University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Higher education. The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

Are lizards feeling the heat? A tale of ecology and evolution under two temperatures

Abstract: Aim Temperature influences most components of animal ecology and life history ‐ but what kind of temperature? Physiologists usually examine the influence of body temperatures, while biogeographers and macroecologists tend to focus on environmental temperatures. We aim to examine the relationship between these two measures, to determine the factors that affect lizard body temperatures and to test the effect of both temperature measures on lizard life history. Location World-wide. Methods We used a large (861 species) global dataset of lizard body temperatures,and the mean annual temperatures across their geographic ranges to examine the relationships between body and mean annual temperatures.We then examined factors influencing body temperatures, and tested for the influence of both on ecological and life-history traits while accounting for the influence of shared ancestry. Results Body temperatures and mean annual temperatures are uncorrelated. However, accounting for activity time (nocturnal species have low body temperatures), use of space (fossorial and semi-aquatic species are ‘colder’), insularity (mainland species are ‘hotter’) and phylogeny, the two temperatures are positively correlated. High body temperatures are only associated with larger hatchlings and increased rates of biomass production. Annual temperatures are positively correlated with clutch frequency and annual longevity, and negatively correlated with clutch size, age at first reproduction and longevity.

Stress Detection Using Wearable Physiological and Sociometric Sensors.

Abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.