Lamb Wave Based Structural Damage Detection Using Stationarity Tests

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey–Fuller (ADF) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips–Perron (PP) test, and Leybourne–McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

Laparoscopic Pectopexy—CUSUM Learning Curve and Perioperative Complications Analysis

The study aimed to examine the learning curve and perioperative complications for laparoscopic pectopexy (LP). A total of 60 women with stage II–IV apical prolapse who underwent LP were dichotomized into groups: LSH(+) with concomitant laparoscopic supracervical hysterectomy (LSH), LSH(−) after previous supracervical/total hysterectomy. Operative time, estimated blood loss and hospitalization length were evaluated with cumulative sum (CUSUM) analysis and the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, separately for two surgeons (A and B). Intraoperative and perioperative complications according to the Clavien–Dindo (C–D) classification were analyzed. Mean operative time, change in hemoglobin level, and postoperative hospital stay were 143.5 ± 23.1 min—1.5 ± 0.5g/dL and 2.5 ± 0.9 days, respectively. LSH during pectopexy was associated with longer operative time (p = 0.01) but not with higher intraoperative bleeding or prolonged hospital stay. Severe complications rate was low (1.7%) with one bowel injury in LSH(−) (C–D grade IIIb). No C–D grade II, IV and V complications were found. Conversion to open pectopexy, return to the operating room or blood transfusion were not required. The KPSS test showed that a steady operative time for Surgeon A was achieved after 28 procedures. A proficiency for laparoscopic pectopexy based on CUSUM analysis was observed after 38–40 procedures.

Testování hypotézy konvergence v regionu střední Evropy pomocí kointegrace

The issue of regional disparities development is usually examined only in terms of beta- and sigma-convergence. To extend the discussion, therefore, it is needed to use approaches based on completely different principles, which are not burden with always the same methodological limits. In this context, the aim of this contribution is to apply the cointegration approach to assess the development of regional disparities in economic performance and income in Central Europe. On the sample of 62 regions in 2004-2018, this contribution applies the disparity evaluation method based on the Pesaran's probabilistic approach. In particular, we test the convergence hypothesis by KPSS test (null of stationary), and the divergence hypothesis by ADF test (null of unit root). The analysis found the regional convergence in economic performance, but not in income. After extending the analysis to the level of individual countries, internal regional convergence within most of countries was found in both economic performance and income. As part of the disparity evaluation in economic performance, "western" (regions of the Austria, Czechia and Germany) and the "eastern" convergence club (Hungary, Poland and Slovakia) were identified. Similarly, in the case of income analysis some signs of convergence club (Czechia, Hungary and Slovakia) were found.

The Relationship between Government Expenditures and Revenues in Algeria During the Period (1990-2019): Granger Causality Approach

The aim of this study is to examines the causal relationship between government revenues and expenditures in Algeria during the period 1990 to 2019. Data properties were analyzed to determine their stationarity using the Dickey-Fuller (ADF) test, Phillips-Perron test and Kwiatkowski, Phillips, Schmidt, Shin (KPSS) test, as well as the Granger Causality Test (1969) of showing the direction. The results show that there is unidirectional causal relationship between government expenditure and revenue with the direction of causality running from government revenues to expenditures.

Modelling the Occurrence of the Novel Pandemic COVID-19 Outbreak– A Box and Jenkins Approach

The corona virus disease 2019 (COVID-19) is a novel pandemic disease that spreads very fast and causes severe respiratory problem to its carrier and thereby results to death in some cases. In this research, we studied the trend, model Nigeria daily COVID-19 cases and forecast for the future occurrences in the country at large. We adopt the Box and Jenkins approach. The time plot showed that the cases of COVID-19 rises rapidly in recent time. KPSS test confirms the non-stationarity of the process (p < 0.05) before differencing. The test also confirmed the stationarity of the process (p > 0.05) after differencing. Various ARIMA (p,d,q) were examined with their respective AICs and Log-likelihood. ARIMA (1, 2, 1) was selected as the best model due to its least AIC (559.74) and highest log likelihood (−276.87). Both Shapiro-Wilk test and Box test performed confirm the fitness of the model (p > 0.05) for the series. Forecast for 30 days was then made for COVID-19 cases in Nigeria. Conclusively, the model obtained in this research can be used to model, monitor and forecast the daily occurrence of COVID-19 cases in Nigeria.

Non-Stationary in Extreme Share Return: World Indices Application

This paper investigates the behaviour of the extreme share return for the 26 different major indices shares by exploring their stationarity. Extreme return for weekly and monthly series is generated by using block maxima method. Four-employed test permits us to spot non-stationarity in extreme movement. The Augmented Dickey-Fuller and Kwiatkowski Phillips Schmidt Shin (KPSS) test scanned the unit root and the stationarity, and Mann-Kendall and Spearman's test inspected the trend and correlation in the series. Our approach approximates global stock returns for weekly and monthly series market activity. We find most of the extreme stock to be active in shift movement, and we have confirmed that the movement of extreme share return for the majority of the stock indices in the weekly and monthly interval is non-stationary. This testified statistical property in the series can be used as the first crucial appraisal when scrutinizing extreme share return for future research. Keywords: Extreme share returns; block maxima method; non-stationary; stock market; major indices