bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data Analytics and Machine Learning Application for Reservoir Potential Prediction in Vuggy Carbonate Reservoirs Using Conventional Well Logging
Authors: Gharieb Amr , Gabry Mohamed Adel , Elsawy Mohamed , Algarhy Ahmed , Ibrahim Ahmed Farid , Darraj Nihal , Sarker M. R. , Adel Samuel 
Published: April 2024

Abstract
The objective of this study is to tackle the challenges associated with assessing the potential of carbonate reservoirs, especially when specialized logs such as image logs are unavailable. The primary aim is to introduce a novel workflow that integrates machine learning and data analytics to improve the evaluation of carbonate reservoir potential using conventional logs.
The recommended technique has several aspects. Beginning with feature engineering, techniques such as geometric and harmonic averaging, combined with Z-score standard deviation, are applied to log data. This process yields a diverse set of over 200 metrics, offering a panoramic view of the reservoir's nature. Following this, Principal Component Analysis (PCA) is employed as a means of dimensionality reduction. This strategic implementation of PCA becomes instrumental in honing in on the pivotal variables paramount for an accurate reservoir evaluation. Complementing these methods, data visualization, particularly through strategic parameter plotting, assumes a pivotal role. It aids in distinguishing inherent relationships, and patterns, and offers insights critical for an informed reservoir assessment.
Out of the diverse outcomes, two observations distinctly stand out. First, through the lens of advanced visualization techniques, there emerges a discernible boundary separating the high-risk from the low-risk reservoir zones. This critical delineation provides a cornerstone, bolstering decision-making with amplified confidence. Second, a machine learning model, crafted leveraging the engineered features, comes to the fore, showcasing an impressive accuracy rate that surpasses 85%. What makes this accomplishment particularly remarkable is that such precision was attained by relying solely on conventional logs, eliminating the customary dependency on specialized tools.
In summation, this study makes an emphatic case for the integration of advanced data science techniques in carbonate reservoir evaluation, especially when confronted with the absence of specialized logs. By offering a comprehensive methodology buoyed by machine learning, the research not only charts a methodological path forward but also presents the industry with a cost-effective reservoir assessment paradigm.
Integration of Real-Time Monitoring and Data Analytics to Mitigate Sand Screenouts During Fracturing Operations
Authors: Hou Lei , Elsworth Derek , Gong Peibin , Bian Xiaobing , Zhang Lei 
Published: April 2024

Summary
Sand screenout, the most frequent incident during hydraulic fracturing, is one of the major threats to operational safety and efficiency. Screenout occurs when advancing hydraulic fractures are blocked by injected proppant-slurry, stall, and develop fluid overpressure. Because massive wells are still being hydraulically fractured every year, operational safety has become a critical and urgent issue that has always been overshadowed by the whether-or-not controversy. However, the suddenness and unheralded surprise of screenout make it extremely difficult to predict and handle. Previous efforts attempt to predict screenout as discrete events by interpreting injection pressure directly. We propose and then demonstrate a self-updating (via data and experience augmentation) and customizable (numerical models and algorithms) data-driven strategy of real-time monitoring and management for screenout based on records of shale gas fracturing. Two new indicators—proppant filling index (PFI) and safest fracturing pump rate (SFPR)—are improved and then integrated into the strategy. The PFI reveals the mismatch between injected proppant and hydraulic fractures and provides a continuous time-historical risk assessment of screenout. A pretrained ensemble learning model is applied to process the geological and hydraulic measurements in real time for the PFI evolution curve during fracturing operations. Integrated with the SFPR, a stepwise pump rate regulation strategy is deployed successfully to mitigate sand screenout for field applications. Four field trials are elaborated, which are representative cases exhibiting the data-driven approach to monitor and manage sand screenout during hydraulic fracturing.
Sorbent selection for the recovery of gallium and indium from aqueous solutions: a sustainable approach to the recovery of strategic metals from LED lamps
Authors: Sáez Patricia , Diez Eduardo , Gómez José María , López Carmen , Conte Naby , Lobete Mercedes , Rodríguez Araceli 
Published: April 2024

Abstract

Gallium and indium, metals present in LED lighting technology, were removed from aqueous solutions by sorption. Carbonaceous materials, such as activated carbon, beer bagasse, spent coffee grounds or peanut sells, and a low-cost zeolite as chabazite, were used. Chabazite protonated, under mild conditions, showed high sorption capacities for gallium (56 mg/g) and indium (92 mg/g), 10 to 30 times higher than carbonaceous materials. Sorption experiments with both metals in solution showed a competitive effect between gallium and indium for the sorption sites of the chabazite, showing more affinity towards gallium than indium. Ga3 + sorbed/In3 + sorbed molar ratio above 2 was achieved for the same initial concentration of both metals, increasing to almost 3 when the initial gallium concentration increased. However, the sorption capacity for both metals was always around 0.35 mmol Ga + In/g. The selectivity of the chabazite was conditioned by different behavior of both metals in aqueous solution at the sorption pH being the predominant species in solution Ga(OH)2+ for gallium and In3+ for indium. Sorption with protonated chabazite can be used in the treatment of spent light emitting diodes (LEDs) leachate for the dual purpose of water purification and selective metal separation.
Glomerulonephritis during Mycobacterium Tuberculosis infection: Scoping Review
Authors: Forster Adam , Sabur Natasha , Iqbal Ali , Vaughan Stephen , Thomson Benjamin 
Published: April 2024

Abstract

Introduction
People with Tuberculosis (TB) infection may present with glomerulonephritis (GN). The range of presentations, renal pathologies, and clinical outcomes are uncertain. Whether clinical features that establish if GN etiology is medication or TB related, and possible benefits of immunosuppression remain uncertain.
Methods
A scoping review was completed, searching MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science and Conference Abstracts from Inception to December, 2023. The study population included patients with TB infection who developed GN and underwent renal biopsy. All data regarding presentation, patient characteristics, renal pathology, management of TB and GN, and outcomes were summarized.
Results
There were 62 studies identified, with 130 patients. These cases included a spectrum of presentations including acute kidney injury, nephrotic syndrome and hypertension, and a range of 10 different renal pathology diagnoses. Cases that included immunosuppression and outcomes ranged from complete remission to long-term dialysis dependence. The presence of granulomas (4/4, 100%), anti-GBM disease (3/3, 100%), amyloidosis (75/76, 98.7%), and FSGS (2/2, 100%) were specific for GN being TB-infection related. On the other hand, minimal change disease was specific for anti-TB therapy related (7/7, 100%). While patients with more aggressive forms of GN commonly were prescribed immunosuppression, this study was unable to confirm efficacy. Only rifampin or isoniazid were implicated in drug-associated GN.
Discussion
This study provides a clear rationale for renal biopsy in patients with TB and GN, and outlines predictors for the GN etiology. Thus, this study establishes key criteria to optimize diagnosis and management of patients with TB and GN.
Research on carbon black and Cerium co-doped Ti4O7-CB-Ce electrocatalytic oxidation of tetracycline-based antibiotics.
Authors: Chen Junxia , He Xinyi , Lei Chongtian , Li Weigang , Yang Zhenzhen , Zhou Qing 
Published: April 2024

Abstract

Elemental doping is a promising way for enhancing the electrocatalytic activity of metal oxides. Herein, we fabricate Ti/ Ti4O7-CB-Ce anode materials by the modification means of carbon black and Cerium co-doped Ti4O7, and this shift effectively improve the interfacial charge transfer rate of Ti4O7 and •OH yield in the electrocatalytic process. Remarkably, the Ti4O7-CB-Ce anode exhibits excellent efficiency of minocycline (MNC) wastewater treatment (100 % removal within 20 minutes) and the removal rate reduces from 100 % to 98.5 % after 5 cycles, which is comparable to BDD electrode. •OH and 1O2 are identified as the active species in the reaction. Meanwhile, it’s discovered that Ti/ Ti4O7-CB-Ce anodes can effectively improve the biochemical properties of the non-biodegradable pharmaceutical wastewater (B/C values from 0.25 to 0.44) and significantly reduce the toxicity of the wastewater (luminescent bacteria inhibition rate from 100 % to 26.6 %). This work paves an effective strategy for designing superior metal oxides electrocatalysts.
Construction of gas-bearing characteristics model based on KPCA-SVR for southern Sichuan shale gas
Authors: Zhongyuan Liu , Diquan Li , Jing Jia , Zhongle Wang , Xuesong Xie , Yunqi Zhu 
Published: April 2024

Abstract

The gas content characteristics in shale gas reservoirs, encompassing both the volume and dispersion of gas, are vital in augmenting the efficiency of extraction and resource utilization of shale gas. The correlation between gas content (Vg) and well logging parameters, for instance, porosity (POR), density (DEN), natural gamma (GR), along with geochemical parameters such as total organic carbon content (TOC), potassium-uranium-thorium ratio (U), organic matter maturity (Ro), and high-precision resistivity (ρ), continues to be ambiguous. Moreover, a gas content characteristic model apt for the southern Sichuan region is still in the process of being developed. As a result, this research introduces a method, grounded on Kernel Principal Component (KPCA) and Support Vector Regression (SVR), for the quantitative prediction of Vg. Initially, we performed a cross-analysis of diverse parameters to pinpoint the sensitive parameters for Vg, and the investigation revealed that POR, TOC, U, ρ, PERM, DEN are sensitive parameters for Vg. Subsequently, we utilized these sensitive parameters to formulate a shale gas content characteristic model based on KPCA-SVR. Ultimately, we implemented this method in the shale gas field in the Changning area of southern Sichuan, and by juxtaposing the predicted values and actual values of three verification wells, we discerned that the discrepancy between the two was minimal, validating the applicability of this model. The principal contribution of this research lies in the successful development of a high-precision model, employing machine learning methods, for the quantitative prediction of shale gas content.
Activated type 17 helper T cells affect tofacitinib treatment outcomes
Authors: Ito Yuki , Okamoto Norihiro , Watanabe Daisuke , Kanzawa Maki , Kohashi Michitaka , Miyazaki Haruka , Tokunaga Eri , Ku Yuna , Ooi Makoto , Hoshi Namiko , Kodama Yuzo 
Published: April 2024

Abstract

Background: The incidence of ulcerative colitis (UC) is on the rise also in Japan. Simultaneously, therapeutic options, including biologics and Janus kinase (JAK) inhibitors, have significantly expanded over the past decade. Although tofacitinib (TOF), one of JAK inhibitors, is a viable option for patients with moderate to severe UC, there is insufficient data to predict responsiveness of TOF treatment.
Aim: The present study aimed to determine whether the infiltration of IL-17A-positive mononuclear cells into the colonic mucosa can predict responsiveness to TOF treatment.
Methods: Patients with UC who underwent TOF treatment were divided into responder and failure groups. Subsequently, we conducted a comparative analysis to identify differences in the infiltration of IL-17A-positive cells into the colonic mucosa through immunohistochemical examination of colon biopsy samples.
Results: The proportion of IL-17A positive mononuclear cells in colon biopsy samples was significantly higher in the failure group than among responders (38.2% vs. 21.2%). Consistent with this finding, our re-analysis of RNA sequence datasets available in the Gene Expression Omnibus (GEO) database suggested that TOF exerts a more pronounced influence on Th1 cells compared with IL-17-producing Th17 cells.
Conclusion: In summary, an abundance of IL-17A-positive mononuclear cells in the colonic mucosa has the potential to predict the responsiveness to TOF treatment.
Intelligent prognostic prediction in patients with hepatoblastoma: a population-based retrospective study
Authors: Zhao Zhuangjie , Qiu Ronglin , Zhang Di , Su Jianhang , Chen Luping , Liao Minyi , Jin Shikai , Xiong Xiangang , Lu Zijie , Peng Junli , Yang Yi , Deng Xiaogeng , Wu Yaohao 
Published: April 2024

Abstract

The study meticulously analyzes pertinent risk variables influencing Hepatoblastoma (HB) prognosis, utilizing data from both the SEER database and a singule-center.Encompassing HB patients from 2000 to 2019 within the SEER database, we partitioned the cohort into modeling and internal validation groups. Following rigorous univariate and multivariate analyses of the SEER group, encompassing risk factors with P < 0.05 and incorporating findings from extant literature, a prognostic risk model was erected and subsequently validated. we pursued a follow-up of HB patients initially diagnosed in Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University from January 2002 to August 2022, serving as an external validation group, and proceeded to authenticate the predictive utility, discriminatory prowess, calibration integrity, and clinical utility of the Nomogram through indices such as C-index, ROC curve, calibration curve, and DCA curve. The external validation ascertained the model's aptitude for broad clinical applicability, culminating in the development of an interactive, web-based Dynamic Nomogram. Within both the SEER database and the external validation group, 835 and 61 patients, respectively, satisfied the inclusion-exclusion criteria for study inclusion. In the SEER group, the 1-, 3-, and 5-year survival rates for HB patients were 86.3%, 80.6%, and 78.0%, respectively, with 671 cases serving as the modeling group and 164 as the internal validation group. In the external validation group, the corresponding rates were 78.60%, 75.00%, and 68.50%. Univariate and multifactorial analyses revealed Age, Stage, Chemotherapy, Surgery, and Lung Metastasis as independent prognostic factors influencing overall survival rate in HB. The C-indexes for the modeling group, internal validation group, and external validation group were 0.796, 0.873, and 0.887, respectively. Calibration curves evinced high concordance, while decision analysis curves underscored the pronounced clinical utility of the presented Nomogram.The study to create a Nomogram for predicting the prognosis of patients with HB was validated to have excellent predictive performance, differentiation, calibration, and clinical benefit to help clinicians in decision making.
Incorporating health literacy principles into the adaptation of a methods motivational interviewing approach: enrolling Black adults in a pilot behavioral trial
Authors: Shiyanbola Olayinka O , Wen Meng-Jung , Maurer Martha A. 
Published: April 2024

Abstract

Suboptimal enrollment and retention efforts can compromise the quality of clinical trials. Barriers to enrolling marginalized populations include low awareness/education about clinical trials. Methods-Motivational Interviewing (MMI) is a patient-centered process that integrates education in providing trial information and engages participants in discussions about the trial before enrollment. Though the MMI approach is effective in enhancing trial recruitment and retention, limited studies have incorporated evidence-based health literacy approaches. In this study, prior to the informed consent process, Black adults completed an interactive orientation followed by a small group discussion. The content used health literacy principles including plain language, infographics, and a pictorial roadmap to guide participants about the purpose of a trial, the randomization process, the study objectives, and timeline. Thereafter, two focus groups explored participants’ perceptions of the enrollment process. Fifteen participants attended the orientation with 14 enrolled in the trial after the orientation, (93% enrollment rate). Retention was 93% (control) to 100% (intervention) at the end of 6 months. Participants reported positive orientation experiences due to a better understanding of the study information, expectations, and rationale behind randomization. Implementing an adapted MMI approach focused on health literacy principles may improve trial enrollment and retention rates among Black adults.
Bullying Behaviors and Stress (Acute and Perceived) Among Undergraduate Nursing and Midwifery Students: A Cross-Sectional Correlation Study
Authors: Almarwani Abdulaziz Mofdy , Alenezi Atallah , Almutairi Mohammed , Alhowaymel Fahad , Alzahrani Naif S. , Alharbi Hanan F. , Abaoud Abdulaziz Fahad 
Published: April 2024

Abstract

Background.Nursing education is facing ongoing challenges, including the departure of newly graduated nurses and a rise in nursing shortages. Nursing and midwifery professionals’ well-being may be affected by various factors, one of which is the bullying of new nurses.Aim. In this study, we analyzed the relationship between bullying behaviors, perceived stress, and acute stress among undergraduate nursing and midwifery students in Saudi Arabia, as well as the moderating role of gender and academic major in this relationship.Method. A cross-sectional correlation design was used, and data were collected from 322 undergraduate nursing and midwifery students enrolled in four major nursing universities in Saudi Arabia. Structural equation modeling using SPSS AMOS version 27 was employed to analyze the results.Results. Educational level, environment, and personal attacks had a significant negative impact on perceived and acute stress (social and non-social), while the other factors, i.e., hostility toward academic achievement and direct negative behaviors, had a significant positive impact on perceived and acute stress (social and non-social). Female nursing students reported a stronger impact of bullying behavior on their perceived stress, while male students reported a greater impact on social and non-social stress. Nursing students reported a stronger impact of bullying on perceived stress, while midwifery students reported a greater impact on social and non-social stress.Discussion.Based on the findings of the study, we recommend taking drastic measures to reduce bullying behaviors among undergraduate nursing students and thereby reduce these students perceived and acute stress. Nursing educators should raise awareness about the harmful effects of bullying and emphasize the importance of creating a safe, supportive learning environment.
On computing the number of distinct values occurring in generalized Dirichlet process samples
Authors: Akell Hassan , Sajadi Farkhondeh-Alsadat , Kazemi Iraj 
Published: April 2024

Abstract

This paper studies the generalized Dirichlet process ( \(\mathcal{GDP}\) ) with its main properties, including moments of random weights and tail moments. We present the truncated \(\mathcal{GDP}\ as a finite mixture distribution and assess the error bounds caused by the truncation. This tactic provides more practicable stick-breaking priors in nonparametric Bayesian settings and facilitates computation. We obtain the joint density of random weights, show that the number of distinct values varies on raising the $\mathcal{GDP}$ samples, and present the impact of the precision parameter on this number. We also show that our results coincide with the Dirichlet process \((\mathcal{DP})\) .
MSC Classification: 62E15 , 60C05 , 97K60
Investigating the seasonality of infectious keratitis by pathogen: A retrospective study
Authors: Yamada Naoyuki , Iwamoto Nanako , Sakuma Ayano , Sunada Junki , Aoki Ren , Higashijima Fumiaki , Yoshimoto Takuya , Nagai Tomohiko , Morita Yukiko , Kimura Kazuhiro 
Published: April 2024

Abstract

This study aimed to investigate the seasonal trends in infectious keratitis by assessing the month of onset and causative microorganisms. Five hundred consecutive cases of infectious keratitis that were diagnosed and treated by a corneal specialist at the Department of Ophthalmology, Yamaguchi University Hospital between January 2009 and January 2021 in whom micro-organisms could be identified formed the study population. The month of onset of infectious keratitis was retrospectively examined based on the medical records. The causative microorganisms were bacteria in 249 eyes, fungi in 51 eyes, Acanthamoeba in 27 eyes, and viruses in 173 eyes. The top 10 causative microorganisms accounted for 402 of 500 eyes (80.4% of the total). The incidence of infectious keratitis was highest from January to March and lowest in June. The total number of bacterial-induced infections was high between October and March. Pseudomonas aeruginosa commonly caused infectious keratitis from August to September. Acanthamoeba-induced infection was common in summer from June to August. HSV infections were common between January and May. A seasonal trend was observed in the occurrence of infectious keratitis by examining the months of onset. We contemplate that these results will assist ophthalmologists in diagnosing infectious keratitis.
A real-world pharmacovigilance study of FDA adverse event reporting system (FAERS) events for Tivozanib
Authors: Ma Xiaohan , Chen Sheng , Xuan Lan , Lian Zuping , Hou Encun 
Published: April 2024

Abstract

This study analyzes adverse drug events (ADEs) associated with Tivozanib, sourced from the Food and Drug Administration Adverse Event Reporting System (FAERS) database. We collected ADE data for Tivozanib from 2021 Q2 through 2023 Q4. Post-standardization, we employed several signal quantification methods for analysis, such as the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propensity for Neural Networks (BCPNN), and the Multi-item Gamma Poisson Shrinker (MGPS). In this analysis of 1,142 ADE reports citing Tivozanib as the primary suspect, we identified 69 adverse reaction terms (PTs) across 21 system organ classifications (SOCs). The three most prevalent SOCs were general disorders and administration site conditions (841 incidents, ROR 1.27, PRR 1.21, IC 0.28, EBGM 1.14), gastrointestinal disorders (669 incidents, ROR 2.52, PRR 2.25, IC 1.17, EBGM 2.25), and investigations (322 incidents, ROR 1.45, PRR 1.42, IC 0.5, EBGM 1.42). Notably, fatigue and hypertension were the most frequently reported adverse events associated with Tivozanib, with hypothyroidism also demonstrating significant signal intensity. While Tivozanib offers therapeutic benefits, it also carries the risk of several adverse reactions. Clinicians should vigilantly monitor patients for signs of fatigue, hypertension, hypothyroidism, and other events during its clinical use.