bluuebunny/crossref_metadata_2025 huggingface dataset:
A Case Report: Treatment of Anaplasmosis and Ectoparasitic Infestation in Mixed-Breed Golden Retriever Dog
Authors: Situmorang Fernando Jose Immanuel Clinton , Suartha I Nyoman , Jayanti Putu Devi
Published: September 2024

Anaplasmosis is a disease in dogs caused by intracellular gram-negative bacteria belonging to the Anaplasmataceae family. A 6-month-old mixed-breed golden retriever was examined at the Laboratory of Veterinary Internal Medicine, Faculty of Veterinary Medicine, Udayana University, with complaints of itching, tick infestation, weakness, and decreased appetite. The clinical examination revealed pale mucous membranes in the mouth and eye conjunctiva, as well as an infestation of Rhipicephalus ticks on the skin. Routine hematological examination indicated the presence of anemia and thrombocytopenia. Blood smear examination confirmed the presence of Anaplasma sp. Treatment was provided in a causative and supportive manner. Causative therapy involved the administration of doxycycline at a dose of 10 mg/kg body weight orally for twenty-eight days, ivermectin at a dose of 0.2 mg/kg body weight injected subcutaneously every two weeks for four weeks. Supportive therapy included the daily administration of Fufang E'jiao Jiang ®(Dong E Ejiao Co, Ltd., Shandong, China) at 2 ml/day and Sangobion® (PT. Merck Tbk, Jakarta, Indonesia) at  one tablet per day for fourteen days. Treatment with doxycycline, ivermectin, Fufang E'jiao Jiang® (Dong E Ejiao Co, Ltd., Shandong, China), and Sangobion® (PT. Merck Tbk, Jakarta, Indonesia) resulted in a positive outcome for the dog, with improved activity, hair growth, good appetite, and the absence of ticks.
A Case Report: Myxomatous Mitral Valve Disease in a Shih Tzu
Authors: Sheren  , Arjentinia I  Putu Yudhi , Widyastuti Sri Kayati
Published: September 2024

Myxomatous Mitral Valve Disease (MMVD) is a degenerative condition of the mitral valve where it weakens and causes regurgitation, eventually leading to cardiac remodeling. Jason, a seven-year-old male Shih Tzu weighing 7.5 kg, was presented with a persistent cough and exercise intolerance lasting over a month. A physical examination revealed a Grade II/VI heart murmur. Radiography and echocardiography were performed as part of the laboratory examinations. Radiography demonstrated cardiac remodeling, with a VHS of 10.3 viscerocranial, an intercostal space of 3, and a VLAS of 2.3. Echocardiography unveiled left atrial enlargement, mitral valve regurgitation, and a reduction in heart function. The dog was treated with Pimobendan (Cardisure® 10mg, Dechra, England) as an inodilatator at 0.25mg, Enalapril Maleate 0.5mg/kg (Tenace® 5mg, Combiphar, Indonesia), and furosemide (Farsix® 40mg, Fahrenheit, Indonesia) at 2 mg/kg via oral route twice a day over the course of seven days. Thereafter, the dose was reduced to 1.5 mg/kg PO twice a day for seven days, and eventually once a day for the remainder of the seven days. Following the three-week treatment, there was a significant reduction in the frequency and intensity of coughing.
Analyzing the Impact of Cost of Funds and Non-Performing Loans on Bank Profitability
Authors: Buchory Herry Achmad
Published: September 2024

The research was conducted to see how cost of funds (COC), and non-performing loans (NPL) on profitability is measured by return on assets (ROA). The research method used is descriptive and verification methods. Secondary data is sourced from Regional Development Banks of West Java and Banten Provinces in Indonesia quarterly financial reports for 2014 – 2022, while the data analysis technique uses multiple linear regression analysis. The results of the research are that COF and NPL has a negative and significant effect on ROA. Simultaneously, COF and NPL have a significant effect on ROA. The results of this research are useful for bank management in determining funding strategies with low cost of funds, and credit management strategies so that non-performing remains healthy.
Automating Portfolio Replication with Stock Market Algorithms
Authors: Gottimukkala Srinivas Raju
Published: September 2024

This study aims to explore methods for automating the replication of correlated portfolios across shares using various algorithms. The goal is to select one algorithm for implementation in a visualization tool that will facilitate portfolio building. The dynamics of the stock market provide a comprehensive view of the global economy, acting as a catalyst for stakeholders to participate and benefit various sectors financially, primarily through investments in companies that yield returns and contribute to the economy. The evaluation and comparison of these algorithms are crucial for making well-informed decisions regarding the core objectives of the study. Ultimately, a determination will be made as to whether the program can effectively analyse data from Yahoo finance to provide a quick overview of the financial market state of companies in the S&P 500 US stock prices.
Financial Network Analysis Using Polymodel Theory
Authors: Cao Zeyu , Zhao Siqiao , Dong Zhikang , Douady Raphael
Published: September 2024

This paper presents a novel approach to financial network analysis by leveraging PolyModel theory. Traditional financial networks often rely on correlation matrices to represent relationships between assets, but these fail to capture the complex, non-linear interactions prevalent in financial markets. In response, we propose a method that quantifies the relationship between financial time series by comparing their reactions to a broad set of environmental risk factors. This method constructs a network based on the inherent similarities in how assets respond to external risks, offering a more robust representation of financial markets. We introduce several network topological properties, such as eigenvalues, degree, and clustering coefficients, to measure market stability and detect financial instabilities. These metrics are applied to a real-world dataset, including the DOW 30, to predict market drawdowns. Our results indicate that this PolyModel-based network framework is effective in capturing downside risks and can predict significant market drawdowns with high accuracy. Furthermore, we enhance sensitivity to smaller market changes by introducing new time-series indicators, which further improve the predictive power of the model.
Effect of Green Cowpea Manure on the Quality Properties of Sandy Soils Under the Specific Conditions of the Dry Climate in Romania
Authors: Draghici Reta , Nanu Ștefan , Diaconu Aurelia , Bîrsoghe Cristina , Netcu Florentina , Paraschiv Alina Nicoleta , Dima Milica
Published: September 2024

As a result of the low organic matter content of the sandy soils in Romania, for the success of most crops, large amounts of chemical fertilizers are needed, which can often lead to the pollution of groundwater with nitrates, given the poor hydrophysical properties in terms of retaining chemical elements by these soils. In this sense, the study carried out in the period 2020-2023 within the rotation cowpea-rye+cowpea successive crop-sorghum for grains, aimed to promote the system of sustainable agriculture, by incorporating into the soil, in the form of green manure, the cowpea sown in succession after the rye. The soil quality analyses, carried out the following year after harvesting sorghum for grains, showed that by incorporating 52.4 t/ha of cowpea biomass into the soil between October 15-20, the quality characteristics of the sandy soil improved significantly. Thus, compared to the initial state of soil fertility, there were annual increases of 0.017-0.029%, in total nitrogen, by 9.5-13.3 ppm, in extractable phosphorus, by 11.333-24 ppm, in exchangeable potassium and by 0.008-0.012%, to organic carbon, also depending on the sorghum fertilization system. Also, the incorporation of green cowpea manure into the soil contributed to reducing the acidity of the soil pH and improving the water holding capacity of the soil profile.
Possible Mechanisms and Strategies for Immune Therapy Resistance in Gastric Cancer
Authors: Yuyu Xu , Huiling Shen
Published: September 2024

Gastric cancer has become one of the most common cancers in the world.Immunotherapy, especially immune point inhibitors targeting programmed cell death 1 or its ligand PD-L1, has been successfully applied in the clinical treatment of GC. However, only a small number of cancer patients experience persistent clinical responses during immunotherapy. In this review, we discussed the current status of immunotherapy for colorectal cancer, elucidated the mechanisms mediating immunotherapy resistance, predicted biomarkers for immunotherapy resistance, and strategies for improving immunotherapy efficacy.
Enhanced SAR Compression through Multi-Look Doppler Compensation and Auto-Focusing Technique
Authors: Kim HyeonSeong , Kwon YongHwi , Kim ChulKi
Published: September 2024

This paper presents a simple and streamlined compensation technique for improving the quality of Synthetic Aperture Radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications.
Coupled System of Dual-Axis Clinostat and Helmholtz Cage for Simulated Microgravity Experiments
Authors: Malczyk Maciej , Blachowicz Tomasz , Ehrmann Andrea
Published: September 2024

The experimental investigation of plant growth under space conditions is a necessary prerequisite of long-term space missions. Besides experiments in space, many studies are performed under simulated microgravity, using a clinostat. However, the Earth magnetic field is usually not taken into account in such investigations. Here, a self-designed and constructed system of coupled devices – a clinostat and a Helmholtz cage – is presented. The clinostat can, on the average, cancel the effective gravity field by using two independent rotations, enabling simulated zero-valued gravity experiments. Additionally, appropriately symmetrically mounted Helmholtz cage can be used to cancel the natural Earth magnetic field in the volume where the clinostat is located. The combination of these two devices offers the opportunity, e.g. for bio-inspired experiments in which plant cultivation can be carried out in conditions that imitate space environment. We provide information about the experimental setup and show first experimental results of growth tests.
Application of Inorganic-Based Nanoparticles and Bionanomaterials as Biocompatible Scaffolds for Regenerative Medicine and Tissue Engineering: Current Advances and Developments
Authors: Saikia Nabanita
Published: September 2024

Regenerative medicine is an interdisciplinary field that combines stem cell technology and tissue engineering to replace or regenerate human cells, tissues, or organs and restore normal functions. The term 'regenerative medicine' was coined by William Haseltine at a 1999 conference on Lake Como. Since its inception in 1968, the field has offered clinical benefits for the regeneration, repair, and restoration of bones, skin, cartilage, neural tissue, and heart, as well as scaffold fabrication. Inorganic biomaterials play a crucial role in regenerative medicine, often surpassing traditional synthetic materials due to their adjustable intrinsic properties, such as size, topography, charge, and chemical stability. These properties enable a flexible cellular response within intracellular matrix environments and provide structural support for cell adhesion and tissue regeneration. Given the wide gamut of near-future potential applications of inorganic biomaterials, this article gives an overview of their emerging roles in stem cell regenerative research, tissue engineering, artificial skin and cartilage regeneration, neural nerve injuries, 3D bioprinting, and development of new inorganic bio-scaffolds. The review also addresses the challenges related to the clinical application and tissue compatibility of inorganic biomaterials, utilizing current state-of-the-art techniques.
Mean Droplet Size Prediction of Twin Swirl Airblast Nozzle at Elevated Operating Conditions
Authors: Miao Jiaming , Wang Bo , Ren Guangming , Gan Xiaohua
Published: September 2024

This study introduces a novel predictive model for atomization droplet size, developed through comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel-air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of ≥0.13 and highlight the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates (q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design.
Association Between the Korean Healthy Diet Score and Metabolic Syndrome: Effectiveness and Optimal Cutoff of the Korean Healthy Diet Score
Authors: Kim Soo Hyun , Joung Hyojee
Published: September 2024

Objectives: Although the prevalence of metabolic syndrome (MetS) has increased among Koreans, the specific health benefits of the Korean Healthy Diet (KHD) score remain unexplored. This study aimed to evaluate the association between the KHD score and MetS, and to identify the optimal cutoff of the KHD score for reducing MetS prevalence. Methods: This cross-sectional study used data from 11,403 participants of the 7th and 8th Korea National Health and Nutrition Examination Survey. The KHD score was calculated based on adherence to 13 dietary components. Logistic regression analysis was used to determine the association between the KHD score and MetS, and to identify the optimal cutoff values for the KHD score. Results: The average KHD score was significantly lower in participants with MetS than in those without MetS (5.03 vs. 5.14, p=0.016). A one- and two-point increase in the KHD score was associated with reduction in MetS prevalence (one-point, odds ratio [OR]: 0.95, 95% confidence interval [CI]: 0.91–0.98; two-point, OR: 0.90, 95% CI: 0.84–0.97). The optimal cutoff for the KHD score was identified as >7 points, which was related to lower odds of MetS (OR: 0.78, 95% CI: 0.65–0.93) compared to 1–4 points, particularly with hypertriglyceridemia (OR: 0.80, 95% CI: 0.69–0.94) and low high-density lipoprotein cholesterol (OR: 0.85, 95% CI: 0.74–0.98). Conclusions: The KHD score was inversely associated with MetS prevalence, and the identified optimal cutoff values can serve as a practical tool for public health interventions aimed at reducing the risk of MetS.
Key factors for BIM Implementation in the Context of ESG and SDGs Integration: A Systematic Literature Review
Authors: Jing Wu , Alias Aidi Hizami
Published: September 2024

Driven by global sustainability trends, Building Information Modelling (BIM) technology is in-creasingly becoming a key tool in the construction industry to improve efficiency and sustaina-bility.This study aims to identify the key factors affecting BIM implementation in the context of Environmental, Social and Governance (ESG) and Sustainable Development Goals (SDGs) and to construct a theoretical framework for BIM implementation based on these factors.To achieve this objective, this study used a systematic literature review (SLR) method to systematically review the relevant literature between 2009 and 2024, and identified 16 key factors from the selected 406 studies through keyword co-occurrence analysis (using VOSviewer) and data coding.These key factors include:Top Management Support for ESG and SDGs,Alignment of SDGs,ESG Integra-tion,Technical Support,BIM Software,BIM Hardware,Structural Adjustment & Collabora-tion,Capacity Building,Change Management,Skill and Attitude,Educational Training and Devel-opment,Incentive Mechanism,Roles & Responsibilities,Sustainable Construction Practic-es,Policies and Regulations,Resource Efficiency.This study categorises these factors under the STOPE framework (Strategy, Technology, Organisation, People, Environment) and proposes a theoretical implementation framework for BIM accordingly. The findings not only provide a practical guiding framework for the sustainable development of construction companies in the context of ESG and SDG integration, but also lay a solid theoretical foundation for future empiri-cal research.
A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel
Authors: Tu Yi-fan , Kwan Mei-ying , Yick Kit-lun
Published: September 2024

Artificial intelligence (AI) is revolutionizing the textile industry by improving the prediction of fabric properties and handfeel, which are essential for assessing textile quality and performance. However, the practical application and translation of AI-predicted results into real-world textile production remain unclear, posing challenges for widespread adoption. This paper systematically reviews AI-driven techniques for predicting these characteristics by focusing on model mechanisms, dataset diversity, and prediction accuracy. Among 811 papers initially identified, 26 were selected for in-depth analysis through both bibliometric and content analysis. The review categorizes and evaluates various AI approaches, including machine learning, deep learning, and hybrid models, across different types of fabric. Despite significant advances, challenges remain, such as ensuring model generalization and managing complex fabric behavior. Future research should focus on developing more robust models, integrating sustainability, and refining feature extraction techniques. This review highlights the critical gaps in the literature and provides practical insights to enhance AI-driven prediction of fabric properties, thus guiding future textile innovations.
Village Organization and Sustainable Growth of Farmers' Income: An Empirical Study Based on Dynamic Survey Data of Labor Force in China
Authors: Wang Qinghao , Gong Mingyue , Zhang Chentao
Published: September 2024

Common prosperity is the core goal of rural revitalization, and sustainable growth of farmers' income is an inevitable requirement. This study uses CLDS2016-2020 village and family level questionnaire data to reveal how the degree of village organization affects the sustainable growth of farmers' income, thus providing reference and decision-making for the local government to promote the increase of farmers' income and narrow the gap from the village level. The results show that the degree of village organization can significantly affect farmers' income, and the analysis results of the degree of formal organization are consistent with them. Specifically, for every 1% increase in the degree of village organization, the per capita household income will increase by 32.4%, among which the wage income will increase by 22.6%, the operational income will increase by 5%, the property income will increase by 0.8%, and the transfer income will increase by 4.2%. The estimation results through instrumental variables are consistent, and the results are robust. At the same time, improving the degree of village organization can also significantly increase the proportion of farmers' non-agricultural income and narrow the income gap among farmers. On the impact of heterogeneity, it is found that the geographical distance between villages and county and township governments, as well as the political identity and education level of farmers will affect the role of the degree of organization. The results of mechanism analysis show that improving the degree of village organization is mainly through promoting the circulation of agricultural land, socialized service of agricultural machinery and industrial and commercial capital to the countryside to increase farmers' income. The research revealed that the modernization of rural governance should be accelerated, and the degree of village organization should be improved, especially the degree of formal organization, so as to build a benign trust and cooperation relationship between the two village committees and the villagers, and implement policies and measures to improve the income of farmers according to local conditions. At the same time, strengthen the publicity and education in party member to improve the proportion of rural party member and the education level of farmers.
Development of Moodle-Based E-Learning Media in Workshop and Entrepreneurship Learning
Authors: Michelle Firsta Karima Sara  , Ambiyar  , Dedy Irfan  , Hasan Maksum
Published: September 2024

The rapid transition to online learning due to the Covid-19 pandemic presented challenges in ensuring effective student engagement and comprehension, particularly in subjects like Practical Work and Entrepreneurship. The problem identified is the lack of suitable digital tools to support interactive learning in these subjects, which often rely on hands-on experience and active participation. To solve this problem, this study developed and implemented Moodle-based e-learning media to enhance student understanding and engagement in Practical Work and Entrepreneurship education. The research objective was to assess the effectiveness of this e-learning media in improving students' learning outcomes compared to traditional learning methods. This study employed the Research and Development (R&D) method, involving two groups of students: an experimental group that used Moodle-based e-learning media and a control group that did not receive any additional digital learning support. Data were collected over a period of six months, using pretests and posttests, with statistical analysis conducted via Wilcoxon and Mann-Whitney tests. The results of the study showed significant improvements in the experimental group, with both statistical tests yielding significance values (sig) less than 0.05. These findings highlight the positive impact of Moodle-based e-learning media on students' comprehension of Practical Work and Entrepreneurship. In conclusion, the development and implementation of Moodle-based e-learning media proved to be an effective solution for enhancing student engagement and learning outcomes, particularly in the context of distance learning during the pandemic. This approach offers a scalable and flexible solution for improving education in similar contexts.
The Effect of Organizational Justice and Organizational Commitment on Teacher Performance with Job Satisfaction as a Mediator in Private Elementary Schools
Authors: Mukti Kiki Trisna , Basrowi  , Khaeruman
Published: September 2024

The purpose of this study is to determine the influence of organizational justice and organizational commitment on teacher performance with job satisfaction as a mediator in Cilegon City Private Elementary School. In this study, a quantitative method is used, where variables are measured using a Likert scale. The data collection method was carried out by distributing questionnaires. The population in this study is private elementary school teachers in Cilegon City. The sample used in this study amounted to 200 respondents. The analysis tool used in this study uses Smartpls v.3.2.9 software. Based on the results of the data analysis, it can be concluded that (1) organizational justice has a positive and significant effect on teacher performance, (2) organizational commitment has a positive and significant effect on teacher performance, (3) organizational justice has a positive and significant effect on job satisfaction, (4) organizational commitment has a positive and significant effect on job satisfaction, (5) job satisfaction has a positive and significant effect on teacher performance, (6) job satisfaction can mediate the relationship between organizational justice and performance (7) job satisfaction can mediate the relationship between organizational commitment and teacher performance significantly.
Design of Religious Moderation Education in Muhammadiyah Middle School and Ma'arif NU Middle School Educational Institutions
Authors: Umam Khairul , Karim Abdul , Abidin Zainal
Published: September 2024

This study aims to analyze the application of religious moderation education curriculum design in educational institutions, including  Mandiri Berbagi and Mandiri Berubah Curriculum Models, collaborative and separate curriculum design, and hidden curriculum design in SMP Muhammadiyah 6 Wuluh and SMP 09 Ma'arif NU Ambulu in Jember. This study used a qualitative approach with the type of case study to describe and analyze the design of religious moderation education in educational institutions. Data collection was conducted by observing the activities of teachers and principals in designing a religious moderation education curriculum. At the same time, interviews were carried out with the principal, subject teacher, and the vice principal of curriculum involved in the design of religious moderation education. Data analysis follows the Miles, Huberman, and Saldana models, including data condensation, data presentation, and inference. Furthermore, the validity of the data was ensured using source and technical triangulation. The findings of this study revealed 1) the application of religious moderation education curriculum with Mandiri Berbagi and Mandiri Berubah Curriculum Models, 2) the design of moderation education curriculum with collaborative model of ISMUBARIS (Islam, to-Muhammadiyah, Arabic and Islamic Cultural History) and a separate model between religious education and the teachings of Nahdatul Ulama, and 3) hidden curriculum on all subjects with the message of Islam wasathiyah and moderate Islamic messages of an-Nahdiya. This research contributes to policymakers, such as the government, the Ministry of Education and Culture, the Ministry of Religion, and all educational institutions, such as junior high schools and MTs, in implementing religious moderation education for application in the curriculum and learning of Islam.
Digital Transformation in Education: Examining the Contribution of Madrasah Principal Leadership to Teacher Teaching Performance
Authors: Abdullah Shodiq , Ratnawati Zulfa
Published: September 2024

This article investigates the influence of madrasah principal leadership on the madrasah teachers' teaching performance in the digital era. The study used a quantitative approach, and the survey method involved 124 respondents randomly selected from seven madrasahs in Jepara, Indonesia, based on cluster techniques. The results revealed that the F-test obtained F-statistic> F-table (39.824 > 3.07) and significance (0.000 < 0.05), indicating that the leadership of the madrasah principal exerted a positive and significant influence on the madrasah teacher's performance. The coefficient of determination test results exhibited an R Square (R2) value of 0.240, meaning that teacher teaching performance is influenced by the leadership variable of the madrasah principal by 24% and 76% by other variables. These findings provide a foundation for understanding the critical role of madrasah principal leadership in improving teacher-teaching performance. Further research is recommended to consider the factors affecting teaching performance and expansion.
Critical Thinking, Literacy, and Numeracy as Factors in STEM: Madrasah Student Learning
Authors: Kuswanto Kuswanto , Abidin Zaenal , Pestano Rayvin D , Ikhlas Muhammad
Published: September 2024

In the era of massive information sources, the ability of the young generation to have critical thinking, literacy, and numeracy skills is needed. These three variables are the focus of this study: how are students' abilities in critical thinking, literacy, and numeracy using STEM-based learning? To answer this problem, the researchers employed mixed research with an explanatory sequential design. The researchers used quantitative analysis and then utilized qualitative. The findings of this study revealed that STEM exerted a good influence on student learning. The findings of this study also stated that the ability of students to think critically was in a good category (n=76,55), literacy was in a good category (n=3,58), and numeracy was in a neutral category (n=58,57). In addition, the researchers uncovered that STEM is an alternative to teaching science and produces critical thinking, literacy, and numeracy skills.
Socio-ecosystem Services for Integrated Heritage Assessment (IHA). A new approach to valuing cultural heritage
Authors: Arcila-Garrido Manuel , Ramírez-Guerrero Gema , García-Onetti Javier , Chica-Ruiz J. Adolfo
Published: September 2024

This paper introduces a conceptual framework that adapts the ecosystem services approach to identify the benefits of conserving cultural heritage. This novel approach aims to enhance the social valorization of this heritage. The proposed service categorization and classification are based on anthropic units that offer benefits, complementing the existing classification for services provided by biotic and non-biotic units with a systemic perspective. The Common International Classification of Ecosystem Services (CICES) and the European Union project "Mapping and Assessment of Ecosystems and their Services" (MAES) serve as the primary references. The main objective is to transcend the concept of heritage as an object-based, historical, and aesthetic treasure and instead develop a more comprehensive definition that encompasses geographical, physical-natural, social, and cultural dimensions while also recognizing the value of usage. This proposal has been validated through the analysis of over 200 cultural asset cases from the European Union. It paves the way for a new avenue of research to better comprehend and appraise cultural heritage, as well as any man-made structure that provides benefits to society. By utilizing a language equivalent to the benefits associated with natural assets, we aspire to contribute to the advancement of a more integrated and interdisciplinary approach to spatial and urban planning.
Experimental and Numerical Investigation of Solids Transport in a Wellbore using Gauge Measurements Complemented with Fiber-Optic DTS
Authors: Shetty Rishikesh , Sharma Jyotsna , Tyagi Mayank , Santos Otto
Published: September 2024

Solids detection and monitoring are critical for maintaining wellbore integrity and efficiency in hydrocarbon production. This study investigates the real-time detection and analysis of solids transport in a full-scale wellbore, using a combination of gauge measurements and fiber-optic distributed temperature sensor (DTS) data. The experiments are conducted in a 5163-ft deep wellbore filled with synthetic oil-based mud. The study involves monitoring the movement of solids-laden mud during circulation tests, with data collected from both downhole and surface pressure gauges as well as density measurements using a Coriolis meter. The experimental results were compared with numerical simulation predictions using an in-house program and commercial software, DrillSIMTM. The results show a good agreement between the experimental data, including the DTS measurements, and the numerical predictions, providing valuable insights into the dynamics of solids transport in multiphase flow systems under well-scale conditions. This underscores the potential for enhanced solids management and optimization through advanced monitoring techniques. Traditional methods face challenges in real-time detection and characterization, especially under complex flow conditions. The results of this study highlight the advantages of fiber-optic sensing technologies, such as DTS, which offer high-resolution spatial and temporal data, improved reliability under harsh conditions, and a comprehensive understanding of flow dynamics. By integrating traditional gauge measurements with fiber-optic sensor data and validated numerical models, this research provides a robust framework for optimizing solids management in oil and gas operations under a variety of field conditions.
AI-Driven Optimization of Refinery Configurations: A Novel Approach to Predicting Production Outputs and Capacities
Authors: Valliyatt Minas Poyil , Tharayil Sarafudheen M. , Razhi Abdulilah A. , AlQarni Shouq , AlThubaiti Leen
Published: September 2024

In the context of conceptualization and operation of refineries, achieving an optimal configuration emerges as a critical endeavor. Our proposed methodology transcends conventional approaches by seamlessly integrating domain expertise, empirical insights, and cutting-edge artificial intelligence (AI). Our approach holistically captures the intricate linkages between different refinery process facilities (also referred as process units or modules in later part of this paper) to make their best use in ever changing market conditions. Key aspects include robust predictive modeling for individual modules, meticulously considering feedstock availability, product demand, price information, process interdependencies and historical data. These predictions serve as foundational inputs during the conceptualization and planning stages, guiding optimal configuration decisions. Additionally, our rigorous cost modeling extends beyond capital costs to encompass financial parameters sufficient for shortlisting of configuration options for detailed economic evaluation. The machine learning models are evaluated based on the error metrices and has confirmed the models performs with in the acceptable tolerance ranges. Moreover, our integration of Large Language Models (LLMs) adeptly transforms complex refinery configurations into actionable insights expressed in business language. Decision-makers gain a holistic view, aligning technical choices with overarching organizational goals. Ultimately, this pioneering approach eases conceptualization process of new refineries and empowers operating refineries toward sustainable production and refinery-wide optimization.
Automating Drilling Practices to Compensate for Interactions Between Tool Joints and the Rotating Head
Authors: Eng David Forrest P. , Fischer Raymond , Waldner James , Eddy Aaron , Eng Sean Scotvold P. , Eng Cristian Tibrea P. , Fernandes Ashley
Published: September 2024

The use of managed pressure drilling (MPD) equipment on North America land rigs has become more common over the past several years. Standard and heavy weight drill pipe tool joints require more weight to pass through the rotating head than the pipe bodies. The negative impacts of this phenomena on both rate of penetration (ROP) and downhole dynamics have been previously studied and published. Various methods of manually adjusting AutoDriller setpoints have been presented as industry best practices. This paper presents the automation of industry best practices through the rig's process control system. Implementation and scaling across a rig fleet will also be discussed.

Using a patented process (Scotvold et al. 2020), an application was developed to run on the rig's process control system, where AutoDriller setpoints are automatically adjusted as the tool joints pass through the rotating head on each stand. Different methods of AutoDriller control can be utilized to achieve the intended result. Design of the application to minimize human error when configuring for specific pipe will be a focus, as well as challenges encountered with this in the past.

Numerous examples, methods, and control methods such as manually adding weight across tool joints, using ROP as a control method, and automating the process with different logic will be compared. Analysis of these scenarios will demonstrate the value of consistency by removing as much human intervention and setup from the operation as possible.

This paper presents the full automation of industry best practices that have historically been conducted through repetitive manual tasks. In addition, there is a strong focus on improving system autonomy going forward to minimize the level of human intervention and setup that is required for optimal performance while drilling with MPD equipment. Ease of scalability for automating this process will also be discussed as it was a major consideration when developing the application.
Novel Method for Automated Advanced Mud Gas Extraction Efficiency Correction (EEC) Characterization in Near Real Time
Authors: Ritzmann N. , Mueller T. , Erdmann S. , Says I.
Published: September 2024

Advanced mud gas logging is progressing to a more established service throughout the industry over the last two decades. It is more and more recognized as a highly valuable addition to standard and advanced downhole logging data and can be used to a certain extent as a continuous formation fluid composition log often comparing well with pressure volume temperature (PVT) fluid sample analysis. Compared to basic mud gas data, the process involves several correction layers starting from a booster dilution and contamination over to recycled gas correction until the final layer of the extraction efficiency correction (EEC). For this last step a new, fast, and online methodology has been developed and will be presented here together with comparison results between PVT data and corresponding, fully corrected, mud gas data. Mud gas is extracted from drilling mud at surface and measured with gas chromatographs, focusing on the hydrocarbon compounds up to C8s. The boiling point and the interaction of the components is different for each one of them and can be to a certain extent correlated to the number of carbons in the molecule. To derive PVT like quantification, up to normal pentane it is critical to accurately determine the extraction efficiency for each component and to be able to respond in case of fluid changes. Therefore, a fully automatic method is presented that determines the EEC on live mud samples in less than 10 minutes any time a significant change to the mud system is made. The method isolates a fixed volume of mud inside the equipment's extraction chamber and derives the correction coefficients by the integral of concentration extracted compared to a reference integral from before the test was started. This simple and fast method enables the methodology to quickly respond to necessary changes.

Several case studies from water as well as oil-based mud demonstrate the applicability of the newly introduced method and unlock the full potential of advanced mud gas data up to normal-pentane.
Minimizing Greenhouse Gas Footprint of Enhanced Oil Recovery Operations Through Optimizing Injected Fluid Composition
Authors: Olofinnika O. A. , Selveindran A. , Okoroafor E. R.
Published: September 2024

Most oil and gas fields face the dual challenge of optimizing recovery and minimizing CO2 and greenhouse gas footprint from depleting fields. Although tertiary recovery strategies including CO2 and produced gas injection are adopted, the cost and miscibility uncertainty associated with pure CO2 and produced gas streams remains a challenge. Therefore, this paper assesses the impact of pure and blended compositions of produced gas and CO2 in simultaneously improving the oil recovery efficiency and minimizing the GHG footprint of petroleum development projects.

We performed an experimental coreflood of a single-stage water alternating gas flood using CO2. Carbonate cores and crude oil with properties typical to a Permian field were studied. X-ray computed tomography (CT) before and after gas injection enabled investigations of fluid retention at the core scale. A sample field model was applied to investigate the impact of injected fluid composition on field-scale GHG reduction and oil recovery.

We observed increased CO2 storage in the optimal produced gas and CO2 blend compared to pure CO2 injection. We benchmarked the GHG emissions and oil recovery across differing injected gas compositions at the end of the WAG flood against a base case of waterflooding only. Our results revealed lower emissions and high recovery by switching to an optimal injection composition. The results suggest optimal compositions of produced gases and CO2 exist with a capacity to further improve the oil recovery and storage efficiency of CO2 while minimizing the overall GHG footprint of oil and gas operations.

We demonstrate experimentally how alteration of injected fluid composition can simultaneously increase oil recovery, reduce GHG emissions, and increase CO2 storage capacity. Adoption of this work has the potential to mitigate the flaring of produced gases and costs associated with pure CO2 injection while ensuring low carbon footprint operations in the oil and gas sector.
Machine-Learning Workflow for Fracture Geometry Characterization Using High-Resolution Distributed Strain Sensing During Well Production
Authors: Ma Wei , Wu Kan , Jin Ge
Published: September 2024

Rayleigh Frequency Shift Distributed Strain Sensing (RFS-DSS) has emerged as a crucial technique for characterizing fracture geometry in unconventional reservoirs by measuring strain responses along optical fibers. Several numerical modeling studies have been conducted to relate the RFS-DSS strain data to the fracture geometry and then to estimate the production performance. However, the issue of solution non-uniqueness remains unaddressed. Multiple combinations of fracture properties can potentially generate identical strain responses, introducing significant uncertainty in interpretation. The objective of this paper is to develop an integrated artificial intelligence (AI) assisted workflow to calibrate uncertain parameters and automatically history match field-measured strain responses and then estimate the well production performance.

The workflow is summarized as follows: First, the uncertain parameters and objectives are identified for this study. Uncertainty analysis is then conducted to investigate the impact of these parameters on strain change objectives. Initial numerical cases are generated using the Latin Hypercube Sampling (LHS) method. These cases are subsequently simulated using a coupled fluid flow and geomechanics simulator during production. Next, four strain change objectives, peak value, minimum strain, strain area, and strain width, are calculated, and efficient machine learning models are constructed as proxies for the simulator. Different machine learning models are developed for predicting the strain change objectives: Neural Network (NN) models for peak value, minimum strain, and strain area, and XGBoost for strain width. The Markov Chain Monte Carlo (MCMC) algorithm is implemented in conjunction with these proxy models to identify appropriate candidate samples from the posterior distributions. The P50 solution is then derived after rigorous history matching. Our AI-driven workflow demonstrates remarkable computational efficiency, capable of proposing 50,000 samples and generating comprehensive posterior distributions in less than 20 minutes.

Validation of the workflow using synthetic cases reveals that the P50 solutions of fracture geometry derived from the posteriors show excellent agreement with the true fracture geometry. The four strain change objectives calculated using the P50 solutions align closely with the true values of the strain change objectives. The results of this study demonstrate the accuracy and robustness of this workflow for predicting strain change objectives and automatically history-matching field-measured strain changes. Furthermore, for field applications, after history matching the measured strain using this workflow to obtain the fracture geometry, we can then estimate production profiles for various completion designs, providing valuable guidance for optimizing completion design and evaluating production performance.
Management as a Technical Discipline in Energy
Authors: Capello Maria Angela , Howes C. Susan , Kirkham Peter , Calleberg Amanda , Nandurdikar Neeraj , Swami Gautam
Published: September 2024

Management is a technical discipline that requires a solid knowledge base and a constant learning approach. Management Practitioners need to master various domains such as Asset and Portfolio Management, Risk Management and Decision-Making, Strategic Planning and Management, Energy Economics, Professionalism, Training, and Education to support an efficient decision-making process, and quantify the risks involved. This paper explores how the practice of management has evolved in the Oil and Gas sector, and the relevance of creating standards or guidelines to guide its practitioners.

The term "Management" is usually associated with people skills, whereas the methodologies of technical disciplines of management are well-known in the energy sector. Our paper focuses on five of the 29 technical sub-disciplines of the SPE taxonomy (Society of Petroleum Engineers [SPE], n.d.) of Management, showing their evolution through time and the progress of related roles in the industry. We highlight challenges and opportunities for the creation of systems and technical approaches. The technical sub-disciplines analyzed in this article are:

Portfolio analysis, management, and optimization Risk, uncertainty, and risk assessment Benchmarking and performance indicators Future of energy/oil and gas Personnel competence

The summary and analysis of the five Management disciplines addressed in this paper produced two deliverables: 1) an evolution timeline that shows how data analytics, changes in economics frameworks, and environmental considerations catapulted management workflows to a systematic approach in the decision-making for all the technical disciplines analyzed, and 2) a list of opportunities and challenges for the creation of guidelines for Management in the energy sector.

The ATCE Centennial Celebration provides a unique opportunity to share knowledge and experience in technical managerial workflows and practices, in looking back and forecasting the future, benefiting the Management technical discipline.

The conclusions highlight the importance of advancing Management disciplines in the technical realm. The examples exemplify the importance for management practitioners of keeping their technical skills updated. Our paper aims to raise awareness about the current practices of Management in oil and gas, how they have evolved, and what are the main future challenges and opportunities in the creation of certifications, best practices, guidelines, and standards in Management. We want to inspire students and young professionals to initiate their path into management early on in their careers.