Mapping of bioavailable strontium isotope ratios in France for archaeological provenance studies

Strontium isotope ratios (87Sr/86Sr) of archaeological samples (teeth and bones) can be used to track mobility and migration across geologically distinct landscapes. However, traditional interpolation algorithms and classification approaches used to generate Sr isoscapes are often limited in predicting multiscale 87Sr/86Sr patterning. Here we investigate the suitability of plant samples and soil leachates from the IRHUM database (www. irhumdatabase.com) to create a bioavailable 87Sr/86Sr map using a novel geostatistical framework. First, we generated an 87Sr/86Sr map by classifying 87Sr/86Sr values into five geologically representative isotope groups using cluster analysis. The isotope groups were then used as a covariate in kriging to integrate prior geological knowledge of Sr cycling with the information contained in the bioavailable dataset and enhance 87Sr/86Sr predictions. Our approach couples the strengths of classification and geostatistical methods to generate more accurate 87Sr/86Sr predictions (Root Mean Squared Error=0.0029) with an estimate of spatial uncertainty based on lithology and sample density. This bioavailable Sr isoscape is applicable for provenance studies in France, and the method is transferable to other areas with high sampling density. While our method is a step forward in generating accurate 87Sr/86Sr isoscapes, the remaining uncertainty also demonstrates that finemodelling of 87Sr/86Sr variability is challenging and requires more than geological maps for accurately predicting 87Sr/86Sr variations across the landscape. Future efforts should focus on increasing sampling density and developing predictive models to further quantify and predict the processes that lead to 87Sr/86Sr variability.

The Viral Founder Effect and Economic-Driven Human Mobility Shaped the Distinct Epidemic Pattern of HIV-1 CRF01_AE in Northeast China

Background: In China, two distinct lineages shaped the epidemic of HIV-1 CRF01_AE among men who have sex with men (MSM), of which the uneven distributions were observed geographically. One lineage spread across China, while another dominated in Northeast China. Understanding the drivers of viral diffusion would provide guidelines for identifying the source and hotspots of HIV transmission among MSM to target interventions in China.Methods: We collected the pol sequences between 2002–2017 to reconstruct the spatiotemporal history of CRF01_AE lineages in Shenyang, one economic center of Northeast China, using the Bayesian phylogeographic and phylodynamic approaches. Importantly, for the datasets with the high sample density, we did the down-sampling to avoid the sampling bias.Results: Two lineages accounted for 97%, including 426 and 1516 sequences, and homosexuals and bisexuals were above 80%. One lineage appeared earlier 7 years than another (1993 vs. 2002) among homosexuals and bisexuals, whereas among heterosexuals, both lineages were observed firstly in 2002. 96% viral migrations within one lineage were from homosexuals toward bisexuals (49%) and male-heterosexuals (46%). Within another, except for homosexuals (72%), bisexuals (23%) served as the top second source, and female-heterosexuals (11%) were the third recipients following bisexuals (44%) and male-heterosexuals (39%). Although the basic reproduction number (R0) of two lineages were similar and both of the effective production number (Re) fell below 1 at the most recent sampling time, the starts of the Re declining varied.Conclusions: Our findings revealed that throughout the viral national spread chain, Shenyang is the source for the initial expanding of one lineage, where is only a sink of another, proving that the viral founder effect and regional human mobility contributed to the uneven distribution of two lineages, and emphasizing the important roles of the area where the virus originated and economy-driven migrants in HIV transmission.

Geometric estimates of variations in space geodetic data

Well-known statistical parameters have some disadvantages when analyzing space geodetic data. Geometric parameters are proposed here for estimating the variation properties of samples for various discrete datasets. The proposed parameters are logically related to each other and are based on the simplest well-known statistical parameters; they do not depend on the type of distribution of the sample under study. “Variation asymmetry” shows the shift of the arithmetic mean relative to the center of the variation interval in the units of the studied sample. “Density of variation” characterizes the level of average variability in sample units. This parameter has several times greater discriminatory sensitivity to extremely different types of variations than linear and standard deviations. The relative parameter “proportion of maximum density” shows the closeness of variation to a uniform distribution in the ranked sample and complements the indicator of variation density. An algorithm for separating different structural levels of the useful signal from emissions (noise) is proposed here based on the calculation of geometric characteristics. The iterations of dividing the sample into structurally homogeneous segments can be stopped at the level of the proportion of maximum density ≥0.9 when analyzing real GPS coordinates.

‘TeeBot’: A High Throughput Robotic Fermentation and Sampling System

When fermentation research requires the comparison of many strains or conditions, the major bottleneck is a technical one. Microplate approaches are not able to produce representative fermentative performance due to their inability to truly operate anaerobically, whilst more traditional methods do not facilitate sample density sufficient to assess enough candidates to be considered even medium throughput. Two robotic platforms have been developed that address these technological shortfalls. Both are built on commercially available liquid handling platforms fitted with custom labware. Results are presented detailing fermentation performance as compared to current best practice, i.e., shake flasks fitted with airlocks and sideports. The ‘TeeBot’ is capable sampling from 96 or 384 fermentations in 100 mL or 30 mL volumes, respectively, with airlock sealing and minimal headspace. Sampling and downstream analysis are facilitated by automated liquid handling, use of 96-well sample plate format and temporary cryo-storage (<0 °C).

A Physics-Driven CNN Model for Real-Time Sea Waves 3D Reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpredictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigating the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC.

Introducing Retro Products İn The Sports Industry With Augmented Reality Applications

The sports industry has caused differentiation in marketing strategies with the effect of globalization. As a result of this differentiation, manufacturers have put the demands of their customers at the center of production. In the study; It is aimed to reveal the interaction elements within the scope of the retro marketing application area activities in the promotional videos of the Nike brand to impress people. The sample of the study consists of 4 promotional videos: Nike Air Max 2090 Cloud, Explore Air Force I City, Build Your Nike Air Max 2090, Nike Air Max 720 Day, featuring augmented reality technology with the retro products of the Nike brand. Density sampling method was used while determining the sample. Density sampling covers the best or most information-rich examples of the investigated phenomenon, rather than extreme or unusual cases. "Basic Qualitative Research Model" was applied as a research method. The main purpose of basic qualitative research is to reveal and interpret the meanings created by people. As a pattern in the study; document analysis technique was used. Documents used in qualitative research consist of four types: Public records and personal documents are the first and second types, popular culture documents are the third type, and visual documents including movies, videos, and pictures are the fourth type. This study consists of videos in the visual documents section of the fourth type. Analysis of the data obtained in our study; It was carried out according to 5 main titles and 10 sub-titles within the framework of "Retro Marketing Application Area in Sports" As a result; The Nike brand used augmented reality technology to present its products with a combination of old and new features to its consumers within the framework of retro marketing and to enable them to establish an emotional connection with the past. Under the title of images of the retro marketing application field in sports; It was observed that the redesign of AirMax and Air Force shoe models, which are identified with the brand and the titles of giveaways and themed games/events were emphasized in game day promotions. Keywords: Sports Industry, Communication, Retro Marketing, Augmented Reality, Promotion.

A Sequential Sampling Generation Method for Multi-fidelity Model Based on Voronoi Region and Sample Density

Multi-fidelity surrogate model-based engineering optimization has received much attention because it alleviates the computational burdens of expensive simulations or experiments. However, due to the nonlinearity of practical engineering problems, the initial sample set selected to produce the first set of data will almost inevitably miss certain features of the landscape, and thus the construction of a useful surrogate often requires further, judicious infilling of some new samples. Sequential sampling strategies used to select new infilling sample during each iteration can gradually extend the dataset and improve the accuracy of the initial model with an acceptable cost. In this paper, a sequential sampling generation method based on the Voronoi region and the sample density, terms as SSGM-VRDS, is proposed. First, with a Monte Carlo-based approximation of a Voronoi tessellation for region division, Pearson correlation coefficients and cross validation (CV) are employed to determine the candidate Voronoi region for infilling a new sample. Then, a relative sample density is defined to identify the position of the new infilling point at which the sample are the sparsest within the selected Voronoi region. A correction of this density is carried out concurrently through an expansion coefficient. The proposed method is applied to three numerical numerical functions and a lightweight design problem via finite element analysis (FEA). Results suggest that the SSGM-VRDS strategy has outstanding effectiveness and efficiency in selecting a new sample for improving the accuracy of a surrogate model, as well as practicality for solving practical optimization problems.

SPATIAL DEPENDENCY OF SOIL CHEMICAL IN PRODUCTION SYSTEMS IN THE ANTHROPOGENIC DARK EARTH

Anthrosols refers to anthropic soils of high fertility in general, but the concentration of these nutrients may vary according to the occupation of indigenous people in the past or due to current soil use. This study aimed to evaluate the spatial variability of the chemical attributes of the soil in areas of guandu bean production and pasture and to compare with natural forest systems on Anthropogenic dark earth (ADE). For this assessment, 88 sampling points were selected in the area with natural forest vegetation and pasture and 90 sampling points in an area of guandu bean production. Soil samples were collected from layers 0.00-0.05, 0.05-0.10 and 0.10-0.20 m. Chemical analyses of the soil were conducted to determine organic matter, pH, Aluminium (Al3+), soil acidity (H+Al), phosphorus (P), potassium (K+), Calcium (Ca2+), Magnesium (Mg2+), cation exchange capacity (CEC), sum of bases (SB) and base saturation (V%). Data were analyzed using descriptive statistics and geostatistics to sample range, sample density was estimated for each attribute. Guandu bean showed high content of soil organic matter in relation to pasture in the superficial layer (0.00-0.05 m). Based on sample density, lower variability and higher spatial continuity were observed for guandu bean in relation to pasture and natural forest in the layers of 0.00-0.05 and 0.05-0.10 m. It was found that the use and continuous management of ADE’s areas alter the content and distribution of soil fertility and in some cases may even improve chemical attributes when compared to areas not used with agricultural crops.

Suscetibilidade magnética na caracterização da variabilidade espacial de atributos do solo em solos subtropicais

Magnetic susceptibility (MS) has been used to estimate soil attributes. With the proposal to increase the information of soils in southern Brazil the objectives of this work were: (i) evaluate the correlation of SM with soil attributes in a slope of subtropical basaltic soils; (ii) to characterize the spatial variation structure of MS and the content of sand, clay and COS; and (iii) identify the sample density that captures the spatial variability to assist future work under similar conditions. In a 22 ha area located in Santo Augusto - RS, Brazil, an 87 points sample grid was collected to determine soil attributes. Samples were also collected in five profiles along the slope. The profile data were analyzed by correlation to verify the degree of Pearson correlation of the SM with the attributes of the soil. In the sample grid spatial dependence analyzes were performed to assess the degree of spatial dependence on soil attributes. The MS presented a high correlation with the attributes of clay soil, Fes, Fed and COS. The evaluation of the spatial variation structure showed that the attributes presented a degree of spatial dependence ranging from weak for COS to strong for MS. The spatial variability pattern suggests a sample density of one point every 4 to 12 ha.

Planning a geostatistical survey for mapping soil micronutrients: eliciting sampling densities

&lt;p&gt;When planning a geochemical survey, it is necessary to make decisions about the sampling density. Sampling density determines both the quality of predictions and the cost of field work. In geostatistical surveys, the relationship between sampling density and map quality, as measured by the kriging variance (mean square error of the prediction) can be computed. When the variogram is known, then the kriging variance at an unsampled site depends only on the spatial distribution of sampling points around that site. It is therefore possible to find the sample density such that the kriging variance is limited to acceptable values. However, the implications of kriging variances are not always straightforward for decision makers or sponsors of survey to understand. Here we present an alternative method to help end-users assess the implications of uncertainty in spatial prediction in so far as this is controlled by sampling.&amp;#160; It is called the offset correlation and is a measure of how far the mapped spatial variation depends on the positioning of a reqular square sampling grid.&amp;#160; The offset correlation increases as the uncertainty in the map, attributable to sample density, decreases.&amp;#160; It is bounded on the interval [0,1], which makes it intuitively easy to interpret as an uncertainty measure.&amp;#160; In this presentation we shall explain the offset correlation concept, illustrate it with some test cases, and provide session participants with an opportunity to join an elicitation of sampling density for a hypothetical survey of soil micronutrient status.&lt;/p&gt;&lt;p&gt;The offset correlation is an intuitive measure of the precision of a geostatistical mapping process because people can more easily grasp bounded measures like a correlation than unbounded ones like a variance.&lt;/p&gt;