Lowland Maya civilization flourished in the tropical region of the Yucatan peninsula and environs for more than 2500 years (~1000 BCE to 1500 CE). Known for its sophistication in writing, art, architecture, astronomy, and mathematics, Maya civilization still poses questions about the nature of its cities and surrounding populations because of its location in an inaccessible forest. In 2016, an aerial lidar survey across 2144 square kilometers of northern Guatemala mapped natural terrain and archaeological features over several distinct areas. We present results from these data, revealing interconnected urban settlement and landscapes with extensive infrastructural development. Studied through a joint international effort of interdisciplinary teams sharing protocols, this lidar survey compels a reevaluation of Maya demography, agriculture, and political economy and suggests future avenues of field research.

Airborne LiDAR produced large amounts of data for archaeological research over the past decade. Labeling this type of archaeological data is a tedious process. We used a data set from Pacunam LiDAR Initiative survey of lowland Maya region in Guatemala. The data set contains ancient Maya structures that were manually labeled, and ground verified to a large extent. We have built and compared two deep learning-based models, U-Net and Mask R-CNN, for semantic segmentation. The segmentation models were used in two tasks: identification of areas of ancient construction activity, and identification of the remnants of ancient Maya buildings. The U-Net based model performed better in both tasks and was capable of correctly identifying 60–66% of all objects, and 74–81% of medium sized objects. The quality of the resulting prediction was evaluated using a variety of quantifiers. Furthermore, we discuss the problems of re-purposing the archaeological style labeling for production of valid machine learning training sets. Ultimately, we outline the value of these models for archaeological research and present the road map to produce a useful decision support system for recognition of ancient objects in LiDAR data.

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A method for the determination of acrylamide traces as a residue of anti-incrustation agents in sugar was developed. Acrylamide was extracted into ethyl acetate after derivatization by bromination to 2,3-dibromopropionamide (2,3-DBPA). The extract was cleaned up on a silica gel column and analysed by gas-liquid chromatography with an alkali flame-ionization detector (AFID). A glass capillary column with OV-1 stationary phase was used for this analysis. The recovery of the method determined in model experiments was 70.0% and 77.1% at 100 micrograms kg-1 and 20 micrograms kg-1, respectively. The limit of detection of acrylamide in sugar is 1-10 micrograms kg-1. Parameters of the described method are compared with other published methods.

Selected volatile compounds in the most widely produced Slovak varietal white wines, Welschriesling, Gru È ner Veltliner and Mu È ller Thurgau, were analysed over a 3 year period. The study aimed to ®nd a combination of volatiles that could serve as an input for the classi®cation of these wines using multivariate data analysis. Signi®cant differences among the wine samples as regards variety, producer and vintage were initially examined using ANOVA. Two classi®cation techniques were also employed, namely linear (LDA) and quadratic (QDA) discriminant analyses. LDA revealed ®ve volatile compounds that enabled a satisfactory classi®cation of the wines by variety, and 10 volatiles for the classi®cation of producers. QDA was slightly less successful in distinguishing the varieties; however, the classi®cation of producers was comparable with the LDA results when using a signi®cantly smaller number of variables.