Large Time Span Research Articles

Modeling and evolution analysis of patent competition strategy system of AI enterprises

The maintenance and strategy operation after patent licensing can bring great market competitiveness and benefits to enterprises. But the large time span from patent licensing to market application makes it challenging to discern the benefits of patent competition strategy. Besides, artificial intelligence (AI) is an emerging industry without ready-to-use experience to formulate patent competition strategy, and particularly current researches have not designed patent competition strategy from the micro patent management perspective of AI enterprises to solve the uncertainty caused by the lag of market application relative to patent licensing. This research builds an expert group discriminant system based on the system dynamics method to address this problem. It integrates expert tacit knowledge to determine the fuzzy variable value and the fuzzy relationship. The patent competition strategy subsystem in national dimension, industry dimension and enterprise dimension for capturing the market from the perspective of enterprise technology competitiveness are constructed. By combining the three subsystems, the enterprise patent competition strategy system dynamics model with evolution analysis is established. Finally, taking typical Chinese AI enterprise iFLYTEK as an example, the multi-scenario simulation is carried out and the results under four different scenarios can provide effective decision supports for managers to formulate reasonable patent competition strategy and gain high market share. This research sheds light on modeling and evolution analysis of the patent competition strategy which comprehensively and systematically considers the operation mechanism of patent management and contributes to dealing with the uncertainty and ambiguity in the system dynamics model effectively.

Changes of In Situ Prokaryotic and Eukaryotic Communities in the Upper Sanya River to the Sea over a Nine-Hour Period

The transition areas of riverine, estuarine, and marine environments are particularly valuable for the research of microbial ecology, biogeochemical processes, and other physical-chemical studies. Although a large number of microbial-related studies have been conducted within such systems, the vast majority of sampling have been conducted over a large span of time and distance, which may lead to separate batches of samples receiving interference from different factors, thus increasing or decreasing the variability between samples to some extent. In this study, a new in situ filtration system was used to collect membrane samples from six different sampling sites along the Sanya River, from upstream freshwater to the sea, over a nine-hour period. We used high-throughput sequencing of 16S and 18S rRNA genes to analyze the diversity and composition of prokaryotic and eukaryotic communities. The results showed that the structures of these communities varied according to the different sampling sites. The α-diversity of the prokaryotic and eukaryotic communities both decreased gradually along the downstream course. The structural composition of prokaryotic and eukaryotic communities changed continuously with the direction of river flow; for example, the relative abundances of Rhodobacteraceae and Flavobacteriaceae increased with distance downstream, while Sporichthyaceae and Comamonadaceae decreased. Some prokaryotic taxa, such as Phycisphaeraceae and Chromobacteriaceae, were present nearly exclusively in pure freshwater environments, while some additional prokaryotic taxa, including the SAR86 clade, Clade I, AEGEAN-169 marine group, and Actinomarinaceae, were barely present in pure freshwater environments. The eukaryotic communities were mainly composed of the Chlorellales X, Chlamydomonadales X, Sphaeropleales X, Trebouxiophyceae XX, Annelida XX, and Heteroconchia. The prokaryotic and eukaryotic communities were split into abundant, common, and rare communities for NCM analysis, respectively, and the results showed that assembly of the rare community assembly was more impacted by stochastic processes and less restricted by species dispersal than that of abundant and common microbial communities for both prokaryotes and eukaryotes. Overall, this study provides a valuable reference and new perspectives on microbial ecology during the transition from freshwater rivers to estuaries and the sea.

Status and recent extensions of the Caribou DAQ system for picosecond timing with an FPGA TDC

Caribou is a flexible open-source DAQ system developed and used within several collaborative frameworks (CERN EP R&D, RD50, AIDAinnova) for laboratory and high-rate beam tests and easy integration of new silicon-pixel detector prototypes. It uses common hardware, firmware and software components that are shared across different projects, thereby reducing the development effort and cost for such readout systems significantly. This contribution introduces the FASTPIX, APTS and DPTS detectors recently integrated in Caribou and their requirements in terms of waveform sampling and timing of digital pulses. A Time-to-Digital-Converter implemented in the Caribou FPGA was developed to overcome the limitations of the previously used oscilloscope-based readout. It allows for precision timing over large time spans required for the Time-of-Arrival, Time-over-Threshold, and position encoding used in the FASTPIX and DPTS test chips with asynchronous digital readout. A time resolution after calibration of better than 10 ps is achieved.

Modeling days suitable for fieldwork using machine learning, process-based, and rule-based models

ContextPrediction of days suitable for fieldwork is important for understanding the potential effects of climate change and for selecting machinery systems to improve efficiency in field operations and avoid soil damage. Yet, we lack predictive models to inform decision-making at scale. ObjectiveWe filled this knowledge gap by developing and testing five new workability models. MethodsOne model follows soil moisture-based methods (APSIM), one uses simple rain and temperature thresholds, and three follow machine learning techniques (Random Forest, Decision Table, Neural Network). We parameterized the models using USDA survey data from Iowa, USA and evaluated their temporal and spatial prediction over twelve US Corn Belt States and different time periods using multiple statistical indexes and sensitivity analysis. The models operate at a 5-arcminute resolution. Results and conclusionsResults indicated that the simple rule model, the Decision Table model, and the process-based model predicted field workable days with an agreement index of 0.88, 0.86, and 0.84, respectively for the testing datasets (n = 22,671), and hence were deemed sufficient for future use. The selected models are better suited for large timespan evaluations of workability (monthly to annual, normalized root mean square error, nRMSE = 8 to 15%)) than weekly predictions (nRMSE = 21%). The machine learning models tended to cluster their predictions around a mean value and were about 50% less responsive to precipitation than the process-based or rule-based models. We concluded that simple approaches are more robust to be applied at scale than complex approaches with many data input requirements. SignificanceThe developed models enhance our capacity to predict climate change impacts on workability, a valuable indicator for decision-making and overall sustainability.

The Application of Augmented Reality Technology in Urban Greening Plant Growth State Detection

The current target detection network in deep learning has been widely used in plant growth state detection. However, with the development of deep learning, within the field of plant growth state detection, the performance of the detection network is no longer the primary factor limiting the detection accuracy and model generalization ability. The construction of high-quality and large-scale plant datasets is more significant for the improvement of model detection accuracy and generalization ability. However, traditional methods for building deep learning datasets for plants have a large time span and low efficiency. And it is difficult to construct and expand the dataset for plants with complex growth environments and difficult image acquisition by existing methods. To address this problem, this paper proposes a method for constructing plant datasets based on augmented reality techniques. The method proposed in this paper allows for the rapid and efficient construction of large-scale field datasets that match the actual inspection environment in the lack of data. Meanwhile, this paper proposes an automatic annotation method for datasets in conjunction with the imaging environment in virtual space. In this paper, we experimentally compare the proposed method with the method of expanding the dataset using GAN networks. Using the virtual dataset constructed by the method proposed in this paper as the training set, the trained YOLOv5 model achieves an average accuracy (@0.5:0.95) of 0.71 for the three detection categories on the test set. The detection accuracy of the six mixed datasets constructed using the two data expansion methods on the test set was experimentally tested. The proposed method in this paper improved the accuracy by 2.2%, 3.1%, and 7.0%, respectively. The smaller the percentage of real images, the greater the accuracy improvement. Experiments show that the method proposed in this paper can well solve the problems faced in the field of plant growth state detection, such as the lack of data, and provides a new idea for the production and expansion of datasets in plant detection tasks.

Modeling and simulation through multi-criteria approach for customized conveyor-type industrial oven handling wide variety of metal structures

Modern ovens are utilized from large span of time for completing different heat treatment cycles like baking, curing, annealing, aging, drying, and so on. There are numerous sorts of industrial ovens namely batch, relieving, drying, baking, reflow, clean room, conveyor types, and so on. Conveyor oven helps to perform the heat treatment cycle in an uninterrupted flow, including the heating of the objects followed by the cooling down of the same object. A multipurpose conveyor-type industrial oven is designed using analytical approach for heat treatment process on various objects. This article presents the modeling and CFD analysis of conveyor-type continuous ovens. Simulation of the entire model of conveyor-type industrial oven is carried out in Solidworks Flow package. Since the boundary conditions of an actual industrial oven is open at both entry and exit points, same condition is maintained in the CFD analysis. With the help of the simulation model, researchers can study and observe the temperature distribution throughout the oven.

Solution of burgers’ equation appears in fluid mechanics by multistage optimal homotopy asymptotic method

In this article, we approximate analytical solution of Burgers? equations using the multistage homotopy asymptotic method which are utilized in modern physics and fluid mechanics. The suggested algorithm is an accurate and simple to-utilize semi-analytic tool for non-linear problems. In the current research we investigation the efficiency and accuracy of the method for the solution of non-linear PDE for large time span. Numerical comparison with the variational iteration method shows the efficacy and accuracy of the proposed method.

Analysis of Economic Cost of Secondary Level Education in Nepal

Investing on educations seeks a large span of time to get its return. Investment on education is the prime factor for transformation of a society. Educational cost is a measure of what a student, an institution of learning, or the public must give up educating an individual or a group of people. The opportunity cost represents the sacrifice of alternative opportunities to use the resource, either for present consumption or for some other forms of investment. The educational system uses up other resources which have alternative uses, even though these are not reflected in normal expenditure on education. The most obvious example of the opportunity cost is the time of students, who deprive the labour market of their services by choosing to continue their education. This represents a loss of current output for the economy, as well as a loss of earnings for the individual. There are, of course, considerable problems involved in measuring the opportunity cost of students. It is also worth emphasizing that estimates of opportunity costs make sense only within a given institutional framework. The concept of opportunity cost and the technique of cost-benefit analysis are not, however, applicable to situations where a total change in the entire educational or economic structure is contemplated. Better educated population relates to lower expenditures in other public sectors: e.g., better educated people tend to use health system less often since they would possess more information and knowledge on sickness prevention, if faced with unemployment they tend to stay unemployed for shorter periods of time thus relying less on welfare support etc. Per unit cost been assessed NRs 30,000/-which comprises unit cost+ average drop-out cost+ direct cost of guardians.Opportunity cost is assessed NRs.58,333/-and total education cost is assessed NRs 88,333/- i.e., $803. The net return from education must exceed NRs 88,333/-.

The ancient farmstead at Lakkos (Kionia) on Tenos. An interdisciplinary study

This paper offers a preliminary presentation of the first, so far, known ancient farmstead on the island of Tenos in the Aegean Sea in Greece, confirming the significance of archaeobotanical research for the interpretation of archaeological data. At the site of Lakkos at Kionia, the building remains of an agricultural installation were discovered during a rescue excavation (2018–2019) conducted by the Ephorate of Antiquities of Cyclades of the Greek Ministry of Culture and Sports. The main period of use of the farmstead is dated in the Hellenistic and Roman eras (2nd century BCE-2nd century CE) covering a large span of time until its final abandonment during the 3rd and 4th centuries A.D. Both archaeological and archaeobotanical evidence confirm activities such as farming, oil, honey, and possibly wine production as well as processing and storage of goods at the site. Moreover, the discovery of an oil pressing installation elucidates aspects related to the oil extraction technology and the specialized agricultural industry in ancient Tenos.The study of Lakkos is expected to offer important new evidence regarding a series of understudied scientific issues related to the landscape, the agricultural economy, the rural habitation, the social organization, and the cultural significance of Tenos in antiquity.

Characteristics of the cognitive function transition and influencing factors among Chinese older people: An 8-year longitudinal study

BackgroundCognitive impairment among older people is an important public health problem in developing countries. Therefore, it is necessary to identify the subtypes of cognitive function among older people in China, and explore the transformation patterns and influencing factors. MethodsLongitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) were used, and included 2140 women and 2049 men aged over 60 years. Latent profile and latent transition analysis (LPA<A) were used to identify subgroups and transitions between the profiles over time. Influencing factors were identified by multinomial logistic regression analysis. ResultsAccording to the LPA model, three subgroups of cognitive function were identified: Cognitive Impairment, Mild Cognitive Impairment (MCI) and Normal Cognitive Function. Concurrently, >50 % of participants were likely to progress to MCI after seven years, whereas participants with cognitive impairment had a probability of 54.2 % of transitioning to a better cognitive profile. Older adults are less likely to experience cognitive improvement, higher levels of education affect changes in cognition, and having depression are at a lower risk of cognitive decline. LimitationsDue to the incompleteness of the cognitive assessment and the large time span, there was a certain bias in the classification and analysis of latent cognitive profiles. ConclusionThis study identified three latent profiles among Chinese older people and showed the stability and heterogeneity. It demonstrated the effects of higher age or levels of education, and depression on changes in cognitive function in older people.

Complete Chloroplast Genome Sequence of Endangered Species in the Genus Opisthopappus C. Shih: Characterization, Species Identification, and Phylogenetic Relationships.

Opisthopappus C. Shih is a rare genus of the Asteraceae family native to the Taihang Mountains in China. Due to the narrow distribution area, poor reproduction ability and human harvesting, Opisthopappus is threatened by extinction. However, the limited genetic information within Opisthopappus impede understanding of the conservation efforts and bioprospecting. Therefore, in this study, we reported the complete chloroplast (cp) genome sequences of two Opisthopappus species, including Opisthopappus taihangensis and Opisthopappus longilobus. The cp genomes of O. taihangensis and O. longilobus were 151,117 and 151,123 bp, which contained 88 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The repeat sequences, codon usage, RNA-editing sites, and comparative analyses revealed a high degree of conservation between the two species. The ycf1 gene was identified as a potential molecular marker. The phylogenetic tree demonstrated that O. longilobus was a separate species and not a synonym or variety of O. taihangensis. The molecular clock showed that two species diverge over a large time span, O. longilobus diverged at 15.24 Mya (Million years ago), whereas O. taihangensis diverged at 5.40 Mya We found that Opisthopappus and Ajania are closely related, which provides new ideas for the development of Opisthopappus. These results provide biological information and an essential basis to understand the evolutionary history of the Opisthopappus species, which will aid in the future the bioprospecting and conservation of endangered species.

Geological Hazards in Al-Anbar Governorate, West Iraq

Al-Anbar Governorate has the largest coverage area among the 18 Iraqi gover-norates. The coverage area is about 138 579 km2 forming about 32% of the whole coverage area of the Iraqi territory. The governorate forms the western part of Iraq representing the Iraqi Western Desert and a small part north of the Eu-phrates River within the Jazira Plain. This large coverage area with diversity of rock types with large time span (Permi-an – Pleistocene) and different topographic forms have contributed to develop-ment of different types of geological hazards, which act at different parts of the governorate with different intensities and different damages caused to the infra-structure and community, even life losses. Three main types of geological hazards are active in the governorate; these are: 1) Floods, 2) Karstification, and 3) Swell-ing (Expensive) clays; however, other types like mass movements, depressions, active tectonic areas are also present but with less significance as their risks and caused damages to the community and infrastructure is concerned.

Deep Learning Based Channel Estimation for OFDM Systems With Doubly Selective Channel

In wireless communication orthogonal frequency division multiplexing (OFDM) systems with high mobility, channel estimation becomes challenging due to the double selectivity of channels. To solve this problem, a deep learning based method which fully considers the channel features and noise effects is proposed in this letter. In the pilot-based channel estimation scheme, a compression-and-reconstruction channel estimation network (CRCENet) is designed to improve the channel estimation performance, which fuses not only the low-level features of the rapid changes between adjacent subcarriers in the short term and the high-level features of the overall channel changes under large time spans in the backbone, but also the features using the attention mechanism in the up-sampling branches. Meanwhile, considering that the samples with different signal-to-noise ratios (SNRs) have different credibility, loss weights based on credibility are assigned to data with different SNRs during network training, to further improve the estimation performance of the network. The experimental results revealed that CRCENet performed better than the traditional least squares estimation algorithm and state-of-the-art learning networks.

Wheat Stem Rust Back in Europe: Diversity, Prevalence and Impact on Host Resistance.

The objective of this study was to investigate the re-emergence of a previously important crop pathogen in Europe, Puccinia graminis f.sp. tritici, causing wheat stem rust. The pathogen has been insignificant in Europe for more than 60 years, but since 2016 it has caused epidemics on both durum wheat and bread wheat in local areas in southern Europe, and additional outbreaks in Central- and West Europe. The prevalence of three distinct genotypes/races in many areas, Clade III-B (TTRTF), Clade IV-B (TKTTF) and Clade IV-F (TKKTF), suggested clonal reproduction and evolution by mutation within these. None of these genetic groups and races, which likely originated from exotic incursions, were detected in Europe prior to 2016. A fourth genetic group, Clade VIII, detected in Germany (2013), was observed in several years in Central- and East Europe. Tests of representative European wheat varieties with prevalent races revealed high level of susceptibility. In contrast, high diversity with respect to virulence and Simple Sequence Repeat (SSR) markers were detected in local populations on cereals and grasses in proximity to Berberis species in Spain and Sweden, indicating that the alternate host may return as functional component of the epidemiology of wheat stem rust in Europe. A geographically distant population from Omsk and Novosibirsk in western Siberia (Russia) also revealed high genetic diversity, but clearly different from current European populations. The presence of Sr31-virulence in multiple and highly diverse races in local populations in Spain and Siberia stress that virulence may emerge independently when large geographical areas and time spans are considered and that Sr31-virulence is not unique to Ug99. All isolates of the Spanish populations, collected from wheat, rye and grass species, were succesfully recovered on wheat, which underline the plasticity of host barriers within P. graminis. The study demonstrated successful alignment of two genotyping approaches and race phenotyping methodologies employed by different laboratories, which also allowed us to line up with previous European and international studies of wheat stem rust. Our results suggest new initiatives within disease surveillance, epidemiological research and resistance breeding to meet current and future challenges by wheat stem rust in Europe and beyond.

Systematic errors on optical-SED stellar-mass estimates for galaxies across cosmic time and their impact on cosmology

Studying galaxies at different cosmic epochs entails several observational effects that need to be taken into account to compare populations across a large time-span in a consistent manner. We use a sample of 166 nearby galaxies that hosted type Ia supernovae (SNe Ia) and have been observed with the integral field spectrograph MUSE as part of the AMUSING survey. Here, we present a study of the systematic errors and bias on the host stellar mass with increasing redshift, which are generally overlooked in SNe Ia cosmological analyses. We simulate observations at different redshifts (0.1 &lt; z &lt; 2.0) using four photometric bands (griz, similar to the Dark Energy Survey-SN program) to then estimate the host galaxy properties across cosmic time. We find that stellar masses are systematically underestimated as we move towards higher redshifts, due mostly to different rest-frame wavelength coverage, with differences reaching 0.3 dex at z ∼ 1. We used the newly derived corrections as a function of redshift to correct the stellar masses of a known sample of SN Ia hosts and derive cosmological parameters. We show that these corrections have a small impact on the derived cosmological parameters. The most affected is the value of the mass step ΔM, which is reduced by ∼0.004 (6% lower). The dark energy equation of state parameter w changes by Δw∼ 0.006 (0.6% higher) and the value of Ωm increases at most by 0.001 (∼0.3%), all within the derived uncertainties of the model. While the systematic error found in the estimate of the host stellar mass does not significantly affect the derived cosmological parameters, it is an important source of systematic error that needs to be corrected for as we enter a new era of precision cosmology.

The Old Avesta and Birth of Iranian Identity

ABSTRACT Zaraθuštra is known as the author of Gāθās and the founder of the ancient Iranian religion. However, his historicity, time and homeland have been matters of controversy amongst scholars throughout the ages. It is argued that the socio-cultural and archeological evidence in conjunction with available texts enables us to delimit the relatively large time span estimated for the time of the Old Avestan texts from linguistic analysis, which contextualises the Gāθās in the period of Indo-Iranian acculturation of southern Central Asia in the second quarter of the second millennium BCE. Furthermore, the evident social crisis in the Gāθās is seen as a likely reflection of the encroachment of Rig Vedic Proto-Indo-Aryan Andronovo nomads into southern Central Asia and the probable ideological motive for the Old Avestan innovations. In particular, it is argued that the peaceful sedentary lifestyle that the Gāθās envision became the impetus to attract indigenous Greater Iranians and ushered the Iranian acculturation of Greater Iran, leading to the formation of an ancient Iranian Identity, later disseminated into the Iranian plateau.

The Image of Chelyabinsk in the 20th century British Media Discourse (1901-1950)

The paper studies the evolution of the image of Chelyabinsk in the 20th century British media discourse. The research proves relevant as it involves both linguistic and historical analyses; it aims at retrospective study of the evolution of the image of the foreign city in British media discourse over a large time span. A wide range of methods is employed in the study: comparative, diachronic, cognitive-matrix, cognitive-discursive methods, source study, and content analysis. The source of the data is a digitized archive of British historical media texts. The authors fixed nine variations of the city name. The frequency of modeling the image of Chelyabinsk is dissimilar: it is rather high at the beginning of the century, declines in the second decade, reaches its minimum in 1921-1930, and rises again in the subsequent decades, which is explained by the interest of the British media to industrialization and the events of World War II. Most of the newspapers and magazines that modelled the image of Chelyabinsk were published in the capitals and large industrial centres, which is explained by the peculiarities of British print media, a higher level of education of large cities residents, and Britains economic interests in Russia / the Soviet Union. The significant difference in the images of Chelyabinsk across the time is in their emotive load: negative images of the beginning of the century are contrasted to positive images generated in the latest time span.

Trajectories of the Growing-Up in the Psychological Autobiography of Adolescent Secondary School Learners

Introduction. Currently, the key tasks of education are associated with successful socialization, the development of the personal maturity of the younger generation. At the same time, the socio-cultural transformations of modernity lead to a change in the ways and models of growing-up of schoolchildren. Despite the considerable interest of researchers in this problem, there are practically no works revealing the variable nature of the ways of growing-up of modern adolescents, reflected through their autobiographical ideas about the future. The purpose of the study is to identify the main structural and substantive characteristics of the trajectories of growing-up, reflected in the psychological autobiography of modern schoolchildren, and to determine their gender specificity. Materials and Methods. To look into the problem, an empirical study was conducted, in which 1 031 people took part – teenagers aged 12 to 17 years. Empirical data was collected by means of a modified version of the method “Psychological Autobiography” by E. Y. Korzhova. Mathematical and statistical analysis of the results was carried out by means of a one-factor analysis of variance (ANOVA), correlation analysis using the Pearson linear correlation coefficient. Results. In general, the subjective time perspective of adolescents is characterized by a large time span, positive emotional coloring. Based on the results of correlation analysis, the main trajectories of growing-up of modern adolescents are determined. The gender specificity in the structural organization and the content of ideas about the future is revealed. For young men, the trajectories of growing-up associated with independence, a variety of impressions, material success, social recognition, creativity, entertainment, and the development of their inner world are more characteristic. Girls are focused on satisfaction with the process of life, communication, integration of spheres of life. Discussion and Conclusion. The conclusions made by the authors contribute to the development of modern scientific ideas about the variable and nonlinear character of building a life perspective in the younger generation. The results of the study can be used to build programs of psychological and pedagogical support of the processes of growing up and socialization in the conditions of modern society.

High-Resolution Genotyping Unveils Identical Ampicillin-Resistant Enterococcus faecium Strains in Different Sources and Countries: A One Health Approach

Multidrug-resistant (MDR) Enterococcus faecium (Efm) infections continue to increase worldwide, although epidemiological studies remain scarce in lower middle-income countries. We aimed to explore which strains circulate in E. faecium causing human infections in Tunisian healthcare institutions in order to compare them with strains from non-human sources of the same country and finally to position them within the global E. faecium epidemiology by genomic analysis. Antibiotic susceptibility testing was performed and transfer of vancomycin-vanA and ampicillin-pbp5 resistance was performed by conjugation. WGS-Illumina was performed on Tunisian strains, and these genomes were compared with Efm genomes from other regions present in the GenBank/NCBI database (n = 10,701 Efm genomes available May 2021). A comparison of phenotypes with those predicted by the recent ResFinder 4.1-CGE webtool unveiled a concordance of 88%, with discordant cases being discussed. cgMLST revealed three clusters [ST18/CT222 (n = 13), ST17/CT948 strains (n = 6), and ST203/CT184 (n = 3)], including isolates from clinical, healthy-human, retail meat, and/or environmental sources in different countries over large time spans (10–12 years). Isolates within each cluster showed similar antibiotic resistance, bacteriocin, and virulence genetic patterns. pbp5-AmpR was transferred by VanA-AmpR-ST80 (clinical) and AmpR-ST17-Efm (bovine meat). Identical chromosomal pbp5-platforms carrying metabolic/virulence genes were identified between ST17/ST18 strains of clinical, farm animal, and retail meat sources. The overall results emphasize the role of high-resolution genotyping as provided by WGS in depicting the dispersal of MDR-Efm strains carrying relevant adaptive traits across different hosts/regions and the need of a One Health task force to curtail their spread.

Measuring and mapping long-term changes in migration flows using population-scale family tree data

ABSTRACT Studying migration over a long period is challenging due to lack of data, uneven data quality, and the methodological challenges that arise when analyzing migration over large geographic areas and long time spans with constantly changing political boundaries. Crowd-sourced family tree data are an untapped source of volunteered geographic information generated by millions of users. These trees contain information on individuals such as birth and death places and years, and kinship ties, and have the potential to support analysis of population dynamics and migration over many generations and far into the past. In this article, we introduce a methodology to measure and map long-term changes in migration flows using a population-scale family-tree data set. Our methodology includes many steps such as extracting migration events, temporal periodization, gravity normalization, and producing time-series flow maps. We study internal migration in the continental United States between 1789 and 1924 using birthplaces and birthyears of children from a cleaned, geocoded, and connected set of family trees from Rootsweb.com. To the best of our knowledge, the results are the first migration flow maps that show how the internal migration flows within the U.S. changed over such a long period of time (i.e. 135 years).