Historical Dataset Research Articles

Evaluating Behavioural Modelling Predictions in the Blue Shark (Prionace glauca) Enables Greater Insight on Habitat Use from Location only Argos Data

The relationship between habitat and behaviour provides important information for species management. For large, free roaming, marine animals satellite tags provide high resolution information on movement, but such datasets are restricted due to cost. Extracting additional biologically important information from these data would increase utilisation and value. Several modelling approaches have been developed to identify behavioural states in tracking data. The objective of this study was to evaluate a behavioural state prediction model for blue shark (Prionace glauca) ARGOS surface location-only data. The novel nature of the six SPLASH satellite tags used enabled behavioural events to be identified in blue shark dive data and accurately mapped spatio-temporally along respective surface location-only tracks. Behavioural states modelled along the six surface location-only tracks were then tested against observed behavioural events to evaluate the model's accuracy. Results showed that the Behavioural Change Point Analysis (BCPA) model augmented with K means clustering analysis performed well for predicting foraging behaviour (correct 86% of the time). Prediction accuracy was lower for searching (52%) and travelling (63%) behaviour, likely related to the numerical dominance of foraging events in dive data. The model's validation for predicting foraging behaviour justified its application to nine additional surface location-only (SPOT tag) tracks, substantially increasing the utilisation of expensive and rare data. Results enabled the critical behavioural state of foraging, to be mapped throughout the entire home range of blue sharks, allowing drivers of critical habitat to be investigated. This validation strengthens the use of such modelling to interpret historic and future datasets, for blue sharks but also other species, contributing to conservational management.

Safe drinking water supply under extreme climate events: evidence from four urban sprawl communities

ABSTRACT In this study, the impacts of climate variability/change on water supply in three urban sprawl communities were examined. Using historical satellite climate datasets and social surveys, the study assesses the water stress during different seasons in urban sprawl communities. The primary data was gathered through structured questionnaires and focused group discussions (FGDs) in various communities throughout the study area. The stress of accessing drinking water was evaluated in different seasons and during climate extreme events. The correlation analysis was used to further examine the relationship between specific variables and people's perceptions of major observed climate change as they induce water stress. The results from local people's perception of climate change impacts on safe drinking water supply reflect meteorological analysis, which indicates that the mean minimum temperature has increased, 1.0° <Tmin> 1.3°C in the urban sprawl communities. The results indicate that age and time living in the neighbourhood have a significant influence on how people perceive and understand climate change as they induce water stress. These have resulted in much stress for women, who are forced to walk a long distance to fetch drinking water for the households, during the extremely dry seasons.

A data reconstruction-based Monte Carlo method for remaining useful life prediction of lithium-ion battery with few historical samples

Lithium-ion battery (LIB) has been widely used in many energy storage systems, and the accurate remaining useful life (RUL) prediction of LIB is essential to ensure the safe operation of these systems. However, for the LIB installed in a new system, there are only few historical samples available for RUL prediction, which makes it difficult to build accurate RUL prediction model for LIB. Therefore, this paper proposes a data reconstruction-based Monte Carlo method to solve the problems caused by few historical samples. First, a Wiener process-based data reconstruction algorithm is used to reconstruct historical dataset, providing sufficient prior information for the Monte Carlo method. Then, Bayesian theory and a parameter update scheme are proposed to update the reconstructed dataset and the corresponding parameter distributions, so that the training data of RUL prediction model can be closer to the data of target LIB. Finally, long short-term memory neural network is applied for RUL prediction. The effectiveness of our model is verified by two real LIB datasets. Compared with some existing RUL prediction models of LIB, the proposed model has better generality and higher prediction accuracy under the condition of few historical samples.

High frequency environmental DNA metabarcoding provides rapid and effective monitoring of fish community dynamics

AbstractLong‐term monitoring is critical to measure the response of biodiversity patterns and processes to human‐mediated environmental pressures. This is particularly pertinent in freshwaters, where recent estimates indicated a third of all fish species are threatened with extinction, making ongoing biomonitoring essential for conservation management. High frequency annual monitoring is critical for identifying temporal changes in fish community composition; however, traditional survey methods are typically less practical over such timeframes. While environmental (e)DNA measurement represents a potentially powerful tool for monitoring temporal community dynamics, studies are lacking. To address this deficit, we generated a high frequency time‐series dataset of entire fish communities using eDNA metabarcoding, to directly assess the repeatability and sensitivity of this method for detecting annual population trends. We targeted two differing environments (freshwater vs. intertidal) within the Thames catchment, UK, where detailed historical records from traditional monitoring were available for comparison. To test how robust eDNA data is for inferring the known community, we applied a hierarchical, nested design encompassing short and longer‐term variation in eDNA data. Our analyses showed that irrespective of environment, eDNA metabarcoding represented known seasonal shifts in fish communities, where increased relative read abundance of eDNA coincided with known migratory and spawning events, including those of the critically endangered native species Anguilla anguilla (European eel). eDNA species detections across a single year included over 75% of species recorded in a ca. 30‐year historical dataset, highlighting the power of eDNA for species detection. Our findings provide greater insight into the utility of eDNA metabarcoding for recovering temporal trends in fish communities from dynamic freshwater systems and insight into the potential best sampling strategy for future eDNA surveys.

Evaluating the Feasibility of a Shared-Fleet Operation in Healthcare Logistics between Public Organisations

Shared-fleet logistics involves collaboration between two or more companies to combine workloads and vehicle capacity to improve vehicle utilisation and transport efficiency and to reduce costs. This study considers the potential environmental and economic benefits of implementing a shared-fleet collaboration between two public organisations: a local government authority and a National Health Service (NHS) Trust. The research focuses on a specific case study using a historical dataset of vehicle movements, wherein the local authority’s fleet of electric courier vehicles is shared with the NHS Trust for transporting pathology samples from 78 doctors’ surgeries to a laboratory for analysis, thereby replacing the reliance on a third-party logistics provider. The benefits suggested by the results included a 17% reduction in costs, a 3% decrease in overall vehicle kilometres travelled, a 69% decrease in carbon dioxide (CO2) emissions, a 40% reduction in vehicle numbers, and a 27% reduction in total duty time. These results emphasise the considerable potential of shared-fleet operations to alleviate both environmental and economic problems in urban logistics, encouraging public sector organisations to actively pursue the implementation of collaborative solutions to enhance the efficiency of their own-account vehicles while making positive contributions to environmental sustainability.

Modelling the historic total global human population

The historic total global human population dataset is available on Wikipedia and provides an opportunity for modelling with simple models such as the exponential and logistic differential equations for population. Using the per-capita population growth rate (PPGR) predicted by these two models and estimated PPGR from the data, we are able to estimate parameters for the exponential model and discover that we cannot estimate parameters for the logistic model. However, we are able to create a new differential equation that models this data using ideas from the derivation of the logistic model. This is the superexponential model that goes to infinity in finite time. While the initial exponential and superexponential models are good for approximating the population data, neither really matches the PPGRs estimated from the data. We switch to a hybrid model that better matches the estimated PPGRs. It is superexponential for the first portion of the data and logistic for the rest. This hybrid model greatly reduces the modelling error and can extrapolate from the data. We note that the model switches between superexponential and logistic around 1960, the year the FDA-approved oral contraceptives, a fascinating historical tie-in with our modelling.

Lepidoptera research in Puerto Rico: Reconnecting with historical legacies to guide future priorities

AbstractThe Caribbean archipelago of Puerto Rico supports a diverse flora and fauna and is located in a region characterized by complex socio‐economic and environmental change. The diversity of entomofauna across Puerto Rico has received considerable attention in wide‐scale research over the last century, with particular emphasis on the order Lepidoptera as the subject of substantial taxonomic and ecological surveys. However, much of this work is incomplete, outdated, or has been obscured in gray literature. Thus, our primary objectives were to contextualize the role of past research in the current understanding of Puerto Rican Lepidoptera and to outline an agenda for future research. Specifically, we provide an overview of taxonomic, ecological, agricultural, and conservation Lepidoptera research in Puerto Rico and highlight key studies and historical datasets. We found that, despite a strong taxonomic legacy, native moth taxonomy remains poorly understood, except for a few major pests. Further, much of the recent Lepidoptera research has focused on short‐term evaluations of agricultural pests, necessitated by immediate economic needs. The current ecological status of Lepidoptera on the islands is unknown. Therefore, prioritizing ecological research could provide timely insight for understanding changing Lepidoptera diversity and distribution and for conserving this biologically and economically significant group. Greater emphasis on long‐term monitoring and digitization of museum collections would be particularly useful for quantifying past and forecasting future impacts of global change.Abstract in Spanish is available with online material.

Phenomenological Studies of Visual Mental Imagery: A Review and Synthesis of Historical Datasets.

This article reviews historically significant phenomenological studies of visual mental imagery (VMI), starting with Fechner in 1860 and continuing to the present. This synthesis of diverse VMI phenomenological studies in healthy adults serves as a unique resource for investigators of individual differences, cognitive development and clinical and neurological conditions. The review focuses on two kinds of VMI, "memory imagery" and "eidetic imagery". Ten primary studies are drawn from three periods of the scholarly literature: early (1860-1929), middle (1930-1999) and recent (2000-2023). It is concluded that memory and eidetic imagery are two forms of constructive imagery, varying along a continuum of intensity or vividness. Vividness is a combination of clarity, colourfulness and liveliness, where clarity is defined by brightness and sharpness, colourfulness by image saturation and liveliness by vivacity, animation, feeling, solidity, projection and metamorphosis. The findings are integrated in a template that specifies, as a tree-like structure, the 16 properties of VMI vividness in healthy adult humans. The template takes into account the weight of evidence drawn from the accounts and reveals an extraordinary degree of consistency in reported VMI characteristics, revealed by specialized studies of healthy adult humans across time, space and culture.

Intelligent Network Maintenance Modeling for Fixed Broadband Networks in Sustainable Smart Homes

Due to the emergence of sustainable smart homes, each smart device requires more bandwidth putting pressure on the existing home networks. A very good solution to ensure high-bandwidth home networks is the Fiber-to-the-Home (FTTH) technology. FTTH delivers high-speed Internet from a central point directly to the home through fiber optic cables. This fixed broadband network can transmit information at virtually unlimited speed and capacity enabling homes to be smarter. Hence, a well-monitored and well-maintained FTTH broadband network is necessary to obtain a high level of service availability and sustainability in smart homes. This study aims to develop a predictive model that will proactively monitor and maintain FTTH networks through the use of sophisticated modeling techniques such as machine learning (ML). The predictive model targets to classify the proposed technician resolution based on the historical FTTH field dataset. The results show that the K-Nearest Neighbors (KNN)-based model obtained the highest accuracy of 89% followed by the Feed-Forward Artificial Neural Network (FF-ANN)-based model with 86%. In addition, the identified anomalies from the dataset affecting service degradation and performance include FTTH access issues, ONU issues, and faults in customer premises equipment.

Large-scale real-world data analysis of source plasma collections using a novel technology-enabled nomogram.

Source plasma collections are needed to satisfy the growing demand for plasma-derived medicinal products. The US plasma collection target volume has been guided by a standard weight-based FDA-issued nomogram (STAN) since 1992. In this research, large-scale US-based real-world data (RWD) were analyzed to confirm the safety and volume gains of a newly introduced personalized nomogram (PERS) that was previously studied in a premarket randomized controlled environment. A non-inferiority (NI) analysis was conducted to compare the novel nomogram's significant hypotensive adverse event (AE) incidence rate with large historical standard nomogram AE datasets. Additionally, the average target volumes and donor return rates were compared for collections following PERS and STAN. A total of 4,816,784 donations (PERS) by 414,957 donors resulted in a rate of 0.0998% (95% CI [0.0970, 0.1027]) significant hypotensive AEs. NI analysis suggested strong non-inferiority of the new technology (Δ = -0.0082%, 95% CI [-0.0113, -0.0050], prespecified NI margin = 0.1080). Average plasma collection target volumes increased by 66.39 mL (8.49%; p < .0001). Consecutive weekly donor return rates were consistent between the two nomograms (PERS: 73.6%, 95% CI [69.6%-76.7%]; STAN: 74.1%, 95% CI [66.1%-77.2%]). This analysis confirms in a large-scale real-world dataset the key safety parameter and collection benefit of a novel, technology-enabled nomogram. The nomogram may help meet the growing demand for plasma-derived therapies by providing approximately 8.5% more plasma per donation on average while maintaining donor safety and return rates.

A Multi-Agent Prediction Method for Data Sampling and Transmission Reduction in Internet of Things Sensor Networks.

Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods.

Grey-box model for model predictive control of buildings

Model predictive control (MPC) can improve energy efficiency and demand-side flexibility in buildings. Developing a grey-box model suitable for MPC is not straightforward, especially in buildings combining, not only ventilation and usual internal loads, but also Thermally Activated Building Structures (TABS) and large glass façades with external shading. To address these complexities, this paper presents a reduced order grey-box approach, considering all these elements. Various single zone model structures are compared, combining resistance–capacitance model, with finite difference or finite volume methods for modelling the TABS. The performance of these various model structures is evaluated using experimental data from a well-equipped living laboratory building. Additionally, the influence of technical parameters on the model's performance is investigated.The best model variant, with an enhanced glass façade model, achieves an accuracy of 0.25 °C of Mean Absolute Error over a year of simulation, on the 24 h zone temperature forecast compared to the measurement. This model has a small number of parameters (8), which are estimated with the least square non-linear method. The stability of the parameter values is analysed. The parameter identification requires only a small historical dataset of 1–2 weeks for startup and 2–4 weeks for training. This provides an adaptive model, in the sense that it is updated regularly (every day or week) based on recent measurement data. This data-driven evolving model is suitable across a wide range of applications involving data-driven Model Predictive Control (MPC) for buildings.

Prospective comparison of prognostic scores for prediction of outcome after out-of-hospital cardiac arrest: results of the AfterROSC1 multicentric study

BackgroundOut-of-hospital cardiac arrest (OHCA) is a heterogeneous entity with multiple origins and prognoses. An early, reliable assessment of the prognosis is useful to adapt therapeutic strategy, tailor intensity of care, and inform relatives. We aimed primarily to undertake a prospective multicentric study to evaluate predictive performance of the Cardiac Arrest Prognosis (CAHP) Score as compare to historical dataset systematically collected after OHCA (Utstein style criteria). Our secondary aim was to evaluate other dedicated scores for predicting outcome after OHCA and to compare them to Utstein style criteria.MethodsWe prospectively collected data from 24 French and Belgium Intensive Care Units (ICUs) between August 2020 and June 2022. All cases of non-traumatic OHCA (cardiac and non-cardiac causes) patients with stable return of spontaneous circulation (ROSC) and comatose at ICU admission (defined by Glasgow coma score ≤ 8) on ICU admission were included. The primary outcome was the modified Rankin scale (mRS) at day 90 after cardiac arrest, assessed by phone interviews. A wide range of developed scores (CAHP, OHCA, CREST, C-Graph, TTM, CAST, NULL-PLEASE, and MIRACLE2) were included, and their accuracies in predicting poor outcome at 90 days after OHCA (defined as mRS ≥ 4) were determined using the area under the receiving operating characteristic curve (AUROC) and the calibration belt.ResultsDuring the study period, 907 patients were screened, and 658 were included in the study. Patients were predominantly male (72%), with a mean age of 61 ± 15, most having collapsed from a supposed cardiac cause (64%). The mortality rate at day 90 was 63% and unfavorable neurological outcomes were observed in 66%. The performance (AUROC) of Utstein criteria for poor outcome prediction was moderate at 0.79 [0.76–0.83], whereas AUROCs from other scores varied from 0.79 [0.75–0.83] to 0.88 [0.86–0.91]. For each score, the proportion of patients for whom individual values could not be calculated varied from 1.4% to 17.4%.ConclusionsIn patients admitted to ICUs after a successfully resuscitated OHCA, most of the scores available for the evaluation of the subsequent prognosis are more efficient than the usual Utstein criteria but calibration is unacceptable for some of them. Our results show that some scores (CAHP, sCAHP, mCAHP, OHCA, rCAST) have superior performance, and that their ease and speed of determination should encourage their use.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT04167891

Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.

Development and Validation of Nonhuman Primate Head-Neck Computational Model for Frontal Impact Injury Analysis

Abstract Severe cervical spine injuries are rare in an automobile crash, however, the recovery for an individual is difficult. With no suitable surrogate in the laboratory setting, the exact head-neck (HN) response to severe impact accelerations is unknown. Therefore, this study aimed to develop a nonhuman primate (NHP) HN model and validate it using a historic NHP kinematic dataset that tested noninjury, as well as injury-inducing impact accelerations. The geometry of the NHP HN model was constructed from a previously CT-scanned skeleton and idealized as a two-dimensional quadrilateral shell mesh. Inertial properties of the vertebra and skull were defined, as well as 1D beam elements representing the spinal ligaments and discs. The model was then driven at the T1 vertebra using a literature-derived 10G acceleration curve to simulate frontal impact. Output peak Head X-acceleration was measured at 19.8G, which fell within the average peak response of 18.8 ± 4.6 G. Capsular ligament and interspinous ligament strains were measured along the cervical spine and the relative magnitudes were consistent with areas of likely injury at more severe impact accelerations. Once tested at more severe impact accelerations, this NHP HN model will provide a suitable way to study potential human cervical spine dynamics during frontal impact.

Water Typology in the Amazon: Close Correlation with the Hydrogeochemistry of River Basins

A study on the typology of Amazonian waters was developed in the North Geographic Region of Brazil, considering a set of historical data from 1995 to 2022. The objective was to discuss and point out which environmental parameters are considered preponderant within the classification criteria of Amazonian waters. Historical data on temperature, density, pH, conductivity, oxygen, transparency, light attenuation, turbidity, color, total and dissolved solids, hardness, alkalinity, CaCO3, BOD, COD, main cations and anions, iron, C, N and P fractions, silicate, trace elements (Al, Cu, Zn and Mn) and chlorophyll a were analyzed. The analysis comprised water samples from the National Hydrometeorological Network (Sub-Basins 10 to 19), with a total of 36 basins, including tributaries, lakes and adjacent streams. The historical series was statistically analyzed through the techniques of Cluster, Principal component analysis (PCA), Piper Diagram and image interpolation by the ordinary kriging method. The results indicated a strong seasonal trend, with an evident distinction between the flood and ebb periods. The typology of white, black and clear waters was also evidenced, this last group being an intermediate class to the others, due to the physical-chemical standards. The PCA highlighted the parameters conductivity, pH, calcium, bicarbonate and Dissolved inorganic carbon (DIC) (Factor 1), and turbidity and alkalinity (Factor 2) as preponderant in the water classification. The ionic balance demonstrated that the white waters stood out for the highest concentrations of calcium and magnesium. The results suggest a pattern of calcic waters oscillating towards sodic and carbonated waters, with a subgroup in the northern eastern region of seasonally sodic-chlorinated waters, due to the influence of ocean tides. Image interpolation suggested a predominance of white waters in the western southern zone (Western Amazon) and white waters transitioning to clear waters in the eastern zone (Eastern Amazon). The analysis also showed a predominant strip of black and clear waters in the northern zone, and a strip of white waters in the central Amazon.

Creating a dataset of historic roads in Sydney from scanned maps

This study creates a historic dataset of road opening dates in Sydney. A method was developed for map digitization to extract spatial data from historic maps and place them in a collective vector layer. The method includes extensive georeferencing of the maps, as well as editing and cleaning the maps through raster and vector analysis. Preferred methods for map digitization used in the project were identified. For a considerable area of Sydney, in which approximately 52000 road links were included, almost half of the links were identified with an open date by the start of the twentieth century. A further half of these links were confined to opening within a thirty-year period. The project has established a strong foundation for a historic road dataset for Sydney. It has also outlined methods and procedures that can be followed to progress the dataset further.

Lowland forest loss and climate-only species distribution models exaggerate a forest-dependent species' vulnerability to climate change

Climate-only species distribution models are commonly used to evaluate a species' vulnerability to future climate change. However, these models may overestimate a species' vulnerability when occurrence data comes from only a subset of the species' historical range due to anthropogenic range contractions. This study investigated the impacts of using climate-only species distribution models with anthropogenically truncated occurrence data on future predictions, using the little-studied leopard cat (Prionailurus bengalensis) of mainland Tropical Asia as a case study. Like most other species in mainland Tropical Asia, the leopard cat's current distribution represents a non-random subset of its historical range due to the disproportionate loss of lowland forests in the region. We found that climate-only species distribution models trained on current occurrence data significantly overestimated the leopard cat's vulnerability to future climate change, predicting considerable range contractions by 2081–2100 due to rising summer temperatures. A similar range contraction was observed using a virtual species whose occurrence was solely predicated on extant forest cover and not climate. This suggested the leopard cat's apparent vulnerability to future climate change was driven by a spurious relationship with summer temperatures, as modern occurrence data primarily came from refugial forest cover in the region's cooler uplands rather than the warmer, human-dominated lowlands. This bias was not apparent, however, from our current predictions, which had high predictive accuracy and were spatially consistent with current knowledge of the species' distribution; an observation highlighting the inadequacy of predictive accuracy alone as a metric for selecting models in studies where transferability to novel environments is critical. We next considered two potential strategies for detecting and mitigating the effects of anthropogenically truncated occurrence data: 1) the use of hybrid models that incorporate both climatic and non-climatic variables, and 2) the use of climate-only models with historical datasets. Despite using different data, both proposed strategies produced spatially similar predictions of present and future leopard cat occurrence with little-to-no range contractions predicted. We thus recommend all species distribution modeling studies interested in assessing a species' vulnerability to climate change compare future predictions derived from climate-only and hybrid models as well as compare predictions from current and historical datasets, as differences in predictions can highlight the presence of potential biases and other limitations. Finally, we discuss our findings as they apply to both our focal species and to the overall field of species distribution modeling.

Digitization of weather records ofSeungjeongwon Ilgi: A historical weather dynamics dataset of the Korean Peninsula in 1623–1910

AbstractHistorical weather records from Europe indicate that the Earth experienced substantial climate variability, which caused, for instance, the Little Ice Age and the global crisis in the period between the 14th and 19th centuries. However, it is still unclear how global this climate variability was because of the scarce meteorological data availability in other regions including East Asia, especially around the 17th century. In this context,Seungjeongwon Ilgi, a daily record of the Royal Secretariat of the Joseon Dynasty of Korea, is a precious source of historical meteorological records for the Korean Peninsula, as it covers 288 years of weather observations made during 1623–1910. We used the digital database ofSeungjeongwon Ilgito construct a machine‐readable weather condition dataset. To this end, we extracted valid weather information from the original weather description text and compiled them into pre‐defined weather categories. Additionally, we attempted to improve the usability of dataset by converting the reported dates in the traditional calendar system to those in the Gregorian calendar. Finally, we outlined promising implications of this dataset for meteorological and climatological studies, while describing the limitations of the dataset. Overall, future studies focusing on the climate and weather of the past could use this meteorological database for investigating long‐term climate variability. Our datasets are publicly available at10.5281/zenodo.8382243.

Minimizing fuzzy makespan in a distributed assembly flow shop by using an efficient artificial bee colony algorithm

Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades; however, the distributed AFSP with DPm → 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented; a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed; the historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances.