This manuscript mainly explores the characteristics of turbulence quantities in the wake of tall building clusters of different array size ($N$) and building spacing ($W_S$) arranged in an aligned and regular grid in the flow direction. Velocity fields are measured in a wind tunnel using three-dimensional laser Doppler anemometry. Results show a delayed recovery of $u_{rms}$ and $v_{rms}$ (defined as the root-mean-square of the streamwise and lateral velocities, respectively) in the wake flow compared with the mean flow. Based on the turbulent fluctuations, the extents of the near-, transition- and far-wake regions in Mishra et al. (Boundary-Layer Meteorol., vol. 189, 2023, pp. 1–25) are revisited. In the near wake, we observe a significant reduction in $u_{rms}$ and $v_{rms}$ in the wake of a $4\times 4$ cluster compared with that of a single building. In the transition region, the turbulence intensity magnitudes within the cluster reduce to below their free-stream counterpart; this reduction is associated with the slowly varying nature of the normalised wake deficit in the streamwise direction. The recovery of the root mean square in the far-wake region is observed for $x \geq 2.5W_A$ (where $W_A$ is the width of the cluster), with the mutual interaction of the wakes formed behind the individual buildings reducing with an increase in $W_S$, resulting in a faster recovery of the turbulent fluctuations. Finally, wavelet analysis suggests the existence of multi-scale vortex-shedding frequencies downwind of tall building clusters.

This paper establishes some stylized facts of the long-run relationship between growth and labor shares using historical data for the USA (1898–2010), the United Kingdom (1856–2010), and France (1896–2010). Performing individual country time–frequency analysis, we demonstrate the existence of long-term cycles in labor share of 30–50 years explaining a major part of the variance in the data. Further, the impact of labor share on growth changes sign with the frequency considered from negative at high frequencies to positive at low frequencies. Finally, the positive coefficient associated with the labor share at low frequencies increases over time.

Glyphosate and paraquat herbicide drift injury to crops may substantially reduce growth or yield. Determining the type and degree of injury is of importance to a producer. This research was conducted to determine whether remote sensing could be used to identify and quantify herbicide injury to crops. Soybean and corn plants were grown in 3.8-L pots to the five- to seven-leaf stage, at which time, applications of nonselective herbicides were made. Visual injury estimates were made, and hyperspectral reflectance data were recorded 1, 4, and 7 d after application (DAA). Several analysis techniques including multiple indices, signature amplitude (SA) with spectral bands as features, and wavelet analysis were used to distinguish between herbicide-treated and nontreated plants. Classification accuracy using SA analysis of paraquat injury on soybean was better than 75% for both 1/2- and 1/8× rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2× rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. Applications of a 1/2× rate of glyphosate produced 55 to 81% soybean injury and 20 to 50% corn injury 4 and 7 DAA, respectively. However, using SA analysis, the moderately injured plants were indistinguishable from the uninjured controls, as represented by the low classification accuracies at the 1/8-, 1/32-, and 1/64× rates. The most promising technique for identifying drift injury was wavelet analysis, which successfully distinguished between corn plants treated with either the 1/8- or the 1/2× rates of paraquat compared with the nontreated corn plants better than 92% 1, 4, and 7 DAA. These analysis techniques, once tested and validated on field scale data, may help determine the extent and the degree of herbicide drift for making appropriate and, more importantly, timely management decisions.

Vibration analysis is the most widely used tool in industrial application for machine’shealth condition assessment. Bearings, however, are very sensitive and require specialattention. Vibration analysis employs various signal processing methods such as spectralanalysis, time-scale, time frequency analysis, etc. these methods are used to analyzebearings’ vibratory behavior by monitoring the evolution of statisticalindicators.However, diagnosing the bearing depending on traditional features only isn’tsufficient to assure effective or reliable assessment of the component’ health condition.This paper proposes a multi-features online dynamic classification as a new method forfault detection and health condition monitoring for bearings; this technique uses multiplefeatures, including traditional features extracted from the raw signal, two specialfeatures extracted by wavelet analysis, the spectral kurtosis, coupled with a nonlinearprincipal component analysis and a dynamic classification to capitalize on the hiddeninformation in the time evolution of the features.Through this article, we introducedifferent measures and techniques used to characterize the health state of rolling, thenwe deploy a methodology using dynamic classification to detect early defect. To ensure analmost continuous surveillance, this methodology is based on a real-time analysis, anduses specific statistical indicators adapted to the experimental bench. Then, themonitoring of the degradation is achieved through the resulting class of the state ofdegradation. New parameters such as the speed of the class, the position of the class, theshape of the class will be discussed to inform the state of damage. The suggestedmethodology is validated by analyzing several fatigue tests from a fatigue bench bearingthrust ball referenced SNR51207.

The work developed in the paper concerns the multivariate fractional Brownian motion (mfBm) viewed through the lens of the wavelet transform. After recalling some basic properties on the mfBm, we calculate the correlation structure of its wavelet transform. We particularly study the asymptotic behaviour of the correlation, showing that if the analyzing wavelet has a sufficient number of null first order moments, the decomposition eliminates any possible long-range (inter)dependence. The cross-spectral density is also considered in a second part. Its existence is proved and its evaluation is performed using a von Bahr–Essen like representation of the function sign(t)|t|α. The behaviour of the cross-spectral density of the wavelet field at the zero frequency is also developed and confirms the results provided by the asymptotic analysis of the correlation.

The solar activity has been proposed as one of the main factors of Earth's climate variability, however biological processes have been also proposed. Dimethylsulfide (DMS) is the main biogenic sulfur compound in the atmosphere. DMS is mainly produced by the marine biosphere and plays an important role in the atmospheric sulfur cycle. Currently it is accepted that terrestrial biota not only adapts to environmental conditions but influences them through regulations of the chemical composition of the atmosphere. In the present study we used different methods of analysis to investigate the relationship between the DMS, Low Clouds, Ultraviolet Radiation A (UVA) and Sea Surface Temperature (SST) in the Southern Hemisphere. We found that the series analyzed have different periodicities which can be associated with climatic and solar phenomena such as El Niño, the Quasi-Biennial Oscillation (QBO) and the changes in solar activity. Also, we found an anticorrelation between DMS and UVA, the relation between DMS and clouds is mainly non-linear and there is a correlation between DMS and SST. Then, our results suggest a positive feedback interaction among DMS, solar radiation and cloud at time-scales shorter than the solar cycle.

Rapid climate warming has been observed in the region of the Antarctic Peninsula since the middle of the last century with the largest warming rate in the winter. Decadal variability of winter temperature on the regional scale was analysed using eight station datasets of the Antarctic Peninsula region. The Scientific Committee on Antarctic Research Reference Antarctic Data for Environmental Research from the period 1950–2009 were used. Fourier and wavelet transforms of the averaged temperature anomaly time series reveal a clear separation between the oscillations with three to eight year periods and a decadal oscillation with a period of around 16 years. On the Antarctic Peninsula region scale, 16 year periodicity in the winter temperature variability has not been described before. Both spectral components show similar spectral power and statistical significance (5–10%). This is evidence of their comparable importance for winter temperature changes in the Antarctic Peninsula region. The three to eight year periods are most probably related to the El Niño–Southern Oscillation and Antarctic Circumpolar Wave signals, but the 16 year oscillation has not been identified within the scope of this analysis. The possible effect of the decadal oscillation in the winter temperature trend estimate is discussed.

We analysed the patterns of variation that characterize 33 catch time series of large pelagic fishes exploited by the Japanese and Taiwanese longline fisheries in the Indian Ocean from 1968 to 2003. We selected four species, the yellowfin (Thunnus albacares), the bigeye (T. obesus), the albacore (T. alalunga), and the swordfish (Xiphias gladius) and aggregated data into five biogeographic provinces of Longhurst (2001). We carried out wavelet analyses, an efficient method to study non-stationary time series, in order to get the time-scale patterns of each signals. We then compared and grouped the different wavelet spectra using a multivariate analysis to identify the factors (species, province or fleet) that may influence their clustering. We also investigated the associations between catch time series and a large-scale climatic index, the Dipole Mode Index (DMI), using cross wavelet analyses. Our results evidenced that the geographical province is more important than the species level when analyzing the 33 catch time series in the tropical Indian Ocean. The DMI further impacted the variability of tuna and swordfish catch time series at several periodic bands and at different temporal locations, and we demonstrated that the geographic locations modulated its impact. We discussed the consistency of time series fluctuations that reflect embedded information and complex interactions between biological processes, fishing strategies and environmental variability at different scales.

Multipath is still one of the major error sources that degrades the accuracy of GPS positioning. The amount of multipath is highly dependent on the antenna's environment, which makes it difficult to isolate. Usually there is at least one in-view satellite which is more susceptible to multipath, particularly the one with the lowest elevation angle. To increase the positioning the best satellites must be selected (i.e. by least square or multipath mitigation) for computing a position. In this paper we propose an algorithm which picks up the best satellites (when there are more than four satellites in view) based on wavelet analysis for calculating a position. In this experiment, code and carrier measurements were collected in 15-minute segments by exploiting a single frequency (L1), stationary, navigation-grade receiver in a high-multipath environment. The magnitudes of these pseudoranges were often inflated by multipath error. We then post-processed the received data by applying wavelet filtering to the residuals (code minus carrier) to approximate the multipath values, and compute the receiver's position based on the selected satellites. Satellites were selected based on the residual values. To compare the results with the raw measurements, statistical elements were computed. The results showed significant improvement in variance of the estimated positions and, most importantly, a normalization of the data scatter-distribution was observed.