The mechanisms and drivers of lithification in slag‐dominated artificial ground

Unconsolidated artificial ground is an ever-increasing feature on the Earth's surface but it poses various challenges such as pollutant release and ground instability. The process of lithification could be an important factor in changing the properties of artificial ground and ameliorating these challenges. In this study, a lithified deposit of a furnace slag associated with a former iron and steel works in Scotland was analysed to determine the mechanisms and drivers of lithification. Scanning Electron Microscope analysis showed that Ca leached from around the edges of clasts of slag through reaction of the chemically unstable slag with water from an adjacent water body. Dissolution of Ca (and OH-) from the slag caused the water in contact with the slag to become hyperalkaline, facilitating ingassing and hydroxylation of CO2 from the atmosphere (fingerprinted through carbon isotope analysis). Reaction of the dissolved Ca and CO2 led to precipitation of calcite. Scanning Electron Microscope analysis showed the calcite is distributed between slag clasts, forming rims around clasts and cementing clasts together into a solid rock-like mass. Understanding the mechanisms and drivers of lithification in artificial ground will be important, given its widespread nature particularly in urban areas where artificial ground is the substrate of most development

Understanding ‘saturation’ of radar signals over forests

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points

The effectiveness and cost-effectiveness of the ‘Walk with Me’ peer-led walking intervention to increase physical activity in inactive older adults:Study protocol for a randomised controlled trial

Background: The proportion of the population aged 65 years or older is increasing. Typically, physical activity and health decline with age, which is why action to promote active ageing is a major public health priority, particularly due to health inequalities in older adults. The aim of this study is to assess the effectiveness and cost-effectiveness of the Walk with Me peer-led walking intervention for older adults. Methods: This study is a two-arm, assessor-blind, randomised controlled trial. The intervention is a 12-week peer-led walking intervention based on social cognitive theory. Participants in the control group will receive information on active ageing and healthy nutrition. The study will target 348 community-dwelling older adults, aged 60 years or over living in areas of socio-economic disadvantage communities. Trained peer mentors will deliver the intervention. The primary outcome will be a mean between-group change in moderate-to-vigorous physical activity at 12 months from baseline, measured using an Actigraph accelerometer. Secondary outcomes will include quality of life, mental wellbeing, blood pressure, BMI and waist circumference. An embedded process evaluation will involve focus groups and participant diaries. Discussion: Evidence-based, cost-effective interventions to promote physical activity in older adults living in socio-economically disadvantaged communities are needed to address health inequalities

Linking soils and human health: geospatial analysis of ground-sampled soil data in relation to community-level podoconiosis data in North West Cameroon

Background Podoconiosis is a form of leg swelling, which arises when individuals are exposed over time to red clay soil formed from alkaline volcanic rock. The exact causal agent of the disease is unknown. This study investigates associations between podoconiosis disease data and ground-sampled soil data from North West Cameroon. Methods The mineralogy and elemental concentrations were measured in the soil samples and the data were spatially interpolated. Mean soil values were calculated from a 3 km buffer region around the prevalence data points to perform statistical analysis. Analysis included Spearman's rho correlation, binary logistic regression and principal component analysis (PCA). Results Six elements, barium, beryllium, potassium, rubidium, strontium and thallium, as well as two minerals, potassium feldspar and quartz, were identified as statistically related to podoconiosis. PCA did not show distinct separation between the spatial locations with or without recorded cases of podoconiosis, indicating that other factors such as shoe-wearing behaviour and genetics may significantly influence podoconiosis occurrence and prevalence in North West Cameroon. Conclusion Several soil variables were statistically significantly related to podoconiosis. To further the current study, future investigations will look at the inflammatory pathway response of cells after exposure to these variables

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Data and metadata are stored on the Open Science Framework website [https://osf.io/mhg94/]

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts. Data and metadata are stored on the Open Science Framework website (https://osf.io/mhg94/).</p&gt

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset)

This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments’ psychometric properties across different languages and contexts