Source regions of infragravity waves recorded at the bottom of the equatorial Atlantic Ocean, using OBS of the PI‐LAB experiment

Infragravity waves are generated along coasts, and some small fraction of their energy escapes to the open oceans and propagates with little attenuation. Due to the scarcity of deep‐ocean observations of these waves, the mechanism and the extent of the infragravity waves energy leakage from the coasts remains poorly understood. Understanding the generation and pathways of infragravity wave energy is important among others for understanding the breakup of ice‐shelves and the contamination of high‐resolution satellite radar altimetry measurements of sea level. We examine data from 37 differential pressure gauges of Ocean Bottom Seismometers (OBS) near the equatorial mid‐Atlantic ridge, deployed during the Passive Imaging of the Lithosphere‐Asthenosphere Boundary (PI‐LAB) experiment. We use the beamforming technique to investigate the incoming directions of infragravity waves. Next, we develop a graph‐theory‐based global back‐projection method of noise cross‐correlation function envelopes, which minimizes the effects of array geometry using an adaptive weighting scheme. This approach allows us to locate the sources of the infragravity energy. We assess our observations by comparing to a global model of infragravity wave heights. Our results reveal strong coherent energy from sources and/or reflected phases at the west coast of Africa and some sources from South America. These energy sources are in good agreement with the global infragravity wave model. In addition, we also observe infragravity waves arriving from North America during specific events that mostly occur during October–February 2016. Finally, we find indications of waves that propagate with little attenuation, long distances through sea ice, reflecting off Antarctica

A self-determination approach to understanding of athletes' automatic self-talk

Grounded on self-determination theory, this study aimed to explore the relationships between basic needs satisfaction, motivational regulations, and athletes' automatic self-talk, and examine the potential mediating role of autonomous and controlled motivation in the relationships between basic needs satisfaction and athletes' automatic self-talk. A crosssectional design was employed. Athletes (N = 381; mean age 16.54) from a variety of individual (n = 132) and team sports (n = 249) completed a multisection questionnaire tapping the targeted variables. Structural equation modeling was used to test the hypothesized relationships through a direct, a fully mediated, and a partially mediated model. The analyses provided best support for the partially mediated model which showed that (a) the relationships between the need for competence and athletes' positive and negative self-talk were partially mediated by autonomous motivation; (b) the relationships between the need for relatedness and athletes' positive and negative self-talk were fully mediated by autonomous motivation; (c) the relationship between the need for autonomy and positive self-talk was partially mediated by autonomous motivation, whereas the relationship between the need for autonomy and negative self-talk was fully mediated by autonomous and controlled motivation. Overall, our self-determination theory-derived hypotheses were largely supported. These findings stress the importance of basic need satisfaction and motivational regulations, as factors that influence athletes' automatic self talk and suggest that sport environments which support athletes' basic psychological needs are likely to foster more autonomous motivation toward sport and maximize athletes' positive self-talk, while minimizing negative self-talk. © 2017 American Psychological Association

Towards the use of artificial intelligence deep learning networks for detection of archaeological sites

Funder: Daphne Jackson Trust; doi: https://doi.org/10.13039/501100000643Funder: National Environment Research CouncilAbstract While remote sensing data have long been widely used in archaeological prospection over large areas, the task of examining such data is time consuming and requires experienced and specialist analysts. However, recent technological advances in the field of artificial intelligence (AI), and in particular deep learning methods, open possibilities for the automated analysis of large areas of remote sensing data. This paper examines the applicability and potential of supervised deep learning methods for the detection and mapping of different kinds of archaeological sites comprising features such as walls and linear or curvilinear structures of different dimensions, spectral and geometrical properties. Our work deliberately uses open-source imagery to demonstrate the accessibility of these tools. One of the main challenges facing AI approaches has been that they require large amounts of labeled data to achieve high levels of accuracy so that the training stage requires significant computational resources. Our results show, however, that even with relatively limited amounts of data, simple eight-layer, fully convolutional network can be trained efficiently using minimal computational resources, to identify and classify archaeological sites and successfully distinguish them from features with similar characteristics. By increasing the number of training sets and switching to the use of high-performance computing the accuracy of the identified areas increases. We conclude by discussing the future directions and potential of such methods in archaeological research.</jats:p

Analytical study of ancient (6th-4th cent.BC) mortars from Aiani, ancient Upper Macedonia, Greece.

The study is focused on the microstructural, chemical and mineralogical characteristics of aracheological mortars from Aiani, ancient Upper Macedonia, Greece in the frame of the ongoing archaeological research in this region. The mortars were dated back to archaic, classical and hellenistic times. The qualitative and quantitavive characterization of ancient mortar were perfomed by XRD, XRF, ESEM-EDS, FTIR, DSC/TGA/DTA, EMPA and optical microscopy.Peer Reviewe

Source regions of infragravity waves recorded at the bottom of the equatorial Atlantic Ocean, using OBS of the PI‐LAB experiment

Infragravity waves are generated along coasts, and some small fraction of their energy escapes to the open oceans and propagates with little attenuation. Due to the scarcity of deep‐ocean observations of these waves, the mechanism and the extent of the infragravity waves energy leakage from the coasts remains poorly understood. Understanding the generation and pathways of infragravity wave energy is important among others for understanding the breakup of ice‐shelves, and the contamination of high‐resolution satellite radar altimetry measurements of sea level. We examine data from 37 differential pressure gauges of Ocean Bottom Seismometers (OBS) near the equatorial mid‐Atlantic ridge, deployed during the Passive Imaging of the Lithosphere‐Asthenosphere Boundary (PI‐LAB) experiment. We use the beamforming technique to investigate the incoming directions of infragravity waves. Next, we develop a graph‐theory‐based global back‐projection method of noise cross‐correlation function envelopes, which minimizes the effects of array geometry using an adaptive weighting scheme. This approach allows us to locate the sources of the infragravity energy. We assess our observations by comparing to a global model of infragravity wave heights. Our results reveal strong coherent energy from sources and/or reflected phases at the west coast of Africa and some sources from South America. These energy sources are in good agreement with the global infragravity wave model. In addition, we also observe infragravity waves arriving from North America during specific events that mostly occur during Oct‐Feb 2016. Finally, we find indications of waves that propagate with little attenuation, long distances through sea ice, reflecting off Antarctica

Fusion of geophysical images in the study of archaeological sites

In the last few years, the idea of combining images, called image fusion, appeared and it has become a critical area of research and development. Image fusion can be defined as the process of combining images, taken from the same scene and create one single image containing all the essential information of the original images. A single sensor is not always sufficient. Different sensors, effective in different environmental conditions, provide different information of a scene. The underlying idea in this article is to combine geophysical images taken with different sensors, from the same location, aiming to improve the detectability of possible archaeological targets. Three different fusion approaches were used; fusion by calculating the average of the individual images, and through the use of wavelet and curvelet transforms. Furthermore, taking advantage of the curvelet domain we exploit possible prior angle information to enhance the angles where the remnants are expected. We applied the methods in seven different pairs of geophysical images taken from two different archaeological areas. In all cases the fused images produced significantly better results than each of the original geophysical images separately

Ancient mitochondrial diversity reveals population homogeneity in Neolithic Greece and identifies population dynamics along the Danubian expansion axis

The aim of the study is to investigate mitochondrial diversity in Neolithic Greece and its relation to hunter-gatherers and farmers who populated the Danubian Neolithic expansion axis. We sequenced 42 mitochondrial palaeogenomes from Greece and analysed them together with European set of 328 mtDNA sequences dating from the Early to the Final Neolithic and 319 modern sequences. To test for population continuity through time in Greece, we use an original structured population continuity test that simulates DNA from different periods by explicitly considering the spatial and temporal dynamics of populations. We explore specific scenarios of the mode and tempo of the European Neolithic expansion along the Danubian axis applying spatially explicit simulations coupled with Approximate Bayesian Computation. We observe a striking genetic homogeneity for the maternal line throughout the Neolithic in Greece whereas population continuity is rejected between the Neolithic and present-day Greeks. Along the Danubian expansion axis, our best-fitting scenario supports a substantial decrease in mobility and an increasing local hunter-gatherer contribution to the gene-pool of farmers following the initial rapid Neolithic expansion. Οur original simulation approach models key demographic parameters rather than inferring them from fragmentary data leading to a better understanding of this important process in European prehistory