Historical Data Research Articles

Development of an inclusive, scalable, and flexible hydrologic modeling system: Establishing integrated flood simulation system at agricultural watersheds

In this study, we developed a comprehensive hydrological modeling system to address the diverse needs of hydrologists and researchers. The system comprised nine modules, each serving a specific purpose. These modules include a multiplicative random cascade model, frequency analysis, inflow simulation, Hydrologic Engineering Center – 5, Hydrologic Engineering Center – River Analysis System, and farmland drainage simulations. The system follows a structured sequence within a large framework, beginning with user inputs for the initial condition information and period specifications for frequency analysis. It then calculates the time-disaggregated precipitation data utilizing the Dask distributed server for efficient computation. Subsequent module computations were conducted on dedicated mask workers. The Integrated Database serves as a comprehensive repository for simulation studies, encompassing historical precipitation data, shared socioeconomic pathways, climate change scenario data, and reservoir and farmland survey data. This system has been used in several studies and has provided cohesive and reliable results for flood simulations.

Interpretation of Ambiguous Segments in the Zo’é Language

This study, conducted as part of a project at the Federal University of Western Pará, re-evaluates the phonological system of the Zo’é language to address discrepancies in phonetic and phonological interpretations identified since the 1990s. Central to this research is the reinterpretation of ambiguous sounds, such as affricates, labialized, and palatalized consonants, as well as approximants. Utilizing historical data and recent recordings, the study identifies systematic processes preserving syllable patterns, including syllable restructuring at morpheme boundaries. The findings propose that sounds like [kʲ], [gʲ], [ŋʲ], [pʲ], [bʲ], [hʲ], [mʲ], [kw], [ʧw], [gw], [ʧ], and [ʤ] function as simple sounds. Additionally, the study explores the complex role of glides [w] and [j], suggesting their dual interpretation as consonants in onset positions and vowels in coda positions. By resolving phonological ambiguities and establishing a cohesive phonetic framework, this research lays the groundwork for a unified orthographic system for the Zo’é language, contributing significantly to its linguistic documentation and preservation.

On Multi-Fidelity Impedance Tuning for Human-Robot Cooperative Manipulation

Abstract We examine how a human-robot interaction (HRI) system may be designed when input output data from previous experiments are available. Our objective is to learn an optimal impedance in the assistance design for a cooperative manipulation task with a new operator. Due to the variability between individuals, the design parameters that best suit one operator of the robot may not be the best parameters for another one. However, by incorporating historical data using a linear auto-regressive (AR-1) Gaussian process, the search for a new operator's optimal parameters can be accelerated. We lay out a framework for optimizing the human-robot cooperative manipulation that only requires input-output data. We characterize the learning performance using a notion called regret, establish how the AR-1 model improves the bound on the regret, and numerically illustrate this improvement in the context of a human-robot cooperative manipulation task. Further, we show how our approach's input-output nature provides robustness against modeling error through an additional numerical study.

Mucosal epithelial homeostasis: Reference intervals for skin, gill lamellae and filament for Atlantic salmon and other fish species.

Mucosal barriers are gatekeepers of health and exhibit homeostatic variation in relation to habitat and disease. Mucosal Mapping technology provides an in-depth examination of the dynamic mucous cells (MCs) in fish mucosal barriers on tangential sections, about 90° from the view of traditional histology. The method was originally developed and standardized in academia prior to the establishment of QuantiDoc AS to apply mucosal mapping, now trademarked as Veribarr™ for the analysis of skin, gills and gastrointestinal tracts. Veribarr™ uses design-based stereology for the selection and measurement of cell area (size) (μm2), the volumetric density of MCs in the epithelium (MCD, amount of the epithelia occupied by MCs, in %) and the calculated abundance of the MCs (barrier status or defence activity). MC production was mapped across the skin and gill epithelia in 12 species, discovering that gills consistently have two distinct groups of MCs, one on the lamellae where MCs are few and small and one on the filament where MCs are larger and more abundant. MCs were usually much larger in the skin than in the gills, with the latter requiring fewer and smaller cells for adequate respiration. The difference observed between MCs in gill lamella and gill filament is likely a result of functional demands. In addition, our findings also highlight a variation in the mucosal parameters between the species skin, which cannot be explained by the weight differences, and a potential link between MC distribution and species-specific lifestyles in the gill lamella. This diversity necessitates the development of species and tissue site-specific reference intervals for mucosal health evaluation. Mucosal bivariate reference intervals were developed for MC production, including size (trophy) and calculated defence activity (plasia) in the skin and gills of Atlantic salmon, to contrast new measurements against historical data patterns. The application of mucosal reference intervals demonstrates that stress from parasites and treatments can manifest as changes in mucosal architecture, as evidenced by MC hypertrophy and hyperplasia within the gill lamellae. These reference intervals also facilitate comparisons with wild Atlantic salmon, revealing a somewhat higher MC level in farmed salmon gill lamellae. These findings suggest that MC hyperplasia and hypertrophy in the gills are stress/environmental responses in aquaculture. They also advocate for developing specific mucosal bivariate homeostatic reference intervals in aquaculture to improve fish health and welfare across all farmed species.

An agroecological approach to preparation and use of a milk protein production baseline.

Commercial dairy production occurs in a complex management environment, but increasingly, the dairy manager is expected to provide detailed reporting of productivity and environmental outcomes, for which conventional research methods double-blind crossover or case:control trials are inappropriate. This paper demonstrates the development of a milk protein production monitoring tool using a temporal (baseline) control in longitudinal, census-type investigations of modulation of system performance in response to factor change. It utilises farm-derived current and historical data, and contrasts seasonal responses with those achieved on neighbouring farms in a 2×2 contingency table. The approach is then shown to be useful in assessing the effect of two approaches to moderating milk urea concentration. Firstly, milk urea content can be monitored as it falls due to reduced feed protein content, and this fall can be arrested when milk protein content starts to decline relative to the value expected for the herd at any lactation stage. Secondly, by providing a dietary intervention aimed at increasing the availability of metabolic energy in the last month before calving, udder development can be augmented, leading to greater protein secretion capacity, meaning greater utilisation of circulating amino acids, and thus more limited substrate for urea synthesis. Thus, the changing impact of differing nutrition practices on dairy herd nitrogen excretion to environment can be followed with daily precision. In principle this approach can provide useful insights into a wide range of practical management interventions.

A new method for thermal runaway warning of electric vehicle charging

Abstract With the large-scale development of electric vehicle (EV) in China, the frequent occurrence of EV fire accidents has attracted the attention of insiders to the fault monitoring and early warning during the charging process of EV. According to data from the National Emergency Management Ministry, there were over 3,000 EV fire accidents nationwide in 2023, underscoring the urgency for enhanced fault monitoring and early warning measures. In response to these issues, this article proposes an EV thermal runaway early warning method based on Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) by combining the two technologies. The method aims to improve EV safety by monitoring battery status for early thermal runaway warning. First, the historical normal charging data of the battery are preprocessed and feature extracted using the TCN, and then features fed into GRU for time-series modeling and forecasting. This combined model not only demonstrates high prediction accuracy and stability but also swiftly responds to abnormal conditions during charging, effectively preventing thermal runaway accidents and ensuring charging safety. Furthermore, this model possesses excellent generalization capabilities and can adapt to different types and specifications of EV battery systems.

Potential formation mechanism and prediction of crude oil price based on underdetermined independent component analysis

In most crude oil price prediction, a few of them explored the potential formation mechanism of oil price. This study proposes a novel multi-scale integrated (MSI) learning paradigm to mine the potential formation mechanism of the crude oil price and predict the crude oil price. Firstly, we propose a novel underdetermined independent component analysis (UICA) model based on extreme-point symmetric mode decomposition (ESMD) and independent component analysis (ICA) to study the potential formation mechanism of crude oil price. Based on the representative independent components (ICs), we found and analyzed some factors affecting the crude oil price from high frequency to low frequency, as well as the potential formation mechanism of crude oil price, such as geopolitical implications, natural disasters, historical pricing data and copper futures price. Moreover, the ICs are predicted and generated the final forecasting result of crude oil price via radial basis function neural network (RBFNN). The experimental results based on the West Texas Intermediate (WTI) crude oil price, confirm that the proposed MSI model is superior to the existing prediction models in terms of statistical and error performance criteria and Diebold-Mariano (DM) test with the optimal D stat = 99.0220 , MAE = 0.0050 , RMSE = 0.0537 , MAPE = 0.0116 , ESD = 0.0007 , R 2 = 0.9960 .

A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems

AbstractGrasslands and savannas are experiencing transformation and degradation due to bush encroachment (BE). BE has been monitored using restrictive traditional techniques that include field surveys and manual long-term observations. Owing to the limitations of traditional techniques, remote sensing (RS) is an attractive alternative to assess BE because of its generally high precision and return interval, cost-effectiveness, and availability of historical data archives. Furthermore, RS has an added advantage in its ability of acquiring global coherent data in near-real time compared to the snapshot acquisition mode with traditional surveying techniques. Despite its extensive application and vast possibilities, a critical synthesis for RS successes, shortcomings, and best practices in mapping BE in savannas and grasslands is lacking. Thus, broadly, the direction, which this type of investigation has taken over the years is largely unknown. This study sought to connect and measure the progress RS has made in mapping BE in grassland and savanna ecosystems through bibliometric analysis. One hundred and twenty-three peer-reviewed English written documents from the Web of Science and Scopus databases were evaluated. The study revealed 13.05% average annual publication growth, indicating that RS and BE mapping research in grasslands and savannas has been increasing over the survey period. Most published studies came from the USA, while the rest came from South Africa, China, and Australia. The results indicate that BE has been extensively mapped in grasslands and savannas using coarse to medium resolution data. As a result, there is a weak relationship (r² = 0.324) between the dependent variable (aerial images) and the independent variable (percentage of woody cover). This connotes the need to improve BE assessments in grasslands and savannas by integrating recent high-resolution data, machine learning algorithms and artificial intelligence.

Advancing Sustainability in a Residential Building in Europe: A Case Study on LCA and LCC across Different Scenarios

This study evaluates the environmental and economic performance of the "ESTIA of Athens" residential building considering its energy consumption and greenhouse gas (GHG) emissions. It also explores the potential for improvements in these areas. The European Union's strategy to reduce GHG emissions from buildings by 55% compared to 1990 levels by 2030, as part of its broader aim for climate neutrality by 2050 is a background for the study. The methodology involves a Life Cycle Assessment (LCA) using SimaPro software, monitoring energy consumption, temperature and humidity of the building. The thirteen impact categories and damage impact assessment that include human health, ecosystem and resources were examined. The study compares various scenarios, including historical data from 2018 to 2022 and two hypothetical scenarios to assess environmental impacts across different categories. Additionally, Life Cycle Costings (LCC) are performed to evaluate the economic aspects of the building's performance. The results highlight substantial differences in energy consumption, GHG emissions and economic costs among the scenarios. The findings suggest that a hypothetical scenario, referred to as Case 3, demonstrates lower environmental impacts and economic costs compared to other scenarios, indicating its potential as an optimal renovation strategy for the building. This includes reductions of 32%, 40% and 58% in the human health, ecosystems, and resources categories, respectively. A similar trend is observed across the impact categories, with reductions ranging from 4% in the Mineral Resource category to 47% in the Global Warming category. The study underscores the importance of holistic assessments in informing energy policy and renovation strategies for achieving both environmental sustainability and economic viability in buildings.

AI-Assisted Work Planning - Extracting expert knowledge from historical data for streamlined efficiency and error mitigation

AI-Assisted Work Planning - Extracting expert knowledge from historical data for streamlined efficiency and error mitigation

The Impact of Political Events on Currency Exchange Rates

This research paper examines the intricate relationship between political events and currency exchange rates. By analyzing historical data, conducting quantitative and qualitative analyses, and developing a predictive model, the study identifies significant impacts of political events on currency volatility, particularly in emerging markets. The research also highlights the importance of political stability and strong institutions in mitigating these effects. Furthermore, it explores the role of economic openness and political systems in shaping currency stability. The findings offer valuable insights for policymakers, investors, and businesses operating in the global market. Keywords: Political events, currency exchange rates, political risk, foreign exchange markets, geopolitical risk, market volatility

Diagnosis of DC-DC Converter Semiconductor Faults Based on the Second-Order Derivative of the Converter Input Current

The deployment of DC microgrids presents an excellent opportunity to enhance energy efficiency in buildings. Among other components, DC-DC converters play a crucial role in ensuring the interface between the microgrid and its energy generation, storage, and consumption components. However, the reliability of these energy conversion solutions remains somewhat limited. Adopting strategies for accurate monitoring and diagnostics of the DC-DC converter topologies that best suit each equipment’s constraints is, therefore, of critical relevance. Solutions available in the literature concerning fault diagnostics on DC-DC converters do not consider the application of such converters in the household and tertiary sector environments and associated constraints—cost effectiveness, robustness against parameter uncertainty of the converter model, and obviation of the need for historical data. On this basis, this paper presents a simple and effective fault diagnostic strategy, based on a time-domain analysis of the second-order derivative of the converter input current. Its implementation is straightforward and can be integrated into the pre-installed converter control unit. The unique features of the fault diagnostic algorithm show good results for a broad range of operating points, along with insensitivity against load transients and supply voltage fluctuations.

Detection and Measurement of Bacterial Contaminants in Stored River Water Consumed in Ekpoma

This study was conducted in Ekpoma, a town dependent on rainwater and river water from nearby areas because of a lack of groundwater sources, and the physicochemical and bacteriological (heterotrophic plate count [HPC], total coliform count [TCC], and fecal coliform count [FCC]) properties of 123 stored river water samples grouped into five collection districts (EK1 to EK5). The results were compared with regulatory standards and previous regional studies to identify water quality trends. While most physicochemical properties met drinking water standards, 74% of samples had pH values > 8.5. Twenty-seven samples were fit for drinking, with EK4 having the highest number of bacterio-logically unsuitable samples. Ten bacterial species were identified, with Gram-negative short-rod species such as Escherichia coli, Klebsiella pneumoniae, and Salmonella typhimurium being predominant. HPC values varied from 367 × 10⁴ to 1320 × 10⁴ CFU/mL, with EK2 (2505 × 10⁴ CFU/mL) and EK5 (1320 × 10⁴ CFU/mL) showing particularly high counts. The TCC values ranged from 1049 × 10⁴ to 4400 × 10⁴ CFU/mL, and the FCC values from 130 × 10⁴ to 800 × 10⁴ CFU/mL, all exceeding WHO limits (1.0 × 102 CFU/mL). Historical data show no improvement in water quality, emphasizing the need for individuals to treat water properly before consumption. The findings provide baseline data for local water authorities and serve as a wake-up call for adequate water treatment, storage interventions, and community education on water security. Additionally, this study offers a practical process for improving the quality of water stored in similar regions.

Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain

AbstractThis paper addresses the challenges of low‐carbon sourcing in intertwined supply chains by proposing a data‐driven control framework and a prey–predator model for sourcing decisions. The objective is to optimize low‐carbon objectives and reduce environmental impact. Existing static models fail to capture the dynamic nature of supply chain systems and overlook the ripple effects when sourcing decisions propagate throughout the interconnected network. To bridge this gap, our study develops a dynamic model that explicitly captures the bullwhip effect and leverages real‐time and historical data. This model conceptualizes suppliers as prey and manufacturers and consumers as predators, employing an ecological analogy to decipher the intricate interactions and dependencies within the supply chain. Through this approach, we identify strategies to promote sustainable practices and motivate suppliers to adopt low‐carbon measures. We assess two data‐driven algorithms, the nonlinear auto‐regressive exogenous (NARX) network and sparse identification of nonlinear dynamic systems with input variables (SINDYc). The results reveal that SINDYc outperforms prediction accuracy and control, offering significant advantages for rapid decision‐making. The study highlights how shifts in market demands and regulatory pressures critically influence the strategies of chemical firms and fertilizer markets. Moreover, it discusses the economic challenges in transitioning from high carbon footprint suppliers (HCFSs) to low carbon footprint suppliers (LCFSs), exacerbated by a notable cost disparity where HCFSs are approximately 30% cheaper. By advancing beyond conventional static models, this research provides a deeper understanding of the environmental impacts and operational dynamics within supply chains, emphasizing the significant “ripple effect” where decisions at one node profoundly affect others within the chain.

Who (actually) gets the farm? Intergenerational farm succession in the United States

AbstractFarm succession is a central issue in agricultural policy. Yet although many studies explore succession planning, little is known about how farms are actually transferred. We provide the first population‐level evidence on intergenerational farm succession by linking US census records for millions of farmers' children in 1900 and 1910 to identify which children own and operate the family farm up to 40 years later. We first show that daughters are rarely successors. Using a within‐family identification strategy, we find that first‐born sons are slightly more likely than their younger brothers to be successors while their parents are working aged. However, birth order is not predictive of who receives the farm when parents are older or deceased. For later farm transfers, sons who were previously tenant farmers are much more likely than their brothers to be successors, possibly because they are better prepared. Fewer than one‐fifth of farmers transfer their farm to any son. Our study relies on rich historical data because current policy prevents the necessary data linkages for studying intergenerational farm succession. Providing a secure system for researchers to link modern agricultural data to population microdata, similar to what has been achieved with other data sources, would yield crucial insights into long‐term agricultural policy issues in the US.

The Prevalence of Anxiety and Depression in Children With Postural Orthostatic Tachycardia Syndrome (POTS): A Retrospective Study.

Postural orthostatic tachycardia syndrome (POTS) is a chronic form of orthostatic intolerance characterized by various symptoms such as dizziness, lightheadedness, and increased heart rate. Conflicting reports exist regarding the prevalence of anxiety and depression in adults with POTS, while data on pediatric POTS remains scarce. A retrospective analysis of pediatric patients aged 11-17 years with POTS, who underwent autonomic testing at Nemours Children's Hospital in Orlando, Florida, was conducted. The patients were screened for anxiety, using the Severity Measure for Generalized Anxiety Disorder-Child Age 11-17 years (GAD-7) questionnaire, and depression, using PHQ-9 Modified for Adolescence (PHQ-A) for depression. The prevalence rates of anxiety and depression in the study cohort were compared to historical data from similar age groups in the existing literature. The study was approved by the Nemours Children's Hospital Institutional Review Board. The cohort comprised 27 children with POTS (26 females, age 15.8±1.6 years). Overall, 74% exhibited moderate-to-severe anxiety, depression, or both, with 44% having comorbid anxiety and depression. In total, 4/27 (14%) had pure depression and 4/27 (14%) had pure anxiety. Six patients had no depression or anxiety. On average, POTS symptoms began 1.9±1.3 years before diagnosis. Eleven patients took stable doses of psychotropic medications. After a follow-up period of 5.1±1.7 months of POTS therapy, seven patients had follow-up questionnaires. In 4/7 patients, the depression severity improved, and in 3/7 patients, the anxiety severity improved. Patients were not actively treated for depression and anxiety during this time. Anxiety and depression are prevalent among pediatric patients with POTS. While preliminary data suggests POTS therapy may alleviate these psychological symptoms, further longitudinal studies are warranted to explore the therapeutic impact in greater detail.

Implementasi Algoritma Naïve Bayes dalam Memprediksi Tingkat Kelulusan Siswa pada Sertifikasi Mikrotik Certified Network Associate (MTCNA)

The development of technology has increased the importance of certification in computer networking, such as the MikroTik Certified Network Associate (MTCNA). This certification is crucial for students who want to validate their skills and enhance their career prospects. However, predicting student graduation rates for the MTCNA certification presents various challenges, such as prior knowledge, study habits, and the complexity of the exam content. These factors can significantly affect the accuracy of predictions regarding student graduation rates. This study aims to address these issues by implementing the Naïve Bayes algorithm to predict student graduation rates for the MTCNA certification. The Naïve Bayes algorithm, known for its simplicity and effectiveness in classification problems, is expected to provide a more accurate predictive model. This study uses historical data on student performance and other relevant attributes. The results show that the Naïve Bayes model has an adequate prediction accuracy, with an accuracy rate of 91.70%. These findings are expected to provide valuable insights for educators and students, thereby improving preparation strategies and success rates in the MTCNA certification exam.

Robustness-Based Approach for Slack Time Optimization in Tram Timetables

AbstractConsidering the unique interplay of trams with road traffic, this study explored the issue of instability in tram operations—a prominent medium-capacity rail transit. Our goal was to design a timetable slack time optimization method for scheduling slack time to improve the stability of tram operations. To facilitate this, we derived the travel/dwelling time distribution from historical data, which assisted in estimating interference times and evaluating the requisite slack time. We then developed an integer programming model to calculate both the punctuality rate and expected delay under varying travel times, enabling the creation of alternative slack time schemes. Using a unique tram operation simulation logic, we assessed the operational efficiency and reliability of these alternate schemes based on specific operational indicators. The results suggest that our novel approach to timetable optimization significantly enhances the tram’s adaptability to disruptions, directly improving the passenger experience and tram competitiveness. This work offers a robust framework for timetable optimization for semi-independent right-of-way public transportation.

Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.

Time series forecasting of price of the agricultural products using data science

Purpose. The purpose of our article is to research and forecast prices for agricultural products using the example of potato prices based on the most effective models using data science techniques. Methodology / approach. Various forecasting models are explored, starting from baseline models like decomposition and exponential smoothing models to more advanced techniques such as ARIMA, SARIMA, as well as deep learning models including neural network. The data is split into training and testing sets, and models are validated using cross-validation techniques and optimised through hyperparameter tuning. Model performance is evaluated using metrics such as MAE, MSE, RMSE, and MAPE. The selected model is then used to generate future price forecasts, with uncertainty quantified through confidence intervals. Results. The study successfully applied advanced data science techniques to forecast potato prices, leveraging a range of effective models. By analysing historical price data and using various forecasting methods, the research identified the most accurate models for predicting future price trends. The results demonstrate that the selected models can provide reliable forecasts. In particular, the results showed that the model could achieve good forecast results when applied to real problems and, thus, can be effectively used for forecasting tasks especially considering seasonality. In addition, it should be noted that the model has a higher prediction accuracy at the time intervals closest to the original data. The obtained results support using both models simultaneously for forecasting, which can compensate for the shortcomings of each of them. The models can be used separately, to more accurately predict the values for the required period, or a combination of them is also possible. Originality / scientific novelty. The study’s originality lies in development of methods for effectively accounting for seasonality in agricultural price data, such as using seasonal decomposition techniques or more advanced techniques that combine statistical and data science approaches. The novelty implies the implementation of real-time data processing and forecasting system allows for the timely prediction of price changes, enabling stakeholders to make more informed decisions. Practical value / implications. Forecasting potato prices holds significant practical value for various stakeholders. For farmers, accurate forecasts enable informed decisions on the optimal times to plant, harvest, and sell their crops, thereby optimising their profits. In the supply chain, distributors and retailers can use these forecasts to manage inventory more effectively and plan contracts, reducing waste and avoiding shortages. Policymakers benefit from forecasts by anticipating market fluctuations and stabilising prices, which supports both consumers and producers. For consumers, stable pricing ensures better budgeting and helps avoid sudden price spikes, making essential foods more affordable. Overall, accurate price forecasting enhances market efficiency by reducing uncertainty and aiding investors in managing risk.