We present a simple, spreadsheet-based method to determine the statistical significance of the difference between any two arbitrary curves. This modified Chi-squared method addresses two scenarios: A single measurement at each point with known standard deviation, or multiple measurements at each point averaged to produce a mean and standard error. The method includes an essential correction for the deviation from normality in measurements with small sample size, which are typical in biomedical sciences. Statistical significance is determined without regard to the functionality of the curves, or the signs of the differences. Numerical simulations are used to validate the procedure. Example experimental data are used to demonstrate its application. An Excel spreadsheet is provided for performing the calculations for either scenario.

The dataset used in the paper is a multivariate normal distribution with a mean of zero and standard deviation of one for each covariate ζi and travel time ξ(i,j) with a standard deviation that matches the deviation present in Kallus and Mao (2023)’s dataset yet both the correlation and mean vector are treated differently.

All the signatures show a very similar behaviour with a small standard deviation.

For this calculation, the same dimensionality (corresponding to the number of electrodes) and the same number of points (corresponding to three times the number of experiments) was used.

We present new Holocene century to millennial-scale proxies for the well-dated piston core MD99-2269 from Húnaflóadjúp on the North Iceland Shelf. The core is located in 365 mwd and lies close to the fluctuating boundary between Atlantic and Arctic/Polar waters. The proxies are: alkenone-based SST°C, and Mg/Ca SST°C estimates and stable d13C and d18O values on planktonic and benthic foraminifera. The data were converted to 60 yr equi-spaced time-series. Significant trends in the data were extracted using Singular Spectrum Analysis and these accounted for between 50% and 70% of the variance. A comparison between these data with previously published climate proxies from MD99-2269 was carried out on a data set which consisted of 14-variable data set covering the interval 400-9200 cal yr BP at 100 yr time steps. This analysis indicated that the 1st two PC axes accounted for 57% of the variability with high loadings clustering primarily into "nutrient" and "temperature" proxies. Clustering on the 100 yr time-series indicated major changes in environment at ~6350 and ~3450 cal yr BP, which define early, mid- and late Holocene climatic intervals. We argue that a pervasive freshwater cap during the early Holocene resulted in warm SST°s, a stratified water column, and a depleted nutrient supply. The loss of the freshwater layer in the mid-Holocene resulted in high carbonate production, and the late Holocene/neoglacial interval was marked by significantly more variable sea surface conditions.

With the development of computer vision and image processing technology, color segmentation of printed fabrics has gradually become a key task in the textile industry. However, the existing methods often face the problems of low segmentation accuracy and poor computational efficiency when dealing with high complexity patterns and similar colors. To address the above problems, a new color segmentation algorithm for printed fabrics is proposed by integrating the self-organizing mapping network (SOM) in adaptive neural network and the density peak clustering algorithm. The method achieves topological mapping learning of color features through SOM, and then uses DPC for density-driven fine clustering division, which effectively improves the accuracy and stability of color segmentation. The experimental results show that the proposed method shortens the execution time by nearly 40% compared with the self-organized mapping network, and the average color difference (ΔE) of each region after color segmentation is as low as 0.7, which is significantly better than other algorithms. Meanwhile, in the detection of the four types of printed fabric samples, the obtained average color value is up to 87.49 (the higher the 0–100 score value indicates that the color is more significant), and the smallest standard deviation is 2.18 (the smaller the value indicates that the color segmentation is more centralized), which further verifies the comprehensive advantages of the algorithm in terms of segmentation accuracy and stability. In conclusion, the proposed method provides an effective reference for improving the quality and efficiency of color segmentation of printed fabrics.

The calculations were reduced by the 2 endpoints of repairing after 15 and 60 minutes of 5Gy irradiation, since these points of time appear to need the lowest sample sizes. With regard to the standard deviation the minimal and maximal values from the original dataset for the two timeframes were selected.

Seasonal patterns in the partitioning of phytoplankton carbon during receding sea ice conditions in the eastern Bering Sea water column are presented using rates of 14C net primary productivity (NPP), phototrophic plankton carbon content, and POC export fluxes from shelf and slope waters in the spring (March 30-May 6) and summer (July 3-30) of 2008. At ice-covered and marginal ice zone (MIZ) stations on the inner and middle shelf in spring, NPP averaged 76 ± 93 mmol C/m**2/d, and in ice-free waters on the outer shelf NPP averaged 102 ± 137 mmol C/m**2/d. In summer, rates of NPP were more uniform across the entire shelf and averaged 43 ± 23 mmol C/m**2/d over the entire shelf. A concomitant shift was observed in the phototrophic pico-, nano-, and microplankton community in the chlorophyll maximum, from a diatom dominated system (80 ± 12% autotrophic C) in ice covered and MIZ waters in spring, to a microflagellate dominated system (71 ± 31% autotrophic C) in summer. Sediment trap POC fluxes near the 1% PAR depth in ice-free slope waters increased by 70% from spring to summer, from 10 ± 7 mmol C/m**2/d to 17 ± 5 mmol C/m**2/d, respectively. Over the shelf, under-ice trap fluxes at 20 m were higher, averaging 43 ± 17 mmol C/m**2/d POC export over the shelf and slope estimated from 234Th deficits averaged 11 ± 5 mmol C/m**2/d in spring and 10 ± 2 mmol C/m**2/d in summer. Average e-ratios calculated on a station-by-station basis decreased by ~ 30% from spring to summer, from 0.46 ± 0.48 in ice-covered and MIZ waters, to 0.33 ± 0.26 in summer, though the high uncertainty prevents a statistical differentiation of these data.

This data set contains 0.5 Degree ECMWF Ensemble Small Domain forecast imagery. The forecast products are available at 6 hourly intervals out to 36 hours and 12 hourly intervals from 36 to 144 hours. The products include a variety of ensemble mean, standard deviation, probabilities, and quartiles.

Measurements [in µm] and pt values of selected morphological structures of individuals of Ramazzottius syraxi sp. nov. from the type locality, mounted in Hoyer’s medium (N – number of specimens/structures measured; RANGE refers to the smallest and the largest structure among all measured specimens; SD – standard deviation, pt – ratio of the length of a given structure to the length of the buccal tube expressed as a percentage,? – lack of measurements due to unsuitable position of the structure).

Our objective was to model minimum flow coefficient of variation (CV) on small, ungaged streams in the Upper Colorado River Basin. Modeling streamflows is an important tool for understanding landscape-scale drivers of flow and estimating flows where there are no gaged records. We focused our study in the Upper Colorado River Basin, a region that is not only critical for water resources but also projected to experience large future climate shifts toward a drier climate. We used a random forest modeling approach to model the relation between minimum flow CV (the standard deviation of annual minimum flows times 100 divided by the mean of annual minimum flows) on gaged streams (115 gages) and environmental variables. We then projected minimum flow CV to ungaged reaches in the Upper Colorado River Basin using environmental variables for each stream cell in the basin. This data layer shows modeled values for minimum flow CV.

Average infections for the target concept for small-world networks in the LTM, with no burn-in time and a r value of 2, with standard deviation in brackets, and the best performing heuristic in bold.

Measurements and pt values of selected morphological structures of the specimens of paratypes of Macrobiotus harmsworthi obscurus ssp. nov. (Dastych 1985) (= Mesobiotus harmsworthi s.s. (Murray, 1907) (N—number of specimens/structures measured, RANGE refers to the smallest and the largest structure among all measured specimens; SD—standard deviation).

Measurements [in µm] of selected morphological structures of eggs of Mesobiotus cf. coronatus, mounted in Hoyer’s medium (N – number of specimens/structures measured, RANGE refers to the smallest and the largest structure among all measured eggs; SD – standard deviation).

This study aims to evaluate the measurement accuracy of computed tomography (CT) systems, focusing on the necessity of using calibration phantoms for enhanced precision. Both clinical CT and micro-CT systems were evaluated using a specially designed two-ball phantom, which provides a reliable reference for spatial resolution and geometric accuracy. The study involved scanning the phantom with two micro-CT devices (the oversize micro-CT SkyScan 1173 and the high-resolution micro-CT SkyScan 1272) and a clinical CT device, a third-generation dual-source CT scanner (SOMATOM Force), measuring the distance between the centres of two ruby balls. The results showed significant differences in measurement accuracy between the devices. The high-resolution micro-CT provided the most consistent measurements with minimal variance, indicating its superiority in applications requiring high precision. In contrast, the oversize micro-CT exhibited larger errors, particularly at smaller voxel sizes, suggesting that internal calibration affected its accuracy. The dual source CT system had the smallest mean error but a larger standard deviation, indicating less consistency compared to micro-CT systems. Calibration with the two-ball phantom improved measurement accuracy across all devices. This improvement underscores the importance of using calibration phantoms to ensure accurate measurements, especially in fields that require high precision, such as clinical diagnostics and materials science. We concluded that routine calibration with phantoms is essential to achieve high measurement accuracy in CT imaging, thereby increasing the reliability of CT-based analyses in various disciplines.

Measurements [in µm] and pt values of selected morphological structures of individuals of Mesobiotus cf. coronatus, mounted in Hoyer’s medium (N – number of specimens/structures measured; RANGE refers to the smallest and the largest structure among all measured specimens; SD – standard deviation, pt – ratio of the length of a given structure to the length of the buccal tube expressed as a percentage,? – lack of measurements due to unsuitable position of the structure).

Measurements and pt values of selected morphological structures of Mesobiotus occultatus sp. nov. mounted in Hoyer’s medium (N—number of specimens/structures measured, RANGE refers to the smallest and the largest structure among all measured specimens; SD—standard deviation).

Measurements [in μm] of selected morphological structures of eggs of Mesobiotus skorackii sp. nov. mounted in Hoyer’s medium (N—number of specimens/structures measured, RANGE refers to the smallest and the largest structure among all measured eggs; SD—standard deviation).

Measurements [in µm] of selected morphological structures of eggs of Macrobiotus sharopovi sp. nov. mounted in Hoyer’s medium (N – number of specimens/structures measured, RANGE refers to the smallest and the largest structure among all measured eggs; SD – standard deviation).

Measurements [in µm] and pt values of selected morphological structures of individuals of Macrobiotus sharopovi sp. nov., mounted in Hoyer’s medium (N – number of specimens/structures measured; RANGE refers to the smallest and the largest structure among all measured specimens; SD – standard deviation, pt – ratio of the length of a given structure to the length of the buccal tube expressed as a percentage,? – lack of measurements due to unsuitable position of the structure).