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The distribution of bumblebee species in Wrocław (SW Poland) was studied based on data from ten UTM grid squares between 1879 and 2018, and the composition and abundance of bumblebee species between 2011 and 2018. The results were compared with historical data available for the last 139 years, including published materials and collections of the Museum of Natural History (University of Wrocław). Twenty-six bumblebee species, including six species of cuckoo bumblebees of the subgenus Psithyrus, were recorded. One species was not confirmed during the later period, nine were strongly decreasing both in the range of distribution, seven other were expanding significantly in Wrocław and three were new in the Wrocław city boundaries. Negative and positive tendencies resulted from both anthropogenic and natural factors. All bumblebees that appeared in the city of Wrocław and increased their abundance were species that prefered forest and shrub areas, while species that disappeared from the city and decreased in numbers were species who prefered open areas. [ABSTRACT FROM AUTHOR]

Background: Adult Changes in Thought (ACT), a prospective cohort study, enrolls older adult members of Kaiser Permanente Washington. We describe an ambitious project to abstract medical records facilitating epidemiological investigation. Methods: Abstracted data include medications; laboratory results; women's health; blood pressure; physical injuries; cardiovascular, neurological, psychiatric and other medical conditions. Results: Of 1419 of 5763 participants with completed abstractions, 1387 (97.7%) were deceased; 602 (42.4%) were diagnosed with Alzheimer's Disease and Related Dementias; 985 (69.4%) had a brain autopsy. Each participant had an average of 34.3 (SD = 13.4) years of data abstracted. Over 64% had pharmacy data preceding 1977; 87.5% had laboratory data preceding 1988. Stroke, anxiety, depression and confusion during hospitalization were common among participants diagnosed with dementia. Conclusions: Medical records are transformed into data for analyses with outcomes derived from other ACT data. We provide detailed, unparalleled longitudinal clinical data to support a variety of epidemiological research on clinical-pathological correlations. [ABSTRACT FROM AUTHOR]

Abstract Understanding regional sea level variations is crucial for assessing coastal vulnerability, with accurate sea level data playing a pivotal role. Utilizing historical sea level marigrams can enhance datasets, but current digitization techniques face challenges such as bends and skews in paper charts, impacting sea level values. This study explores often‐overlooked issues during marigram digitization, focusing on the case study of Dún Laoghaire in Ireland (1925–1931). The methodology involves digitizing the original marigram trace and underlying grid to assess offsets at the nearest ft (foot) interval on the paper chart, corresponding to changes in the water level trace for each hour interval. Subtracting the digitized value from the known value (the actual measurement) allows for the determination of differences, which are then subtracted from each hourly trace value. After adjusting for offsets ranging from −3.962 to 13.716 mm (millimetres), the study improves the final accuracy of sea level data to approximately the 10 mm level. Notably, data from 1926 and 1931 exhibit modest offsets (9–14 mm), emphasizing the importance of adjustments for accuracy. Such 10 mm accuracy is compatible with requirements of the Global Sea Level Observing System. Comparing the adjusted digitized data with other survey data shows similar amplitudes and phases for Dún Laoghaire in both the historical and modern datasets, and there is an overall mean sea level rise of 1.5 mm/year when combined with the available data from the Dublin region.

Understanding regional sea level variations is crucial for assessing coastal vulnerability, with accurate sea level data playing a pivotal role. Utilizing historical sea level marigrams can enhance datasets, but current digitization techniques face challenges such as bends and skews in paper charts, impacting sea level values. This study explores often‐overlooked issues during marigram digitization, focusing on the case study of Dún Laoghaire in Ireland (1925–1931). The methodology involves digitizing the original marigram trace and underlying grid to assess offsets at the nearest ft (foot) interval on the paper chart, corresponding to changes in the water level trace for each hour interval. Subtracting the digitized value from the known value (the actual measurement) allows for the determination of differences, which are then subtracted from each hourly trace value. After adjusting for offsets ranging from −3.962 to 13.716 mm (millimetres), the study improves the final accuracy of sea level data to approximately the 10 mm level. Notably, data from 1926 and 1931 exhibit modest offsets (<7 mm), while other years show more substantial offsets (>9–14 mm), emphasizing the importance of adjustments for accuracy. Such 10 mm accuracy is compatible with requirements of the Global Sea Level Observing System. Comparing the adjusted digitized data with other survey data shows similar amplitudes and phases for Dún Laoghaire in both the historical and modern datasets, and there is an overall mean sea level rise of 1.5 mm/year when combined with the available data from the Dublin region. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low computing efficiency in big data analysis and model construction, this paper intended to deeply explore the big data analysis programming model, DAG (Directed Acyclic Graph) and other contents, and on this basis, it adopted a distributed matrix computing system Octopus for big data analysis. Octopus is a universal matrix programming framework that provides a programming model based on matrix operations, which can conveniently analyze and process large-scale data. By using Octopus, users can extract functions and data from multiple platforms and operate through a unified matrix operation interface. The distributed matrix representation and storage layer can design data storage formats for distributed file systems. Each computing platform in OctMatrix provides its own matrix library, and it provides a matrix library written in R language for the above users. SymboMatrix provides a matrix interface to OctMatrix that is consistent with OctMatrix. However, SymboMatrix also retains the flow diagram for matrix operations in the process, and it also supports logical and physical optimization of the flow diagram on a DAG. For the DAG computational flow graph generated by SymbolMatrix, this paper divided it into two parts: logical optimization and physical optimization. This paper adopted a distributed file system based on line matrix, and obtained the corresponding platform matrix by reading the documents based on line matrix. In the evaluation of system performance, it was found that the distributed matrix computing system had a high computing efficiency, and the average CPU (central processing unit) usage reached 70%. This system can make full use of computing resources and realize efficient parallel computing. [ABSTRACT FROM AUTHOR]

It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust meta-analytic predictive prior, provide blanket discounting. Thus, if only a subset of participants in the historical data are exchangeable with the current data, these priors may not be appropriate. In order to combat this issue, propensity score approaches have been proposed. However, these approaches are only concerned with the covariate distribution, whereas exchangeability is typically assessed with parameters pertaining to the outcome. In this paper, we introduce the latent exchangeability prior (LEAP), where observations in the historical data are classified into exchangeable and non-exchangeable groups. The LEAP discounts the historical data by identifying the most relevant subjects from the historical data. We compare our proposed approach against alternative approaches in simulations and present a case study using our proposed prior to augment a control arm in a phase 3 clinical trial in plaque psoriasis with an unbalanced randomization scheme. [ABSTRACT FROM AUTHOR]

This article explores a stochastic volatility model that incorporates fractional Brownian motion (fBm) into the constant elasticity of variance (CEV) framework. We use time series models to estimate the drift and volatility parameters of the model and validate its performance. We also examine the fractional Black-Scholes (BS) equation arising from the CEV model with fBm. To solve this equation numerically, we apply a Chebyshev collocation method and analyze its convergence properties. We demonstrate the effectiveness of the numerical method with an example and apply it to the option pricing problem. [ABSTRACT FROM AUTHOR]

In this study, we consider historical geomagnetic satellite data obtained during a strong magnetic storm on March 8−9, 1970. In addition to the data of the Soviet satellite Kosmos-321, data from the American satellite OGO-6, which performed geomagnetic measurements at the same time, were used. We analyzed time variations of external magnetic fields recorded in satellite and ground-based observations of the magnetic field. The research also gave impetus to the creation of the improved software implementation of the auroral oval model APM, which enables reconstruction of its position and precipitation intensity in both the past and near real time. The magnetic variations originating in the near-Earth space from various sources were identified. In particular, we revealed the signatures of the storm-time ring current and equatorial and auroral electrojects. The paper highlights the enduring value of historical data of magnetic field observations stored in data centers and continuously digitized by their staff. [ABSTRACT FROM AUTHOR]

The success of the D-Day landings during World War II was significantly influenced by the detailed reconnaissance and scientific analysis of coastal substrate, particularly peatlands, by Allied wetland scientists. This paper examines the critical role of wetland science in ensuring the feasibility of the Normandy invasion. Initial geological intelligence raised concerns about the stability of the beaches due to extensive peat deposits underlying the Normandy coast. To address uncertainties, the Combined Operations Pilotage Parties (COPP) conducted covert beach surveys, collecting substrate samples crucial for operational planning. These missions, undertaken under challenging conditions, identified suitable landing areas by analysing sediment composition and bearing capacities. The success of D-Day was, in part, attributed to the insights provided by wetland scientists, who highlighted the significance of substrate properties in operational success. Their contributions underscored the interdisciplinary nature of wartime planning, integrating scientific expertise with military strategy. This study illuminates the often-overlooked role of wetland science in pivotal historical events, emphasising its influence on strategic decision-making and operational outcomes during one of the 20th century’s defining battles. [ABSTRACT FROM AUTHOR]

Considering currently operative European and worldwide regulations, preserving and/or improving the state of remnant alluvial floodplains is a high-priority goal for experts. One of the threats is the decrease of lateral connectivity: due to the erosion in the mainstem riverbed and the sedimentation of the floodplain and its channels, the bed elevation gap slowly increases between the main channel and the side branches and oxbows of the floodplain. Without revitalization measures, this progress predicts severe ecological consequences. As an example, and as a continuation of our earlier work, we considered the Gemenc floodplain forest along the Danube, in Hungary. We set up a two-dimensional coupled hydrodynamic and sediment transport computational model to describe floodplain deposition dynamics. Model validation was carried out with historical data, i.e., two ground elevation sets measured in 1990 and in 2009, respectively. Our aim was 1. to show, how coarse resolution measured data can be used for validating a large-scale model in terms of sediment deposition processes, and 2. to interpret the first results on some areas exposed to strong deposition, after validation. Showing good agreements in three pillars: magnitude of estimation, spatial tendencies and spatial patterns, the model was deemed valid. We were also able to observe a clear gradient, along which areas could be categorized with high, medium and small extent of sediment deposition. With this model, the sediment dynamics in the Gemenc floodplain forest can be assessed, with special attention to the impact analysis of restoration measures to improve lateral connectivity conditions. [ABSTRACT FROM AUTHOR]

Genebanks, other than their pivotal role as diversity conservation repositories, regenerate part of their collection every year to maintain their material in optimal conditions. During regeneration cycles, morpho-physiological data are collected, contributing to the creation of large datasets that offer a valuable resource of information. In Italy, rice cultivation has been documented since the second half of the 15th century, and nowadays, Italy contributes more than 50% of the total European rice production. The ex situ collection of rice (mainly Oryza sativa L. subgroup japonica) held at the Research Center for Cereal and Industrial Crops (CREA-CI) of Vercelli is quite unique in Italy and its establishment dates back to the beginning of the 20th century. The collection is hereby presented through the analysis of 21 years of historic data, from 2001 to 2022, in 17 different locations in Northern Italy, for a total of 6592 entries, 677 genotypes analyzed and 9 phenotypic traits under investigation. An R script has been developed to analyze the dataset. The BLUEs calculation, heritability, PCA and correlation with weather data provided a comprehensive overview of the germplasm stored in the genebank. The great variability and phenotypic diversity were assessed, key aspects from the perspective of breeding programs. This work starts a re-evaluation of historic data, historic cultivars, and represents the first step toward the shift of the genebank to a bio-digital resource center. [ABSTRACT FROM AUTHOR]

A growing body of research documents the relevance of parental education as a marker of family socio-economic status for children's later-life health outcomes. A strand of this literature evaluates how the early-life environment shapes mortality outcomes during infancy and childhood. However, the evidence on mortality during the life course and old age is limited. This paper contributes to the literature by analysing the association between paternal education and children's old-age mortality. We use data from Social Security Administration death records over the years 1988–2005 linked to the United States 1940 Census. Applying a family(cousin)- fixed-effects model to account for shared environment, childhood exposures, and common endowments that may confound the long-term links, we find that having a father with a college or high-school education, compared with elementary/no education, is associated with a 4.6- or 2.6-month-higher age at death, respectively, for the child, conditional on them surviving to age 47. [ABSTRACT FROM AUTHOR]

Water scarcity and the need for efficient water re-source management have driven the development of innovative solutions in the field of agriculture. This paper introduces "Hydrosense," a smart irrigation system that combines moisture sensors, real-time weather forecasting data, and historical weather data to optimize irrigation practices. As water resources become increasingly strained due to changing climate patterns and population growth, the importance of precision irrigation cannot be overstated. Hydrosense utilizes moisture sensors embedded in the soil to monitor the real-time moisture levels. These sensors enable the system to determine the exact water requirements of the crops, thereby avoiding both over- and under-irrigation. However, what sets Hydrosense apart is its integration of weather fore-casting data. By accessing up-to-the-minute weather forecasts, the system can make informed decisions about whether to initi-ate irrigation cycles or withhold them to conserve water. The ability to anticipate upcoming rainfall events and adjust irriga-tion schedules accordingly minimizes water wastage and pro-motes sustainable farming practices. In addition to current weather data, Hydrosense harnesses the power of historical weather data specific to the region. By ana-lyzing long-term weather patterns and precipitation trends, the system can better predict the likelihood of rainfall. This histori-cal data serves as a valuable parameter in the decision- making process, allowing for even more precise irrigation scheduling. Hydrosense's intelligent approach to irrigation not only con-serves water resources but also enhances crop yields by provid-ing optimal moisture conditions for plant growth. This paper explores the technological aspects and methodologies employed in the development of Hydrosense, emphasizing the synergy be-tween real-time data, historical weather analysis, and soil mois-ture sensing. The system's potential impact on sustainable agri-culture and water resource management is discussed, highlight-ing its contribution to conserving one of our most precious re-sources in the face of a changing climate. [ABSTRACT FROM AUTHOR]

Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer's revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany. [ABSTRACT FROM AUTHOR]

This paper contributes to the still unresolved issue of the growth impact of government size by analysing a historical panel data set of 17 developed countries that ranges from 1880 to 2016. The unique feature of the long-time dimension allows for conducting a kind of natural experiment. Government size is closely related to economic-policy paradigms. The time span covers different economic policy paradigms, in particular, ‘laissez-faire’ before World War II and Keynesian economic policy after World War II. Before WW II government size is small, after WW II it is (has grown) big. Furthermore, this paper contributes to filling a gap in the literature by testing the non-linear hypothesis (Armey curve). We take particular attention to a key shortcoming of panel-data analysis – parameter or individual heterogeneity. Overall, this analysis suggests a systematic positive, albeit quite small, linear relationship of government size with economic growth. As a consequence, rather than concentrating their attention to the sheer size of government, policy makers are advised caring for an efficiently run and high)quality government sector as a prerequisite for a steady growth path. [ABSTRACT FROM AUTHOR]

Complete eradication of invasive plants is often infeasible but in some cases it is possible to reduce the invader to low levels with reduced ongoing management costs, referred to as functional eradication. Non-native Phragmites australis has challenged land managers across North America but functional eradication may yet be possible in some scenarios. Here we present data from Chesapeake Bay brackish tidal wetlands where two approaches to Phragmites management (long-term, continuous management and short-term, non-continuous management) were used. We demonstrate that the application of herbicides will lead to the establishment of native species, but long-term, continuous management is required to facilitate functional eradication of Phragmites by keeping it at low levels of occurrence and restoring native plant communities. Ultimately, historical data from sites that were sampled as part of this study indicate that if management is successful and results in functional eradication of Phragmites, then the recovering vegetation will include native species that were present before the sites were invaded by Phragmites. [ABSTRACT FROM AUTHOR]

Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability. [ABSTRACT FROM AUTHOR]

The Slovenian climate has undergone significant fluctuations, and an understanding of the past climate is necessary to improve models and recognise long-term patterns. The cryosphere environment, such as ice core samples, provides valuable palaeoclimate data. Palynology and dendroclimatology are also effective ways to study long-term changes in vegetation and reconstruct past climates using pollen and tree proxies. Sediment cores from various locations in Slovenia have been studied to understand past environmental changes. Borehole temperature profiles as well as historical records were also used to reconstruct past climate conditions. Studies have shown specific periods when climatic changes likely played a major role, but a complete timeline of the Slovenian climate throughout the Holocene has not yet been fully developed. [ABSTRACT FROM AUTHOR]

Long after a hurricane passes over and through any region or coastal area, the memories of it often linger. There may be visual reminders of the storm and the day lives may have been changed, or lost, forever. Hurricanes can be long-lived storms. They can also be structurally large, so their impact can cover enormous swaths of land and water, often with large populations in the way, particularly coastal areas. Hurricanes are the only officially named natural disasters, as well as the only hazards whose names can be retired. This unique characteristic provided us an opportunity to develop an activity that allows students to gain a greater understanding and appreciation into the: a) climatology of hurricanes, b) change over time with respect to naming and their frequency, and c) socio-scientific impact of the storms. In this activity students make sense of various data by analyzing, comparing and contrasting, and thinking critically about the various patterns that emerge, enhancing scientific literacy as well as gaining an appreciation for the phenomenon and the research around tropical systems. [ABSTRACT FROM AUTHOR]

The past years have sharpened the industry's understanding of a Quality by Design (QbD) approach toward clinical trials. Using QbD encourages designing quality into a trial during the planning phase. The identification of Critical to Quality (CtQs) factors and specifically Critical Data and Processes (CD&Ps) is key to such a risk-based monitoring approach. A variable that allows monitoring the evolution of risk regarding the CD&Ps is called a Quality Tolerance Limit (QTL) parameter. These parameters are linked to the scientific question(s) of a trial and may identify the issues that can jeopardize the integrity of trial endpoints. This paper focuses on defining what QTL parameters are and providing general guidance on setting thresholds for these parameters allowing for the derivation of an acceptable range of the risk. [ABSTRACT FROM AUTHOR]

A devastating earthquake took place on 8 April 1893, close to the town of Svilajnac, central Serbia. Over the past decade, significant historical data on the effects of this earthquake has been collected from a variety of sources, including books, scientific publications, reports, newspapers, and coeval chronicles. Additionally, this earthquake was recorded 750 km from the epicenter at the seismological station Rocca di Papa in Rome, Italy. Based on critical review and analysis of the historical data, we demonstrate that the epicentral area of this earthquake was 531 km2, and the macroseismic effects were recorded at epicentral distances up to 600 km towards the west (Vienna, Austria) towards the north, up to 500 km (Košice–Michalovce, Slovakia), towards the east up to 460 km (Brašov–Borsec, Romania); and towards the south up to about 300 km (Radoviš, North Macedonia). Finally, we show that the key parameters of the 1893 Svilajnac earthquake are as follows: (1) epicentral intensity, I0 = IX EMS-98, (2) the estimations of the moment magnitude and focal depth based on the observed intensities, MW = 6.8 and h = 13 km, respectively, and (3) the epicenter coordinates, 44.160° N and 21.354° E. [ABSTRACT FROM AUTHOR]

Electrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‐rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted. [ABSTRACT FROM AUTHOR]

Abstract Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

AbstractThere is growing interest in Bayesian clinical trial designs with informative prior distributions, for example for extrapolation of adult data to pediatrics, or use of external controls. While the classical Type I error is commonly used to evaluate such designs, it cannot be strictly controlled and it is acknowledged that other metrics may be more appropriate. We focus on two common situations—borrowing control data or information on the treatment contrast—and discuss several fully probabilistic metrics to evaluate the risk of false positive conclusions. Each metric requires specification of a design prior, which can differ from the analysis prior and permits understanding of the behavior of a Bayesian design under scenarios where the analysis prior differs from the true data generation process. The metrics include the average Type I error and the pre-posterior probability of a false positive result. When borrowing control data, our empirical cases demonstrate that the average Type I error is asymptotically controlled (in certain cases strictly) when the analysis and design prior coincide. We illustrate use of these Bayesian metrics with real applications, and discuss how they could facilitate discussions between sponsors, regulators and other stakeholders about the appropriateness of Bayesian borrowing designs for pivotal studies. [ABSTRACT FROM AUTHOR]

Data from breeding, including phenotypic information, may improve the efficiency of breeding. Historical data from breeding trials accumulated over a long time are also useful. Here, by organizing data accumulated in the National Agriculture and Food Research Organization (NARO) rice breeding program, we developed a historical phenotype dataset, which includes 6052 records obtained for 667 varieties in yield trials in 1991? 2018 at six NARO research stations. The best linear unbiased predictions (BLUPs) and principal component analysis (PCA) were used to determine the relationships with various factors, including the year of cultivar release, for 15 traits, including yield. Yield-related traits such as the number of grains per panicle, plant weight, grain yield, and thousand-grain weight increased significantly with time, whereas the number of panicles decreased significantly. Ripening time significantly increased, whereas the lodging degree and protein content of brown rice significantly decreased. These results suggest that panicle-weight-type high-yielding varieties with excellent lodging resistance have been selected. These trends differed slightly among breeding locations, indicating that the main breeding objectives may differ among them. PCA revealed a higher diversity of traits in newer varieties. [ABSTRACT FROM AUTHOR]

This article introduces and explores the 3.5-million-word Victorian Anti-Vaccination Discourse Corpus (VicVaDis). The corpus is intended to provide a (freely accessible) historical resource for the investigation of the earliest public concerns and arguments against vaccination in England, which revolved around compulsory vaccination against smallpox in the second half of the 19th century. It consists of 133 anti-vaccination pamphlets and publications gathered from 1854 to 1906, a span of 53 years that loosely coincides with the Victorian era (1837–1901). This timeframe was chosen to capture the period between the 1853 Vaccination Act, which made smallpox vaccination for babies compulsory, and the 1907 Act that effectively ended the mandatory nature of vaccination. After an overview of the historical background, this article describes the rationale, design and construction of the corpus, and then demonstrates how it can be exploited to investigate the main arguments against compulsory vaccination by means of widely accessible corpus linguistic tools. Where appropriate, parallels are drawn between Victorian and 21st-century vaccine-hesitant attitudes and arguments. Overall, this article demonstrates the potential of corpus analysis to add to our understanding of historical concerns about vaccination. [ABSTRACT FROM AUTHOR]

Copyright of Diversitas Journal is the property of Diversitas Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Art History / Sanat Tarihi Dergisi is the property of Ege University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Playing multimedia documents in ubiquitous systems may require content adaptation based on gathered context information and accumulated historical data. Several approaches have already been proposed, in which adaptation actions are performed to provide adapted documents. Nevertheless, these approaches focus mainly on efficient use of context information without involving historical users data to improve the adaptation process. Thus, this paper allows for consideration of historical users data during the execution of the adaptation process. To do so, the context elements and the adaptation actions are first modeled using the oriented-object approach and then converted into relational and NoSQL databases schemes. Finally, algorithms for storing, retrieving and analysing data are designed. The proposal is validated by implementing scenarios through a real prototype. At a first step, the performances are measured to estimate the cost of data processing. The experiments show that NoSQL databases excel in data storage and ease of implementation, while relational databases perform well in data retrieve. At a second step, the proposal usefulness is highlighted by showing how historical data contribute to adaptation rules personalization using datadriven rule learning mechanisms rather than defining them explicitly. The analysis algorithm could retain personalized adaptation rules with confidence degree greater than 90%. Overall, the results are satisfactory. [ABSTRACT FROM AUTHOR]

The entrance in the Sulina channel in the Black Sea is the target area of this study. This represents the southern gate of the seventh Pan-European transport corridor, and it is usually subjected to high navigation traffic. The main objective of the work is to provide a more comprehensive picture concerning the past and future expected dynamics of the environmental matrix in this coastal area, including especially the extreme wind and wave conditions in connection with the possible navigation risks. The methodology considered assumes analyses performed at three different levels. First, an analysis of some in situ measurements at the zero-kilometer point of the Danube is carried out for the 15-year period of 2009–2023. Together with the maximum wind speed and the maximum value of the wind gusts, the water level variation was analyzed at this point. As a second step, the analysis is based on wind speed data provided by regional climate models. Two periods, each spanning 30 years, are considered. These are the recent past (1976–2005), when comparisons with ERA5 reanalysis data were also performed, and the near future (2041–2070), when two different models and three climate scenarios were considered. The focus was on the extreme wind speed values, performing comparisons between the past and future expected extreme winds. Finally, the third analysis is related to the wave conditions. Thus, using as a forcing factor each of the wind fields that was previously analyzed, simulations employing a spectral wave model were carried out. The wave modeling system was focused using three different computational domains with increasing resolution towards the target area, and the nearshore wave conditions were evaluated. The results show that both the extreme wind and wave conditions are expected to slightly increase in the future. Especially in the wintertime, strong wind fields are often expected in this area, with wind gusts exceeding more than 70% of the hourly average wind velocity. With regard to the waves, due to the complex nearshore phenomena, considerable enhancements in terms of significant wave heights are induced, and there is also an elevated risk of the occurrence of rogue waves. This work is still ongoing, and taking into account the high navigation risks highlighted, the next step would be to elaborate the risk assessment of severe shipping conditions, particularly related to the likelihood or probability of adverse conditions with the potential of generating hazardous situations in this coastal environment. [ABSTRACT FROM AUTHOR]

How does war affect states' tolerance toward foreign culture? It is well documented by historians that democratic countries, despite their heralded values of liberty and diversity, acquiesce and even promote the practice of cultural intolerance in wartime. The available evidence, however, remains either anecdotal or limited to a specific context, and the extent to which war-induced cultural intolerance persists has so far rarely been examined. In order to investigate the short- and long-term effects of war on the degree of foreign cultural acceptance, this article focuses on patterns of classical music performances before, during, and after the two World Wars, based on a novel dataset assembled from concert program notes of ten renowned symphony orchestras from five countries (Austria, Germany, Japan, the United Kingdom, and the United States) between 1900 and 1960, covering 29,135 concerts and 125,530 pieces. Quantitative analysis suggests that the rate of performing pieces originating from belligerent countries in wartime declines markedly for music written in relatively modern times and that the defeat in wars led to a swift recovery of the exclusionary tendency against former enemy music. These findings demonstrate that states' security concerns and relative power, rather than political regime types, dictate international transaction patterns, including cultural flows across sovereign state borders. [ABSTRACT FROM AUTHOR]

This paper studies the long-run mortality effects of in utero and early-life economic conditions. We examine how local economic conditions experienced during the Great Depression, proxied by county-level banking deposits during in utero and first years of life, influences old-age longevity. We find that a one-standard-deviation rise in per capita bank deposits is associated with an approximately 1.7 month increase in males’ longevity at old age. Additional analyses comparing state-level versus county-level economic measures provide insight on the importance of controlling for local-level confounders and exploiting more granular measures when exploring the relationship between early-life conditions and later-life mortality. [ABSTRACT FROM AUTHOR]

Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture. [ABSTRACT FROM AUTHOR]

The ongoing replication crisis in science has increased interest in the methodology of replication studies. We propose a novel Bayesian analysis approach using power priors: The likelihood of the original study's data is raised to the power of α , and then used as the prior distribution in the analysis of the replication data. Posterior distribution and Bayes factor hypothesis tests related to the power parameter α quantify the degree of compatibility between the original and replication study. Inferences for other parameters, such as effect sizes, dynamically borrow information from the original study. The degree of borrowing depends on the conflict between the two studies. The practical value of the approach is illustrated on data from three replication studies, and the connection to hierarchical modeling approaches explored. We generalize the known connection between normal power priors and normal hierarchical models for fixed parameters and show that normal power prior inferences with a beta prior on the power parameter α align with normal hierarchical model inferences using a generalized beta prior on the relative heterogeneity variance I 2 . The connection illustrates that power prior modeling is unnatural from the perspective of hierarchical modeling since it corresponds to specifying priors on a relative rather than an absolute heterogeneity scale. [ABSTRACT FROM AUTHOR]

The evolution of educational systems is closely tied to technological advancements, particularly the emergence of machine learning. This technology offers a sophisticated system capable of predicting, explaining, and influencing behavior. Many efforts have aimed to integrate machine learning into education, focusing on specific cases using ad-hoc models. This paper introduces an intelligent educational system that relies on data-driven student models, aiming to surpass the limitations of these ad-hoc systems. The approach outlined in this endeavor adopts a comprehensive and methodical modeling methodology centered on machine learning techniques. By employing Long Short-Term Memory (LSTM), the proposed approach enables predictive student models based on historical educational data. The effectiveness of this method was tested through experimentation on an intelligent tutoring system using 5-fold cross-validation, revealing that the smart educational system achieved a remarkable 96% accuracy rate. Furthermore, a comparison between the importance scores of features with and without the student models demonstrated the practicality and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Intensive agriculture and deep plowing caused topsoil erosion and dust storms during the 1930s, affecting agricultural income and land values for years. Given the growing literature on the relevance of in utero and early-life exposures, it is surprising that studies focusing on links between the Dust Bowl and later-life health have produced inconclusive and mixed results. We reevaluate this literature and study the long-term effects of in utero and early-life exposure to topsoil erosion caused by the 1930s Dust Bowl on old-age longevity. Specifically, using Social Security Administration death records linked with the full-count 1940 census, we conduct event studies with difference-in-differences designs to compare the longevity of individuals in high- versus low-topsoil-erosion counties before versus after 1930. We find intent-to-treat reductions in longevity of approximately 0.85 months for those born in high-erosion counties after 1930. We show that these effects are not an artifact of preexisting trends in longevity. Additional analyses suggest that the effects are more pronounced among children raised in farm households, females, and those whose mothers had lower education. We also provide suggestive evidence that reductions in adulthood income are a likely mechanism for the effects we document. [ABSTRACT FROM AUTHOR]

The Slovenian climate has undergone significant fluctuations, and an understanding of the past climate is necessary to improve models and recognise long-term patterns. The cryosphere environment, such as ice core samples, provides valuable palaeoclimate data. Palynology and dendroclimatology are also effective ways to study long-term changes in vegetation and reconstruct past climates using pollen and tree proxies. Sediment cores from various locations in Slovenia have been studied to understand past environmental changes. Borehole temperature profiles as well as historical records were also used to reconstruct past climate conditions. Studies have shown specific periods when climatic changes likely played a major role, but a complete timeline of the Slovenian climate throughout the Holocene has not yet been fully developed.

The growing importance of Territorial Digital Twins (TDT) in the built environment is becoming increasingly apparent. The TDT can play a crucial role in helping governments make informed decisions based on a thorough understanding and interpretation of the evolution of both the physical territory and its urban, social, and economic development. This study develops the Territorial Digital Twin of Guadalajara (Spain), within the knowledge domain of urban planning and architectural and natural heritage. It allows the analysis of the evolution of the city, using the variable time, taking as origin an unpublished flight of Guadalajara from 1934. To be able to count on the widest possible time span, it is essential to recover the unpublished documentation that remains in historical archives. In this sense, aerial images have proved to be invaluable source for the recovery of historical heritage and territorial analysis. During the study, different techniques of image georectification have been combined, and a historical vector graphic base with topology has been created, plus an associated database accompanied by files with town planning and heritage information. As a result, the changes produced in the urban planning of the city are analysed and shown on the open-source website developed for the consultation of Territorial Digital Twin by all the stakeholders (collectivity, public bodies, university, investigation bodies, etc).

Background: Adult Changes in Thought (ACT), a prospective cohort study, enrolls older adult members of Kaiser Permanente Washington. We describe an ambitious project to abstract medical records facilitating epidemiological investigation. Methods: Abstracted data include medications; laboratory results; women’s health; blood pressure; physical injuries; cardiovascular, neurological, psychiatric and other medical conditions. Results: Of 1419 of 5763 participants with completed abstractions, 1387 (97.7%) were deceased; 602 (42.4%) were diagnosed with Alzheimer’s Disease and Related Dementias; 985 (69.4%) had a brain autopsy. Each participant had an average of 34.3 (SD = 13.4) years of data abstracted. Over 64% had pharmacy data preceding 1977; 87.5% had laboratory data preceding 1988. Stroke, anxiety, depression and confusion during hospitalization were common among participants diagnosed with dementia. Conclusions: Medical records are transformed into data for analyses with outcomes derived from other ACT data. We provide detailed, unparalleled longitudinal clinical data to support a variety of epidemiological research on clinical-pathological correlations.

The change in the volume of the Kozelsky Glacier in Kamchatka for the period 1977–2022 (1977–2015 and 2015–2022) was estimated using historical data and modern DEM. During this period, the area of the glacier did not change much. At the same time, its length increased by about 0.7 km, while the width decreased over its almost whole extent. The volume of the glacier decreased by 34.15 ± 6.74 million m3, and its surface became lower by 17.30 m, on the average. The cumulative mass balance amounted 14.70 ± 3.94 m w.e., and the mean annual value –0.33 m w.e. yr–1. In the last 45 years, the ice loss and redistribution to lower hypsometric levels took place on the Kozelsky Glacier. In 1977–2015, the average area change in the altitude of the glacier surface was equal to –17.84 m, the volume decreased by 35.21 ± 7.20 million m3, the cumulative mass balance amounted –15.16 ± 4.17 m w.e., and the mean annual balance –0.40 m w.e. yr–1. In the period 2015–2022, an elevation of the glacier surface was recorded by 0.59 ± 1.55 m on the average, the volume increased by 1.01 ± 2.65 million m3, the cumulative mass balance amounted to 0.50 ± 1.35 m w.e., and the mean annual balance – to 0.07 m w.e. yr–1. During the last decade, a slowdown in the movement of the glacier front down the valley was recorded. In 2012–2022, the glacier front advanced with a velocity of about 5.2 m/year, while it was 17.9 m/year in 1977–2007, and 20.0 m/year in 2007–2012. The current climatic conditions are not favorable for development of glaciers. In 1977–2022, a trend of the summer air temperature rise was observed with a relatively stable amount of precipitation falling during the cold period. The almost continuous (except 1978–1981) advance of the glacier in 1977–2022 can be explained by the influence of the volcanic factor. A thick surface moraine covers more than 2/3 of the glacier area and, thus, prevents the surface ablation. Increased seismic activity associated with active volcanism promotes the ice movement.

In determining the utilization of the area, many government regulations have been made from the Central, Provincial, and Regional levels using a production data approach. However, it is getting different results with the same slice. The purpose of the study is to identify areas and determine priority agricultural commodities for food crops, horticulture, and plantation groups in Pasaman Regency. Analytical techniques used in Pasaman Regency are LQ analysis, shift share (SS) analysis, and field observation with a stakeholder approach. Based on the research, it is found that LQ and SS were able to determine the leading commodities of food crops, horticulture, and plantations in Pasaman Regency. The main food crop is corn, with the central development area in Tigo Nagari District. The superior horticultural crops and their development areas are chilies in Panti District and mangoes in Mapat Tunggul. The main plantation crop is rubber in a central development location in South Rao District.