Production Data Research Articles

AIGD-PFT: the first AI-driven global daily gap-free 4 km phytoplankton functional type data product from 1998 to 2023

Abstract. Long time series of spatiotemporally continuous phytoplankton functional type (PFT) data are essential for understanding marine ecosystems and global biogeochemical cycles as well as for effective marine management. In this study, we integrated artificial intelligence (AI) technology with multisource marine big data to develop a spatial–temporal–ecological ensemble model based on deep learning (STEE-DL). This model generated the first AI-driven global daily gap-free 4 km PFT chlorophyll a concentration product from 1998 to 2023 (AIGD-PFT). The AIGD-PFT significantly enhances the accuracy and spatiotemporal coverage of quantifying eight major PFTs: diatoms, dinoflagellates, haptophytes, pelagophytes, cryptophytes, green algae, prokaryotes, and Prochlorococcus. The model input encompasses (1) physical oceanographic, biogeochemical, and spatiotemporal information and (2) ocean colour data (OC-CCI v6.0) that have been gap-filled using a discrete cosine transform–penalized least squares (DCT-PLS) approach. The STEE-DL model utilizes an ensemble strategy with 100 residual neural network (ResNet) models, applying Monte Carlo and bootstrapping methods to estimate the optimal PFT chlorophyll a concentration and assess the model uncertainty through ensemble means and standard deviations. The model's performance was validated using multiple cross-validation strategies – random, spatial-block, and temporal-block methods – combined with in situ data, demonstrating STEE-DL's robustness and generalization capability. The daily updates and seamless nature of the AIGD-PFT data product capture the complex dynamics of coastal regions effectively. Finally, through a comparative analysis using a triple-collocation analysis (TCA) approach, the competitive advantages of the AIGD-PFT data product over existing products were validated. The complete product dataset (1998–2023) can be freely downloaded from https://doi.org/10.11888/RemoteSen.tpdc.301164 (Zhang and Shen, 2024a).

Vertical larynx actions and intergestural timing stability in Hausa ejectives and implosives.

The current project undertakes a kinematic examination of vertical larynx actions and intergestural timing stability within multi-gesture complex segments such as ejectives and implosives that may possess specific temporal goals critical to their articulatory realization. Using real-time MRI (rtMRI) speech production data from Hausa non-pulmonic and pulmonic consonants, this study illuminates speech timing between oral constriction and vertical larynx actions within segments and the role this intergestural timing plays in realizing phonological contrasts and processes in varying prosodic contexts. Results suggest that vertical larynx actions have greater magnitude in the production of ejectives compared to their pulmonic counterparts, but implosives and pulmonic consonants are differentiated not by vertical larynx magnitude but by the intergestural timing patterns between their oral and vertical larynx gestures. Moreover, intergestural timing stability/variability between oral and non-oral (vertical larynx) actions differ among ejectives, implosives, and pulmonic consonants, with ejectives having the most stable temporal lags, followed by implosives and pulmonic consonants, respectively. Lastly, the findings show how contrastive linguistic 'molecules'- here, segment-sized phonological complexes with multiple gestures- interact with phrasal context in speech in such a way that it variably shapes temporal organization between participating gestures as well as respecting stability in relative timing between such gestures comprising a segment.

Fusing Climate Data Products Using a Spatially Varying Autoencoder

Fusing Climate Data Products Using a Spatially Varying Autoencoder

EarthCODE – ESA’s Earth Science Collaborative Open Development Environment

Abstract. Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

Adverse events associated with herbal medicine products reported in the Korea Adverse Event Reporting System from 2012 to 2021.

Systematic collection of diverse adverse events during herbal medicine administration is crucial. The Korea Adverse Event Reporting System (KAERS) compiles spontaneously reported adverse event data for medicinal products including herbal medicines. This study focused on extracting and analyzing adverse event data specifically related to herbal medicine products from the KAERS database. Individual case safety reports (ICSRs) encompassing 84 types of herbal medicine products, identified by item codes from 2012 to 2021, were extracted from the KAERS database. Descriptive statistics were employed to analyze the characteristics of the extracted reports, and adverse event information was systematically categorized and analyzed based on the MedDRA System Organ Class and preferred term classification. In total, 1,054 ICSRs were extracted, with some documenting multiple adverse events in a single ICSR, resulting in 1,629 extracted adverse events. When categorized by the MedDRA System Organ Class, gastrointestinal disorders were the most prevalent (28.7%), followed by skin and subcutaneous tissue disorders (20.1%). Based on the preferred terms, the most frequently reported adverse events were diarrhea (5.8%), urticaria (5.3%), pruritus (4.7%), rash (4.4%), and abdominal discomfort (4.2%). The most frequently reported herbal medicines were Bangpungtongseong-san (297 cases), Kyeongok-go (144 cases), and Eunkyo-san (108 cases). Spontaneously reported adverse events associated with herbal medicine products were systematically documented using the KAERS database. This study, which focused on voluntarily reported adverse reactions, underscores the need for additional research to estimate the incidence rate of adverse events and assess causality.

A Spatiotemporal Empowerment Framework for China’s National 3D Mapping Program 3dRGLM

Abstract. China has launched a national 3D mapping program to build 3D Realistic geospatial Landscape Model (3dRGLm) over the whole country, covering both cities and countryside. The 3dRGLM is a digital description and representation of the real 3D geospatial spaces, and is defined as a new generation geospatial data product with 3D structures, realistic scenes, and geospatial entities. Such a national or local 3dRGLm will set up a digital 3D realistic geospatial space which can facilitate the vital connection with the real geospatial space, and will serve as a new generation of geospatial information infrastructure for our society. With the acquisition of complex 3D spatial data continues to advance, managing and utilizing the massive volumes of 3D data to provide spatiotemporal empowerment applications has become a significant technological challenge. This paper analyzes the technical characteristics of 3d realistic geospatial landscape model, and then proposes the main content and basic structure of a 3d realistic geospatial landscape model database, as well as the spatiotemporal service empowerment methods based on the database. Finally, a case analysis is provided to serve as a reference for the construction and application of 3d realistic geospatial landscape.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

Spatio-temporal and multi-mode prediction for blast furnace gas flow

The reasonable and stable distribution of blast furnace (BF) gas flow is the basis for maintaining the smooth operation of BF. Therefore, the accurate detection of the gas flow distribution is essential in the BF ironmaking process due to the direct impact on productivity, stability, and efficiency. However, there is a significant challenge to capture the complex interactions and dynamic changes of the ironmaking process by single predictive mode and two-dimensional (2D) distribution, leading to a lack of flexibility and interpretability in dealing with different abnormalities. To address this issue, a novel spatio-temporal multi-mode approach for three-dimensional (3D) BF gas flow prediction is proposed in this article. First, Pearson correlation analysis is employed to evaluate correlated variables in the spatial dimension. The precise temporal correlations among the multiple variables are matched with mutual information (MI) to extract spatio-temporal variables. Next, the spatio-temporal variables are decomposed utilizing variation mode decomposition (VMD), and the noise is removed with integrated correlation analysis and Fourier transform (FT) to identify and retain the relevant information. Finally, the MI-VMD-Informer is innovatively proposed to establish three different prediction modes based on spatio-temporal features, thus obtaining 2D and 3D gas flow distributions. The superiority of the proposed method is verified by actual BF production data.

Remote sensing and computer vision for marine aquaculture.

Aquaculture, the cultivation of aquatic plants and animals, has grown rapidly since the 1990s, but sparse, self-reported, and aggregated production data limit the effective understanding and monitoring of the industry's trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000 to 2021 including 4010 cages (average cage area, 69 square meters). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production and develop a flexible framework to quantify uncertainty in these estimates. Overall, our study presents an efficient, scalable, and adaptable method for monitoring aquaculture production from remote sensing imagery.

3D Data Integration in the Voxel Domain

Abstract. The interest in 3D data integration is growing as the concept of Spatial Digital Twins gains traction among local governments and stakeholders. Spatial data is typically organized into standardised themes such as buildings, transport, and vegetation, or as ad hoc data products in either 2D or 3D dimensions. However, integrating these data sets into a cohesive 3D model presents several challenges. The foundational data sets may vary in terms of accuracy, resolution, representation, and actuality due to differences in creation standards and procedures. Consequently, inconsistencies often arise within the 3D models, making them difficult to identify and rectify. Many of these data integration issues can be traced back to the 3D vector representation. Vector 3D models exhibit significant geometric diversity, which complicates the computational operations required for examining and validating the models. In this paper, we propose a novel voxel-based approach for integrating 3D data. Voxels, analogous to pixels in 2D raster images, inherit several key properties. We employ a dedicated voxel data structure and a set of operations tailored for this purpose. First, we voxelise vector datasets, converting continuous geometric information into discrete voxel representations. Next, we merge voxel layers using a specialized voxel overlay operation. The resulting 3D model adheres to most validity and integrity requirements. To highlight the flexibility of our voxel data structure, we transformed it into point clouds for visualisation in ArcGIS Pro, enabling the addition of 3D base maps and GIS layers. We then published these datasets to Scene Services, granting public access. This allows users to easily explore the 3D scenes through a web browser, making complex geographic data more accessible. By leveraging voxels, our approach facilitates efficient and accurate 3D data integration, making it a valuable contribution to the field.

State of continental discharge estimation and modelling: challenges and opportunities for Africa

ABSTRACT Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience.

Visualising traceability data of tobacco products

Visualising traceability data of tobacco products

SUSTAINABLE GROWTH THROUGH GREEN ECONOMY: AN ECONOMETRIC ANALYSIS

Objective: In recent years, the growing interest and awareness of environmental issues have once again brought the issue of population growth to the forefront, exerting significant pressure on production. The increased pressure on production and the resultant rise in production have led to a faster trend in consumption. As a natural consequence of both trends, detrimental effects on ecological balance and the environment have begun to emerge. The concept of "green economy" has emerged as an approach to restore the disrupted ecological balance and operationalize this order. This widely spreading concept has begun to generate significant policies at both national and international levels. Method: This study aims to examine the relationship between renewable energy production, consumption, wastewater, and solid waste data, covering the years 1980-2022, using econometric methods to explore the relationship between green economy and economic growth in the context of Turkey. The objective is to analyze the impact of the recent and rapidly increasing transformation towards "green economy" on the long-term trajectory of economic growth in Turkey. Result and Discussion: Based on the findings of the study, it has been determined that, at the 0.05 level of statistical significance, there is no long-term relationship between the variables and economic growth, as well as no long-term significant relationship between any of the variables. When the statistical significance is accepted at the 0.10 level, a significant long-term relationship is identified between the amount of wastewater and renewable energy, with a 1% increase in wastewater leading to a 0.82% increase in renewable energy. Implications: The findings of this study and the transformation towards a green economy, widely covered in the literature, have been recognized in Turkey and an unnamed green transformation movement has begun. As the results of the analysis of the study indicate, the policies introduced for the transition to a green economy and transformation efforts have a positive and significant impact on economic growth in the long term, but the same cannot be claimed for the short term, but in the future, advances in scientific and technological progress and over-specialization will inevitably have positive consequences on global and domestic sustainable development in the short term. Originality/Value: The distinctive feature of this study from similar studies in the literature is that the relationship between the green economy and sustainable growth was analyzed empirically and with five separate econometric models. Recommendations: Turkey needs serious reforms in education in its transition to a green economy. In addition, there is a need for political determination to use environmentally friendly sectors that are more open to innovation and competition, such as renewable energy and green building sectors.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

X-Ray Emission of Nearby Low-mass and Sunlike Stars with Directly Imageable Habitable Zones

Stellar X-ray and UV radiation can significantly affect the survival, composition, and long-term evolution of the atmospheres of planets in or near their host star’s habitable zone (HZ). Especially interesting are planetary systems in the solar neighborhood that may host temperate and potentially habitable surface conditions, which may be analyzed by future ground- and space-based direct-imaging surveys for signatures of habitability and life. To advance our understanding of the radiation environment in these systems, we leverage ∼3 Ms of XMM-Newton and Chandra observations in order to measure three fundamental stellar properties at X-ray energies for 57 nearby FGKM stellar systems: the shape of the stellar X-ray spectrum, the luminosity, and the timescales over which the stars vary (e.g., due to flares). These systems possess HZs that will be directly imageable to next-generation telescopes such as the Habitable Worlds Observatory and ground-based Extremely Large Telescopes. We identify 29 stellar systems with L X/L bol ratios similar to (or less than) that of the Sun; any potential planets in the HZs of these stars therefore reside in present-day X-ray radiation environments similar to (or less hostile than) modern Earth, though a broader set of these targets could host habitable planets. An additional 19 stellar systems have been observed with the Swift X-ray Telescope; in total, only ∼30% of potential direct imaging target stars has been observed with XMM-Newton, Chandra, or Swift. The data products from this work (X-ray light curves and spectra) are available via a public Zenodo repository (doi:10.5281/zenodo.11490574).

The Application of Fuzzy Time Series Method for Web-Based Prediction of Household Chemical Product Sales Stock

The seasonal and trend factors of the market demand greatly influence the sales of household chemical products. Therefore, PT. XYZ company requires an accurate sales prediction system to optimize production and inventory. This research aims to develop a web-based sales prediction system for household chemical products using fuzzy time series method. This method considers the factors that influence sales and generates a prediction model that can assist the company in decision-making. This method allows the use of uncertain and unstructured variables in the prediction. The research collected historical sales data of household chemical products from January to December 2023 through interviews, observations, and literature studies. The system was designed using a structured approach and PHP programming language. The Fuzzy Time Series method was used for decision-making. The accuracy of the Fuzzy Time Series method for sales data from January to December 2020 was calculated through program simulation and manual methods, resulting in an accuracy rate of 13.71%. The application provides information on the sales of each product, with the hope that users can reduce the accumulation of stock every month.

Classification of Tile Productivity Data Based on Tile Type Using Random Forest Algorithm in Langkat Regency

This study aims to classify data on the productivity of census in Langkat Regency based on the type of census by using the Random Forest algorithm. Ubinan is a method used to measure the productivity of food crops, and in this study, the data was processed with various variables such as planting area, type of fertilizer, type of pesticide, and production volume. The Random Forest algorithm was used to build a classification model that could predict the productivity of the tares with very high accuracy, reaching 99.58% in the training stage. The model categorizes the productivity of the samples into several levels, namely Very Low, Low, Medium, High, and Very High. The implementation of this system is also equipped with a MATLAB GUI interface, which makes it easier for users to train and test data efficiently. With this system, users can see the prediction results through intuitive visualization. This research is expected to help farmers and policy makers in improving agricultural productivity through data-based analysis.

THE REALITY OF DATE PRODUCTION IN ALGERIA AND ITS RELATIONSHIP WITH NATIONAL INCOME: AN ECONOMETRIC STUDY USING THE AUTOREGRESSIVE DISTRIBUTED LAG (ARDL) MODEL DURING THE PERIOD 2000-2019

Objective: The objective of this study is to investigate the evolution of date production in Algeria and its impact on the country's national income (GDP) from 2000 to 2019. This research aims to analyze the short-term and long-term relationships between date production and national income, assessing the significance of agricultural policies in enhancing the productivity and economic contribution of the date sector. Ultimately, the study seeks to highlight the role of date production in diversifying Algeria's exports and contributing to sustainable economic growth. Theoretical Framework: This study explores the relationship between date production and national income in Algeria, using agricultural productivity theory, endogenous growth theory, export diversification theory, and the ARDL model. It aims to understand the impact of agricultural policies, human capital investment, and export diversification on Algeria's date production, highlighting the potential for economic diversification. Method: This study analyzes the relationship between date production and Algeria's GDP from 2000 to 2019, using the Autoregressive Distributed Lag (ARDL) model. Data sources include Algeria's GDP and date production data. The study uses unit root tests, bounds testing, and error correction models to estimate the relationship. Statistical software like EViews or R is used for analysis. Results and Discussion: The study reveals a positive relationship between date production and national income in Algeria from 2000 to 2019. The data suggests that agricultural policies have led to an upward trajectory in date production, while GDP has grown significantly. The study also highlights the importance of date production in Algeria's economy, particularly in diversifying exports. The long-term relationship suggests sustained investment in date production will yield continued benefits for national income. Research Implications: The study highlights the importance of supporting agricultural policies that enhance productivity in Algeria's date sector. It suggests broader export diversification strategies, infrastructure development, and economic development. Strengthening date production can create job opportunities, stimulate rural development, and contribute to food security. Future research should explore other agricultural products, climate change, and technological innovations to improve yields and sustainability in the sector. Originality/Value: The study explores Algeria's date production and national income relationship, providing insights for policymakers to diversify the economy beyond hydrocarbons and foster sustainable growth.

A practitioner-oriented regional hydrology data product for use in site-specific hydraulic applications

In the 465,000 km2 Canadian Prairies ecozone, robust hydrological input data for hydraulic model applications are uncommon because of the sparse monitoring network and the intermittently connected stream network. New hydrological datasets can offer a valuable advancement for making water management decisions and designing infrastructure in this water stressed region. The Prairie Hydrology Design and Analysis Product (PHyDAP) was created to address existing limitations, and provides a comprehensive regional dataset for use in hydraulic modelling applications. PHyDAP is a collection of outputs from a physically based hydrological modelling framework, run for periods ranging from 38 to 150 years, according to three climate forcing datasets. The dataset includes vertical and lateral fluxes (rainfall, snowmelt, upland runoff, and open water evaporation) and basin streamflow, at hourly or 3-hourly intervals for the >4000 small basins of approximately 100 km2 that span the region. This contribution describes the motivation for this work and methodology used to derive the data product, summarizes the data and its accessibility, and provides an overview of potential use cases.