residential building with a basement (ATP) floor and a complex configuration in plan. The structural scheme of the complex is a monolithic reinforced concrete frame with stiffening cores. The stability and rigidity of the frame is ensured by diaphragms of stiffness in the form of reinforced concrete walls, elevator-stair nodes and rigid nodes connecting pylons and horizontal disks of monolithic reinforced concrete floor slabs. During the examination, visual and instrumental methods of research were performed. Before the field work, measures were taken to collect design and archival information about the research object. Instrumental studies to determine the actual concrete strength of reinforced concrete frame elements and to determine the diameter, protective layer and pitch of the working rods of the reinforcing frames of the load-bearing elements were carried out by non-destructive methods. The calculation model of the spatial scheme of the structure of the frame of the residential building, taking into account the damage, was made in the subsystem (PS) "COMPOSITION" of the software complex (PC) "MONOMAH CAD". The result of the survey is conclusions and recommendations, which indicate the specifics of damage and types of defects, destruction and debris in various structural elements that were discovered during the survey. The general technical condition of the residential building is classified as emergency. As for the recommendations, two options for carrying out repair and restoration works were provided. The first option is to restore the plan-elevation position of the building, the design marks of vertical and horizontal structures and the unloading of overloaded structures, to be performed with the help of an automatic hydraulic complex. The second option is the arrangement of temporary strengthening of emergency structures with the help of a metal space frame, without restoring the planned height position of the residential building.

Identification of coating layer pipeline defects based on the GA-SENet-ResNet18 model

Detecting damage in coated pipelines is a challenging and costly task. This study proposes a method for pipeline defect identification based on VMD-DWT noise reduction and GA-SENet-ResNet18. Combining wavelet transform to convert denoised defect signals into time-frequency representations enhances the model's ability to capture both time-domain and frequency-domain features of defect signals, thereby improving its recognition capability for different types of defects. The study analyzed the feature extraction capabilities of ALexNet, GooleNet, VGG16, ResNet18, SENet-ResNet18, and GA-SENet-ResNet18 models in pipeline defect recognition. Experimental results show that SENet-ResNet18 achieved an accuracy of 0.9591 on the training set in 9m38s, significantly outperforming the first four models. GA-SENet-ResNet18 achieved 96.83 % accuracy, 96.67 % precision, 96.73 % recall, and 96.68 % F1 score in pipeline defect signal recognition. Compared to ResNet18, it improved accuracy by 2.06 %, precision by 1.94 %, recall by 2.09 %, F1 score by 2.37 %, with a reduction in time by 1m1s. The study demonstrates that the combined improvement of GA and SENet enhances ResNet18 not only in feature selection and response enhancement but also significantly improves its performance compared to traditional ResNet18 networks, making it more effective in pipeline defect recognition tasks. This research is crucial for ensuring pipeline system integrity and preventing pipeline accidents.

Impact of damaged housing on the performance of field aged composite High Voltage Insulators

Composite High Voltage Insulators (HVIs) are lightweight, inexpensive, and offer superior pollution performance. Consequently, they have replaced ceramic HVIs in highly contaminating coastal and desert regions. However, they are prone to degradation in harsh environments. In this paper, we assess the performance of three identical field-aged 380 kV composite HVIs with a service life of twelve years near the western coastal region of the Kingdom of Saudi Arabia. The housing of all the HVIs showed widespread damage, including cracks on sheds and shanks, partially or fully broken sheds, and rusted end fittings. The changes in morphology and material composition were assessed through SEM and EDX. An electrical breakdown test in the presence of steam fog revealed lowered withstand strength. Electric field distribution, a key design criterion for an HVI, was studied using the Finite Element Model (FEM) of the insulator bearing physical defects. The results reveal localized electric field intensification caused by all types of defects, notably smaller and narrower ones. Our findings indicate that while physical defects in HVIs can cause localized electric field intensification, they are not a significant concern unless accompanied by severe hydrophobicity loss, compromised electrical withstand levels, or extensive erosion exposing the fiberglass rod and jeopardizing mechanical stability.

Construction of "Metal Defect/Oxygen Defect Junction" in ZnFe2O4-NiCo2O4 Heterostructures for Enhancing Electrocatalytic Oxygen Evolution.

Defect engineering is a promising approach to improve the conductivity and increase the active sites of transition metal oxides used as catalysts for the oxygen evolution reaction (OER). However, when metal defects and oxygen defects coexist closely within the same crystal, their compensating charges can diminish the benefits of both defect structures on the catalyst's local electronic structure. To address this limitation, a novel strategy that employs the heterostructure interface of ZnFe2O4-NiCo2O4 to spatially separate the metal defects from the oxygen defects is proposed. This configuration positions the two types of defects on opposite sides of the heterojunction interface, creating a unique structure termed the "metal-defect/oxygen-defect junction". Physical characterization and simulations reveal that this configuration enhances electron transfer at the heterostructure interface, increases the oxidation state of Fe on the catalyst surface, and boosts bulk charge carrier concentration. These improvements enhance active site performance, facilitating hydroxyl adsorption and deprotonation, thereby reducing the overpotential required for the OER.

An Automated Analysis of Homocoupling Defects Using MALDI-MS and Open-Source Computer Software.

Conjugated organic polymers have substantial potential for multiple applications but their properties are strongly influenced by structural defects such as homocoupling of monomer units and unexpected end-groups. Detecting and/or quantifying these defects requires complex experimental techniques, which hinder the optimization of synthesis protocols and fundamental studies on the influence of structural defects. Mass spectrometry offers a simple way to detect these defects but a manual analysis of many complex spectra is tedious and provides only approximate results. In this work, we develop a computational methodology for analyzing complex mass spectra of organic copolymers. Our method annotates spectra similarly to a human expert and provides quantitative information about the proportions of signal assigned to each ion. Our method is based on the open-source Masserstein algorithm, which we modify to handle large libraries of reference spectra required for annotating complex mass spectra of polymers. We develop a statistical methodology to analyze the quantitative annotations and compare the statistical distributions of structural defects in polymer chains between samples. We apply this methodology to analyze commercial and lab-made samples of a benchmark polymer and show that the samples differ both in the amount and in the types of structural defects.