bluuebunny/crossref_metadata_2025_split huggingface dataset:
FPU Mooring Footprint Reduction in Buzios Field: Key Driver to its Successful Subsea Layout
Authors: Cabral Gabriel Rodrigues , da Silva Helvio Ferreira , Oshiro Anderson Takehiro , Trovoado Leandro Cerqueira , Hernalsteens Thierry , Britto Joao Francisco Fleck Heck , Pinto Leandro De Assis 
Published: August 2021

Abstract
Buzios field development has the potential to implement several production systems due to large reservoir volumes. Considering the oil specification, the drive to use standard solutions already in place in Pre-salt area, associated with the high production indexes of the wells, Petrobras decided to tie back all production wells in satellite configuration. These facts, together with geological hazards in the area, lead to a potentially congested seabed scenario. Hence, FPU positioning has been challenging and demanding innovative engineering solutions to optimize FPU mooring as to overcome these challenges and enable FPU positioning close to wells. This optimization gave birth to new issues, such as risk of clashing between mooring lines and lazy-wave flexible risers. Integrated riser and mooring lines dynamic analysis, together with subsea layout assessment were performed to ensure technical and economic feasibility. Furthermore, due to the Buzios reservoir, well design requirements and subsea layout specificities, all FPU were located on the edge of the reservoir and flexible risers were tied back mainly from only one board of each FPU. Hence, enhancing clearance between bow and stern mooring clusters and the optimization of the risers’ configuration were of paramount importance for enabling most of the risers’ connections on the desirable board. FPU mooring optimization led to up to 30% of mooring lines’ radius reduction (horizontal projection), and an average of up to 500m per flowline reduction, saving CAPEX, OPEX and increasing the return on investment.
Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning
Authors: Figueirêdo Ilan Sousa , Carvalho Tássio Farias , Silva Wenisten José Dantas , Guarieiro Lílian Lefol Nani , Nascimento Erick Giovani Sperandio 
Published: August 2021

Abstract
Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.
Development of a Computationally Intelligent Model to Estimate Oil Formation Volume Factor
Authors: Khan Mohammad Rasheed , Kalam Shams , Khan Rizwan Ahmed 
Published: August 2021

Abstract
This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art computational intelligence (CI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserve evaluation studies and reservoir engineering calculations. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can aid in optimizing time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of Pakistan, Iran, UAE, and Malaysia. Resultantly, this allows to move step forward towards the creation of a generalized model.
Multiple CI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models for CI are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, and crude oil API gravity. Comparative analysis of various CI models is performed using visualization/statistical analysis and the best model pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented.
Graphical analysis using scatter plots with a coefficient of determination (R2) illustrates that ANN equation produces the most accurate predictions for oil FVF with R2 in excess of 0.96. Moreover, during this study an error metric is developed comprising of multiple analysis parameters; Average Absolute Error (AAE), Root Mean Squared Error (RMSE), correlation coefficient (R). All models investigated are tested on an unseen dataset to prevent the development of a biased model. Performance of the established CI models are gauged based on this error metric, which demonstrates that ANN outperforms the other models with error within 2% of the measured PVT values. A computationally derived intelligent model proves to provide the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.
Structural Digital Twin of FPSO for Monitoring the Hull and Topsides Based on Inspection Data and Load Measurement
Authors: Bhat Shankar , Nadathur Varadarajan , Knezevic David , Aalberts Pieter , Kolsters Hans , Amuda Daniel , Atebe Orobome , Pasala Dharma , Hoang Tuyen , Luong Trong , Huynh Phuong , Righetti Riccardo , Hageman Remco , Yu Jim 
Published: August 2021

Abstract
A high-fidelity FPSO Structural Digital Twin (SDT) based on Reduced Basis Finite Element Analysis (RB-FEA) coupled with inspection data and physical sensor measurements (advisory hull monitoring system) is presented to demonstrate a complete FPSO "digital thread" that combines operational data feeds, detailed structural analysis based on as-is asset condition, and automated structural integrity reporting. This lays the groundwork for a philosophical shift for asset lifecycle management by enabling the use of "as-measured" conditions in lieu of assumed "design-conditions" for a more accurate, and robust understanding of asset health. We demonstrate the deployment of this methodology for the Bonga FPSO and discuss the value that it brings during day-to-day operations.
A Comparative Study of the Course and Outcome in Hypoxic COVID-19 Patients with and Without Comorbidities
Authors: Wajekar Shreyas Deepak , Kurundkar Gajanan Balaji , Shah Pushkar P. , Kadam Dileep B. , Bhat Shreepad M. 
Published: August 2021

Background:
Severe acute respiratory illness caused by SARS-CoV-2 has been a health emergency of great concern in the year 2020. This study was undertaken to identify characteristics of hospitalized patients with Coronavirus Disease 19 (COVID-19) and hypoxia in the form of disease course and outcome with special reference to the presence or absence of comorbidities.

Materials and Methods:
A prospective observational study was conducted at a tertiary hospital recognized as Dedicated COVID Hospital during the period of June 2020 to September 2020. The study included a total of 249 patients of COVID-19 with hypoxia who required oxygen or noninvasive ventilation/invasive ventilation. Patients were divided into two groups as per the presence or absence of comorbidity (175 and 74 patients, respectively). Their clinical and laboratory findings, course in the hospital, and outcomes were noted. Data were analyzed using SPSS software.

Results:
Among all the study patients, more patients from comorbidity group presented with a N:L ratio >3.5 and raised inflammatory markers (like serum ferritin) than patients in the no comorbidity group. In patients with comorbidities, 47.43% required noninvasive or invasive ventilation as against 18.92% in those without any comorbidities. Development of deranged renal function was noted in 32.57% of patients in the comorbidity group and only 9.46% in the noncomorbid group. All except one death during the study period were in the patients with comorbidities.

Conclusion:
COVID-19 patients with hypoxia and the presence of comorbidities in this study had more complications and a worse outcome.
Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning
Authors: Shi Qing-Yang , Tan Li-Zi , Seng Lim Lian , Wang Hui-Jun 
Published: August 2021

Objective:
This study aimed to construct an intelligent prescription-generating (IPG) model based on deep-learning natural language processing (NLP) technology for multiple prescriptions in Chinese medicine.

Materials and Methods:
We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation, and the Yellow Emperor's Canon of Internal Medicine, the Classic of the Miraculous Pivot, and the Classic on Medical Problems as supplementary datasets for fine-tuning. We selected the word-embedding model based on the Imperial Collection of Four, the bidirectional encoder representations from transformers (BERT) model based on the Chinese Wikipedia, and the robustly optimized BERT approach (RoBERTa) model based on the Chinese Wikipedia and a general database. In addition, the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model. Multiple IPG models were constructed based on the pretraining strategy and experiments were performed. Metrics of precision, recall, and F1-score were used to assess the model performance. Based on the trained models, we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns.

Results:
Among all the trained models, the RoBERTa-large model performed the best, with a test set precision of 92.22%, recall of 86.71%, and F1-score of 89.38% and 10-fold cross-validation precision of 94.5% ± 2.5%, recall of 90.47% ± 4.1%, and F1-score of 92.38% ± 2.8%. The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer's patterns showed the associations of symptom–symptoms, disease–symptoms, and symptom–punctuations, while the between-layer's patterns showed a progressive or dynamic symptom and disease transformation.

Conclusions:
Deep-learning-based NLP technology significantly improves the performance of IPG model. In addition, NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.
Epidemiology of Rickettsial Infection in the Municipality of Uramita, Colombia
Authors: Quintero-Vélez Juan C. , Cienfuegos-Gallet Astrid V. , Quintero Lisardo Osorio , Úsuga Andrés F. , Cifuentes Sebastian , Solari Sergio , Rodas Juan D. , Diaz Francisco J. , Rojas Carlos A. 
Published: August 2021

ABSTRACT.
This study aimed to analyze epidemiological indicators related to seroprevalent and seroincident cases of spotted fever group rickettsiae (SFGR) and to identify housing conditions related to tick infestation. A prospective study (2016–2018) was conducted to examine rickettsial seropositivity in humans, domestic animals, and wild mammals in the municipality of Uramita, Antioquia, Colombia, where a previous Rickettsia rickettsii outbreak was reported from 2014 to 2015. The seroprevalence and its associated factors were estimated at baseline, and the seroincidence and its risk factors for humans were estimated 20 months later. A cross-sectional analysis was performed to evaluate the housing conditions linked to tick infestation. The SFGR seroprevalence was 26.70% (95% confidence interval [CI], 20.79–31.37), and the factors associated with SFGR seropositivity were male sex (adjusted prevalence ratio [PRa], 1.67; 95% CI, 1.19–2.32), age (evaluated in 5-year increments) (PRa, 1.05; 95% CI, 1.01–1.09), and household proximity (PRascattered vs. very near=3.87; 95% CI, 1.12–8.66). The overall seroincidence was 7.40% (95% CI, 4.71–11.06), and the factors associated with SFGR seroincident cases were the presence of wild animals (adjusted relative risk [RRa], 2.46; 95% CI, 1.06–4.72) and the presence of trees in the peri-domiciliary area (RRa, 0.47; 95% CI, 0.23–0.94). The prevalence of house tick infestation was 27.81% (95% CI, 21.93–34.80), and the factors associated with infestation were dirt floors (PRa, 5.88; 95% CI, 2.28–10.31), fiber cement roofs (PRa, 1.76; 95% CI, 1.07–2.83), and the presence of canines in peri-domiciliary or intra-domiciliary areas (PRa, 5.05; 95% CI, 3.31–7.19). Seropositivity rates for canines and opossums were 35.62% (26/73) and 100% (6/6), respectively. Identification of these factors will help to implement efficient surveillance programs in Colombia.
Mixed Plasmodium malariae Infections Were Underdetected in a Malaria Endemic Area in the Amazon Region, Brazil
Authors: Cunha Maristela G. , Santos Camille S. , Raiol Milene , Costa Sheyla Patrícia T. , Ventura Ana Maria R. , Póvoa Marinete M. , Ribeiro-dos-Santos Ândrea 
Published: August 2021

ABSTRACT.
Plasmodium malariae infections are often asymptomatic and long-lasting. Mixed infections are often underdetected in areas where P. malariae, P. vivax, and P. falciparum are coendemic. In this study, we described the occurrence of these species circulating as single or mixed infections in Pará state, Brazil, in the Amazon region, with the purpose of clarifying the impact of misidentification of parasite species based only on morphological description using thick blood smear. By using real-time polymerase chain reaction based on the amplification of the mitochondrial DNA, we detected a prevalence of 46% (58/126) mixed infections with 33.3% P. malariae/P. vivax which were read as P. vivax monoinfections by microscopy detection. Our findings confirmed the high circulation of P. malariae in a malaria endemic area in the Brazilian Amazon region.
SISTEM INFORMASI PENDATAAN BARANG MASUK DAN KELUAR PADA PT. SHAMROK MANUFACTURING CORPORA
Authors: Tajrin Tajrin 
Published: August 2021

Abstrak— PT. Shamrok Manufacturing Corpora merupakan perusaan yang bergerak di bidang sarung tangan, saat ini perusahaan masih menggunakan proses manual untuk melakukan proses pendataan barang masuk dan keluar. Tentunya hal tersebut akan memakan waktu yang cukup lama untuk melakukan proses terhadap data sehingga tidak efisien kemungkinan lainnya yang dapat terjadi yaitu data yang dicatat pada lembaran kertas tersebut kotor, rusak, maupun hilang. Untuk mengatasi kekurangan tersebut maka di bangun sebuah sistem informasi pendataan barang masuk dan keluar untuk dapat mempermudah dalam proses pendataan surat masuk dan keluarKata Kunci— Sistem Informasi, barang masuk dan barang keluar. Abstract— PT. Shamrok Manufacturing Corporation is a company engaged in the field of gloves, currently the company is still using a manual process to collect data in and out of goods. Of course this will take a long time to process the data so that it is not efficient that other possibilities that can occur are the data recorded on the sheet of paper is dirty, damaged, or lost. To overcome these deficiencies, an information system for data collection of goods in and out was built to be able to facilitate the data collection process for incoming and outgoing mailsKeywords - Information System, incoming and outgoing goods
ترجيح اسباب النزول في جامع البيان عن تاويل اى القران للطبري في ضوء سورة الإخلاص
Authors: Boihaqi Boihaqi 
Published: August 2021

Praises be to Allah who created causality events and revealed the miraculous Holy Quran in which wisdom and good tidings are rewarded. Peace and salutation be upon Prophet Muhammad SAW, the noble human being amongst all. Exegesis is an exalted field of sciences which functions to decipher Quranic verses and clarify sharia sciences related thereto, it is infact often be seen as references to any other field of sciences. Hence, the early exegesis scholars had put enormous effort in order to decipher the meaning of Quran and they bequeath tafseer to their future successors which contained compilations of hadith, the saying of His companion and tabiâ€™in. Also, the scholars had made every effort to improve human comprehension of Quran. Among all the noted exegesis scholars was Ibnu Jarir Atthabary who wrote â€œJamiâ€™al Bayan Taâ€™wil Al-Quranâ€ which used distinctive method to decipher Quran. For instance, when interpreting Quranic verses, he said, â€œthe following is the Quranic interpretationâ€¦â€, and he vowed that the tafseer are results of sanad up to a Prophetâ€™s companion or Successors, which called tafseer bilmaâ€™sur. Furthermore, when there were dispute about the meaning of Quranic Verses, he quoted various opinions, sanad, and references. Then, he would explain the most acceptable argumentation amongst all, viewed them semantically if needed, and also vowed likewise.
This method, along with other Islamic theorems, was also used in deducing regulation/law (istimbath). The tafseer of Ibnu Jarir are invaluable reference for exegesis scholars. For this reason, the writer was interested in conducting discussions on â€œThe Background of Quranic Revelation Based on Atthabaryâ€™s Jamiâ€™al Bayan Taâ€™wilayat Al Quran and the Tafseerâ€™s Influences in Analyzing Most Righteous Argumentation (A study on Quranic Verses Al Ikhlash)â€. This study is a comprehensive causative study which especially discusses the contradictory argumentation in interpreting Quranic verses which has revelation background. As a matter of fact, the important findings highlighted of this research were Ibnu Jarir Atthabari is an obidient human being who mastered various branches of science related to his curriculum and was not a adhered to any specified realms. Moreover, his tafseer contains a lot of beneficial sciences for those who seek for knowledge. Finally, it is only to Allah, the writer raised his hands for help, protection, and success. Praises be to Allah for His mercy which pours all the goodness amongs us.
Modelling of beyond budgeting, competitor accounting, transparency, competitive advantage, and organizational performance: The case of Indonesia SMEs
Authors: Harbi Valdiansyah Riyan , Augustine Yvonne 
Published: August 2021

SMEs are the backbone in driving the Indonesian economy and play an essential role in creating jobs throughout Indonesia. Indonesian SMEs are the key to designing inclusive development through the merger of large & small businesses so that economic equity can touch all levels of society. This study examines several factors of Indonesian SMEs' organizational performance, such as beyond budgeting, competitor accounting, and transparency through competitive advantage. This research was conducted by distributing online questionnaires to business owners, financial managers, or financial supervisors of SMEs in Indonesia spread across six islands. A total of 155 questionnaires were obtained in this study and analysed by SMART PLS 3.2.9. The results show that beyond budgeting and transparency influence competitive advantage, while competitor accounting does not. Other results show that competitor accounting, transparency, and competitive advantage influence business performance, while beyond budgeting does not. Path analysis in this study shows that competitive advantage mediates beyond budgeting and transparency, while competitor accounting is not mediated. Researchers are aware that this study has limitations in data dissemination and was distributed during the COVID-19 pandemic conditions, which allowed for differences in results with other similar studies. Hopefully, with this research that practitioners will be able to provide other views beyond budgeting, competitor analysis, and the side of transparency for Small and Medium Enterprises in Indonesia. Policymakers can encourage SMEs to be more innovative and responsive to science through programs to increase business scale, considering the contribution of SMEs to Indonesia's gross domestic product. Further research is expected to analyze more deeply what factors affect the competitive advantage and organizational performance of small and medium enterprises, such as the availability of working capital, SME access to banks, how digitalization plays a role, etc.