An analytical approach to evaluating the residual life Nfс of a defective pipeline element is proposed. The analytical approach considers the effects of the internal pressure (p) of a hydrogen-containing operating environment, defect geometry (axis ratio a/c), and the total hydrogen concentration in the metal (СH). Experimentally determined cyclic fracture resistance parameters – the threshold ΔKth and critical ΔKfc stress intensity factor ranges, as well as the Paris law constants A and n, were approximated by analytical relations as functions of СH. This enabled the derivation of closed-form dependences for residual life Nf without numerical simulation. A parametric analysis was conducted over a wide range of pressure p, axis ratios a/c, and СH values, allowing for the assessment of their influence on the residual life of the structural element. The obtained analy­tical relations can be used for engineering assessment of the safe service life of pipelines.

Purpose Phased array antennas utilized for different space applications, such as deep space vehicles, satellite communication, telemetry tracking and control communication systems, may suffer from single or multiple element failure because of the harsh space environment or long period operation of amplifiers present in them. Design/methodology/approach Phased array antennas are capable of generating highly directed signals, rapid beam scanning and high gain by using multiple antenna steering in a desirable direction. However, this could also lead to a higher probability of defective elements in the array that are difficult to replace if the application is distant or space-borne. Faulty elements in a circular antenna array (CAA) can rigorously distort the radiation pattern, which results in degrading the array efficacy by increased sidelobe levels (SLL), beam broadening and gain reduction. Findings In this research article, the issue of recovering the desired side lobe power pattern from the remaining active elements can be achieved successfully by reoptimizing the array excitation parameters. Research limitations/implications While the results are promising, the study is limited to simulations and theoretical modelling, which might not capture all real-world variables affecting space-borne antenna systems. Future research should focus on empirical testing in actual space conditions to validate the effectiveness of the artificial rabbits optimization (ARO) technique. Additionally, the study concentrates on a specific type of antenna array (CAA), and the generalizability of ARO to other array geometries like linear, rectangular and concentric circular arrays remains to be explored. Practical implementation may also reveal unforeseen challenges in the scalability and adaptability of the ARO technique in diverse operational scenarios. Practical implications The practical implications of this study are significant for the design and maintenance of antenna systems in space environments. By implementing artificial rabbits optimization (ARO), engineers can enhance the fault tolerance of circular antenna arrays without necessitating physical replacements, thus reducing maintenance costs and extending the operational lifespan of space-borne equipment. ARO’s ability to adaptively optimize the array’s performance post-fault occurrence offers a reliable method for maintaining communication integrity in critical missions. This approach can be integrated into existing systems, providing a robust solution for optimizing performance amidst the challenging conditions of space operations. Social implications The application of artificial rabbits optimization (ARO) in maintaining the integrity of space-borne communication systems has broader social implications. By ensuring reliable and uninterrupted communication capabilities in space missions, ARO contributes to enhanced safety for spacecraft and satellites, which is crucial for manned missions and high-stakes satellite operations. Furthermore, improved communication reliability supports better data transmission from space exploration, impacting scientific research and global communication networks. This technology could lead to more sustainable and cost-effective space missions, potentially making space more accessible and fostering greater international collaboration in space exploration and satellite deployment. Originality/value The mechanism of self-recoverable CAA with the prior knowledge of faulty elements can be attained effectively by implementing a novel metaheuristic artificial rabbits optimization (ARO) technique. This proposed approach also compares some of the state-of-the-art metaheuristics in element failure correction of faulty CAA.

Defect element segmentation-based vibration analysis of ball bearings: dynamic modelling and experiments

Abstract Background LTR-retrotransposons (LTR-RT) are a major component of plant genomes and important drivers of genome evolution. Most LTR-RT copies in plant genomes are defective elements found as truncated copies, nested insertions or as part of more complex structures. The recent availability of highly contiguous plant genome assemblies based on long-read sequences now allows to perform detailed characterization of these complex structures and to evaluate their importance for plant genome evolution. Results The detailed analysis of two rice loci containing complex LTR-RT structures showed that they consist of tandem arrays of LTR copies sharing internal LTRs. Our analyses suggests that these LTR-RT tandems are the result of a single insertion and not of the recombination of two independent LTR-RT elements. Our results also suggest that gypsy elements may be more prone to form these structures. We show that these structures are highly polymorphic in rice and therefore have the potential to generate genetic variability. We have developed a computational pipeline (IDENTAM) that scans genome sequences and identifies tandem LTR-RT candidates. Using this tool, we have detected 266 tandems in a pangenome built from the genomes of 76 accessions of cultivated and wild rice, showing that tandem LTR-RT structures are frequent and highly polymorphic in rice. Running IDENTAM in the Arabidopsis, almond and cotton genomes showed that LTR-RT tandems are frequent in plant genomes of different size, complexity and ploidy level. The complexity of differentiating intra-element variations at the nucleotide level among haplotypes is very high, and we found that graph-based pangenomic methodologies are appropriate to resolve these structures. Conclusions Our results show that LTR-RT elements can form tandem arrays. These structures are relatively abundant and highly polymorphic in rice and are widespread in the plant kingdom. Future studies will contribute to understanding how these structures originate and whether the variability that they generate has a functional impact.

Rejection of defective parts and elements is an important part in quality improvement, maintenance and accident prevention. Some imaging instrumental methods of test and inspection have improved ability to reveal defects, but currently have disadvantages, such as low accuracy of defect classification and need of human operator. Therefore, there is a high demand for a solution that is reliable enough, can exclude human element and mathematically grounded unlike probabilistic and neural network models. The paper presents generally applicable algorithmic solution for a system that can analyze images and reveals defects automatically on par or better than a human operator. The general principle is based on approach that some general properties about the analyzed part (periodicity) is known beforehand. The method of defects reveal by pattern-recognizing not defects themselves but periodic background is proposed. Mathematical formulation for an error function adapted for approximating images and data with defects is proposed, and viability of the functions analyzed, including computational experiment to verify its validity. Using ab initio considerations, a new type of error function is suggested, it specially tailored to approximating datasets with high density of outliers, with performance comparable and in certain situations exceeding -norm in that respect. Thus, the algorithm suggested, by using which image of the object examined can be reconstructed in its «idealized», defect-less state and, further by comparing image to the reconstruction, the defects can be revealed. Furthermore, statistical model used in formulation of the error function allows estimation of defect probability in the area based on analysis of deviation and measurement errors. The approach is demonstrated and tested on a practical example. All essential stages of the algorithm, such as preliminary analysis, and model building are outlined. It is demonstrated capability of revealing defects without human operator intervention and without machine learning, only having basic information about object investigated.

The article describes the stages of automated material defect control. It is noted that the technical inspection system uses high-resolution cameras to obtain a detailed image of the surface material used for the production of equipment. Pre-processing of the image is necessary to eliminate possible interference. This provides a clean image for further analysis. Algorithms for determining key image parameters, namely: textural characteristics, color differences and geometric features, help identify your defects. After detecting defects, an initial distribution of defective elements is carried out. The detected defects are distributed by categories depending on their criticality. This allows you to make operational decisions about the further use of the material. The module automatically decides if the material can be used, whether it needs to be rejected or sent for additional processing. This stage is integrated with production systems for instant response. The analysis results are displayed graphically: defective areas can be marked on the image, and a report is generated that can be used for further quality analysis and process optimization. All information about detected defects is used for continuous learning and improvement of algorithms. This allows the system to adapt to new conditions and increase the accuracy of analysis over time. Analysis of literature sources showed that the use of deep learning algorithms significantly increases the accuracy of defect detection, which can be adapted for material quality control. The use of machine vision, neural network algorithms and intelligent systems allows you to reduce costs, improve product quality and increase the efficiency of production processes. Further research and development in this area will contribute to the further improvement of control technologies and increase the competitiveness of enterprises

Rolling element bearings (REBs) constitute a crucial element in rotating machinery, and their malfunction is one of the most common reasons for unplanned outages and shutdowns. As a result, monitoring the health of REBs, detecting and diagnosing the size and location of faults, and assessing their degradation have been the subject of extensive research. In the last few decades, several signal processing techniques such as Fast Fourier transform, Wavelet transform, Hilbert transform, artificial neural network, and recurrence plot (RP), among others, have been proposed for the diagnosis and prognosis of REBs. However, most of these techniques provide only a qualitative diagnosis while ignoring the quantitative aspect of the faults. The current work proposes a novel entropy-based fault detection approach and grading the condition of bearings from noisy time history measurements. Entropy-based measures based on Kullback–Leibler and Shannon entropy from vibration signals have been used for grading the condition of bearings. Subsequently, these entropy basis measures have been successfully tested on vibration signatures from various bearing fault types occurring at different locations. The results indicate a one-to-one relation between fault severity and its entropy measure. Moreover, each type of bearing fault has been shown to have a well-defined entropy measure, for different faults lie on a nearly linear locus. In addition to the field and experimental data obtained in the workshop, a nonlinear vibration model under the combined effect of the unbalanced and non-Gaussian Poisson loading has been developed to obtain the acceleration time waveform for the fault severity assessment. The noisy nonlinear model takes into account the Hertizian contact force between the ball and races, internal clearance, race waviness, varying compliances, and localized defects. The adaptive time stepping (ATSP) numerical integration combined with the Brownian tree is used to obtain the nonlinear vibration response. The results show the effectiveness of the proposed algorithm in the diagnosis and prognosis of various types of REB faults.

An effective vibration-based feature extraction method for single and multiple damage localization in thin-walled plates using one-dimensional wavelet transform: A numerical and experimental study

In the game theoretical approach of the basic problem in Combinatorial Search an adversary thinks of a defective element d of an n-element pool X, and the questioner needs to find x by asking questions of type is d∈Q? for certain subsets Q of X. We study cooperative versions of this problem, where there are multiple questioners, but not all of them learn the answer to the queries. We consider various models that differ in how it is decided who gets to ask the next query, who obtains the answer to the query, and who needs to know the defective element by the end of the process.

Method for Deselecting Defective Photosensitive Elements that Reduce the Signal/Noise Ratio in the Channels of an Infrared Photomodule with a Time Delay and Accumulation Mode

This article aims to elucidate the distinct political development in post-communist countries after 1989, focusing on the deviations from democratic governance. Drawing upon the insightful analysis provided by Czech political scientist Michal Klima in his work titled "Informal Politics in Post-Communist Europe: Political Parties, Clientelism and State Capture", this review article aims to synthesize and present key findings related to clientelism, the challenges of countering the state, and the strategies employed in managing the state. The analysis encompasses elements such as clientelism, corruption, and the patronage system, highlighting the nuanced perspectives on these issues within the framework of the post-communist political landscape. The overarching goal is to contribute to a comprehensive understanding of the post communist political dynamics, encompassing diverse elements such as clientelism, state governance, and the intricate interplay of political forces. In conclusion, the analysis serves as a precursor to further exploration and understanding of the post-communist political landscape, offering valuable insights into the complex nature of state governance, clientelism, and other challenges to revive the civil society to counter these defective elements of the democratic regime.

The hAT family transposable element, hopper, was originally discovered as a defective 3120-bp full-length element in a wild-type strain of the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae), and subsequently a functional 3131-bp element, hopperBdwe, was isolated from a white eye mutant strain. The latter study showed that closely related elements exist in melonfly, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae), a closely related subgenus, suggesting that hopper could have a widespread presence in the Bactrocera genus. To further understand the distribution of hopper within and beyond the B. dorsalis species complex, primer pairs from hopperBdwe and its adjacent genomic insertion site were used to survey the presence and relatedness of hopper in five species within the complex and four species beyond the complex. Based on sequence identity of a 1.94 kb internal nucleotide sequence, the closest relationships were with mutated elements from B. dorsalis s.s. and species synonymized with B. dorsalis including B. papayae, B. philippinensis and B. invadens, ranging in identity between 88.4% and 99.5%. Notably, Bactrocera carambolae (Drew & Hancock) (Diptera: Tephritidae), which is most closely related to B. dorsalis beyond the synonymized species, shared hopper identities of 97.3%-99.5%. Beyond the B. dorsalis complex, Z. cucurbitae,Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) and Bactrocera zonata (Saunders) (Diptera: Tephritidae) shared identities of 83.1%-97.1%, while hopper was absent from the Bactrocera oleae (Gmelin) (Diptera: Tephritidae) strain tested. While the functional autonomous hopperBdwe element was not detected in these species, another closely related hopper element isolated from a B. dorsalis genetic sexing strain has an uninterrupted transposase open reading frame. The discontinuous presence of hopper in the Bactrocera genus has implications for its use for genomic manipulation and understanding the phylogenetic relationship of these species.

Software bugs are a noteworthy concern for developers and maintainers. When a failure is detected late, it costs more to be fixed. To repair the bug that caused the software failure, the location of the bug must first be known. The process of finding the defective source code elements that led to the failure of the software is called bug localization. Effective approaches for automatically locating bugs using bug reports are highly desirable, as they would reduce bug-fixing time, consequently lowering software maintenance costs. With the increasing size and complexity of software projects, manual bug localization methods have become complex, challenging, and time-consuming tasks, which motivates research on automated bug localization techniques. This paper introduces a novel bug localization model, which works on two levels. The first level localizes the buggy classes using an information retrieval approach and it has two additional sub-phases, namely the class-level feature scoring phase and the class-level final score and ranking phase, which ranks the top buggy classes. The second level localizes the buggy methods inside these classes using an information retrieval approach and it has two sub-phases, which are the method-level feature scoring phase and the method-level final score and ranking phase, which ranks the top buggy methods inside the localized classes. A model is evaluated using an AspectJ dataset, and it can correctly localize and rank more than 350 classes and more than 136 methods. The evaluation results show that the proposed model outperforms several state-of-the-art approaches in terms of the mean reciprocal rank (MRR) metrics and the mean average precision (MAP) in class-level bug localization.

This work is an introduction of acoustic emission (AE) signals used in order to detect the malfunction of selected semiconductor elements. The authors proposed the use of internally generated signals (elastic waves) of acoustic emission leading to the detection of the pre-fail state of switching IGBT transistors. The analysis of the AE signals allows the creation of a reference pattern of properly working transistors and at the same time the identification of abnormal signals, which are generated by a defective element. Unlike many papers, this article shows experimental results demonstrating a comparison of undamaged, properly working and defective IGBT transistors which can be used, for example, as a reference for diagnostic tools. Analysis of the signal in the frequency domain obtained from the faulty transistor (overheated or with damaged casing) shows the presence of additional frequencies which can indicate the imminent occurrence of critical damage.

Abstract In production, quality monitoring is essential to detect defective elements. State-of-the-art approaches are single-sensor systems (SSS) and multi-sensor systems (MSS). Yet, these approaches might not be suitable: Nowadays, one component may comprise several hundred meters of the weld seam, necessitating high-speed welding to produce enough components. To detect as many defects as possible in time, fast yet precise monitoring is required. However, information captured by SSS might not be sufficient and MSS suffer from long inference times. Therefore, we present a confidence-based cascaded system (CS). The key idea of the CS is that not all data are analyzed to obtain the quality weld, but only selected ones. As evidenced by our results, all CS outperform SSS in terms of accuracy and inference time. Further, compared to MSS, the CS has hardware advantages.

The obstacle faced by PT Alis Jaya Ciptatama is that the production process carried out in 2022 still has many product defects. For this reason, it is necessary to improve the quality of the product. In this case the method used is the Six Sigma method as a quality improvement tool. The results of analysis using Pareto diagrams obtained the highest type of defect with a percentage of 38.2%, and there are three elements that influence this type of defect, namely human, machine and material elements. Through DMAI analysis, the company's average sigma value of 2.70 is still far from the company's expectations of 6, with a Defect Por Million Opportunities (DPMO) of 115362.894. This value is the most critical failure mode so it needs to be repaired. The proposed improvement is by implementing 5S and providing Standard Operating Procedures (SOP) so that operators work in accordance with operational standards in the mill 1 work area. Then a comparison is made between before and having been given the SOP, so the DPMO value is 72,666 and the sigma value is increased by 0.53.

Incomplete data poses challenges in accurately assessing structural health and detecting damage. It limits the ability to capture the complete behavior and response of the structure, which may hinder the identification and localization of potential damage or anomalies. Addressing the issue of incomplete data requires developing strategies and algorithms that can effectively handle missing or limited measurements. Using incomplete and noisy measurements, we propose an optimization-based damage detection method for laminated composite plates with closely-spaced eigenvalues. The proposed method consists of two stages. In the first stage, the most probable defective elements are identified by utilizing condensed mode shapes as incomplete noisy inputs for modal residual vectors. This approach significantly reduces the computational effort for damage estimation. The second stage introduces an objective function based on incomplete and noisy Condensed Frequency Response Functions (CFRFs). To optimize the damage quantification, the Improved Particle Swarm Optimization (IPSO) algorithm is employed to minimize errors in the proposed objective function based on CFRFs of damaged and intact laminates. The proposed method is demonstrated on laminated composite plates with different lamination schemes, ply orientations, and multiple damaged elements in different damage scenarios. By evaluating the method on numerical results and comparing it with previous studies, its superiority is demonstrated. Furthermore, the proposed method exhibits robustness to changes in mass distribution in the system investigated by retrofitting extra masses to the plate structures that lead to worsening closely-spaced eigenvalues.

Railway track accidents continue to occur despite manual inspections, which are often inaccurate and can lead to catastrophic events. While artificial intelligence has been applied in the railway sector, few studies have focused on defect detection using object detection tools. Additionally, there is a lack of studies that compare different models using the same dataset.This paper proposes new data-driven techniques that identify railway track faults using three object detection models: YOLOv5, Faster RCNN, and EfficientDet. These models are compared by testing a dataset of 31 images that contain three different railway track elements (clip, rail, and fishplate), both faulty and non-faulty. Six classes were differentiated in the training of the models: one faulty and one non-faulty for each of the three classes. Image pre-processing steps included data augmentation techniques and image resizing. Results show good precision (equivalent to 1) in detecting non-defective elements, but recall values for defective elements vary among models, with Faster RCNN performing the best (0.93), followed by EfficientDet (0.81), and YOLOv5 (0.68). The full paper discusses the strengths and weaknesses of these proposed techniques for railway fault detection.

To investigate the optrA-carrying genetic elements and their transferability in two linezolid-resistant Streptococcus dysgalactiae subsp. equisimilis (SDSE) strains of swine origin. SDSE strains (V220 and V1524) were phenotypically and genotypically characterized. Transferability of oxazolidinone resistance genes (filter mating), genetic elements and relatedness between isolates (WGS) were analysed. Excision of the genetic elements was assayed by inverse PCR. SDSE isolates were resistant to chloramphenicol, florfenicol and linezolid, but susceptible to tedizolid and both carried the optrA gene.In SDSE V220 optrA was located on a 72.9-kb ICESdyV220 inserted in the 3' end of the chromosomal rum gene. It was 94%-96% identical (coverage, from 31% to 61%) to other optrA-carrying ICEs. In-depth ICESdyV220 sequence analysis revealed that optrA was carried by an IMESdyV220 (17.9 kb), also containing the tet(O/W/32/O) gene. Inverse PCR assays excluded the ICESdyV220 mobility. In SDSE V1524, optrA was carried by the ΦSdyV1524 prophage, integrated near the 5' end of the chromosomal had gene, showing a genetic organization similar to that of other streptococcal phage. Conjugation and transduction assays failed to demonstrate the optrA transferability to streptococcal recipients. V220 and V1524 belonged to two novel sequence types (ST704 and ST634, respectively). To the best of our knowledge, this is the first identification of the optrA gene on a prophage and an ICE in SDSE isolates from swine brain.These findings are consistent with the current belief in the key role of bacteriophages and ICEs in the streptococcal evolution and adaptation.

Dynamic modeling and analysis of rolling bearings with rolling element defect considering time-varying impact force