Production Data Research Articles

Scallop fishing activity characterization in Southern New England: Offshore wind demands and fisheries-dependent methods

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational.

Ethanol Production From Sugarcane Molasses: Effects of Ph, Supplementation, and Refrigeration in Simulated Industrial Conditions at a Microdistillary

Objective: This study aims to evaluate the effects of pH, ammonium sulfate supplementation, and refrigeration on fermentation performance in sugarcane molasses, with the goal of optimizing ethanol production under simulated industrial conditions. Theoretical Framework: The research builds on key concepts in biofuel production, emphasizing the role of pH, nutrient supplementation, and temperature control in influencing yeast metabolism and fermentation efficiency. Theories on enzymatic activity and nutrient absorption (Vidal et al., 2013; Gutierrez, 1993) provide the foundation for understanding the interactions between these factors. Method: A full 2³ factorial design was employed, evaluating the effects of pH (3.5 and 5.0), supplementation (0.0 and 1.0 g/L), and refrigeration (with or without) on fermentation efficiency, process efficiency, ethanol productivity, and substrate-to-cell conversion in a microdistillery. Analytical methods included spectrophotometry, refractometry, and chromatography. Results and Discussion: The results indicate that fermentation conditions with pH 3.5, without supplementation, and with refrigeration yielded the best performance, with significant increases in ethanol productivity and fermentation efficiency. The interaction between variables suggests that these factors should be considered jointly to optimize the process. Research Implications: The findings offer practical insights for improving fermentation in industrial biofuel production, particularly in optimizing conditions for ethanol yield and process efficiency under near-industrial conditions. Originality/Value: This study contributes by using a microdistillery to simulate industrial conditions, providing more accurate data for scaling ethanol production. It adds value by highlighting how specific combinations of pH, supplementation, and refrigeration can improve biofuel sustainability.

Climatological Evaluation of Three Assimilation and Reanalysis Datasets on Soil Moisture over the Tibetan Plateau

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major supplements for TP climate research. Based on observations from 1992 to 2013, this study provides a comprehensive evaluation of three sets of assimilation and reanalysis products (GLDAS, ERA5-Land, and MERRA-2) on the climatic mean and variability of soil moisture over the Tibetan Plateau (TPSM). For the climatic mean, GLDAS captures the spatial distribution and annual cycle of TPSM better than other datasets in terms of lower spatial RMSE (0.07 m3×m-3) and bias (0.06 m3×m-3). In terms of the climatic variability of TPSM, the multi-data average (MDA) highlights its advantages in reducing the bias relative to any single data product. MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. When considering both the climatic mean and spatial variability, the performance of MDA is more accurate and balanced than that of a single data product. This study overcomes the deficiency of limited time and space in previous evaluations of TPSM and indicates that multi-data averaging may be a more effective approach in the climate investigation of TPSM.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

A Study on Data Scraping of Cosmetic Products from E-Commerce Website

The popularity of cosmetic products has snowballed over the past few years. Hence, there have been many more brands and makeup products on the market, especially in the online market. To analyze the online marketing trends in the Indian cosmetic industry, we have used some of the most popular Indian websites like Amazon, Flipkart, Ulta and Sephora to scrape the product data. This dataset also contains the ingredients of these products, which can be further analyzed

Impact of FTC's Allegation on Amazon's Marketplace: Evidence From Coffee Products

ABSTRACTHow do firms respond to regulatory allegations? The monopolist lawsuit by the Federal Trade Commission (FTC) against Amazon provides an avenue to answer this question. Applying difference‐in‐differences to panel data of ground coffee products from Amazon's marketplace in the United States and Canada, we find that Fulfillment by Amazon (FBA) fees were reduced by $0.27–$0.29 per product in the United States due to the FTC's monopolist allegation. We also find that the allegation led to price reductions of $0.79–$0.92 per product in the country. Since 42% of the 7.5 billion products sold on Amazon United States in 2023 were by FBA sellers, we estimate that the allegation led to an annual savings of $0.85 billion to $0.91 billion for the FBA sellers and $5.92 billion to $6.85 billion for Amazon's customers on average. These results suggest that the FTC's allegation had sizeable and positive welfare impacts on Amazon's third‐party sellers and customers, making it a significant win for the regulatory agency. Moreover, our study provides insights into the implications of the FTC allegation for agribusiness stakeholders and the future of grocery shopping.

"Guilds" as Worker Empowerment and Control in a Chinese Data Work Platform

Data work plays a fundamental role in the development of algorithmic systems and the AI industry. It is often performed in business process outsourcing (BPO) companies and crowdsourcing platforms, involving a global and distributed workforce as well as networks of collaborative actors. Previous work on community building among data workers centers organization and mutual support or focuses on the structuring and instrumentalization of crowdworker groups for complicated projects. We add to these lines of research by focusing on a specific form of community building encouraged and facilitated by platforms in China: guilds. Based on ethnographic work on a Chinese crowdsourcing platform and 14 semi-structured interviews with data workers, our findings show that guilds are a form of both worker empowerment and control. With this work, we add a nuanced empirical case to the interconnection of BPOs, online communities and crowdsourcing platforms in the current data production sector in China, thus expanding previous investigations on global perspectives of data production. We discuss guilds in relation to individual workers and highlight their effects on data work, including efficient coordination, enhanced standardization, and flattened power structure.

Identifying genomic data use with the Data Citation Explorer

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

RADIATION PATIENT AND WORKER DOSE APPLICATION (SI PADI)

Medical procedures for diagnostic and therapeutic purposes currently use many sources of ionizing radiation. Monitoring patient and radiation worker doses is needed to keep the radiation dose received to a minimum. The purpose of the patient and radiation worker dose application is to facilitate the task of Medical Physicists in conducting patient and radiation worker dose audits so that they become faster and more effective. The patient and radiation worker dose application is made based on dose audits conducted by Medical Physicists by monitoring patient doses on CT Scan examinations using CT dose index volume (CTDIvol) and dose length product (DLP) parameters on CT Scans and patient doses of the Siemens Somatom 128 slice Cathlab using Dose Area Product (DAP) data on the Philips Azurion 7 C-Arm. Radiation worker dose audits are conducted using evaluation data from TLD badges, TLD eyes, and pocket dosimeters of radiation workers working at Karawang Regional Hospital. Data processing uses a spreadsheet program. The menus on the dashboard are combined with formulas so that the data automatically appears in tables, graphs, or special formats as needed. The radiation patient and worker dose information system (Si Padi) was successfully developed at Karawang Regional Hospital to realize efforts to optimize radiation protection and safety for patients and radiation workers. Keywords: patient dose, radiation worker dose, dose audit, CT dose index (CTDI), dose length product (DLP), dose area product (DAP), Si Padi application.

Single-machine scheduling with setup time: a case study in the cutting of large-scale structural parts

This article considers a single-machine scheduling problem with setup time to optimize the cutting schedule for structural parts within a railway equipment manufacturing enterprise. When performing parts-cutting operations for various orders, factors such as priority, the material attribute of the steel plate and part specifications need to be considered. Notably, it is beneficial to ensure continuous cutting to minimize steel waste when these parts need the same steel plates. A mixed integer programming model and a two-stage heuristic are proposed to determine the cutting sequence of the parts. The two-stage heuristic involves preprocessing and local search. Finally, the performance of the proposed approaches is assessed using real-world cutting production data. This includes scenarios with more than 10,000 parts, allowing for a thorough evaluation. The results indicate that the proposed two-stage heuristic exhibits significant efficiency and quality advantages compared to the Gurobi tm solver and local search.

Result lexicalization with manner verbs in French: an experimental investigation of the material/product alternation and the packaging of meaning

Abstract French is a typical verb-framed language, in which manner verbs cannot freely combine with result-denoting constituents in a single VP. Drawing on experimentally elicited production data on Hexagonal French, this study examines how the syntactic (in)flexibility of manner-of-creation verbs influences the lexicalization of the event result. As for result lexicalization within the VP headed by the manner verb, the study explores the occurrence of effected objects and resultative PPs. Thus, it addresses the availability of the material/product alternation (sculpter une poupée à partir du bois/sculpter le bois en (une) poupée) as a type of argument alternation, whose existence has been questioned for the Romance languages. Furthermore, it is explored how the verbs’ syntactic flexibility influences whether manner and result are lexicalized within a single VP at all or distributed onto different VPs. The results show that the material/product alternation does occur, but that only a limited set of verbs has the syntactic flexibility required for it. Additionally, it is shown that syntactic flexibility favors a denser packaging of conceptual components, since with verbs that admit an effected object, the result is realized more often in the VP than with verbs that do not.

Road Freight Demand Forecasting Using National Accounts’ Data—The Case of Cereals

This paper investigates the potential of utilising historical agricultural production data for enhancing road freight transport forecasting, focusing on cereal production. This study applies a multiple linear regression analysis using national statistical accounts and secondary data. The data were sourced from Finland’s Statistics Agency and the Natural Resources Institute. The analysis identifies an observable correlation between agricultural production and road freight volumes, although this correlation is not statistically significant. The highest adjusted R² observed in the models was 0.62. The analysis reveals that previous years’ production data can help forecast future road freight volumes, with vehicle mileage estimable from recent production and stock levels. Additionally, annual percentage changes in the volume of transported cereals can be partially predicted by the changes in total available cereals and opening stocks from two years prior. This exploratory research highlights the untapped predictive potential of agricultural production variables in forecasting road freight demand, suggesting areas for further forecasting enhancement.

Intricate Supply Chain Demand Forecasting Based on Graph Convolution Network

Globalization has contributed to the increasing complexity of supply chain structures. In this regard, precise demand forecasting for the intricate supply chain holds paramount importance in effective supply chain management. Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this issue, this paper proposes an intricate supply chain demand forecasting method based on graph convolution networks adept at handling non-Euclidean data. First, the companies within the supply chain are treated as nodes in the graph structure, and the relationships between them are treated as edges, with demand data serving as the features of these edges. Then, a graph convolutional network is constructed to aggregate node and edge information. Through a multi-layer network, the relationships among nodes, edges, and historical demand are established to facilitate the prediction of supply chain demands. In this process, the graph convolutional network incorporates supply chain connectivity information into demand time series analysis. This integration of surface-level topological features and deeper latent correlation attributes across the supply chain’s nodes refines the demand forecasting precision across the entire supply chain. The validation experiment in this paper is grounded in sales data of a singular product from multiple sales nodes of an electronics company. The results demonstrate that the proposed method surpasses four other traditional demand forecasting algorithms significantly in terms of accuracy, providing substantial evidence for the superior performance of graph networks in the analysis of intricate supply chain relationships.

High frequency radar error classification and prediction based on K-means methods

This study aims to characterize the high frequency radar and numerically simulated low-frequency filtered currents in the south-eastern Bay of Biscay (study area) using a K-means classification algorithm based on an improved Euclidean Distance calculation method that does not take missing values. The errors between observations and simulations was estimated and predicted based on this classification method. Results indicate that predominantly eastward (northward) currents over the Spanish (French) continental shelf/slope in winter and more variable currents in the west and south-west in summer. The model classification results for circulation characteristics are in relatively good agreement with HF radar results, especially for currents on the Spanish (French) shelf/slope. In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. This study provides a foundation for subsequent model error testing, forecast product improvement and data assimilation.

Friedman Test Technique for Optimizing a Seasonal Box-Jenkins ARIMA Model Building

This study employs the Box-Jenkins methodology for time series modelling to analyze Nigerian crude oil production data. To enhance the model-building process, the Friedman rank test for seasonality was utilized. Data were sourced from the Nigerian Petroleum Corporation’s Annual Statistical Bulletin, covering the period from January 1997 to September 2014. The findings indicate that the yearly and monthly mean distributions exhibit non-constant behavior over time, implying that both natural and governmental factors significantly impact crude oil production (COP) dynamics. Consequently, a seasonal ARIMA(0,1,1) (0,1,1)12 model was fitted and deemed appropriate for the data.

Seeing it in others versus doing it yourself: Social desirability judgements and conversation production data from autistic and non-autistic children.

During a conversation, on average, autistic individuals are often more likely than non-autistic people to provide an off-topic comment and/or to pause for longer before providing a response. One possible explanation for this is that autistic individuals prefer, or are more tolerant of, unconventional communication styles. To explore this possibility, we investigated whether autistic and non-autistic 9-13-year-olds find off-topic or delayed responding a deterrent to friendship or interaction. Participants listened to scripted conversations and then rated social desirability statements, such as 'I would enjoy chatting to the [target speaker]'. We also examined the prevalence of these behaviours in children's own conversational responses. We found that autistic children were just as likely as non-autistic children to dis-prefer unconventional conversational responding. Both groups indicated that they were less likely to want to be friends with the speaker, or to chat with them, when they provided off-topic or delayed responses. However, despite their judgements of others, the same autistic children were more likely to provide off-topic responses themselves than their non-autistic peers, as well as giving fewer on-topic responses which facilitate back-and-forth conversation. Overall, this is problematic for autistic children, as our findings suggest that the tendency to exhibit unconventional conversational behaviours will have negative social consequences, even when interacting with other autistic peers.

A framework and pilot study for assessing usability of flood data portals for interdisciplinary research

There is a lack of datasets to study the climate and human outcomes nexus. There are many flood data portals due to recent improvements in flood identification using satellites, providing opportunities to study the human impacts. The development of these portals is rapid and there is currently no standard for evaluating their usability for interdisciplinary research. This paper addresses this important data gap. We put forth a usability framework that includes data availability, approaches to flood identification, alignment, velocity, variety, and user feasibility aspects. We piloted it through an in-depth review and user survey of NASA Worldview (NW), Global Flood Awareness System (GloFAS), Global Flood Monitoring System (GFMS), Global Surface Water Explorer (GSWE), and Dartmouth Flood Observatory (DFO) GSWE and GloFAS were rated most favorably. Respondents had discrepancies in their opinions on the clarity of the goals and platform accessibility for GFMS, DFO, and NW, and in data and visualization quality for all portals. Historical data and measures of flood recurrence and other characteristics are needed. Flood data products should be provided in multiple formats, aggregated by sub-national boundaries, with mechanisms that delineate incomplete or unreliable data. Flood data portals should include interdisciplinary research as part of their mission. Their longevity and maintenance should be secured to preserve these important data sources for future research. This framework can be adapted and used to enable interdisciplinary spatial and survey data linkages.

Remote Sensing Data Quality in the Era of AI

Abstract. The era of Artificial Intelligence ‘AI’ with all the benefits brought along, has raised new and additional challenges to the ongoing efforts of assessing, defining, formulating, and implementing the quality aspects of geospatial remote sensing data. Developed practices using artificial intelligence leveraged techniques such as image interpretation, classification, thematic mapping, and even image quality enhancement, necessitating by that the reassessment and redevelopment of some of the related emerging quality aspects. Moreover, technology also made the generation of false images and false data possible, this matter constrained and increased precaution and doubtfulness, altogether making some practices based on that data almost halt to further notice. This paper presents the collaborative research work to assess and clarify the quality aspects that arose with the advent and implementation of AI and associated technologies; the concerns and issues that can accompany the generation of false satellite and aerial images including the generated geospatial data out of which, how the new emerged quality aspects fit into the currently existing methods through the lifecycle of remote sensing data production and usage, and consequently how the quality dimensions are affected and should be further developed and improved to tackle the changes and innovations. Also, lame a bit on investigating how to accommodate the new challenges in standards, and practical procedures and raise the awareness to users, the level of dependency on improved and enhanced satellite images when it comes to data collection interpretation and classification, and finally define the research gaps, future expected challenges and thus enclose suggestions and recommendations in that respect.

Business Intelligence for Agricultural Foreign Trade: Design and Application of Power BI Dashboard

In today’s rapidly evolving business landscape, data analytics has become a critical tool for organizations striving to remain competitive and make informed decisions. Although technology facilitates data analysis, many decision-makers need more technical knowledge to use these tools. This project focuses on developing a business intelligence system in Power BI to analyze agricultural exports from Mexico as part of the Agro-logistics Observatory, aiming to bridge this gap and make data more accessible for non-technical users. The objective is to create an interactive panel that would allow users to visualize and compare export and import data, thereby supporting decision-making for both business and academic purposes. The methodology involved collecting and cleaning data from various sources, such as public institutions, and a modeling process that integrates geospatial data and economic classifications. Key performance indicators (KPIs), such as trade balance and the export share by-product, were designed and calculated to identify critical products and trade patterns. Although similar approaches have been presented in the literature, the novelty of this research lies in the detailed presentation of its methodology, which provides a step-by-step guide for its replication that is often lacking in other studies. In addition, it offers a dashboard that integrates data from various sources, presenting them through easy-to-interpret visualizations so that stakeholders can quickly and efficiently access valuable information beyond traditional data visualization. Furthermore, the project highlights opportunities for improvements in data visualization, including integrating more specific product data and enhancing the dashboard’s capabilities through predictive analysis.

Condensate Banking Analysis Optimizes Reservoir Development in Permian Basin

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4044947, “Numerical Analysis of Condensate Banking and Development Optimization in the Deep Formation of the Permian Basin,” by Jichao Han, SPE, Sunil Lakshminarayanan, and Jiasen Tan, SPE, Oxy, et al. The paper has not been peer reviewed. _ In the context of unconventional reservoir development, the issue of condensate banking—a factor contributing to reduced productivity in gas condensate reservoirs when the reservoir pressure falls below the dewpoint pressure—poses a constant concern. Before implementation of any mitigation strategies, it is prudent to assess the potential of condensate banking in the future, based on the limited early production data. A specialized workflow was devised for a deep formation in the Permian Basin. This workflow aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies. Introduction Well A represents a single appraisal well situated within deep formations of the Permian Basin. With only 5 months of production data available, pressure/volume/temperature analysis and modeling identified it as a gas condensate reservoir, with the dewpoint very close to the critical point. This prompts two critical business inquiries: Are there any concerns regarding condensate banking for this well? And what are the optimal drawdown strategies tailored to this specific format surrounding the appraisal well? Model Setup and Calibration Modeling Workflow. This study uses the comprehensive simulation workflow derived from Hydraulic Fracturing Test Site 2, which can effectively handle integrated models to provide valuable insight for condensate banking analysis and reservoir development optimization (Fig. 1). Hydraulic fracture and reservoir simulations were conducted using GOHFER 3D and Navigator software, respectively. The integration of fracture and reservoir models poses a significant challenge in accurately transferring fracture geometry and conductivity information from fracture simulators to reservoir simulators. To address this challenge, an in‑house preprocessor was developed, designed to project results from the fracture simulator into the reservoir simulator seamlessly, ensuring the fidelity of complex fracture geometries. This preprocessor enables the extension or contraction of fracture geometries in proportion to reduce uncertainties imposed by production data. Additionally, fracture conductivity is determined through regional correlations among proppant concentration, net closure stress, and fracture conductivity. Model Introduction. The geological model has been simplified to enhance modeling efficiency while still meeting the project requirements. The 3D geological model adopts a layer-cake structure with a flat geostructure. To ensure accuracy, the well trajectory is adjusted to align with the desired formation. The reservoir model measures 4,000×10,000×330 ft, with a grid resolution of 200×25×10 ft. Fracture geometry is imported from the fracture simulator, while fracture conductivity is primarily determined based on proppant concentration data exported from the fracture simulator. Fracture height remains unaltered in the reservoir simulator, reflecting available geochemical measurements. A compositional simulation approach is adopted instead of a black-oil model. To improve the accuracy of pressure drop and condensate dropout predictions, the local grid refinement (LGR) approach is selected, resulting in denser grids. This approach proves superior to the dual-porosity, dual-permeability (DPDP) method.