Detection Of Spatiotemporal Patterns Research Articles

Isotopes prove advanced, integral crop production, and stockbreeding strategies nourished Trypillia mega-populations

After 500 y of colonizing the forest-steppe area northwest of the Black Sea, on the territories of what is today Moldova and Ukraine, Trypillia societies founded large, aggregated settlements from ca. 4150 BCE and mega-sites (>100 ha) from ca. 3950 BCE. Covering up to 320 ha and housing up to 15,000 inhabitants, the latter were the world's largest settlements to date. Some 480 δ13C and δ15N measurements on bones of humans, animals, and charred crops allow the detection of spatio-temporal patterns and the calculation of complete agricultural Bayesian food webs for Trypillia societies. The isotope data come from settlements of the entire Trypillia area between the Prut and the Dnieper rivers. The datasets cover the development of the Trypillia societies from the early phase (4800-4200/4100 BCE), over the agglomeration of mega-sites (4200/4100-3650 BCE), to the dispersal phase (3650-3000 BCE). High δ15N values mostly come from the mega-sites. Our analyses show that the subsistence of Trypillia mega-sites depended on pulses cultivated on strongly manured (dung-)soils and on cattle that were kept fenced on intensive pastures to easy collect the manure for pulse cultivation. The food web models indicate a low proportion of meat in human diet (approximately 10%). The largely crop-based diet, consisting of cereals plus up to 46% pulses, was balanced in calories and indispensable amino acids. The flourishing of Europe's first mega-populations depended on an advanced, integral mega-economy that included sophisticated dung management. Their demise was therefore not economically, but socially, conditioned [Hofmann et al., PLoS One. 14, e0222243 (2019)].

Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis.

Numerous investigations of the spatiotemporal patterns of infectious disease epidemics, their potential influences, and their driving mechanisms have greatly contributed to effective interventions in the recent years of increasing pandemic situations. However, systematic reviews of the spatiotemporal patterns of communicable diseases are rare. Using bibliometric analysis, combined with content analysis, this study aimed to summarize the number of publications and trends, the spectrum of infectious diseases, major research directions and data-methodological-theoretical characteristics, and academic communities in this field. Based on 851 relevant publications from the Web of Science core database, from January 1991 to September 2021, the study found that the increasing number of publications and the changes in the disease spectrum have been accompanied by serious outbreaks and pandemics over the past 30 years. Owing to the current pandemic of new, infectious diseases (e.g., COVID-19) and the ravages of old infectious diseases (e.g., dengue and influenza), illustrated by the disease spectrum, the number of publications in this field would continue to rise. Three logically rigorous research directions-the detection of spatiotemporal patterns, identification of potential influencing factors, and risk prediction and simulation-support the research paradigm framework in this field. The role of human mobility in the transmission of insect-borne infectious diseases (e.g., dengue) and scale effects must be extensively studied in the future. Developed countries, such as the USA and England, have stronger leadership in the field. Therefore, much more effort must be made by developing countries, such as China, to improve their contribution and role in international academic collaborations.

Fiber-Optic Distributed Sensing Network for Thermal Mapping of Gold Nanoparticles-Mediated Radiofrequency Ablation.

In this work, we report the design of an optical fiber distributed sensing network for the 2-dimensional (2D) in situ thermal mapping of advanced methods for radiofrequency thermal ablation. The sensing system is based on six high-scattering MgO-doped optical fibers, interleaved by a scattering-level spatial multiplexing approach that allows simultaneous detection of each fiber location, in a 40 × 20 mm grid (7.8 mm2 pixel size). Radiofrequency ablation (RFA) was performed on bovine phantom, using a pristine approach and methods mediated by agarose and gold nanoparticles in order to enhance the ablation properties. The 2D sensors allow the detection of spatiotemporal patterns, evaluating the heating properties and investigating the repeatability. We observe that agarose-based ablation yields the widest ablated area in the best-case scenario, while gold nanoparticles-mediated ablation provides the best trade-off between the ablated area (53.0–65.1 mm2, 61.5 mm2 mean value) and repeatability.

Cartography and geographic information science at Penn State

Current research in the Cartographic and Geographic Information Sciences at Penn State emphasizes map design, geovisual analytics, spatiotemporal pattern detection, spatial cognition, CyberGIS, geographic information retrieval, image analysis, and novel methods for geospatial education. Cartography and GIScience research and education at Penn State is organized through groups such as the GeoVISTA Center and the Peter R. Gould Center in the Department of Geography, the Institute for CyberScience, and the John A. Dutton e-Education Institute. Recent achievements in these units have included advances in extracting geographic information from social media, novel methods for pattern analysis from event data, and the development of the first massive open online course in geography.

An Unsupervised Approach to Learning and Early Detection of Spatio-Temporal Patterns Using Spiking Neural Networks

This paper addresses the problem of learning and recognizing spatio-temporal patterns, which are typically encountered when representing gestures or other human actions. Existing approaches to learning such patterns are typically supervised, rely on extensive amounts of training data and require the observation of the entire pattern for recognition. We propose an approach that brings the following main contributions: i) it learns the patterns in an unsupervised manner, ii) it uses a very small number of training samples, and iii) it enables early classification of the pattern from observing only a small fraction of the pattern. The proposed method relies on spiking networks with axonal conductance delays, which learn encoding of individual patterns as sets of polychronous neural groups. Classification is performed using a similarity metric between sets, based on a modified version of the Jaccard index. The approach is evaluated on a data set of hand-drawn digits that encode the temporal information on how the digit has been drawn. In addition, the method is compared with three other standard pattern classification methods: support vector machines, logistic regression with regularization and ensemble neural networks, all trained with the same data set. The results show that the proposed approach can successfully learn these patterns from a significantly small number of training samples, can identify patterns before their completion, and it performs better than or comparable with the three other supervised methods.