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I evaluated trends in spatial and temporal variability in historical levels of rainfall, water elevation, and breach events for lakes Earl, Tolowa, and their combined lagoon system along the coast of northern California. I examined the efficacy of time series analyses to model and forecast rainfall and lake elevation at a regional scale from 2008 to 2021. I employed semi-parametric Generalized Additive Model regression to investigate the historical relationship between anthropogenic breaching of the lagoon and simultaneous occurrences of environmental parameters to better understand conditions surrounding each breach event. Evaluation of the central tendency of rainfall and surface lake elevation showed high fluctuations in their mean, positive skewed, and leptokurtic curves. Augmented Dickey-Fuller tests found that seasonal rainfall was stationary, but surface lake elevation attained stationarity only after the first seasonal difference. Decomposition of each time series and MannKendall and Sen’s slope estimators, found a significant decreasing trend in seasonal surface lake elevation but no trend was found in rainfall. Seasonal Autoregressive Integrated Moving Average (SARIMA) time series analysis and diagnostic tests of stability and reliability found best fit models for rainfall (SARIMA[1,0,0] [2,1,1]12) and surface lake elevation (SARIMA [1,1,2] [1,0,0]12) used to forecast future values for each parameter. Multiple regression of variables obtained at each breach event showed that the proportion of variance (55.0%) and null deviance (72.1%) explained by the combination of rainfall, hightide, and wave height was the “best” model with the lowest Generalized Cross-Validation statistic of all other models evaluated. All models agreed that rainfall was the most significant factor within each set of predictor attributes used to model surface lake elevation. A declining trend in surface elevation in combination with variation in the historical area and extent of wetland plant communities may be attributable to systematic breaching of the lagoon annually.

Since ancient times, the shells of marine molluscs have been used as a therapeutic and/or prophylactic resource. In Spain, they were part of practical guides for doctors or pharmacists until the 19th century. In general, seashells were prepared by dissolving in vinegar and were part of plasters or powders used as toothpaste, or to treat dyspepsia, heartburn and leprosy. Thus, the nacre or mother-of-pearl of various molluscs was regularly used in the Royal Colleges of Surgery and in hospitals during the times of the Cortes of Cadiz, as a medicine in galenic preparations based on powders. In contemporary Spanish ethnomedicine, seashells, with a high symbolic value, have been used as an amulet to prevent cracks in the breasts and promote their development during lactation, to avoid teething pain in young children, to eliminate stains on the face or to cure erysipelas. But, as in other countries, products derived from seashells have also been empirically applied. The two resources used traditionally have been the cuttlebone, the internal shell of cuttlefish and the nacre obtained from the external shells of some species. Cuttlebone, dried and pulverised, has been applied externally to cure corneal leukoma and in dental hygiene. In the case of nacre, a distinction must be made between chemical and physical remedies. Certain seashells, macerated in lemon juice, were used in coastal areas to remove spots on the face during postpartum. However, the most common practice in Spain mainland was to dissolve mother-of-pearl buttons in lemon juice (or vinegar). The substance thus obtained has been used to treat different dermatological conditions of the face (chloasma, acne), as well as to eliminate freckles. For the extraction of foreign bodies in the eyes, a very widespread traditional remedy has been to introduce small mother-of-pearl buttons under the lid. These popular remedies and practices are compared with those collected in classic works of medicine throughout history, and data on the pharmacological activity and pharmaceutical applications of the products used are provided. The use of cuttlebone powders is supported by different works on anti-inflammatory, immune-modulatory and/or wound healing properties. Nacre powder has been used in traditional medicines to treat palpitations, convulsions or epilepsy. As sedation and a tranquilisation agent, nacre is an interesting source for further drug development. Likewise, nacre is a biomaterial for orthopaedic and other tissue bioengineering applications. This article is a historical, cultural and anthropological view that can open new epistemological paths in marine-derived product research.

Numerous riverbeds and floodplains in the Western Mediterranean Area (WMA) have been affected by anthropogenic modifications during the last centuries. In recent decades, an increase in floods in the coastal WMA has been observed. Variations in the rainfall regime and anthropisation have influenced the relevant geomorphological processes. The coastal floodplains analysed include those in Italy, France, and Spain. Geomorphological and land use changes that occurred in the last two centuries were examined using historical and recent maps, historical data, and European big data since the 1800s for 65 basins, for which over 670 flood events and more than 1300 victims were identified. Anthropogenic activities have changed the patterns of floodplains. In most cases, narrowing of the riverbeds, especially in the lower river sections, has been observed. The riverbeds have also changed from braided- to single-channel morphologies. GIS analysis shows reductions in the coastal watercourse widths ranging from 10% to 95%, with an average of 55%. Other changes are related to the deviation in the watercourses, with trends that did not respect the natural river flow. In some cases, the watercourses were covered and have vanished from recent maps. This aspect has reduced or eliminated the perception of the risk not only for the residents but also for land planners.

The digital twin workshop contains a vast quantity of heterogeneous data from multiple sources, such as the historical state of workshop equipment, which is essential for analyzing implicit problems and bottlenecks in manufacturing tasks. Nevertheless, the current unidirectional and irreversible time flow of the digital twin workshop makes it difficult to optimize workshop productivity using historical data. This paper proposes a scene equipment saving and loading method for the digital twin workshop to address this issue. The initial steps involve defining a workshop information model which represents multiple pieces of workshop equipment in the virtual space and the content of the data it covers. This model stores data for each object type on the workshop using distinct data structures; a workshop element data saving and loading method is proposed, which can save the historical scene equipment data of the digital twin workshop and load the saved data into the digital twin software; finally, a case study is conducted to determine the data compatibility, the saving and loading efficiency, and the system’s ability to save and load actual workshop scenes. The results demonstrate that this method can efficiently save and load scene equipment data on the workshop, enabling workshop administrators to identify problems and bottlenecks in historical manufacturing tasks and then take steps to increase workshop productivity.

Imprecision is commonly encountered with respect to powers and predictive powers in clinical trials. In this article, we investigate the imprecision issues of four powers (Classical Power, Classical Conditional Power, Bayesian Power, and Bayesian Conditional Power) and eight predictive powers. To begin with, we derive the probabilities of Control Superior (CS), Treatment Superior (TS), and Equivocal (E) of the four powers and the eight predictive powers, and evaluate the limits of the probabilities at point 0. Moreover, we conduct extensive numerical experiments to exemplify the imprecision issues of the four powers and the eight predictive powers. In the numerical experiments, first, we compute the probabilities of CS, TS, and E for the four powers as functions of the sample size of the future data when the true treatment effect favors control, treatment, and equivocal, respectively. Second, we compute the probabilities of CS, TS, and E for the eight predictive powers as functions of the sample size of the future data under the sceptical prior and the optimistic prior, respectively. Finally, we carry out a real data example to show the prominence of the methods. [ABSTRACT FROM AUTHOR]

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the 'lme4' R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the 'STPGA' R package. Third, for each TP, phenotypic values and SNP data were incorporated into the 'rrBLUP' mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development. [ABSTRACT FROM AUTHOR]

Anthropogenic and climatic factors affect the survival of animal species. Chinese pangolin is a critically endangered species, and identifying which variables lead to local extinction events is essential for conservation management. Local chronicles in China serve as long‐term monitoring data, providing a perspective to disentangle the roles of human impacts and climate changes in local extinctions. Therefore, we established generalized additive models to identify factors leading to local extinction with historical data from 1700–2000 AD in mainland China. Then we decreased the time scale and constructed extinction risk models using MaxEnt in a 30‐year transect (1970–2000 AD) to further assess extinction probability of extant Chinese pangolin populations. Lastly, we used principal component analysis to assess variation of related anthropogenic and climatic variables. Our results showed that the extinction probability increased with global warming and human population growth. An extinction risk assessment indicated that the population and distribution range of Chinese pangolins had been persistently shrinking in response to highly intensive human activities (main cause) and climate change. PCA results indicated that variability of climatic variables is greater than anthropogenic variables. Overall, the factors causing local extinctions are intensive human interference and drastic climatic fluctuations which induced by the effect of global warming. Approximately 28.10% of extant Chinese pangolins populations are confronted with a notable extinction risk (0.37 ≤ extinction probability≤0.93), specifically those in Southeast China, including Guangdong, Jiangxi, Zhejiang, Hunan and Fujian Provinces. To rescue this critically endangered species, we suggest strengthening field investigations, identifying the exact distribution range and population density of Chinese pangolins and further optimizing the network of nature reserves to improve conservation coverage on the landscape scale and alleviate human interference. Conservation practices that concentrate on the viability assessment of scattered populations could help to improve restoration strategies of the Chinese pangolin. [ABSTRACT FROM AUTHOR]

The mapping of mangrove alliance distributions is limited because of lack of training data and inaccessibility of sites. Mapping mangrove alliances is important for monitoring carbon storage as well as socio-economic services to local communities. This research uses alliance field data from 1978 along with current mangrove distribution for Rewa River Delta and Suva-Navua coast in Fiji to train a Random Forest model. Classification of mangrove alliances used Sentinel-1 and − 2 images along with elevation data which resulted in a 94% classification accuracy for Rewa River Delta and 74.5% Suva-Navua. Alliances within the Rewa River Delta, including Mixed, Dogo, Boreti, Landward and Tiri, were classified with greater than 85% accuracy. In comparison, most alliances in the Suva-Navua had less than 67% accuracy; the exception was Coastal Fringing alliance which represents 56% of the area and had a 92% classification accuracy. White and red mangroves were better classified when they had larger area coverage. The Random Forest model identified SWIR, NIR and elevation data as the most important variables for discriminating mangrove alliances. Compared to limited other studies that mapped mangrove alliances using optical data alone, this analysis resulted in equal or better classification results. These results show the potential of using historic data for mapping contemporary mangrove alliances in regions that often have limited validation data or accessibility. The next step is mapping recovery of mangrove alliances post-cyclone events to help conservation groups identify focused mitigation efforts. [ABSTRACT FROM AUTHOR]

The value of biodiversity and of documented biodiversity surveys is well established. Extracting historical biodiversity data and synthesizing them with current data can provide a more comprehensive estimate of total diversity and guide future monitoring. We demonstrate the utility of compiling historical and recent biodiversity data to better characterize taxon richness and composition. Our focus is an otherwise unmonitored habitat in an unmonitored British Columbia provincial park, in a heavily impacted region of the Salish Sea that was designated a United Nation Biosphere Reserve in 2021. We conducted surveys and compiled historical records that together spanned three intertidal habitats and 43 years. From these combined data we report a total of 99 taxa, an order of magnitude increase over the number listed in the park's Master Plan. These include seven non-native species, of which four are newly reported here. Rarefaction, extrapolation, and multivariate dissimilarity analyses revealed the roles of methods and habitat types in contributing to differences in taxon richness and composition among surveys. This data compilation illustrates many of the challenges and opportunities in aligning and assembling independent space-time snapshots of alpha (i.e., local) diversity to better understand the gamma (i.e., regional) diversity of a marine protected area and provides the foundational data needed to design effective future monitoring at molecular to ecosystem scales. [ABSTRACT FROM AUTHOR]

Background: We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process.Methods: We illustrate the application of the outlined Bayesian approaches on an example data set, retrospective re-analyzing a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study.Results: In the retrospective re-analysis of the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package.Conclusions: The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of considerably shortening the length of clinical trials, and provides a richer set of inferences. [ABSTRACT FROM AUTHOR]

Introduction: Real-world data (RWD) can contextualize findings from single-arm trials when randomized comparative trials are unethical or unfeasible. Findings from single-arm trials alone are difficult to interpret and a comparison, when feasible and meaningful, to patient-level information from RWD facilitates the evaluation. As such, there have been several recent regulatory applications including RWD or other external data to support the product's efficacy and safety. This paper summarizes some lessons learned from such contextualization from 20 notable new drug or biologic licensing applications in oncology and rare diseases. Methods: This review focuses on 20 notable new drug or biologic licensing applications that included patient-level RWD or other external data for contextualization of trial results. Publicly available regulatory documents including clinical and statistical reviews, advisory committee briefing materials and minutes, and approved product labeling were retrieved for each application. The authors conducted independent assessments of these documents focusing on the regulatory evaluation, in each case. Three examples are presented in detail to illustrate the salient issues and themes identified across applications. Results: Regulatory decisions were strongly influenced by the quality and usability of the RWD. Comparability of cohort attributes such as endpoints, populations, follow-up, index and censoring criteria, as well as data completeness and accuracy of key variables appeared to be essential to ensure the quality and relevance of the RWD. Given adequate sample size of the clinical trials or external control, the use of appropriate analytic methods to properly account for confounding, such as regression or matching, and pre-specification of these methods while blinded to patient outcomes seemed good strategies to address baseline differences. Discussion: Contextualizing single-arm trials with patient-level RWD appears to be an advance in regulatory science; however, challenges remain. Statisticians and epidemiologists have long focused on analytical methods for comparative effectiveness but hurdles in use of RWD have often occurred upstream of the analyses. More specifically, we noted hurdles in evaluating data quality, justifying cohort selection or initiation of follow-up, and demonstrating comparability of cohorts and endpoints. [ABSTRACT FROM AUTHOR]

Understanding the breadth and complexity of changes in phenology is limited by the availability of long‐term historical data sets with broad geographic range.We compare a recently discovered historical data set of plant phenology observations collected across the state of New York (1826–1872) to contemporary volunteer‐contributed observations (2009–2017) to evaluate changes in plant phenology between time periods. These multi‐site, multi‐taxa phenology data matched with temperature data uniquely extend historical observations back in time prior to the major atmospheric effects of the Industrial Revolution.The majority of the 36 trees, shrubs and forbs that comprised our analysable data set flowered and leafed out earlier in contemporary years than in the early to mid‐19th century. This shift is associated with a warming trend in mean January‐to‐April temperatures, with flowering and leafing advancing on average 3 days/°C earlier. On average, plants flowered 10.5 days earlier and leafed out 19 days earlier in the contemporary period. Urban areas exhibit more advanced phenology than their rural counterparts overall, and insect‐pollinated trees show more advanced phenology than wind‐pollinated trees and seasonality and growth form explain significant variation in flowering phenology. The greatest rates of temperature sensitivity and change between time periods for flowering are seen in early‐season species, particularly trees. Changes in the timing of leaf out are the most advanced for trees and shrubs in urban areas.Synthesis. Citizen science observations across two centuries reveal a dramatic, climate‐driven shift to earlier leaf out and flowering. The magnitude of advancement varies across settings, species and functional groups, and illustrates how long‐term monitoring and citizen science efforts are invaluable for ecological forecasting and discovery. [ABSTRACT FROM AUTHOR]

Objectives Although education is a key determinant of cognitive function, its role in determining Black–White disparities in cognitive function is unclear. This may be due, in part, to data limitations that have made it difficult to account for systemic educational inequities in the Jim Crow South experienced by older cohorts, including differences in the number of days Black students attended school compared to their White counterparts or Black peers in better-funded southern states. We determine if accounting for differential rates of school attendance across race, years, and states in the Jim Crow South better illuminates Black–White disparities in trajectories of cognitive function. Methods We linked historical state-level data on school attendance from the 1919/1920 to 1953/1954 Biennial Surveys of Education to the Health and Retirement Study, a nationally representative, longitudinal study of U.S. adults older than age 50. We restricted our sample to Black and White older adults who attended school in the Jim Crow South and began primary school in/after 1919/1920 and completed primary/secondary school by 1953/1954 (n = 4,343). We used linear mixed models to estimate trajectories of total cognitive function, episodic memory, and working memory. Results Self-reported years of schooling explained 28%–33% of the Black–White disparity in level of cognitive function, episodic memory, and working memory. Duration of school, a measure that accounted for differential rates of school attendance, explained 41%–55% of the Black–White disparity in these outcomes. Discussion Our study highlights the importance of using a more refined measure of schooling for understanding the education–cognitive health relationship. [ABSTRACT FROM AUTHOR]

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.

Abstract Anthropogenic and climatic factors affect the survival of animal species. Chinese pangolin is a critically endangered species, and identifying which variables lead to local extinction events is essential for conservation management. Local chronicles in China serve as long‐term monitoring data, providing a perspective to disentangle the roles of human impacts and climate changes in local extinctions. Therefore, we established generalized additive models to identify factors leading to local extinction with historical data from 1700–2000 AD in mainland China. Then we decreased the time scale and constructed extinction risk models using MaxEnt in a 30‐year transect (1970–2000 AD) to further assess extinction probability of extant Chinese pangolin populations. Lastly, we used principal component analysis to assess variation of related anthropogenic and climatic variables. Our results showed that the extinction probability increased with global warming and human population growth. An extinction risk assessment indicated that the population and distribution range of Chinese pangolins had been persistently shrinking in response to highly intensive human activities (main cause) and climate change. PCA results indicated that variability of climatic variables is greater than anthropogenic variables. Overall, the factors causing local extinctions are intensive human interference and drastic climatic fluctuations which induced by the effect of global warming. Approximately 28.10% of extant Chinese pangolins populations are confronted with a notable extinction risk (0.37 ≤ extinction probability≤0.93), specifically those in Southeast China, including Guangdong, Jiangxi, Zhejiang, Hunan and Fujian Provinces. To rescue this critically endangered species, we suggest strengthening field investigations, identifying the exact distribution range and population density of Chinese pangolins and further optimizing the network of nature reserves to improve conservation coverage on the landscape scale and alleviate human interference. Conservation practices that concentrate on the viability assessment of scattered populations could help to improve restoration strategies of the Chinese pangolin.

This paper presents the historical fog climate dataset from 1886 to 1919 for Hungary and its neighbouring countries in the Carpathian Basin. The dataset was obtained from the yearbooks of the Royal Hungarian Central Institute of Meteorology and Earth Magnetism (RHCIMEM) established in 1870 to investigate the climatic features of Hungary during the time of the Austro-Hungarian Monarchy. Monthly observations were recorded from 1871 and daily observations were recorded from 1886. The yearbooks contain daily meteorological records of temperature, relative humidity, rainfall, pressure, wind speed and direction, cloudiness and surface weather conditions along with monthly summaries for 24 meteorological stations. The daily weather observations were recorded three times a day, namely at 07:00, 14:00 and 21:00 local time. Station information (location, environment, instrumentation, observations etc.) can also be found in the yearbooks as metadata. For example, the definition of fog in the case of historical observations is the same as that of today, i.e., fog is detected if the maximum horizontal visibility is less than 1 km. In this way fog observations are easily comparable to today's observations without requiring further data correction and homogenisation. The longest 13 continuously recorded fog observation datasets have the length between 15 and 34 years. The stations are located in 5 countries of the Carpathian Basin at present. These datastests are suitable for conducting historical climatic investigations and can also serve as reference datasets. The historical dataset can be used to study the annual and seasonal changes in frequency and duration of fog events in the Carpathian Basin as a reference, thus facilitating research in the field of fog climatology and forecast.

Abstract This article presents daily rainfall data and metadata for Ireland transcribed from historical manuscript and printed copies of rainfall registers located in Met Éireann's archives. To facilitate the transcription of rainfall observations from paper records, the historical manuscripts were scanned and integrated into Met Éireann's digital archives. The transcription from digital image to data format was undertaken in collaboration with students at Maynooth University as part of a novel crowdsourcing initiative to integrate data rescue activities into the classroom. In total, 3,616 station years of rainfall data (~1.32 million daily values) were transcribed. The data, which was double keyed, have undergone basic quality assurance to check for transcription errors and the resultant raw data and associated metadata are presented here. Ongoing work involves the application of further quality assurance and homogenization techniques to develop a long‐term, quality assured daily rainfall network for Ireland.

Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve model data efficiency is quantified, specifically for: reuse of prediction models, reduction of training data duration, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic. • Forecasting models for MPC tested using energy system simulation with historic data. • Impact of data on forecast accuracy and MPC operational performance quantified. • Simple linear MLP model provides equivalent accuracy to state-of-the-art models. • More than 2 years of training data did not significantly improve forecast accuracy. • Screening training data using change-points improves accuracy and data efficiency. [ABSTRACT FROM AUTHOR]

Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science. [ABSTRACT FROM AUTHOR]

We investigate whether US government spending multipliers are higher during periods of heightened uncertainty or economic slumps as opposed to normal times. Using quarterly data from 1890 onward and local projections, we estimate a cumulative 1‐year multiplier of 2 during uncertain periods. In contrast, the multiplier is about 1 in times of high unemployment and about 0.4–0.8 during normal times. While we find positive employment effects in slumps as well as in uncertain times, two transmission channels can explain the higher multipliers in the latter: greater price flexibility leading to short‐term inflation (lowering the real interest rate) and diminishing risk premiums. [ABSTRACT FROM AUTHOR]

Investment in the capital market can help boost a country's economic growth. Without a doubt, in investing, a technical analysis of the condition of the stock is needed at that time. One of the technical analyses that can be done is to look at the historical data of stocks. Candlestick charts can summarize historical data that contain price value for Open, High, Low, and Close (OHLC) in the form of a chart. A group of candlesticks will form a pattern that can help investors to see whether the stock is trending up or down. The number of candlestick patterns and the manual determination of candlestick patterns may take time and effort. Feedforward Neural Network (FNN) is one of the algorithms that can help map the input and output of a given dataset. This study aims to implement FNN to classify candlestick patterns found in historical stock data. The test results show that the accuracy for each model scenario does not guarantee whether all patterns can be properly recognized. This is mainly caused by an imbalanced dataset and the classification process cannot be done properly. Testing with the original data has an accuracy of above 85% on each stock, but the average F1-score is below 45%. Further experiments using random under-sampling and Synthetic Minority Oversampling Technique (SMOTE) result in decreased accuracy value, where the lowest is 59% in PT Bukit Asam Tbk share, and an increased average F1-score, but less than 15%. [ABSTRACT FROM AUTHOR]

Historical data from plant breeding programs provide valuable resources to study the response of genotypes to the changing environment (i.e. genotype‐by‐environment interaction). Such data have been used to evaluate the pattern of genotype performance across regions or locations, but its use to evaluate the long‐term pattern of genotype performance across environments (i.e. locations‐by‐years) has been hampered by the lack of common genotypes across years. This lack of common genotypes is due to the structure of the breeding program, especially for annual crops, where only a proportion of selected genotypes are tested in subsequent years. This has resulted in a sparse prediction of the performance of genotypes across years (i.e. a genotype‐by‐year table). A genomic prediction method that fitted both a relationship matrix among genotypes and a relationship matrix among environments (i.e. years) could overcome this limitation and produce a dense genotype‐by‐year table, thereby enabling some evaluation of long‐term genotype performance. In this paper, we applied the genomic prediction model to the yield data from CIMMYT's Elite Spring Wheat Yield Trials (ESWYT) to visualise the pattern of genotype performance over 25 years. [ABSTRACT FROM AUTHOR]

Digital libraries have a key role in cultural heritage as they provide access to our culture and history by indexing books and historical documents (newspapers and letters). Digital libraries use natural language processing (NLP) tools to process these documents and enrich them with meta-information, such as named entities. Despite recent advances in these NLP models, most of them are built for specific languages and contemporary documents that are not optimized for handling historical material that may for instance contain language variations and optical character recognition (OCR) errors. In this work, we focused on the entity linking (EL) task that is fundamental to the indexation of documents in digital libraries. We developed a Multilingual Entity Linking architecture for HIstorical preSS Articles that is composed of multilingual analysis, OCR correction, and filter analysis to alleviate the impact of historical documents in the EL task. The source code is publicly available. Experimentation has been done over two historical document corpora covering five European languages (English, Finnish, French, German, and Swedish). Results have shown that our system improved the global performance for all languages and datasets by achieving an F-score@1 of up to 0.681 and an F-score@5 of up to 0.787. [ABSTRACT FROM AUTHOR]

How did Christianity expand in Africa to become the continent's dominant religion? Using annual panel census data on Christian missions from 1751 to 1932 in Ghana, and pre-1924 data on missions for 43 sub-Saharan African countries, we estimate causal effects of malaria, railroads and cash crops on mission location. We find that missions were established in healthier, more accessible, and richer places before expanding to economically less developed places. We argue that the endogeneity of missionary expansion may have been underestimated, thus questioning the link between missions and economic development for Africa. We find the endogeneity problem exacerbated when mission data is sourced from Christian missionary atlases that disproportionately report a selection of prominent missions that were also established early. [ABSTRACT FROM AUTHOR]

Climate change affects the life cycle of many species. Yet, responses to yearly variation of weather can either help species track optimal conditions or be maladaptive.We analysed phenological data of 46,479 pine processionary moths (Thaumetopoea pityocampa) during 15 years along an altitudinal gradient in southern France. These larvae were sampled in situ and allowed to pupate in a common garden at lower elevation.Individuals originating from higher elevation emerged earlier than those sampled at low elevation, which suggests local adaptation. Yearly variations in temperature also affected phenology. Warm springs caused an earlier adult emergence, while autumn temperatures had an opposite effect. Environmental cues could thus induce contradictory plastic responses.Synchronization mechanisms were identified. Variability in the duration of the pupal phase is a key parameter to synchronize adult emergence in spite of different larval development rates that only marginally influenced emergence dynamics. Semivoltine individuals experiencing prolonged diapause were synchronized with univoltine individuals emerging the same year.These data highlight some contradiction in the effect of spatial versus temporal variations of the temperature on adult emergence. This suggests that phenological responses to the current climate change cannot easily be anticipated by space‐for‐time substitution designs. [ABSTRACT FROM AUTHOR]

Debris flow is one of the most dangerous natural processes in mountain regions and it occur in a wide variety of environments throughout the world. In the Italian Alps, some tens of thousands of damaging debris flow and, in general, torrential floods associated to intense sediment transport in secondary catchments have been documented in the last 300 years. These have caused socio-economic damage, damage to anthropogenic structures or infrastructures and in many cases casualties. Often, in the same basins, the occurrence of debris-flow processes recurs many years later. Prediction can often be spatial and based on the magnitude of the largest known process, while the temporal forecast is the most uncertain. It is also possible to increase the resilience of the population and of the territory. The present study aims at investigating different levels of debris-flow hazard in urban areas on Alpine alluvial fans and proposes a strategy for debris-flow prevention based on historical research and on a simplified analytical approach, methods that also involve relatively low costs. For such analysis, Ischiator stream catchment (ca. 20 km2) and its alluvial fan (NW Italy) were selected. This area was partly affected by historical torrential flood associated to intense sediment transport and debris-flow processes. Present-day instability conditions along the slope and the stream network were detected and synthesized through surveys and aerial photo interpretation integrated by satellite images (period 1954–2021). An estimation of the potential amount of moving detritus, referred to as debris flow, was carried out regarding the June 1957 debris-flow event, based on the predictive models. The individual hazard index value was estimated based on different methods. The results indicate that 56% of the area is exposed to flood associated to intense sediment transport hazard, which fluctuates from high to very high levels; such results are supported by debris-flow historical records. Since today almost half of the settlement (Bagni di Vinadio) is located on potentially risk-exposed areas, the urban evolution policy adopted after the 1957 event failed to manage the risk connection to debris-flow activity. [ABSTRACT FROM AUTHOR]

This research work introduces a novel hybrid geometallurgical approach to develop a deep and comprehensive relationship between geological and mining characteristics with metallurgical parameters in a mineral processing plant. This technique involves statistically screening mineralogical and operational parameters using the Historical Data (HD) method. Further, it creates an intelligent bridge between effective parameters and metallurgical responses by the Deep Learning (DL) simulation method. In the HD method, the time and cost of common approaches in geometallurgical studies were minimized through the use of available archived data. Then, the generated DL-based predictive model was enabled to accurately forecast the process behavior in the mineral processing units. The efficiency of the proposed method for a copper ore sample was practically evaluated. For this purpose, six representative samples from different active mining zone were collected and used for flotation tests organized using a randomizing code. The experimental results were then statistically analyzed using HD method to assess the significance of mineralogical and operational parameters, including the proportions of effective minerals, particle size, collector and frother concentration, solid content and pH. Based on the HD analysis, the metallurgical responses including the copper grade and recovery, copper kinetics constant and iron grade in concentrate were modeled with an accuracy of about 90%. Next, the geometallurgical model of the process was developed using the long short-term memory neural network (LSTM) algorithm. The results showed that the studied metallurgical responses could be predicted with more than 95% accuracy. The results of this study showed that the hybrid geometallurgy approach can be used as a promising tool to achieve a reliable relationship between the mining and mineral processing sectors, and sustainable and predictable production. [ABSTRACT FROM AUTHOR]

In this paper, a data mining based methodology for process identification from historical data was proposed. Thereon, it considers the phases of process understanding, data collection, data preparation, data modeling, and model evaluation. As some parts of historical data are irrelevant, a data selection step, based on the Gaussian Mixture Model (GMM) clustering algorithm, was considered. Additionally, the methodology includes a data informativity step to study the richness of data. In this regard, the condition number (CN) and the extended CN for ridge regression (RR CN) were used. To evaluate the approach, 2 years of industrial thickener historical data were used. Thereafter, data were prepared and an ARX (Auto-Regressive with eXogenous inputs) model structure was adopted to identify the model. To estimate input delays, Granger causality was used. As for fit criteria, least square regression was tested and compared to ridge regression as a less sensitive method to multicollinearity. The results were then evaluated based on the 20-step ahead prediction and compared to existing methods. In this context, the proposed approach gave the best results with an R2 of 98.11% and 62.70% for 1 and 20-step ahead predictions, respectively. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine how diverse the academic children's library picture book collection is at San Diego State University Library and how well it represents the members of the community who use it. Using the results of a Diverse BookFinder's Collection Analysis Tool (DBF CAT) report, the researcher compares ethnic group representation in a library's holdings to local patron demographics to analyze how well the collection represents the community that uses it. More specifically, the DBF CAT data were compared to demographic statistics of the university student body and to demographic statistics of local school children whose teachers use the collection to develop lesson plans. While this comparison broadly identified some strengths and gaps in the collection, the demographic category mismatch between the DBF CAT data categories and the demographic statistical sources was problematic. Assessment of the historical part of the picture book collection was also explored and led to a recommendation for further collection diversity assessment research using historical bibliographies and book reviews. [ABSTRACT FROM AUTHOR]

Spruce–fir (Picea–Abies) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce (Picea rubens), black spruce (Picea mariana), and balsam fir (Abies balsamea), while white spruce (Picea glauca) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated. [ABSTRACT FROM AUTHOR]

A recent expert elicitation showed that model validation remains one of the largest barriers for commercial wind farm control deployment. The Gaussian-shaped wake deficit model has grown in popularity in wind farm field experiments, yet its validation for larger farms and throughout annual operation remains limited. This article addresses this scientific gap, providing a model comparison of the Gaussian wind farm model with historical data of three offshore wind farms. The energy ratio is used to quantify the model's accuracy. We assume a fixed turbulence intensity of I ∞ = 6 % and a standard deviation on the inflow wind direction of σ w d = 3 ° in our Gaussian model. First, we demonstrate the non-uniqueness issue of I ∞ and σ w d , which display a waterbed effect when considering the energy ratios. Second, we show excellent agreement between the Gaussian model and historical data for most wind directions in the Offshore Windpark Egmond aan Zee (OWEZ) and Westermost Rough wind farms (36 and 35 wind turbines, respectively) and wind turbines on the outer edges of the Anholt wind farm (110 turbines). Turbines centrally positioned in the Anholt wind farm show larger model discrepancies, likely due to deep-array effects that are not captured in the model. A second source of discrepancy is hypothesized to be inflow heterogeneity. In future work, the Gaussian wind farm model will be adapted to address those weaknesses. [ABSTRACT FROM AUTHOR]

Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43–0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains. [ABSTRACT FROM AUTHOR]

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ∼1,900,000 animals, and ∼5,900 genotyped elite artificial insemination rams. For the ∼8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare "partial" and "whole" predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the "official" (routine) evaluation, which is a mixture of both single and multiple trait, and "deletion" of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records. [ABSTRACT FROM AUTHOR]

The article explores American Kestrels (Falco sparverius) are declining across much of North America, yet the initial timing of the population decrease is unclear. In an attempt to elucidate when kestrel declines began, we examined historical descriptions of abundance within the northeastern United States. Within The Peregrine Fund's research library, we found 54 descriptions of kestrel abundance in northeastern states dating from 1839 to 2013.

In the present research, an overview of the wind climate on the northwestern coast of the Black Sea basin is assessed, using a total of 6 years of data (2015–2020) provided by the National Institute of Marine Geology and Geoecology (GeoEcoMar). It is well known that the enclosed/semi-enclosed basins are complex environments and to accurately represent the features of wind and wave are necessary high resolution spatial fields. For the Black Sea, which is an enclosed basin with complicated regional geography, the main weather parameters reported (wind direction, wind speed, air temperature, air pressure) give a more comprehensive picture of how energetic the area of interest is, and represent the features of the Black Sea's diversified marine environment. Finally, the results obtained in this paper cover a broad range of applications in marine studies, being useful for future research in the area of wind climate in the Black Sea. [ABSTRACT FROM AUTHOR]

Visual expression is increasingly used in historical research due to its intuitiveness and distinctness. However, most of the common research contents focus on the spatial concept, but lack the visualization analysis of the attribute characteristics of the research elements. In order to achieve this goal, based on a case study of the coastal military defense system in Ming Dynasty, the Geographic Information System (GIS) platform was adopted to reconstruct the historical map and its spatial data were extracted. On this foundation, the attribute characteristics of the military settlements, accessibility, was quantified by constructing a hierarchy evaluation model, and then the results were projected into the spatial geographic coordinates to realize the visualization of the accessibility of the military settlements in Ming Dynasty. The results showed that the combined method of quantification and visualization not only enabled more comprehensive and intuitive display of historical information, but also promoted data extraction and correlation analysis, creating a possibly for more in-depth future research. [ABSTRACT FROM AUTHOR]

Abstract Spruce–fir (Picea–Abies) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce (Picea rubens), black spruce (Picea mariana), and balsam fir (Abies balsamea), while white spruce (Picea glauca) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated.

Landslides are one of the most significant natural hazards worldwide. They can have far-reaching negative impacts on societies in different socio-economic sectors as well as on the landscape. Among the different types and processes that can also affect infrastructure and land use planning, slow-moving landslides are often underestimated. Therefore, studying areas affected by slow movements provide an opportunity to better understand the spatial and temporal patterns of these processes, their forcings, mechanisms, and potential risks. This study aims to investigate the importance of historical data for improving landslide hazard assessment in Lower Austria (Austria), which is particularly prone to landslides. This paper focuses on how historical information formed the basis for the establishment of three long-term landslide monitoring observatories in this region. The analysis conducted highlights the importance of using historical data to better assess the frequency and magnitude relationships and phases of landslide activity. In particular, they can extend the temporal window and provide relevant information on past events and accelerations to improve knowledge of landslide dynamics and the resulting socio-economic impacts. In order to better assess the landslide hazard associated, it is necessary to integrate historical data and monitoring datasets obtained by surface and subsurface methods. Both components allow for the characterization of the spatio-temporal evolution of slow movements and the analysis of the hazard over time. Based on a variety of historical sources, it was possible to install the instruments constituting the long-term landslide monitoring observatories in a meaningful manner. The results demonstrate the influential role of human impact on the stability conditions, which may also contribute to landslide occurrence. In this regard, the attempt to combine historical data and long-term, continuous monitoring systems in the presented landslide observatories can improve landslide risk reduction measures in the region. The integration of different techniques and tools, along with ongoing research and collaboration with local authorities, will further improve our understanding of these slow-moving processes and the development of effective management strategies.