An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

We present a statistical framework to identify regional signals in station-based CO2 time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this ±2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

Towards the closure of Collar’s triangle by optical diagnostics

An experimental methodology is proposed for the study of aeroelastic systems. The approach locally evaluates the forces involved in Collar’s triangle, namely aerodynamic, elastic, and inertial forces. The position of flow tracers as well as of markers on the object surface is monitored by a volumetric PIV system. From the recorded images, the flow tracers and surfare markers are separated based on their optical characteristics. The resulting images are then analysed by Lagrangian particle tracking. The inertial and elastic forces are obtained solely analysing the motion and the deformation of the solid object, whereas the aerodynamic force distribution is obtained via the pressure-from-PIV technique. Experiments are conducted on a benchmark problem of fluid-structure interaction, featuring a flexible panel installed at the trailing edge of a cylinder. A polynomial fit of the markers’ positions is carried out to determine the panel’s instantaneous shape, from which the inertial and elastic forces are evaluated. The pressure loads on the panel are determined via solution of the Poisson equation for pressure, imposing adaptive boundary conditions that comply with the panel. The simultaneous measurement of the three forces allows to assess the equilibrium of forces, and in turn to close Collar’s triangle.

Plasmonic Sensor based on Microstructure PCF: Performance Analysis with Outside Detecting Approach

In this microarticle, we design a microstructure photonic crystal fiber (PCF) based external sensing surface plasmon resonance (SPR) sensor. The performance of the design is numerically evaluated incorporating the finite element method (FEM) with Perfectly Matched Layer (PML) boundary condition of scattering case. Modal analysis is performed using finer mesh anlaysis. At the optimized thickness (40nm) of chemically stable gold(Au) layer, the ever been maximum reported wavelength sensitivity (WS) and standard amplitude sensitivity (AS) are to 75,000 nm per RIU and 480 per RIU correspondingly. The sensor also exposed high polynomial fit (𝐑𝟐 = 𝟎. 𝟗𝟗) as well as high figure of merit (FoM) of 280.77 per RIU. Since very much high sensitivity, high detecting range and figure of merit, lowing the cost of fabrication, the proposed design can be a pleasant competitor in detection of the analyte refractive index (RI). At the last, to prove performance ability of our designed sensor all the performance parameter calculated results compare with the existing sensors.

A Statistical Analysis on COVID19 Global Vaccination by Best Fit Regression Method

In this analysis a best fit regression is executed in between two variables - population of specific country in million vs percentage of first dosage vaccinated till 25.04.21. The analysis is carried out by a standard statistical software with a significant Pearson correlation (r) of 4th order polynomial fit. The basis of first dosage already vaccinated is 1030 million globally.

Dynamic Microclimate Boundaries across a Sharp Tropical Rainforest–Clearing Edge

As landscapes become increasingly fragmented, research into impacts from disturbance and how edges affect vegetation and community structure has become more important. Descriptive studies on how microclimate changes across sharp transition zones have long existed in the literature and recently more attention has been focused on understanding the dynamic patterns of microclimate associated with forest edges. Increasing concern about forest fragmentation has led to new technologies for modeling forest microclimates. However, forest boundaries pose important challenges to not only microclimate modeling but also sampling regimes in order to capture the diurnal and seasonal dynamic aspects of microclimate along forest edges. We measured microclimatic variables across a sharp boundary from a clearing into primary lowland tropical rainforest at La Selva Biological Station in Costa Rica. Dynamic changes in diurnal microclimate were measured along three replicated transects, approximately 30 m in length with data collected every 1 m continuously at 30 min intervals for 24 h with a mobile sensor platform supported by a cable infrastructure. We found that a first-order polynomial fit using piece-wise regression provided the most consistent estimation of the forest edge, relative to the visual edge, although we found no “best” sensing parameter as all measurements varied. Edge location estimates based on daytime net shortwave radiation had less difference from the visual edge than other shortwave measurements, but estimates made throughout the day with downward-facing or net infrared radiation sensors were more consistent and closer to the visual edge than any other measurement. This research contributes to the relatively small number of studies that have directly measured diurnal temporal and spatial patterns of microclimate variation across forest edges and demonstrates the use of a flexible mobile platform that enables repeated, high-resolution measurements of gradients of microclimate.

An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

We present a statistical framework for near real-time signal processing to identify regional signals in CO2 time series recorded at stations which are normally uninfluenced by local processes. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally-adjusted noise component, equal to two standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which rise above this 2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale weather events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

Damage Identification of Structures Based on Smooth Orthogonal Decomposition and Improved Beetle Antennae Search Algorithm

A novel damage identification method that utilizes the smooth orthogonal decomposition (SOD) combined with the improved beetle antennae search algorithm (BAS) presented by previous scholars is proposed. Firstly, the damage index which can track the curvature changing of mode shape identified by the SOD method is generated by an adaptive polynomial fit method. The locations of structure damages are determined according to the damage index. Thus, the number of possible damaged elements needed to be taken into account can be reduced when calculating the degree of damage. Then, the reduction in the stiffness at the damage location of the structure is calculated by the improved BAS in which the fitness function is constructed by calculated frequencies of the damaged structure in each iteration and the modal frequencies obtained by SOD. The BAS algorithm is improved through a fusion strategy of simulated annealing theory. Thus, the improved BAS algorithm is efficient and adaptive. The effect of this combined application in damage identification has been verified by numerical examples of a simply supported beam with single damage and a cantilever beam with double damage. The numerical results show that this combined algorithm exhibits high reliability in damage identification of beam-like structures.

An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.

Low error Kramers-Kronig estimations using symmetric extrapolation method

Kramers-Kronig (KK) equations allow us to obtain the real or imaginary part of linear, causal and time constant functions, starting from the imaginary or real part respectively. They are normally applied on different practical applications as a control method. A common problem in measurements is the lack of data in a wide-range frequency, due to some of the inherent limitations of experiments or practical limitations of the used technology. Different solutions to this problem were proved, such as several methods for extrapolation, some of which based on piecewise polynomial fit or the approach based on the expected asymptotical behavior. In this work, we propose an approach based on the symmetric extrapolation method to generate data in missing frequency ranges, to minimize the estimated error of the KK equations. The results show that with data from impedance measurements of an electrode-electrolyte interface, the adjustment error of the transformed functions can be drastically reduced to below 1%.