bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic-Geomechanics Coupled Modelling Helps Improve Understanding of the Reservoir and Improve Storage Capacity Estimate of Depleted Carbonate Gas Reservoir for Bulk Carbon Dioxide Sequestration
Authors: Chidambaram P. , Mohd Ali S. , Tewari R. D. , Tan C. , Mazeli A. H. , Das D. P. , Tiwari P. K. , Widyanita A. 
Published: September 2023

Abstract
A depleted gas reservoir in Central Luconia field, located offshore Sarawak, was evaluated for future carbon dioxide (CO2) storage. This carbonate reservoir has experienced seafloor subsidence during producing life. When coupled dynamic-geomechanics model was history matched, it was discovered that earlier understanding of reservoir performance along with aquifer extent and size were incomplete. This new understanding has led to a significant change in forecasted CO2 storage capacity of the reservoir.
In this reservoir, as production rate declined, reservoir pressure decline started reversing. During the producing life of the reservoir, standalone history matched dynamic model was used to forecast reservoir performance. This model sufficiently explained production performance and in fact production forecasts made from the model were reliable. After the reservoir had ceased to produce, it was evaluated for CO2 storage. As part of the CO2 storage studies, a coupled dynamic-geomechanics model was built. This was when it was noted that seafloor continued to subside even as reservoir pressure was increasing post cessation of production. This highlighted the fact that something other than hydrocarbon reservoir section was compacting. Further studies revealed that regional aquifer that was not included in the standalone dynamic model was undergoing compaction post-production.
This reservoir has a bottom acting aquifer, which has a barrier/baffle zone at the bottom of the aquifer that separates it from an extended/regional aquifer. Standalone dynamic model was truncated at the barrier/baffle zone. This aquifer was sufficient to explain the pressure support received but was insufficient to explain sea floor subsidence post-production. Further studies revealed that the hydrocarbon section was in communication with extended aquifer. And the baffle zone has a major role to play in understanding CO2 storage. Using dynamic-geomechanics coupled model with extended aquifer, it was discovered that baffle zone undergoes pore collapse during production. This translates to significantly lower aquifer efflux across the baffle zone during CO2 injection compared to aquifer influx during production. This directly impacts the storage capacity of the reservoir since the invaded aquifer could not be efficiently displaced by injected CO2. Storage capacity estimate from coupled dynamic-geomechanics model is about 50% lower compared to standalone dynamic model.
Projecting Petrophysical Logs at the Bit through Multi-Well Data Analysis with Machine Learning
Authors: Sharma A. , Burak T. , Nygaard R. , Hoel E. , Kristiansen T. , Hellvik S. , Welmer M. 
Published: September 2023

Abstract
The vertical distance from logging while drilling (LWD) sensors to the bit is often more than 30m (98 ft), which leads to difficulty in performing real-time comparison of LWD and drilling data. This study aims to predict the petrophysical data at the drill bit with the objective of determining the best supervised machine learning algorithm to incorporate to reduce the sensor offset problem. The bulk density and porosity logs are predicted at the bit in this paper using petrophysical and drilling parameters. The results of the model will be used to perform lithology identification in real-time that can be used in real-time drilling analysis.
To predict the bulk density and porosity logs at the bit, data from four different wells located in the Norwegian continental shelf in the North Sea was used as a training dataset. The data from a fifth well from the same field was used as a validation dataset. The prediction was based on input variables of the Gamma ray (GR) log data recorded close to the bit, along with other drilling parameters measured at the bit using Measurement while Drilling (MWD) sensors. The five regression models used for prediction and comparative analysis were: Multi-linear regression (MLR), K-nearest neighbor (KNN) regression, Random forest regression (RFR), Support vector machine (SVM) regression and Artificial neural network (ANN).
All five models were tested for their accuracy in predicting porosity and bulk density, and it was determined that the KNN model was more effective for predicting both porosity and bulk density. The coefficient of determination (R2) value for the KNN model for porosity and bulk density predictions were 86% and 74% respectively with the least mean square error (MSE) calculated on the blind dataset (data from a well not included in model training). SVM was found to be the least effective model for predicting both porosity and bulk density, as it had the highest MSE value.
Prediction of porosity and bulk density logs at the bit using multiple machine learning techniques to eliminate the sensor offset problem have not been performed extensively in the past. The developed machine learning model will improve real-time drilling analysis.
Field Experience Using Flow Positioned Ditch Magnet Systems - Contribution to Efficient Drilling
Authors: Ånesbug G. O. , Pallin J. E. , Aase B. , Andresen O. I. , Sandvik M. , Khalili P. , Saasen A. 
Published: September 2023

Abstract
The presence of magnetic debris in the drilling fluid has a detrimental effect on the drilling process. The BHA compass becomes shielded and most Logging While Drilling operations are affected. Magnetic particles will be present whenever no efficient method to remove these are used. The following text shows how removal of such contamination contributed to successfully drill 6624m horizontal 8 ½" section of in one run on with high average ROP on a North Sea field.
Benchmarking of the flow positioned magnets with simpler systems has shown that it may be several times as effective in removal of magnetic debris than competing systems. Furthermore, this removal significantly eased the directional control during drilling. For the particular well the removal of magnetic debris was very effective. It was stated that due to the improved directional control they did not observe the severe whirl typically expected in long tangent sections. Directional control is known to be affected by the magnetic particle content in the drilling fluid. Neither was there any need for changing any logging tools during drilling. Such changing of logging tools is not uncommon because of agglomeration of metal on the measurement ports. Also, the lifetime of the turbine in the MWD increased partly because of less metallic debris in the drilling fluid. In the article it is summarised the experience of operating the ditch magnets along with an outline of the drilling performance. The amount of removed steel is quantified. The effect on directional drilling is assessed. At the same time, the workload to clean the magnets properly is shown as well as the need for the crew to operate the system properly.
Constructing Three-Phase Envelopes Using a Trust-Region-Based Algorithm
Authors: Xu Lingfei , Chen Zhuo , Li Sirui , Li Huazhou 
Published: September 2023

Summary
We develop a new trust-region (TR)-based algorithm to construct complete three-phase envelopes for reservoir fluid mixtures. The new algorithm is developed based on a basic algorithm for two-phase envelope constructions (Xu and Li 2023). A state-of-the-art TR method with a realistic exact subproblem solver is implemented in the algorithm, and an integrated strategy is adopted to construct complete phase envelopes with two-phase and three-phase branches. We test the performance of the TR-based algorithm by constructing multiphase envelopes for a total of 15 fluid mixtures that include hydrocarbons-CO2, hydrocarbons-asphaltene, and hydrocarbons-water mixtures. Comparison against the conventional Newton-based algorithm indicates that the TR-based algorithm leads to a much higher computational efficiency and an enhanced robustness.
Diversity of bacterial symbionts associated with the tropical plant bug Monalonion velezangeli (Hemiptera: Miridae) revealed by high-throughput 16SrRNA sequencing.
Authors: Navarro-Escalante Lucio , Benavides Pablo , Acevedo Flor Edith 
Published: September 2023

Abstract
Insects and microbes have developed complex symbiotic relationships that evolutively and ecologically play beneficial roles for both, the symbiont and the host. In most Hemiptera insects, bacterial symbionts offer mainly nutritional, defense and reproductive roles and have promoted the adaptive radiation of several hemipteran phytophagous lineages. The tropical plant bug Monalonion velezangeli (Hemiptera: Miridae) is a polyphagous herbivore considered an important insect pest for several economically-relevant tropical crops; however, information about the composition of its bacterial microbiota was missing. In this study, we describe the diversity and structure of the bacterial microbiota in the nymph and adult life stages of M. velezangeli using Illumina high-throughput sequencing of 16S ribosomal RNA gene amplicons (meta-barcoding). We found that both insect life stages share a similar microbiota in terms of bacterial diversity and community structure. The intracellular symbiont Wolbachia (~92%) dominated the overall microbiome composition in these life stages, and along with bacteria genera Romboutsia, Ignavibacterium, Clostridium, Allobaculum, Paracoccus, Methylobacterium, Faecalibacterium, Collinsella, Rothia, Sphingomonas and other 4 undetermined genera, were found as members of the core microbiota. Based on PCR screening and DNA sequencing of wsp gene, Wolbachia infection was confirmed in almost 80% of samples, represented by two different isolates or strains within the supergroup B. This data opens new questions and avenues to better understand the contribution of symbiotic bacteria in the biological performance of this insect pest and provide bases to explore other insect control methods.
Enhancing SPARQL Query Performance with Recurrent Neural Networks
Authors: Chen YiHui , Lu Eric Jui-Lin , Lin Jin-De 
Published: September 2023

Abstract

DBpedia is one of the most resourceful link databases today,
and to access information in DBpedia databases, we need to use
query syntax (e.g., SPARQL). However, not all users know SPARQL, so
we must use a natural language query system to translate the user's
query into the corresponding query syntax. It is costly and
time-consuming for the query system to generate query syntax.
Therefore, this paper proposes generating query syntax from natural
language. Two multi-label learning methods are used for question
transformation: Binary Relevance (BR) and Classifier Chains (CC).
To predict all the labels that match the query intentions, we use
Recurrent Neural Networks (RNNs) to build a multi-label classifier
for generating RDF triples. To better consider the relationship
between RDF triples, the Binary Relevance is integrated into an
ensemble learning approach to propose an Ensemble BR. The
experiments perform better than the other research to improve the
query accuracy. (This article is accepted by IEEE Access with
Digital Object Identifier: 10.1109/ACCESS.2023.3308691 entitled
Enhancing SPARQL Query Performance with Recurrent Neural
Networks)
Association between testosterone and cancers risk in women: a two-sample Mendelian randomization study
Authors: Li Zhizhou , Wang Maoyu , Hua Meimian , Wang Ziwei , Ying Yidie , Zhang Zhensheng , Zeng Shuxiong , Wang Huiqing , Xu Chuanliang 
Published: September 2023

AbstractObjective:Previous observational studies have explored the correlation between testosterone and cancer risk. However, the causal association between testosterone and various cancer types in women remains inconclusive. The objective of this Mendelian randomization study is to evaluate the causal links between total testosterone (TT) and bioavailable testosterone (BT) with cancer risk in females.Methods:Initially, a rigorous quality control process was used to identify suitable instrumental single nucleotide polymorphisms (SNPs) linked with the exposure under investigation that exhibited a significant association. The genetic causal relationship between female testosterone levels and the risk of developing cancers was examined via two-sample Mendelian randomization. A variety of analytical methods, including inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode, were employed in the investigation. Key findings were primarily based on the results obtained via IVW (random effects), and sensitivity analyses were conducted to assess the reliability of the obtained results. Moreover, maximum likelihood, penalized weighted median, and IVW (fixed effects) methods were utilized in order to further validate the robustness of the results.Results:Based on the results of IVW analysis, our study indicated a positive causal relationship between BT and breast cancer (OR = 1.1184, 95%CI: 1.0448-1.1971, P = 0.0083) and endometrial cancer (OR = 1.4995, 95%CI: 1.3179-1.7061, P = 9.94E-09). Moreover, our findings also showed a positive causal association between TT and breast cancer (OR = 1.1403, 95%CI: 1.0574-1.2298, P = 0.0043), cervical cancer (OR = 1.0017, 95%CI: 1.0006-1.0028, P =0.0122), and endometrial cancer (OR = 1.5046, 95%CI: 1.3103-1.7277, P = 9.06E-08). However, no causal relationship was found with BT and TT on other types of cancer (corrected P> 0.05).Conclusions:This study elucidates the role of testosterone in the development of breast cancer, endometrial cancer, and cervical cancer, while also indicating a potential tenuous link between testosterone and bladder cancer as well as skin cancer. Nonetheless, no statistically meaningful relationship between testosterone and various other types of cancer in females was observed.
Efficacy of IL-23 inhibitors and IL-12/23 inhibitors in the induction treatment of Crohn's disease: a meta-analysis based on randomized controlled trials
Authors: Guo Wenying , Shentu Haojie , Sha Suyong , Wang Dongying , Chen Xi , Huang Zhengwei , Dong Nan , Lai Haijia , Chen Meiling , Xu Jianying , Huang Jianing , Zhong Ziwei 
Published: September 2023

Abstract
Background
A growing number of randomized controlled trials (RCTs) have demonstrated interleukin (IL)-23 and IL-12/23 inhibitors effectiveness in treating Crohn's disease (CD). This study evaluated the efficacy of IL-23 and IL-12/23 inhibitors in the induction phase for the treatment of CD.
Methods
We searched the following databases from inception until December, 2022: Medline, Embase, Web of Science and the Cochrane Library. The primary outcome was the proportion of CD patients who achieved clinical remission at the end of the induction therapy period. Secondary outcomes included clinical response, endoscopic remission, endoscopic response and normalized C-reactive protein (CRP).
Results
After screening, 7 RCTs were included in our study. The meta-analysis showed that, in the induction period, more patients treated with IL-23 inhibitors and IL-12/23 inhibitors achieved clinical remission than patients with placebo therapy (RR = 2.11; 95%CI: 1.83–2.44; RR = 1.94; 95%CI: 1.64–2.29; respectively). The IL-23 inhibitor group and the IL-12/23 inhibitor group showed higher clinical response rates than the placebo group (RR = 1.92; 95%CI: 1.74-2,11; RR = 1.83; 95%CI: 1.61–2.09; respectively). In addition, IL-23 inhibitor group had higher endoscopic remission rate and endoscopic response rate than placebo group, the corresponding pooled RR were 3.40 (95%CI: 2.57–4.50) and 2.65 (95%CI: 2.65–3.12), respectively.
Conclusions
IL-23 and IL-12/23 inhibitors were efficiency methods in the induction treatment of CD.
A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records
Authors: Li Qiuying , Li Jiaxin , Chen Jiansong , Zhao Xu , Zhuang Jian , Zhong Guoping , Song Yamin , Lei Liming 
Published: September 2023

Abstract
Background: Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.
Methods: The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (logistic regression, support vector machine, K-nearest neighbors, Naïve Bayes classifier, perceptron, decision tree classifier, and random forest classifier) were used to develop delirium prediction models under full (n=32) and simple (n=20) feature sets, respectively.
Result: The area under the receiver operating characteristic curve (AUC) was higher under the full feature set (ranging from 0.61 to 0.85) than under the simple feature set (ranging from 0.31 to 0.76). Among all machine learning methods, the random forest classifier showed excellent potential for predicting delirium in patients using the full or simple feature set.
Conclusions: We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.
The effect of macroscopic herd inputs on individual investment behaviour
Authors: Nielsen Kristian Roed , Kaiser Micha , Glückstad Fumiko Kano 
Published: September 2023

Abstract
The influence of others' behaviour on our own is pervasive and is often argued to result from an information cascade, where decisions in a sequential setting are influenced by the early decisions of others. However, the degree to which individuals modify behaviour through the integration of social information (i.e. other people’s decisions) varies significantly between individuals. We propose that the scale and longevity of the herd itself at the time when an individual decides, defined as macroscopic herd inputs, significantly influences whether individuals integrate social information. To observe the influence of macroscopic herd inputs on behaviour we employ data from a social trading platform finding that it exerts a strong linear influence on individual investment decisions. The effect of information cascades on individual behaviour is therefore dependent not only on personal factors but also on macroscopic herd inputs that shape the degree to which an individual integrates social information.
How ESG Reporting Effects on Green Credit , Green Investment : Moderating Role of Growth Sales
Authors: Rauf Fawad , Wanqui Wang , Naveed Khwaja , Qadri Syed Usman , Ali Muhammad Sibt E 
Published: September 2023

Abstract
Scholars and authorities are becoming more interested in environmental challenges, and experts have revealed their organizational consequences. At the same time, ESG reporting is regarded as a critical component of financial performance in developed and developing nations. In light of this, this study aims to investigate how China's manufacturing industry sales growth is impacted by ESG reporting, green innovation performance, green credit, green investment, and R&D intensity. Non-financial trade manufacturing companies listed on the Shanghai and Shenzhen stock exchanges between 2015 and 2020 were selected for this study. For data estimation, panel regression estimations using OLS, fixed effects models have been used. The results demonstrate a significant positive correlation between manufacturing industry growth sales in China and ESG reporting, green innovation performance, green credit, green investment, and R&D intensity. Lastly, several practical takeaways are offered to boost green innovation performance among ESG reporting enterprises and increase the effectiveness of R&D intensity. These findings, including Policy recommendations, will benefit all parties involved.
Healthcare efficiency scenario: A structured literature review, implications and research agenda
Authors: Rella Angela , Raimo Nicola , Vitolla Filippo , Esposito Paolo 
Published: September 2023

Abstract

Background Despite the great number of studies and interests in this field, research in this area is still scarce, and some aspects don’t emerge. For this reason, it is important to conduct an international healthcare system debate by showing the findings of SLR putting attention to healthcare efficiency, and organizing the articles in a more systematic and holistic way.Methods The present study provides a comprehensive structured literature review (SLR) of the papers dealing with efficiency in the healthcare sector from an international perspective. For this purpose, this study examines articles published in journals until 2023, by extracting crucial insights about healthcare efficiency, in terms of structures and services.Results A sample of 292 articles extracted from Scopus is preliminarily submitted for bibliometric analysis, then 126 articles are examined with bibliographic coupling and content analysis to identify the different areas of topic categorizations. In light of this, the study through the PRISMA framework and, then the content analysis provides the areas most affected by academic literature, and consequentially, the areas that have not yet been explored systematically.Conclusions Due to this, the research agenda underlines the unexplored areas and lays the foundation for future academic studies.