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The structural condition of the cable-stayed towers anchorage on power transmission lines requires constant monitoring. Maintenance routines must be able to identify faulty anchor rods and substitute them to avoid tower collapses and power delivery interruptions. Modern statistical diagnostic systems based on machine learning requires the generation of several distinct rod sample signals to be trained, which is a time-consuming process in typical electromagnetic solvers. Aiming to generate samples in a feasible time span, this paper presents a modeling strategy based on the cascade analysis of the transmission line composed by the anchor and the reference rods, interconnected by a dedicated high-frequency connector. Each distinct transmission line part is modeled by a quadrupole as a partial ABCD matrix. The complete response for each experimental setup is obtained from the cascaded-multiplying of the partial matrices. The proposed modeling proved to be accurate and provides a faster way to obtain the S-parameters from distinct faulty rods if compared to the traditional methodologies using electromagnetic tools.

Structural faults on anchor rods due to corrosive processes are among the major natural causes worldwide for the collapse of guyed towers. As such towers are widely used on both power transmission lines and antenna systems, their collapse may cause unexpected interruptions of telecommunications and power delivery services. Despite being of utmost importance, underground corrosion monitoring is still a technological challenge, because it is a highly nonlinear problem, influenced by several soil characteristics and its complex iterations with the metallic structure of the rods. Some techniques have already been proposed to locate such faults; however, either they require previous knowledge about the medium or they need the interpretation of the acquired signal by a human specialist. These requirements make it difficult to use such solutions in automatic monitoring as well as to make their integration with Internet of Things (IoT) systems. In this article, a novel technique is proposed to automatically locate structural faults on anchor rods, based on a two-step machine learning (ML) solution. The system is fed by a vector composed of frequency-domain samples of the input impedance electromagnetic (EM) signals measured on the rods through a dedicated designed connector. Experimental results have demonstrated that the proposed combination of EM signal acquisition and ML data processing was able to perform the fault location on anchor rods accurately and with a total independence of any additional human analysis.

Detecting faults on the anchor rods of power transmission line towers is an essential procedure for guaranteeing the safety, operability, and availability of the electric power system. Such a measure aims to minimize the risk of tower collapse and the associated costs due to the lack of energy, injuries to people, and damage to the reputation of the companies involved. In this letter, we report an alternative noninvasive and nondestructive detection technique through the acquisition of electromagnetic scattering parameters. A novel machine learning (ML) approach is proposed in order to achieve the advantages of such a technique and to deal with the respective drawbacks due to the complexity of signals acquired. A strategy to artificially increase the number of relevant samples in the dataset through the simulations of computational models is performed. Different ML classification algorithms are compared in the task of detecting structural faults in anchor rods in order to define the most suitable to this application. Finally, experimental results show that the methodology proposed achieves a high detection accuracy, surpassing other known methods.

This paper introduces a new strategy to determine the soil permittivity and its loss tangent which uses simulation and measurement result for comparison. The setup of the measurement system includes a high frequency connector and a two-rod transmission line (TRTL) where one of the rods is embedded in an unknown-permittivity soil. In order to validate the simulation model by optimizing the complex permittivity of the soil from the measurement result a smart searching tool performed by the PSO (Particle Swarm Optimization) algorithm is applied. The measurements were carried out by a VNA (Vector Network Analyzer) to obtain the reflected signals from the TRTL for a non-parameterized soil. The effectiveness of the PSO algorithm is demonstrated by equalizing both simulated and measurement results of the soil complex permittivity in the simulation model.

Eye bolts are critical elements of the electrical power distribution systems and structural failures on such devices can lead to service interruption, financial losses and hazard to civilians. Due to their installation characteristics, their common maintenance routine is costly, time-consuming and ineffective, because it depends on the de-energization of the circuit, disassembly and visual inspection of the bolts. In this paper, a new approach for detecting structural failures on eye bolts is proposed. An intelligent system based on an artificial neural network is used to process the reflectometry signals measured in order to detect the condition of the eye bolt automatically. The high accuracy in the experimental results suggests that the method proposed can improve the efficiency of the preventive maintenance routine performed on eye bolts, and, consequently, increase the reliability of the power distribution systems., Grant numbers : 5G-TRIDENT - 5G Technologies for ReconfIgurable and efficient raDio accEss iNfrastrucTure and mobile devices (RTI2018-099841-B-I00).

Metallic anchor rods are used to fix transmission tower guy wires. In order to perform visual inspections on the integrity of the anchor rods, electricity companies are obliged to undertake soil excavation, which is both costly and hazardous. Despite this, Frequency domain reflectometry (FDR) may be used to detect faults on anchor rods, and is enabled by a robust, high-frequency microwave device for support and connection. Impedance changes occur along the rod due to faults, thereby causing reflections, which are detected during FDR measurement analysis. However, auxiliary structures on the anchorage system also provide similar results during the analysis, and may therefore be confused with faults. An innovative, new approach is proposed in order to minimize the effects of small structures positioned at a distance from the fault, so that the fault reflection remains the same. Preliminary results have indicated reductions of up to 95% of the undesired reflection values.

Network parameters are capable of conveying information about the constitution of a medium in which a highfrequency wave propagates. In this paper, this characteristic is exploited in order to design a nondestructive system to detect corrosion in anchor rods of guyed towers. A compound database is built with the simulated and measured parameters return loss, input impedance, and voltage standing wave ratio for normal and faulty rods examples. Artificial neural networks are used to capture underlying characteristics of data and establish relationships between these parameters and the presence of corrosion in the rods, without the need for physical models. Experimental results show that the innovative use of a multiparameter strategy achieves a high accuracy and enhances the detection capacity of the system.

Anchor rods are used to fix the guy cables of transmission towers into the soil. Thus, anchor rods are buried or encapsulated in concrete beneath the ground, thereby forming a reliable support system. However, incomplete or imperfect anchor grouting often occurs during construction, which may cause the anchor to become ineffective and lead to engineering accidents. Therefore, such anchors require periodic maintenance. Frequency domain reflectometry is used to locate and quantize possible deterioration on the concrete anchors. Experimental results have shown that the reflectometry approach may be a new method for the noninvasive maintenance of anchor rods measuring one meter.

This paper presents a field application system for detecting structural faults on anchor rods of cable-stayed towers of power transmission lines, based on a nondestructive technique using frequency domain reflectometry analysis. A specific high frequency connector was designed to interface a portable vector network analyzer and the buried rods in the test field. The soil electric permittivity was modeled using a full-wave electromagnetic simulation software and measurements. A machine learning structure was developed for the measured S11-parameter signals from the distinct buried rods to binary classify them as normal or faulty. An accuracy greater than 98% was achieved by characterizing the system as reliable and feasible for a novel predictive maintenance process for ground-anchored metallic rods.

Sao Paulo State Univ UNESP, Ctr Human Reprod Prof Franco Jr, Paulista Ctr Diag Res & Training, Dept Gynecol & Obstet,Botucatu Med Sch, Ribeirao Preto, Brazil

Many theories have been proposed to explain the development of endometriosis, and recently, autoimmune aetiology has been suggested. Besides, it is well known that endometriosis, especially the advanced disease, may impair fertility. B lymphocyte stimulator (BLyS) is a cytokine produced by macrophages and is necessary for normal B cell development. One of the most studied polymorphisms is the -817C/T in the promoter region of the gene. We aimed to assess the association between endometriosis-related infertility and idiopathic infertility and the BLyS -817C/T polymorphism in a Brazilian population. We performed a case-control study comprising 165 infertile women with endometriosis, 83 with idiopathic infertility and 145 fertile and assessed the association with BLys -817C/T polymorphism. BLyS -817C/T polymorphism was detected using TaqMan PCR. The results were analysed statistically, and a P-value0.05 was considered significant. The results disclosed similar genotype and allelic frequencies between endometriosis-related infertility (P = 0.225) and control group, regardless of the disease stage (P = 0.213 and P = 0.462, respectively). However, a statistically significant difference was observed regarding idiopathic infertile group (P = 0.048) compared with controls. Considering the dominant and recessive inheritance models, no significant differences in both endometriosis and idiopathic infertility group were found. The genotype frequencies were in Hardy-Weinberg equilibrium in all studied groups. The results point to a possible association between BLyS -817C/T polymorphism and idiopathic infertility in Brazilian population.

An autoimmune aetiology has been suggested for endometriosis mostly on the basis of an increased prevalence of autoimmune diseases in affected women. Cytotoxic T lymphocyte antigen (CTLA) 4 gene is recognized as a primary determinant for autoimmunity, since specific polymorphisms have been associated with predisposition to most autoimmune disorders. Thus, the objective of the study was to evaluate CTLA4 polymorphism (+49A/G) in a group of infertile women with and without endometriosis and controls. Case-control study comprising 244 infertile women (177 with endometriosis and 67 without endometriosis) and 172 fertile women as controls. CTLA4 polymorphism was identified by qPCR. The results were analysed statistically and a P-value0.05 was considered significant. We found relatively similar CTLA4 polymorphisms genotype frequencies in women with and without endometriosis and controls (P=0.158 and P=0.262, respectively). When the patients with minimal/mild endometriosis and moderate/severe endometriosis were studied separately, no difference was also found related to controls (P=0.560 and P=0.11, respectively). The data suggest that the CTLA4 polymorphism is not associated with endometriosis and/or infertility in Brazilian women.

Individuals with Turner syndrome (TS) clearly have an increased risk for autoimmune diseases. Recently, an allelic variation (C1858T) of the PTPN22 gene was revealed to be associated with the development of autoimmunity. Thus, the aim of this study was to determine the frequency of the PTPN22 C1858T polymorphism in women with Turner syndrome (TS) compared to controls. Case-control study comprises 142 women with TS (cases) and 180 healthy and fertile women without a history of autoimmune disease (controls). Detection of the PTPN22 C1858T polymorphism (rs2476601) was performed by TaqMan real-time PCR. The chi-square test was used to compare allele and genotype frequencies between groups and to estimate the Hardy-Weinberg equilibrium. All P-values were two-tailed, and 95% confidence intervals (CIs) were calculated. A P-value0.05 was considered statistically significant. Genotypes CC, CT and TT of the PTPN22 C1858T polymorphism presented frequencies of, respectively, 67.6%, 28.2% and 4.2% in the TS, and 82.8%, 16.1% and 1.1% in the control group (P = 0.0043). Alleles C and T were present in, respectively, 81.7% and 18.3% of the patients with TS (P = 0.001, OR = 2.22, 95% CI = 1.39-3.54) and in 90.8% and 9.2%, respectively, of the controls. The data suggest that in Brazilian patients with TS, the PTPN22 C1858T polymorphism may be an important genetic factor predisposing to autoimmune disease risk.

The frequency of ciliated fibroblasts in skin, gingiva, molar and incisor periodontal ligaments and incisor enamel-related periodontium of the mouse was estimated by straight counting or by methods based on the probability of observing a basal body in relation to other cell structures. Transmission electron microscopy of ultra-thin sections mounted in single slot grids was used. The results obtained with these methods differed, but indicated that periodontal ligament fibroblasts from molars or incisors generally had a higher ciliation index than the fibroblasts from skin and gingiva. These differences may not be real since the detection of cilia and/or centriolar structures seems to depend very much upon the plane of sectioning relative to the long axis of the fibroblasts, a situation which favours the more regularly arranged periodontal fibroblasts. This arrangement makes the periodontal tissues, particularly those of rodent incisors, a valuable model for studying ciliation in vivo because of the prompt response to experimental manipulation.

Regarding the event of an adenocarcinoma of the colic type of the cecal appendix, operated on at the ABC Medical College Hospital, the authors summarize the subject stating that, among the appendiceal carcinoma, this particular one tends to spread, by the veins or lymphatics, besides spreading by contiguity. This carcinoma hardly presents a symptomology of its own. It appears fairly often as acute appendicitis. The authors also state the difficulty for a macroscopic diagnosis. Thus, its real nature is determined, in general, only after the histologic exam of the removed part. The right hemicolectomy is the most indicated surgery and best results are obtained in the first surgery or 30 days afterwards. About the reported case the patient was operated on with the pre and intra operatory diagnosis of appendicitis and the histologic exam, on top of confirming it, pointed out the presence of neoplasm. After the indication for reoperation, the prompt spreading of the neoplastic disease offered no means to perform any other surgery disclosing its malignant potential.