bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Learning Approaches in Tight Gas Field Pay Zone Classification
Authors: Hu Zijian , Bai Fengjun , Wang Huajie , Sun Chuanhui , Li Pinwei , Li Haoyan , Fu Yunlong , Zhang Jie , Luo Yin , Nunez Perozo Herick Yoander 
Published: March 2023

AbstractLog interpretation is critical in locating pay zones and evaluating their potential. Conventional log interpretation is done manually. In our work, deep learning methods are utilized to deal with preliminary pay zone classification. In this way, human expertise can be liberated from trivial and repetitive tasks during logging interpretation.In a fluvial depositional environment, the sand distribution varies both vertically and horizontally. Thus, a large dataset covering a large area may lead to a too "averaged" model. In our work, we select a relatively small dataset (e.g., seven wells) to reflect the regional features.Standard deep learning processes are employed. The log data are cleaned, visualized, and preprocessed for the algorithms. A preliminary random forest (RF) model is used to separate the sand (interpretation needed) from the shale (interpretation not needed) facies. In the classification model building and training stages, various types of algorithms are tried and compared, from the simple K-nearest neighbor (KNN) to dense neural network (DNN).To account for the continuity and influence of adjacent depths, a 1D convolutional neural network (CNN) model is tested. With the model, a simple self-training model is developed and discussed. K-fold validation methods are used to fully reflect the model's performance in such relatively small dataset. With the given dataset, common deep learning methods generate only moderate accuracy and are easily overfitted.On the other hand, the CNN outperforms the other approaches due its features for pattern recognition. With special caution, a self-learning approach can also further improve the performance. A comparison of different deep learning approaches in terms of time of computation, accuracy, and stability is established. Even trained from a small dataset, with the CNN model, it is possible to identify the zones of interest automatically and consistently.Due to the size of dataset, a series of techniques is utilized to reduce the impact of overfitting, including balance sampling, drop out, regularization, and early stopping, among others. During the optimization of critical hyperparameters, grid search with Bayesian statistics is used together with K-fold validation.
Improved Total Organic Carbon (TOC) Prediction for Vaca Muerta Shale
Authors: Abd Karim Rahimah , Aguilera Roberto , Fraga Camila , Toledo Laura Estela 
Published: March 2023

AbstractTOC evaluation in Vaca Muerta is challenging due to its complex mineralogy and depositional settings. Laboratory measurements can be affected by sample preparation, especially for wells drilled with oil-based mud. Empirical methods like Passey ∆log R relies on resistivity as one of the inputs, which can be affected by clay and mineralogy in shale. In this study, an improved TOC prediction using a multiple regression equation is proposed. The findings reflect the vertical variability of TOC.The method developed in this study first evaluates the TOC correlation with available electrical logs from a vertical well that includes spectral gamma ray, neutron porosity, density and resistivity. It also assesses the correlation with clay and inorganic mineralogy available from X-ray diffraction. This study also incorporates for the first time, thin bed heterogeneity that comprises calcite beef, ash beds and nodules. They make up a considerable portion of the facies, especially in the organic-rich unit of Lower Vaca Muerta (LVM). Despite the complexity, the modelled TOC calibrate well with the laboratory-measured TOC.The TOC regression equation is developed based on two key findings. First, the TOC is positively correlated with uranium and resistivity; and negatively correlated with dolomite and calcite. High TOC is observed in low Ca (calcite, dolomite and ankerite) and high QFP (quartz, k-feldspar and plagioclase) intervals, and vice versa. This negative correlation is unique to Vaca Muerta, which is attributed to the mixed carbonate-siliciclastic depositional system (Kietzmann et al., 2014). Second, the TOC is also strongly affected by thin bed heterogeneity that is identified through micro-resistivity image log and high-resolution logs. Their effect is more pronounced on resistivity log; therefore, an adjustment factor is applied to the regression to account for their presences. Results show that the modelled TOC match well the core TOC as compared to Passey ∆log R method. An important observation is that the Passey ∆log R technique would overestimate the TOC at the top of Upper Vaca Muerta due to high and resistive Ca content; and underestimate it in LVM due to conductive clay in the argillaceous ash beds. Consequently, it would mislead the estimation of reservoir thickness, identification of sweet spot for landing zones, as well as resource estimation in Vaca Muerta shale.This paper develops an original regression equation that models TOC in the presence of thin bed heterogeneity in Vaca Muerta. The results compare well with the laboratory-measured TOC. The study reveals the vertical variability of TOC across the five stratigraphic units in a vertical well. More importantly, it highlights potential TOC discrepancy by Passey ∆log R technique that could mislead reservoir thickness estimation due to the effects of mineralogy and thin bed heterogeneity on resistivity.
Multi-Resolution Graph-Based Analysis for Fissility Evaluation in the Vaca Muerta Formation (Neuquén Basin, Argentina).
Authors: Martín Lucía Inés , Marchal Denis Andre , Naides Claudio Hugo , Barredo Silvia Patricia 
Published: March 2023

AbstractFine-grained rocks exposed at outcrops can remain massive or may split along relatively smooth surfaces parallel to the bedding when the rock is fissile. Fissility is an intrinsic structural characteristic of the rock revealed by weathering through the opening along weakness planes. Those weakness (or fissility) planes depend on numerous factors such as mineralogical composition, rock fabric, diagenetic processes and organic matter content and can exert an influence in the hydraulic stimulation of unconventional plays such as Vaca Muerta Formation.The fissility analysis performed on four cores encompassing more than 300 meters of the Vaca Muerta Formation results in a 4-class index, designated DAD (Drying Alcohol Discontinuities), that represent different fissility intensities. This information is related to the well log response of the cored rocks through a supervised classification using the MRGC (Multi-Resolution Graph-based Clustering) algorithm. This methodology allows considering multiple fissility controls simultaneously: gamma ray and photoelectric factor logs represent the mineralogical composition; sonic log as a proxy of geomechanical behavior; and the shallowest resistivity, representative of the texture of the rock. This classification reproduces potential fissility classes with a reasonable match on the four cores. To improve the accuracy of the resulting model, a high-resolution gamma ray curve is calculated from the processing of the microresistivity image. A second model is built replacing the raw gamma ray log with the HR-GR with an outstanding improvement, especially in the case of high fissility rock classes. Finally, the analysis is complemented with rock mechanics measurements coming from laboratory test to establish the effect of fissility on the geomechanical behavior of the rock.
Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms using Software Defined Radio
Authors: BABJAN PATAN , V V. Rajendran 
Published: March 2023

Abstract

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by implementing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyze the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy.
Clinical effect of pseudarthrosis excision, processus styloideus radii osteotomy combined with headless compression screw fixation on the nonunion of scaphoid
Authors: Lyu Jingtong , Li Yuanqiang , Wang Zhenyu , Wang Yunjiao , Wang Wei , Tang Kanglai , Tao Xu 
Published: March 2023

Abstract
Background
The high incidence of scaphoid nonunion occurs after the scaphoid fracture due to the vulnerable blood supply. The complications of scaphoid nonunion, such as predicatable pattern of carpal collapse and degenerative arthritic will take place without treatment. Patients will suffer from pain of bone and poor functional in the early and late stage of the progression, which can significantly affect quality of life. Therefore, it is of great important to chose an operative technique which can get a high success rate for union.
This paper introduced a technique for the treatment of scaphoid nonunion with pseudarthrosis excision, processus styloideus radii osteotomy combined with headless compression screw fixation.
Methods
A retrospective review a senior surgeon's internship at a private university from 2010 to 2021 was performed. Demographic information and the characteristics of patients were obtained for preoperative and postoperative assessment. Student’s t tests was used to evaluate pre-operative, post-operative, and the last follow-up differences. p≤0.05 was indicated that the difference was statistically significant.
Results：
Totally 18 patients were identified for inclusion in this study. 100% of the patients achieved union at the last follow-up. The average age was 27.22 (17-49). The time from injury to surgery was 12.94 (1-48) months, with the median of 41.86 weeks. The average healing time from surgery was 15.27 (10-27)weeks. The time of follow-up was 49.5 (4-119)months. We evaluated the ideal range of scaphotomy by comparing scaphoid length and postoperative scaphoid shortening rate. The rate of scaphoid shortening was 4.288% (0.40%-9.05%). There were statistically significant differences among the scapholunate angle, the radiolunate angle, the mean scapholunate interval, the post-op Quick DASH, the VAS pain score, the Grip strength and the range of motion before, after surgery and at the last follow-up all had significant improvement(p＜0.05). There were no difference in time of union bewteen different patient ages, different fracture locations, smoking or not, alcohol consumption or not , or time of treatment.
Conclusions：
Our method have equal or superior union rate comparing to other techniques in the literature. This paper highlighted the keys to use this method successfully.
Reversing the Negative Effects of Poor Agricultural Drainage Using Risks Management
Authors: Elnashar Walaa , Elyamany Ahmed 
Published: March 2023

Abstract
Agricultural drainage is essential to the success of irrigated agriculture because it prevents ponding, waterlogging, and salt. This Paper presents a systematic method for identifying, assessing, and mitigating the risks associated with poor agricultural drainage (PAD). Using Monte Carlo simulation within a risk management process, the cumulative impact of all conceivable PAD risks on agricultural productivity was examined. The minimum and maximum predicted values of loss in crop production as predicted by the Monte Carlo simulation model are 60.2% and 69.2%, respectively. According to the interpretation of these findings, crop production might be reduced to around two-thirds of its current level over a century. This necessitates prompt action to limit the impact of these hazards in the coming years. Key contributions of this work include identifying and categorizing the negative effects of PAD on the environment and crop production, analyzing these risks quantitatively and qualitatively, and developing the appropriate responses.
Effect of Contrast Media Nephropathy on Renal Function Prognosis in Patients with Acute Myocardial Infarction after PCI
Authors: Zhang Baojia , Wang Lin 
Published: March 2023

Abstract
Purpose To explore the effect of contrast-induced nephropathy (CIN) on the prognosis of renal function in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI).
Methods This study was a retrospective analysis conducted on 235 patients with acute myocardial infarction who underwent emergency or elective PCI in the Department of Cardiology of the Affiliated Hospital of Jiangsu University from January 2018 to March 2022. We collected the clinical data and laboratory examination data of patients and then divided them into the CIN group and non-CIN group (according to whether CIN had occurred) in order to explore the impact of CIN occurrence on the prognosis of renal function.
Results Of the 235 included patients, 28 were in the CIN group and 207 were in the non-CIN group. There were no obvious abnormalities in the clinical data and laboratory examination data of the patients in the two groups before treatment. The glomerular filtration rates of the CIN group vs. the non-CIN group, respectively, were 87.8 ml/min vs. 87.7 ml/min (at admission), 63.4 ml/min vs. 86.6 ml/min (within 3 days of treatment), 64.1 ml/min vs. 83.4 ml/min (within 3 days to 1 month of treatment), and 63.7 ml/min vs. 82.8 ml/min (within 1 month to 1 year of treatment). The incidence of acute renal failure (AKI) in the CIN group was 46.4% higher than that in the non-CIN group (0.5%).
Conclusion The short-term and long-term prognosis of AMI patients with CIN is significantly worse than that of patients without CIN. The occurrence of AKI in AMI patients is related to CIN.
The implementation of Green Supply Chain Management (GSCM) and Environmental Management System (EMS) practices and its impact on Market Competitiveness during Covid-19
Authors: Das Ghansham , Li Shan , Tunio Raza Ali , Jamali Riaz Hussain , Ullah Ihsan , Fernando Kurukulasuriya Weerasinghe Tharindu Madushanka 
Published: March 2023

Abstract
Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility towards these associations in the context of the Covid-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness. Recent research shows that supply chain professionals, policy makers, managers and researchers are turning to formal EMS, BDA-AI and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.
Needs Assessment for a Leadership Course in Saudi Medical Schools: the Student Perspective
Authors: Alblihed Mohammed , Alzghaibi Haitham 
Published: March 2023

Abstract
Introduction: Leaders in healthcare no longer need to hold formal management positions; instead, leadership is perceived to be the responsibility of all healthcare professionals. Despite changes in curricula and teaching design, however, this review of the content taught in medical colleges in Saudi Arabia reveals a lack of leadership and a failure to equip young graduates to compete on the global stage.
Methods: This cross-sectional study combines a quantitative approach with a self-administered questionnaire-based survey. The sampling procedure is a non-probability convenience technique, adapted for 700 male and female undergraduate medical students from four medical colleges.
Results: Of the students, 75.46% had minimal leadership experience and 22.69% had some leadership experience; 3.02% of 464 students considered themselves highly experienced in leadership.
Conclusion: As the needs assessment and other relevant factors show, leadership should be introduced as a skilled subject. There will always be a growing demand for competent medical graduates, who are capable of becoming future leaders. Although elementary leadership concepts remain fundamentally the same, curricular development must focus on the needs of society and stakeholders.
Comparative Analysis of Radiation Response in Chemo-Treated BT20, 4T1 Breast Cancer, and Neuroblastoma Cancer Cell Lines through Single and Multiple Cell Ionization Using Infrared Laser Trapping
Authors: Goangul Mulugeta S. , Chen Li , Gao Ying , Erenso Daniel , Eshun Kwame , Alvarez Gisela , Crogman Horace T. 
Published: March 2023

Abstract
The aim of our study was to use laser trapping technique to evaluate the radiation sensitivity of cancer cells, both untreated and chemo-treated. We focused on a specific human breast tumor cell line, called BT20, that was treated with an antitumor compound known as 2-Dodecyl-6-methoxycyclohexa-2, 5-diene-1, 4-dione. We investigated an untreated control group, as well as two groups of BT20 cells that were subjected to different treatment durations. We utilized a high-power infrared laser (at 1064 nm) trap to determine the absorbed threshold ionization energy (TIE) and threshold radiation dose (TRD) for single and multiple cells trapping and ionization. We performed statistical analyses, including descriptive and one-way ANOVA, on the results. We also analyzed the relationship between TIE and TRD to the mass of the individual cells for different hours of treatment, in comparison to the control group. Our findings revealed that both TIE and TRD decreased as treatment duration increased. However, the TRD decreased with mass irrespective of the treatment. Moreover, our analyses consistently showed the same behavior of TRD for single vs. multiple cell ionizations within each group, regardless of treatment. In addition, we conducted a comparative analysis on the effects of radiation dosage in BT20, 4T1 breast cancer, and N2a neuroblastoma cell lines, and discovered that the TRD decreased in mass across all three cell lines.
Evaluation of polymorphisms in IGFBPs genes and their influence on meat quality traits in different rabbit breeds (Oryctolagus cuniculus)
Authors: Migdał Łukasz , Migdał Anna , Pałka Sylwia E. , Kmiecik Michał , Semik-Gurgul Ewelina , Otwinowska-Mindur Agnieszka , Bieniek Józef 
Published: March 2023

Abstract
Rabbits are important livestock animals, popular mostly in theMediterranean area. In Poland, the consumption of rabbit meat is constantly increasing as a result of its nutritional value. Nowadays, molecular background of traits influencing on quality of meat and products are highly demanding.Therefore, inour study we analysed the influence of five polymorphisms within the IGFBPs genes in different rabbits breeds. Statistical significances were found for growth, carcass and meat quality traits however not for all analysed breeds. Most promising polymorphism seems to be g.158093018A > T within IGFBP5. The values of pH24on m.longissimus lumborum (m.l.l.) and b.biceps femoris (m.b.f.) were higher for AT genotypes compared to AA genotypes for Termond White and New Zealand White x Flemish Giant crossbreds. We conclude that this polymorphism may be promising for better quality meat in rabbits
Comparison fabrication, identification and avoidance of corrosion potential of M-CuO NPs / S-CuO NPs to suppress corrosion on steel in an acidic solution
Authors: Hyba Amira M. , Refay Heba M. El , Shahen Seham , Gaber Ghalia 
Published: March 2023

Abstract
Due to its simplicity and eco-friendly, the use of plants to create metal oxide nanoparticles has recently attracted a lot of attention. Consequently, the purpose of this study was to generate CuO NPs utilizing two different techniques, including a synthetic approach and an aqueous extract of Moringa oleifera leaves (M-CuO NPs) (S-CuO NPs). Nanoparticles produced were evaluated by SEM, EDX, TEM and XRD, to considering the creation of CuO NPs and to determine the morphological, elemental constitution and size the sample. The average size 14.95 and 35.73 nm for S-CuO and M-CuO NPs respectively, having potentials for application as inhibitor in corrosion of MS. The inhibition and adsorption properties of prepared M-CuO NPs and S-CuO NPs on MS in 1M HCl were investigated using the wt reduction assessment. The maximal IEof prepared M-CuO NPs and S-CuO NPs was 95.06% and 92.10%, respectively, at 1000 ppm. The IE % improves with greater concentration of the prepared M-CuO NPs and S-CuO NPs. According to the findings, M-CuO NPs are the most effective green potential inhibitor for MS in acidic conditions. It is demonstrated that the Langmuir isotherms are obeyed by the produced CuO NPs and MS substrate.
AlphaFold Prediction of Structural Ensembles of Disordered Proteins
Authors: Brotzakis Z. Faidon , Zhang Shengyu , Vendruscolo Michele 
Published: March 2023

Abstract

Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. Here we show that the inter-residue distances predicted by AlphaFold for disordered proteins are accurate, and describe how they can be used to construct structural ensembles. These results illustrate the possibility of making structural predictions for disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.