Signal Values Research Articles

A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

Abstract In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square (RMS) value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through Kernel Principal Component Analysis (KPCA), thereby enabling a more nuanced representation of the gearbox's degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process's parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox's RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic predictive maintenance for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of predictive maintenance in the context of gearboxes.

Overeducation and economic mobility

We assess the hypothesis that declining intergenerational economic mobility in Norway is attributable to a rising signaling value of education accompanied by more overeducation particularly among upper-class offspring. We identify five empirical facts that together point in this direction:• The educational earnings premium has risen, but only through the extensive (employment) margin.• The rising earnings premium is associated with completed degrees only. When educational attainment is measured as time actually invested, the premium has declined.• Both educational attainment and the labor market's skill-requirements (as predicted by the occupational distribution) have increased, but attainment has risen faster than requirements such that the incidence of overeducation has increased.• There is a steep positive social gradient in overeducation: Overeducation is more frequent and has risen faster among offspring in upper-class families.• There is a steep negative social gradient in non-employment: Non-employment is more frequent and has risen faster among offspring in lower-class families.

A fast modularity hardware Trojan detection technique for large scale gate-level netlists

Hardware Trojans (HTs) are a kind of malicious circuit implanted by adversaries and induce malfunction under rare situations. Attackers may insert HTs into untrusted third-party intellectual properties (3PIPs), thus severely threatening the hardware security of ICs. To overcome this issue, state-of-art HT detection techniques are proposed based on feature extraction of gate-level netlists (GLNs). However, these techniques may take a long time to extract HT signals for large scale GLNs. In this paper, we propose a fast modularity HT detection (FMTD) method for large scale GLNs. The GLN modularity algorithm can divide the whole GLN into several small modules with the boundaries of D flip-flops (DFFs) of each module. By analyzing the transition rate of critical signals, preserving suspicious DFFs, and repairing the ring circuit, we can ensure the integrity of HT circuits during the GLN modularity process. Then, the calculation of the testability of each module is conducted in parallel with our self-designed tool. In the self-designed tool, we repair the ring circuit, calculate the testability values, and calibrate the testability values of module boundary signals. Compared with the EDA tools, our self-designed tool has no upper limit of testability values. Then, the testability values are sent to the unsupervised K-means clustering simultaneously to diagnose the HT signals. Facilitated by the modularity of the GLN, the detection time of 105 order signals sample is reduced by up to 90 % when compared to the traditional COTD method, while our MFTD method shows a similar HT detection performance to that of the traditional COTD method. For all 20 kinds of GLN samples in Trust-hub, our FMTD method can obtain a detection accuracy of 100 %, and signal diagnosis precision of more than 93 % with a diagnosis false positive rate lower than 1 %.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.

Evaluation of the Effect of the Injected Dose and Body Mass Index on the Signal-to-Noise Ratio (SNR) Detected using a PET/CT Scan.

This study aims to investigate, using patient studies, the impact of Injected Dose (ID), Body Mass Index (BMI), and image noise assessments in PET imaging. This study included 59 liver cancer patients weighing between 45 and 107 kg. After intravenously injecting 0.1 millicurie (mCi) of 18F-FDG per kilogram of body weight, PET scans were obtained for 1, 1.5, and 3 min/bed position based on the patient's weight. Weight, height, and body mass index were calculated using a spreadsheet. Five regions of interest (ROIs) were placed at the same site in the liver, which was considered to have a homogeneous metabolism, in five successive slices of PET/CT scans to determine the mean uptake (signal) values and their standard deviation (noise). The ratio of both gives the liver's Signal-to-Noise Ratio (SNR). Graphs were created to determine the relationship between these characteristics. The plots demonstrated that the dose injected increased when body weight and/or BMI increased, and that the SNR fell even as the dose administered increased. This is owing to the fact that heavier patients having a higher administered dose and, have a lower SNR even when greater 18F-FDG doses are delivered. These data indicate that the image quality, as measured by the SNR, is inferior in heavier persons compared to those who are thinner.

Research on ultrasonic guided wave-based high-speed turnout switch rail base flaw detection

Turnout switch rail fracture detection is currently a serious issue in the field of railway transportation. Guided wave detection, a non-destructive testing method, is a good way of studying this issue and looking for a suitable solution. For this paper, a guided wave mode and an excitation position were selected based on the phase velocity dispersion curve and the wave structure. After this, a model was devised for the turnout switch area using the finite element (FE) method. This model considered the straight switch rail, curved stock rail, bolt hole, spacer block, and sub-rail foundation, and verifies the validity of the simulation model through the experiment. The propagation characteristics of guided waves in the switch rail were then simulated in different fracturing states by means of a 30-kHz excitation applied vertically to the rail base. The results showed that a single mode of the guided wave could be generated by this excitation method, showing that it could be used as an effective means for fracture detection. The growth rate of the root-mean-square (RMS) value of the time-domain acceleration signal could then be analysed to identify the state of fracture in the straight switch rail. This discrimination method is suitable for finding fractures parallel to the rail cross-section with a width of 5 mm or more at the bottom of the straight switch rail.

Light-induced fading effects on TL and OSL signals and feasibility of dose re-assessment with PTTL signals in BeO dosimeters

Abstract This study aims to investigate both light-induced fading effects on thermoluminescence (TL) and optically stimulated luminescence (OSL) signals under three different light sources (fluorescent, UV-254 and daylight) and dose reassessment with phototransferred TL (PTTL) signals in Beryllium oxide dosimeters. TL and OSL signals were deconvoluted for each light source. Accordingly, variations in the maximum peak temperature, activation energy, peak area value for the TL signal, and intensity and lifetime values for the OSL signal were monitored. Each peak, OSL component, and total area value exhibited different behaviors depending on the light source. Considering the total area condition, the TL intensity decreased by ∼90%, ∼80%, and ∼70% in UV-254, daylight, and fluorescent light exposure, respectively, at the end of the 120 min. On the other hand, the OSL total area intensity faded quickly for both UV-254 and daylight, while it decreased by ∼45% for fluorescent light. According to these results, regardless of TL and OSL measurements, the dosimeters should be kept primarily away from daylight and fluorescent light after irradiation, instead of UV-254, which is rarely encountered in daily life. The feasibility of dose reassessment using PTTL signals under UV-254 light was investigated within a wide dose range from 0.1 to 128 Gy. It is feasible to reassess doses between 0.5 and 32 Gy considering the total area intensity of PTTL signals. In conclusion, PTTL signals can be easily used in fields of the order of Gy, such as in reevaluating doses in radiotherapy applications.

Application study of apnea-hypopnea duration for assessing adult obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a common sleep disordered breathing disorder, which can cause serious damage to multiple human systems. Although polysomnography (PSG) is the current gold standard for diagnosis, it is complex and expensive. Therefore, it is of great significance to find a simple, economical and rapid primary screening and diagnosis method to replace PSG for the diagnosis of OSA. The purpose of this study is to propose a new method for the diagnosis and classification of OSA, which is used to automatically detect the duration of sleep apnea hypopnea events (AHE), so as to estimate the ratio(S) of the total duration of all-night AHE to the total sleep time only based on the sound signal of sleep respiration, and to identify OSA. We performed PSG tests on participants and extracted relevant sleep breathing sound signal data. This study is carried out in two stages. In the first stage, the relevant PSG report data of eligible subjects were recorded, the total duration of AHE in each subject's data was extracted, and the S value was calculated to evaluate the severity of OSA. In the second stage, only the sleep breath sound signal data of the same batch of subjects were used for automatic detection, and the S value in the sleep breath sound signal was extracted, and the S value was compared with the PSG diagnosis results to calculate the accuracy of the experimental method. Among 225 subjects. Using PSG as the reference standard, the S value extracted from the PSG diagnostic data report can accurately diagnose OSA(accuracy rate 99.56%) and distinguish its severity (accuracy rate 95.11%). The accuracy of the S value detected in the sleep breathing sound signal in the diagnosis of severe OSA reached 100%. The results show that the experimental parameter S value is feasible in OSA diagnosis and classification. OSA can be identified and evaluated only by sleep breathing sounds. This method helps to simplify the diagnostic grading of traditional OSA and lays a foundation for the subsequent development of simple diagnostic grading equipment.

Exploring the top 30 drugs associated with drug-induced constipation based on the FDA adverse event reporting system.

This project aims to identify the top 30 drugs most commonly associated with constipation and their signal values within the FDA Adverse Event Reporting System database. We extracted adverse drug events (ADEs) related to constipation from the FAERS database spanning from January 1, 2004, to September 30, 2023. We compiled the 30 most frequently reported drugs based on the frequency of constipation events. We employed signal detection methodologies to ascertain whether these drugs elicited significant signals, including reporting odds ratio, proportional reporting ratio, multi-item gamma Poisson shrinker, and information component given by the Bayesian confidence propagation neural network. Furthermore, we conducted a time-to-onset (TTO) analysis for drugs generating significant signals using the medians, quartiles, and the Weibull shape parameter test. We extracted a total of 50, 659, 288 ADEs, among which 169,897 (0.34%) were related to constipation. We selected and ranked the top 30 drugs. The drug with the highest ranking was lenalidomide (7,730 cases, 4.55%), with the most prevalent drug class being antineoplastic and immunomodulating agents. Signal detection was performed for the 30 drugs, with constipation risk signals identified for 26 of them. Among the 26 drugs, 22 exhibited constipation signals consistent with those listed on the FDA-approved drug labels. However, four drugs (orlistat, nintedanib, palbociclib, and dimethyl fumarate) presented an unexpected risk of constipation. Ranked by signal values, sevelamer carbonate emerged as the drug with the strongest risk signal [reporting odds ratio (95% CI): 115.51 (110.14, 121.15); PRR (χ2): 83.78 (191,709.73); EBGM (EB05): 82.63 (79.4); IC (IC025): 6.37 (4.70)]. A TTO analysis was conducted for the 26 drugs that generated risk signals, revealing that all drugs exhibited an early failure type. The median TTO for orlistat was 3days, the shortest of all the drugs, while the median TTO for clozapine was 1,065days, the longest of all the drugs. Our study provides a list of drugs potentially associated with drug-induced constipation (DIC). This could potentially inform clinicians about some alternative medications to consider when managing secondary causes of constipation or caring for patients prone to DIC, thereby reducing the incidence and mortality associated with DIC.

Platinum-based fluorescent nanozyme-driven “loong frolic pearls” multifunctional nanoplatform for tri-mode ultrasensitive detection and synergistic sterilization of Burkholderia gladioli

Rapid and accurate detection of Burkholderia gladioli (B. gladioli) and effective sterilization are crucial for ensuring food safety. Hence, a novel “loong frolic pearls” platform based on platinum-based fluorescent nanozymes (Pt-OCDs) and strand exchange amplification (SEA) was reported. Magnetic nanoparticles were modified on primer SEAF, while Pt-OCDs were covalently coupled with primer SEA-R. The highly efficient amplification capability of SEA permitted the accumulation of a large number of double-labeled amplicons. After magnetic adsorption, the supernatant was detected in reverse direction to collect colorimetric-fluorescence-photothermal signal values, enabling ultra-precise detection within 1 h. Furthermore, the Pt-based multifunctional nanoplatform generated abundant •OH and 1O2, which synergistically attacked B. gladioli and its biofilm, resulting in significant bactericidal efficacy within 30 min. This “triple-detection and double-sterilization” platform has been successfully applied in the field of food analysis with good recovery rates and immediate control over B. gladioli, thus demonstrating promising prospects for broad applications.

Development of fetal magnetic resonance imaging (MRI) phantom.

Anthropomorphic phantoms play an important role in routine clinical practice. They can be used to calibrate magnetic resonance imaging (MRI) scanners, control the diagnostic equipment quality, and reduce the acquisition time. The latter is especially critical for diagnosing fetal anomalies, which requires optimal image quality within the shortest possible time. This paper aims to develop an MRI fetal phantom and determine the materials that best mimic the magnetic resonance (MR) characteristics of its internal organs. Future phantom features will include simulations of fetal limb movements. A single MRI study of a pregnant woman at 20 weeks 3 days of gestation was used as a reference and for image segmentation. Anonymized Digital Imaging and Communication in Medicine (DICOM) files were imported into 3D Slicer v. 5.2.1 for segmentation of the uterus, fetus, and internal organs. Based on the performed segmentation, a three-dimensional model was obtained for printing on a 3D printer. The mold was 3D printed on an Anycubic Photon M3 Max printer. The paper showcases the selection and manufacturing of compositions to simulate the relaxation times of the fetal organs. Formulations for emulsions and carrageenan- and agar-based hydrogels are presented. The selected compositions were used to fill the 3D printed model. Statistical analysis showed no significant differences in absolute and relative signal values obtained from scans of a pregnant woman at 20 weeks and 3 days and a fetal phantom. During the study, an anthropomorphic fetal phantom was constructed, filled with compositions with relaxation times T1 and T2 similar to the control values of the corresponding tissues. The phantom can be used to set up and optimize fetal MRI protocols, train and educate medical students, residents, graduate students, and X-ray technicians, as well as to timely control image quality and equipment serviceability.

ЕКСПЕРИМЕНТАЛЬНІ ДОСЛІДЖЕННЯ БЕЗКОНТАКТНИХ МАГНІТОЕЛЕКТРИЧНИХ ТАХОГЕНЕРАТОРІВ

The paper is devoted to the study of the characteristics and operating modes of a brushless magneto-electric tachogenerator, which is implemented on the basis of a traditional radial electric machine with a slotless stator and permanent magnets on the rotor. Options for generating an DC output signal using diode and transistor rectifiers have been studied. The dependences of the average value of the output signal, the slope of the output signal, the coefficient of deviation of the characteristic from the specified value and the pulsation coefficient were obtained depending on the rotation speed of the rotor shaft. The influence of the capacitance of the filter capacitor on the magnitude of the output signal ripple amplitude has been studied. Ref. 13, fig. 9, table.

Simultaneous frequency and angle-of-arrival measurement of microwave signals utilizing the Talbot effect and pulse interference

We propose and demonstrate a simple and efficient scheme for simultaneous frequency and angle-of-arrival (AOA) measurement of microwave signals using the Talbot effect and pulse interference. This method maps the frequency of the microwave signal to the time intervals of output pulses through the Talbot effect, using a specialized sampling and dispersive structure. Concurrently, the phase difference related to the AOA is encoded onto the relative amplitudes of the output pulses via pulse interference. By analyzing both the time intervals and relative amplitudes of the output pulses, precise frequency and AOA values of the microwave signal are simultaneously derived. Simulation results indicate that the system can perform real-time, gap-free frequency and AOA measurements of multiple signals. Proof-of-concept experiments show this method achieves high accuracy, with AOA measurement errors within ±2.9° over a range of −63° to 63°, and frequency measurement errors within ±60 MHz for frequency bands of 2–3 GHz and 12–13 GHz.

An analysis of the safety of Sevoflurane drugs: A disproportionality analysis based on Food and Drug Administration Adverse Event Reporting System.

Sevoflurane is a volatile anesthetic that can tolerate inhalation induction and is widely used for inducing anesthesia due to its pleasant odor. As a drug that has been on the market for nearly 30 years, the vast majority of adverse reactions have been documented. This study aims to improve the adverse reactions related to Sevoflurane through the mining, organizing and analysis of Food and Drug Administration Adverse Event Reporting System database data. We collected, organized, and analyzed reports from the first quarter of 2004 to the fourth quarter of 2022. We performed disproportionality analysis algorithms, including reporting odds ratio, the proportional reporting ratio values, to quantify the signal values of different adverse events (AEs). A total of 1126 AEs and 27 system organ classes were identified by performing statistics analysis system software. By combining algorithm calculations, we create a forest map of the top 30 AEs of the reporting odds ratio signal. Based on the reviewing relevant literature, we found that the vast majority of AEs have been reported in relevant studies. However, there is currently no study revealing the correlation between atrial fibrillation and Sevoflurane, which means that atrial fibrillation may be an unreported AE of Sevoflurane. In the present study, we found that atrial fibrillation may be a new adverse reaction of Sevoflurane through the Food and Drug Administration Adverse Event Reporting System database, which can function as a novel guideline to guide us in the more standardized use of Sevoflurane in clinical practice.

Study on the Effect of Starved Lubrication on the Dynamic Characteristics of Locally Failed Roller Bearings

When the lubricant is not enough to maintain the ideal lubrication state of the bearing, the deep groove ball bearing will enter the starved lubrication state, which leads to the change of the vibration response of the deep groove ball bearing with localized defects. Previous bearing dynamics models mainly consider the lubrication state as an ideal lubrication condition, which may not reveal the effect of starved lubrication on the vibration characteristics of defective bearings. In this paper, the internal interaction of the system under starved condition is taken into account, and a dynamic model of defective deep groove ball bearings with insufficient lubricant is established to investigate the effects of different starved lubrication degrees on the contact force, friction force, cage speed, and system vibration characteristics. The results show that the lubricant insufficiency significantly changes the contact force inside the bearing, and the increase in friction caused by the increase in the degree of starved lubrication leads to the suppression of the degree of bearing slipping. The root mean square (RMS) value of the time-domain signal and the outer ring fault frequency both increase with the increase of the starved degree. The results of the study are helpful for fault diagnosis and condition monitoring of related equipment.

Comparative analysis of predose effects on TL and OSL signals in BeO dosimeters

This study investigates in detail the effects of predose on TL and OSL signals for BeO dosimeters. The TL and OSL signals were deconvoluted to each peak and component. As a result of deconvolution, the variations in the kinetic parameters (E, Tmax, b) for the TL signals and the lifetime values of the OSL signals were investigated. In addition, sensitivity changes according to predose were monitored for each peak and component. Finally, dose response curves were studied using the dose linearity index (f(D)) for each peak and component. Accordingly, the peak structures and kinetic parameters did not change according to the predose for the TL signal, whereas variations in the lifetime values for the OSL signal were observed, especially at the initial dose values (0.1 and 0.2 Gy). There was no change in sensitivity according to the predose for the total area condition although each peak and component exhibited independent behavior. Therefore, TL and OSL signals should be evaluated based on the total area in predose applications. The TL and OSL dose response curves exhibited different behaviors according to predose. TL dose response curves were not affected by the predose except for 1000 Gy, while the OSL dose response curves were affected by the predose considering the total area condition. The possible reason for the differences between the TL and OSL dose response curves is the significant transfer effect in the OSL signal at low doses, which results in greater changes at low doses compared to the TL signal. Also, thermal quenching effects may have resulted in lower intensity in the case of the TL signal. In future studies, preheating tests and thermal quenching corrections on TL peaks at high predoses may increase our understanding of deep trap interactions in BeO dosimeters.

Reliability of digital filtering on surface electromyographic activity of respiratory muscles in patients with COVID-19

Introduction: Surface electromyography (sEMG) is valuable for assessing respiratory muscle activity, but the influence of electromagnetic fields can impair the accuracy of the signals. This study aims to compare the Root Mean Square (RMS) values of sEMG signals from respiratory muscles in COVID-19 patients receiving oxygen therapy in the ICU before and after digital filtering. Methods: This exploratory analysis is part of a cross-sectional study registered on ClinicalTrials.gov (NCT05572853). sEMG signals from the sternocleidomastoid, scalenus, diaphragm, and rectus abdominis muscles were recorded in COVID-19 patients receiving low-flow oxygen therapy. The RMS data were analyzed and processed using Scilab 6.1.1 software before and after applying a filter with a 60 Hz notch and a passband between 20-500 Hz. Results: 71 patients with a mean age of 55 ± 14 years, predominantly male (73.2%), were included. Muscle’s activity remained unchanged after signal filtering, showing higher RMS intensity (pre-filter/post-filter) for the scalene (14.0 ± 8.6 / 8.5 ± 6.2), sternocleidomastoid (12.0 ± 8.8 / 6.8 ± 7.8), diaphragm (11.7 ± 8.1 / 5.4 ± 7.1), and rectus abdominis (10.4 ± 4.7 / 3.1 ± 1.9) muscles. RMS attenuation was significant for all muscles (p < 0.001), with no agreement between pre- and post-filtering from Bland-Altman analysis. Conclusions: The applied digital filter attenuated RMS values while maintaining the same respiratory muscle activation, enhancing signal identification by reducing noise.

Sensor-Head Distance and Signal Strength in Whole-Head Magnetoencephalography: Report of 996 Patients With Epilepsy.

Although the sensor-to-head distance is theoretically known to affect the signal strength in magnetoencephalography (MEG), these values have not been reported for a whole-head MEG system in a large population. We measured the distance and signal strength in 996 patients with epilepsy. The MEG sensor array consisted of 102 measurement sites, each of which had two gradiometers and one magnetometer. The sensor-head distance was defined as the minimum distance between each site and a set of digitized scalp points. For the signal strength, we calculated the root-mean-square of the signal values in each sensor over a recording of 4 minutes. For analyses at the individual and sensor levels, these values were averaged over the sensors and patients, respectively. We evaluated the correlation between distance and signal strength at both individual and sensor levels. At the sensor level, we investigated regional differences in these measures. The individual-level analysis showed only a weak negative correlation between the sensor-head distance and the signal strength. The sensor-level analysis demonstrated a considerably negative correlation for both gradiometers and magnetometers. The sensor-head distances showed no significant differences between the regions, whereas the signal strength was higher in the temporal and occipital sensors than in the frontal and parietal sensors. Sensor-head distance was not a definitive factor for determining the magnitude of MEG signals in individuals. Yet, the distance is important for the signal strength at a sensor level. Regional differences in signal strength may need to be considered in the analysis and interpretation of MEG.

Prediction of amplitude of fault generated travelling wave in medium-voltage grids for fault type classification

This paper proposes a new solution for the classification of short-circuits in medium-voltage distribution grids. The solution requires the measurements of the phase voltages at the time of the short-circuit and the voltage amplitudes of the short-circuit wave resulting from the fault. The values of the voltage before the short-circuit are used to calculate the expected values of the short-circuit wave amplitudes for the different types of short circuits. These calculations are based on an analysis of the charge flow between the capacitances of the power line due to the disturbance. The short-time matrix pencil method was used to detect the incoming short-circuit waves. Tests of the algorithm were carried out on the IEEE 34-bus Feeder model, taking into account the influence of voltage sensors on the measured signal values. The classification algorithm uses only non-zero components due to their relatively low attenuation. Despite that, it achieves high performance in the classification of single- and two-phase short-circuits.

Discrimination or a Competitive Climate? Why Women Cannot Translate Their Better High School Grades into University Grades

While girls have better grades than boys in high school, this does not translate into better performance of young women, as compared to young men, in university. Due to the high signalling value of university grades for subsequent income and employment outcomes, this has important consequences for gender inequalities at labour market entry. However, previous studies have not yet examined the potential barriers that might limit women’s ability to maintain their previous academic achievement at the university level. Drawing on the nation-wide Student Survey, this study addresses this shortcoming by investigating perceived discrimination against women and perceived competition among students as two potential correlates. Our findings first confirm that while girls have better grades in high school than boys, this has reversed at the university level. Further, high school grades are less strongly correlated with university grades for girls compared to boys. Our results highlight that young women perceive there to be more discrimination against women as well as higher levels of competition within their field of study, than do their male peers. The study further demonstrates that an increased level of perceived discrimination is strongly associated with lower university performance for young women, thereby plausibly hindering their ability to reach their full academic potential.