bluuebunny/crossref_metadata_2025_split huggingface dataset:
MOF-808 as an Efficient Catalyst for Valorisation of Biodiesel Waste Production: Glycerol
Authors: Mirante Fátima , Leo Pedro , Dias Catarina , Cunha-Silva Luís , Balula Salete S. 
Published: October 2023

Glycerol is the main residue in the biodiesel production industry; therefore, their valorisation is crucial. Acetalization of glycerol towards fuel additives as solketal (2,2-dimethyl-1,3-dioxolan-4-methanol) is of high interest, promoting circular economy since it can be added to the biodiesel or even fossil-diesel to improve their quality and efficiency. Straightforward prepared metal-organic framework (MOF) materials of MOF-808 family were applied to the valorisation of glycerol for the first time. In particular, MOF-808(Hf) revealed to be an effective heterogeneous catalyst to produce solketal under moderate conditions: small amount of the MOF material (only 4wt% of glycerol), ratio 1:6 of glycerol/acetone and temperature of 60 ºC. The high efficiency of MOF-808(Hf) was associated with the high amount of acid centres present in its structure. Furthermore, its structural characteristics such as window opening cavities size and pore diameters showed to be ideal to reuse this material during at least ten consecutive reaction cycles without losing activity (conversion &amp;gt; 90% and selectivity &amp;gt; 98%). Remarkably, it was not necessary the washing or other activation treatment of the MOF-808(Hf) catalyst between cycles (no pore blockage occurred) and it maintained structural integrity after the ten cycles, confirming to be a sustainable heterogeneous catalyst to the glycerol valorisation.
Pivoting a Comprehensive, Custom Onboarding Program for Upstream: Lessons Learned
Authors: Rabaa Ali S , Sulami Sulaiman L. , Shaiji Humoud A. 
Published: October 2023

Abstract
This paper will discuss methods, best practices, and lessons learned to optimize a highly successful Saudi Aramco Upstream Professional Onboarding Program (UPOP), meeting the business needs to deliver the same program in a shorter time frame.
The need to optimize the program emerged due to an influx of new hires in conjunction with a global pandemic that reduced the number of participants per class due to social distancing measures. UPOP was originally designed to walk newly hired Upstream professionals through the upstream lifecycle by using a project that demonstrates the upstream workflow. Participants in this program gain confidence by working on real projects and are expected to give presentations to senior employees explaining the key decisions at each stage of the upstream lifecycle. These activities enhance their skills in leadership, networking, presentation, and communication.
This project-based onboarding program was originally designed by the Upstream Professional Development Center (UPDC) as an 8-week program. As the program has reached a mature stage with over forty (40) successful deliveries and multiple opportunities to apply participant feedback, it was recently optimized to five (5) weeks for in-class delivery without sacrificing quality. The global pandemic pushed the need for a 3-week Virtual Instructor Led Training (VILT) version. The development process involved updating and developing program content to remove scrap learning while enhancing key outcomes. The UPDC team of geoscience subject matter experts (SMEs), petroleum engineering SMEs, soft skills instructors, and learning technology professionals identified and removed course content that was planned to be covered in other courses planned in the young professionals’ prescribed learning curriculum. The revised UPOP optimized participant in-class training to maximize participants’ on-the-job performance by removing redundancies and providing new hires with an overview of the company's upstream lifecycle. Additionally, creating an optimized version of the onboarding program has made it possible to offer the program four times per year to meet the increasing demand for onboarding new hires.
The current customized UPOP is designed to minimize the amount of time required for new hires to complete the program without sacrificing content for on-the-job tasks and objectives. The number of sessions per year has doubled, and the number of participants has increased by 75% since the program's inception in 2010.
Improving 4D Seismic History Matching Through Data Analysis; A Localised Sensitivity Analysis Workflow
Authors: Kolajoobi R. A. , MacBeth C. , Landa J. 
Published: October 2023

Abstract
4D seismic history matching (4D SHM) uses 4D seismic data to calibrate reservoir models to reduce production forecast uncertainty and improve reservoir surveillance. 4D seismic becomes very valuable in field developments with sparse well configurations and high areal uncertainty, such as offshore and carbon sequestration projects. Unlike the production data, the conventional uncertainty and sensitivity analysis (SA) with 4D seismic data might return misleading results. Due to the smooth nature of 4D seismic data, it is highly likely that the effects of different model parameters overlap, and low-frequency signals mask the high-frequency signals. Consequently, some significant parameters are wrongly excluded from the 4D SHM process. Our work aims to address this issue by localising the SA of 4D seismic data. The idea is first to identify specific seismic signals on the seismic maps and then perform the SA only at the individual locations rather than the entire map. This way we overcome the overlapping effects of different input parameters.
Several approaches to localise the SA are utilized. In one approach we defined sliding windows to scan the seismic maps and then executed an SA inside the windows at each location. Other localisation approaches employ dimensionality reduction and feature extraction tools. We used principal component analysis (PCA) and advanced machine learning (ML) methods such as autoencoders (AE) and variational autoencoders (VAE) to transform the 4D seismic maps into a latent space. The information content (the 4D seismic signals) in the high-dimensional 4D seismic maps is represented by a few features in the latent space. Implementing an SA for each feature in the latent space is equivalent to performing SA with the seismic signals in the original map.
The localised SA scheme is coupled with the Ensemble Smoother with Multiple Data Assimilation (ESMAD) algorithm to carry out 4D SHM. Three 4D SHM scenarios were defined: full parameterisation with no SA, conventional SA analysis using the entire map, and localised SA. We ran these scenarios for a complex synthetic reservoir model based on a real field in the North Sea to match to 4D P-wave seismic impedance. The results confirmed the superiority of the localised SA scenario which returned the final ensemble with the lowest error and the best match among the three scenarios. It also turned out that the PCA, for this specific case, is the most suitable methodology to localise the SA.
A novel SA workflow for 4D seismic was proposed to select a better set of parameters for 4D SHM. It relies on ML and data analysis solutions to localise the SA and avoid missing the important parameters for 4D SHM, hence, improving the reservoir model quality and the production forecasts.
Case Study of a Rigless Intervention for the Completion of an Offshore Multi-String Producing Well Using Slimhole Sand Jet Perforating
Authors: Dotson Thomas , Dearborn Earl , Dearborn Richard , Dixon Jacob , Nguyen Jimmy , Piercy Matt 
Published: October 2023

Abstract
Cost effective methods for completing new zones in existing offshore wells present challenges for operators that wish to extend the life of their wells by adding production with new zone completion and stimulation. Workover rigs add significant cost to service operations that often challenge the feasibility of the desired work. Challenges for rigless intervention work include running tools through the existing production tubing for perforating and other functions, having to perforate multiple tubing and casing strings while in the production tubing, and logistical constraints on the platform.
This paper examines the completion and stimulation of an upper zone in a Gulf of Mexico offshore well using a commercially viable program with rigless recompletion where a traditional recompletion was not cost effective. The objective of the project was to utilize a rigless intervention with coiled tubing for the perforation and stimulation of an upper zone above a production packer while sand jet perforating through two cemented casings. This paper explores a successful and cost effective method used to complete and stimulate an upper well zone. Factors that added to the complexity of the project include running tools through the production tubing and production packer, perforating a 2-7/8" production tubing Y block assembly, a 7" casing string, and a 11-7/8" casing string with cement in the annular space, and logistics for offshore equipment rig up and operation. Multiple rounds of surface bench testing were performed to refine the perforating process and develop procedures for well completion.
This paper identifies the challenges for the operation, frac design considerations, and optimizing well production during the initial flowback. The results of the frac, initial injection, and flow testing are included. Cost comparison between the traditional recompletion and this new approach are also important results that help determine overall success of the project. The project was a success in its ability to achieve the technical goals while also being commercially viable.
Without this novel rigless approach, recompletion of this zone would not have been feasible. By completing the new zone, the overall production from the well is increased and the life of the well is extended, maintaining its value as an asset instead of a potential P&A liability.
An Open-Access Data Set of Hospitalized Cardiac Arrest Patients: Machine Learning-Based Predictions Using Clinical Documentation
Authors: Rajapaksha Lahiru T. W. , Vidanagamachchi Sugandima M. , Gunawardena Sampath , Thambawita Vajira 
Published: October 2023

Abstract
Cardiac arrest is a sudden loss of heart function with serious consequences. In developing countries, healthcare professionals use clinical documentation to track patient information. These data can be used to predict developing cardiac arrests. We are publishing the data set through open access to advance the research domain. Based on this data set, our work revolves around generating and utilizing synthetic data by harnessing the potential of synthetic data vaults. We conducted a series of experiments by employing state-of-the-art machine learning techniques. These experiments were aimed to assess the performance of our developed predictive model in identifying the likelihood of developing cardiac arrests. This approach is effective in identifying the risk of cardiac arrest in inpatients, even in the absence of electronic medical recording systems. The study evaluated 112 patients who were transferred from the Emergency Treatment Unit to the Cardiac Medical Ward. The developed model achieved 96\% accuracy in predicting the risk of developing cardiac arrest. In conclusion, our study showcases the potential of leveraging clinical documentation and synthetic data to create robust predictive models for cardiac arrest. The outcome of this effort will provide valuable insights and tools for healthcare professionals to preemptively address this critical medical condition.
Universal production of 2D materials on arbitrary insulators
Authors: Li Junzhu , Yuan Yue , Lanza Mario , Abate Iwnetim , Tian Bo , Zhang Xixiang 
Published: October 2023

Abstract
Synthesizing two-dimensional (2D) materials on insulating substrates is a fundamental requirement for applying low-dimensional materials towards fabricating next-generation nanodevices. However, preparing a 2D layered material, such as graphene or hexagonal boron nitride (hBN), on insulators is challenging owing to the lack of suitable metal catalysts, lattice matching, and other factors. Therefore, developing a generally applicable and efficient protocol remains crucial despite numerous strategies having been explored. Herein, we introduce a universal strategy for highly efficient preparation of wafer-scale single- crystal 2D materials on arbitrary insulating substrates. The foil in a non-adhered metal– insulator-substrate system was almost melted by a short-time high-temperature treatment to obtain 2D layers on the insulator. High quality, large-area, single-crystal, smooth with essentially wrinkle-free mono-, bi-, and tri-layered graphene, and monolayered hBN were fabricated on various insulating substrates. The developed strategy provides ideal growth templates for future wafer-scale syntheses of other single-crystal 2D materials on arbitrary insulating substrates.
Trans-Lesion Synthesis and Mismatch Repair Pathway Crosstalk Defines Chemoresistance and Hypermutation Mechanisms in Glioblastoma
Authors: Cheng Xing , An Jing , Lou Jitong , Gu Qisheng , Ding Weimin , Droby Gaith , Wang Yilin , Wang Chenghao , Gao Yanzhe , Shelton Abigail , Satterlee Andrew , Mann Breanna , Hsiao Yun-Chung , Liu Chih-Wei , Lu Kun , Hingtgen Shawn , Wang Jiguang , Liu Zhaoliang , Miller C , Wu Di , Vaziri Cyrus , Yang Yang 
Published: October 2023

Abstract
Almost all Glioblastoma (GBM) are either intrinsically resistant to the chemotherapeutical drug temozolomide (TMZ) or acquire therapy-induced mutations that cause chemoresistance and recurrence. The genome maintenance mechanisms responsible for GBM chemoresistance and hypermutation are unknown. We show that the E3 ubiquitin ligase RAD18 (a proximal regulator of TLS) is activated in a Mismatch repair (MMR)-dependent manner in TMZ-treated GBM cells, promoting post-replicative gap-filling and survival. An unbiased CRISPR screen provides a new aerial map of RAD18-interacting DNA damage response (DDR) pathways deployed by GBM to tolerate TMZ genotoxicity. Analysis of mutation signatures from TMZ-treated GBM reveals a role for RAD18 in error-free bypass of O6mG (the most toxic TMZ-induced lesion), and error-prone bypass of other TMZ-induced lesions. Our analyses of recurrent GBM patient samples establishes a correlation between low RAD18 expression and hypermutation. Taken together we define novel molecular underpinnings for the hallmark tumorigenic phenotypes of TMZ-treated GBM.
Survival of osseointegrated implants in head and neck cancer patients submitted to multimodal treatment: a systematic review and meta-analysis
Authors: Camolesi Gisela Cristina Vianna , Veronese Henrique Rocha Mazorchi , Celestino Miriã Andrade , Blum Davi Francisco Casa , Márquez-Zambrano José Agustín , Carmona-Pérez Francisca Andrea , Jara-Venegas Tamara Andrea , Pellizzon Antonio Cassio Assis , Bernaola-Paredes Wilber Edison 
Published: October 2023

Abstract
Purpose: to investigate the survival rate in implants placement in irradiated and non-irradiated bone in patients undergoing head and neck cancer (HNC) treatment. We focused on the consequences of the main complications, such as osteoradionecrosis, peri-implantitis.
Methods: An electronic search conducted by PRISMA protocol was performed. Full texts were carefully assessed, and data were assimilated into a tabular form for discussion and consensus among the expert panel. The quality assessment and the risk of bias are verified by Joanna Briggs Institute checklist (JBI) and The Newcastle-Ottawa Scale (NOS), and Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) assessment tool.
Results: A total of 452 records were identified in the based on our PICOs strategy and after screening, 19 articles were included in the descriptive analysis of the review. Totaling 473 implants placed in irradiated and non-irradiated bone, and 31.6 % of the patients were over 60 years of age. 57.9%) performed implant placement in a period of 12 months or more after the ending of radiotherapy. Only 5 studies had a follow-up period longer than 5 years after implant placement, of which three were used for the meta-analysis. In the meta-analysis of 5-year survival rate analysis of implants in irradiated bone was assessed; a random effect model was used and a weighted proportion (PP) of 93.13% (95% CI: 87.20 - 99.06; p<0.001), and in the 5-year survival rate analysis of implants in non-irradiated bone was analysed, a fixed effect model was used and a weighted proportion (PP) of 98.52% survival (95% CI: 97.56-99.48, p<0.001).
Conclusions: Survival rates of implants placed in irradiated bone is clinically satisfactory after a follow-up of 5 years, with a fewer percentage than in implants placed in non-irradiated bone after metanalyses performed.
The incretin effect in type 2 diabetes in a Sub-Saharan African population
Authors: Nielsen Signe , Kweka Belinda , Praygod George , Filteau Suzanne , Olsen Mette Frahm , Friis Henrik , Faurholt-Jepsen Daniel , Krogh-Madsen Rikke 
Published: October 2023

Abstract

Aim
The pathophysiology of type 2 diabetes in sub-Saharan Africa is poorly investigated. In this study we explored the incretin effect.
Methods
In a cross-sectional study in Tanzania including 20 adults with type 2 diabetes based on oral glucose tolerance test (n = 10) or on glycated hemoglobin A1c (n = 10), and 10 non-diabetes controls we investigated the difference between the insulin response during an oral glucose tolerance test and that of the corresponding intravenous glucose infusion.
Results
The incretin effect did not differ between groups, suggesting that the pathophysiology of type 2 diabetes in sub-Saharan Africa differs from what we see in Western populations. We found no difference in insulin resistance between groups, but the beta-cell function was reduced in participants with type 2 diabetes based on oral glucose tolerance test.
Conclusion
The results indicates that type 2 diabetes in this sub-Saharan African population comprise at least two entities, where those diagnosed using HbA1c have intact beta-cell function, and those with Type 2 diabetes diagnosed using oral glucose tolerance test resemble type 2 diabetes in Western populations.
clinicaltrials.gov: NCT03106480, date of registration: 04/10/2017
https://clinicaltrials.gov/ct2/show/NCT03106480?term=NCT03106480&draw=2&rank=1
MPPT techniques for PV systems: A systematic review in current trends and recent advances in AI
Authors: Boubaker Olfa 
Published: October 2023

Abstract
Over the past decades, solar photovoltaic (PV) energy has been the most valuable green energy. It is renowned for its sustainability, environmentally friendly nature, and minimal maintenance costs. Several methods aiming to extract the highest photovoltaic energy are found in the vast literature. The aim of this systematic review is to focus on current trends and the most recent advances in the field. A “Scopus” bibliographic survey is conducted around survey and research articles published over the past three years (2019–2022) according to the PRISMA selection methodology. Over the selected works, different taxonomies of maximum power point tracking (MPPT) approaches are found. The list of associated performance criteria is also established, current trends, future directions and challenges in the field are well identified. This survey paper could be a useful reference for researchers and companies concerned by the sustainable development goals (GSD) for clean energy production and climate change.
Effectiveness of peer-led intervention in control of non-communicable diseases in rural areas of Khordha district: Study protocol for a cluster randomized controlled trial
Authors: Behera Priyamadhaba , Patro Binod Kumar , Singh Arvind Kumar , Dora Susmita , Bandhopadhyay Debapriya , Saharia Gautom Kumar , Dey Anupam , Behera Surama Manjari , Subba Sonu H 
Published: October 2023

Abstract
Background
The main contributors to death and disability from chronic illnesses in developing nations are elevated blood pressure (hypertension), blood sugar (diabetes mellitus), and blood cholesterol (dyslipidemia). Even though there are affordable treatments, the treatment gap for these conditions is still significant. Few pilot studies from industrialized nations discuss the value of peer-led interventions for achieving community-level management of blood pressure and blood sugar. This study aims to evaluate the effectiveness of peer-led intervention compared to standard care in achieving control of selected Non-Communicable Diseases (NCDs) in Indian context at one year of intervention among people of 30-60 years with hypertension and/or diabetes mellitus and/or Dyslipidaemia.
Methods
A Cluster-randomized Controlled Trial will be conducted in villages of two rural blocks of Khordha district of Odisha from August 2023 to December 2024. A total of 720 eligible participants (360 in the intervention group and 360 in the control group) will be recruited and randomized into two study arms. The participants in the intervention arm will receive a peer-led intervention model for six months in addition to standard care. The sessions will be based on the six domains of NCDs - self-care, follow-up care, medication, physical activity, diet, limiting substance use, mental health and co-morbidities. Mean reduction in blood pressure, HbA1C, and blood cholesterol in the intervention arm compared to the standard care arm will be the main outcome.
Discussion
Increasing burden of NCDs demands for newer strategies for management. Peer led interventions have proven to be useful at international level. Incorporating it in India will have remarkable results in controlling NCDs.
Trial registration
Clinical Trial Registry of India, CTRI/2023/02/050022. Registered on 23-02-2023.
A comprehensive experimental comparison between federated and centralized learning
Authors: Garst Swier , Dekker Julian , Reinders Marcel 
Published: October 2023

Abstract
Purpose: Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks.
Methods: We have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various datasets exploring influences of different sample distributions as well as different class distributions across the clients.
Results: The results show similar performances under a wide variety of settings between the federated and central learning strategies. Federated learning is able to deal with various imbalances in the data distributions. It is sensitive to batch effects between different datasets when they coincide with location, similar as with central learning, but this setting might go unobserved more easily.
Conclusion: Federated learning seems robust to various challenges such as skewed data distributions, high data dimensionality, multiclass problems and complex models. Taken together,t he insights from our comparison gives much promise for applying federated learning as an alternative to sharing data.