bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Machine-Learning Assisted Phase-Equilibrium Calculation Model for Liquid-Rich Shale Reservoirs
Authors: Chen Fangxuan , Luo Sheng , Wang Shihao , Nasrabadi Hadi 
Published: March 2023

Abstract
In composition reservoir simulation, fluid phase behavior is determined by vapor-liquid equilibrium (VLE) calculations. VLE calculations can consume more than half of the CPU time of compositional reservoir simulations. To accelerate the VLE calculations, machine learning (ML) technique is introduced. In this work, we developed a novel ML-assisted VLE calculation model for shale reservoirs. Our model has two main innovations compared with previous ML-assisted VLE calculation models. Firstly, the extended Peng-Robinson equation of states (PR-C EOS) is incorporated for VLE calculation. Previous models used the conventional Peng-Robinson equation of states (PR EOS), which becomes inaccurate when the pore diameter reduces to the scale of nanometers. With PR-C EOS, fluid characteristics can be accurately modeled under nano-scale conditions, making our model applicable to shale reservoirs. Secondly, in our model, a general set of pseudo components is selected to cover different fluid types. Previous models are designed for a specific type of hydrocarbon mixture. There are two parts to our model: stability analysis and flash calculation. In the stability analysis, the multi-layer perceptron (MLP) is trained to predict whether the fluid is in single-phase or two-phase condition. The equilibrium ratios are estimated using a physics-informed neural network (PINN) in the flash calculation. The application of ML techniques accelerates the CPU time by two orders of magnitude without losing too much accuracy. This work provides the framework of incorporating ML into VLE calculation and develops a ML-assisted VLE calculation model that is suitable for various hydrocarbon mixtures in shale reservoirs.
Advanced Modeling of Diffusion and Convection in Multiphase Compositional Simulation for Tight Porous Media
Authors: Achour Sofiane Haythem , Okuno Ryosuke 
Published: March 2023

Abstract
Small pore sizes influence the mass transport in gas enhanced oil recovery (EOR) in tight formations through complex phenomena such as diffusion and sorption. However, conventional simulators use relatively simple models for diffusion and sorption that do not properly represent non-ideal fluid interactions at high pressures. This paper presents a newly developed simulator for advanced compositional modeling for tight formations. A case study highlights the impact of diffusion and sorption on compositional transport in tight porous media.
The simulator uses multicomponent diffusion based on the dusty-gas model (DGM), which uses the fugacity gradient as the driving force. The adsorption model used is an approximate solution based on the Multicomponent Theory of Adsorption (MPTA) using the sorbed and bulk regions. The capillary pressure is included both in the flow equations and the flash calculations by minimizing the Helmholtz free energy using the Peng-Robinson equation of state at a fixed temperature, pore volume, and overall composition.
The simulator is applied to model CO2 injection into a 1-D tight porous medium with a ternary mixture of methane, n-butane, and n-decane as the initial oil. Before CO2 injection, n-decane is most attracted to the pore wall and contained at a high concentration in the sorbed region. During CO2 injection, CO2 displaces n-decane from the pore wall, causing the counter-current transport of CO2 and n-decane.
Simulation results also show that the mixing of reservoir oil with CO2 in small pores results in local pressure changes, which tend to drive both the diffusion and convection. In all cases, convection enhances multicomponent transport by dissipating these pressure changes. When CO2 is strongly attracted to the pore walls, a large pressure change on mixing accelerates the CO2 diffusion into the reservoir through the sorbed region and also the counter-current transport of oil components through the central region.
Molecular Simulation of Competitive Adsorption of Hydrogen and Methane: Analysis of Hydrogen Storage Feasibility in Depleted Shale Gas Reservoirs
Authors: Chen Fangxuan , Wang Shihao , Nasrabadi Hadi 
Published: March 2023

Abstract
As a clean energy carrier, hydrogen (H2) is considered as an indispensable part of energy transition roadmap. To meet the increasing energy demand, extremely large storage capacity is required. Previous studies focus on the underground H2 storage in conventional depleted gas reservoirs, salt caverns and saline aquifers. With the development of shale reservoirs, more and more depleted shale gas reservoirs may be good candidates for H2 storage. In this work, we analyze the potential of H2 storage in depleted gas reservoirs using Monte Carlo simulations. The competitive adsorption of methane-hydrogen (C1-H2) system under nanoscale condition is investigated, including the effect of pore size, temperature, pressure, boundary material, and fluid composition. Our results show that C1 is preferentially adsorbed in C1-H2 system. C1 forms the adsorption layer near the boundary surface while H2 molecules are freely distributed in the pore. The fluid distribution indicates that H2 can be easily produced during H2 recovery process, which contributes to H2 storage in depleted shale gas reservoirs. In addition, the effect of water (H2O) on C1-H2 competitive adsorption is analyzed. The strong interactions between H2O and boundary atoms force C1 molecules away from the adsorbed region. If we consider C1 as cushion gas, 50% of C1 is enough for H2 storage in depleted shale gas reservoirs. This work provides foundation for H2 storage in depleted shale gas reservoirs at a molecular level.
Investigating Acidizing in Carbonate Reservoirs: Global Sensitivity Analysis
Authors: Sahu Qasim , Fahs Marwan , Hoteit Hussien 
Published: March 2023

Abstract
Reservoir stimulation is a common technique used to improve the productivity of carbonate reservoirs such as carbonate acidizing. This process involves injecting a reactive fluid to dissolve the rock mineral, creating a conductive path for hydrocarbon flow (i.e., wormhole). With the development of tight and unconventional reservoirs, stimulation has become more critical for optimal economic production. This study aims to simulate carbonate dissolution with a two-scale continuum model and investigate how the uncertainty propagates in that model. The petrophysical properties of carbonate encounter significant uncertainty that affects the predictivity of the model. A simulation model was developed to simulate the initiation and propagation of wormhole in carbonate rock. We investigate the effect of varying the injection rate on the dissolution channel and the efficiency of the acidizing fluid. Next, we utilized design of experiments in a rigorous multi-step methodology to build a surrogate model based on polynomial chaos expansion. The surrogate model is used to perform global sensitivity and uncertainty propagation analysis. We utilize the surrogate model and Sobol indices to identify the most significant parameter affecting stimulation process using dimensionless groups, including the Damkohler, Peclet, and acid capacity number. The results showed that the dissolution channel and pore volume to breakthrough depends on the injection rate. The surrogate model reproduces the simulation model results for the dissolution channel, the pore volume to breakthrough, and the effective permeability. The global sensitivity analysis shows that the acid capacity number is the most significant parameter for the pore volume to breakthrough with the highest value of the total Sobol index. For effective permeability, the initial mean porosity is the primary source of uncertainty. The marginal effect calculated for the individual parameter confirms the results from Sobol indices. This work provides a systematic workflow for uncertainty analysis and optimization applied to the processes of carbonate stimulation.
Role of Inelasticity in Production-Induced Subsidence and Fault Reactivation in the Groningen Field
Authors: Zhao Xiaoxi , Jha Birendra 
Published: March 2023

Abstract
Long-term production of gas from the Groningen field has led to subsidence and seismicity in the region. Most of the prior Groningen modeling studies assumed elastic deformation of the reservoir due to the challenges in modeling poroplasticity in a reservoir with hundreds of faults and decades of production history. Here we quantify the role of inelastic deformation in production-induced subsidence and seismicity in the field via 3D high-resolution multiphysics modeling which couples multiphase flow and elastoplastic deformation in a complex geologic system made of claystone overburden, carboniferous underburden, and the gas-bearing sandstone reservoir compartmentalized with 100+ faults. We drive the model with four decades of historical production, spanning the period of induced seismicity, and two decades of future production under gas injection-enhanced recovery. We calibrate the model using the available pressure and subsidence data and analyze compartmentalized depletion and deformation due to spatially varying production and fault distribution. We analyze stress and strain in the caprock-reservoir depth interval to elucidate the role of inelasticity. We use the evolution in shear and normal tractions on seismogenic faults that hosted 1991-2012 seismicity to quantify the evolution in Coulomb stress and geomechanical stability of the faults.
BiLSTM_SAE:A Hybrid Deep Learning Framework for Efficient Predictive Big Data Analytics System
Authors: Goswami Shubhashish , Kumar Abhimanyu 
Published: March 2023

Abstract
Big data has been utilized and attracted various researchers due to the phenomenal increase in computational application which has developed an overwhelming flow of data. Further, with an expeditious blooming of emerging applications such as social media applications, semantic Web, and bioinformatics applications, data heterogeneity is increasing swiftly. Accordingly, a variety of data needs to be executed with less high accuracy and less. However, effective data analysis and processing of large-scale data are compelling which is considered a critical challenge in the current scenario. To overcome these issues, various techniques have been developed and executed but still, it is significant to improve in accuracy. The current study proposed a hybrid technique of BiLSTM-SAE has been proposed for business big data analytics. Bidirectional LSTM is an advanced version of the conventional LSTM approach. The performance comparison of the proposed method BiLSTM-SAE with existing Random forest-RF has been processed. The final result reported that the proposed method BiLSTM-SAE had been procured with better accuracy of 0.836. Moreover, the training and validation accuracy and loss on different performance metrics have been conducted and studied in the research.
The existing pattern of mother-daughter communication of sexual and reproductive health (SRH) matters among Sinhalese adolescent girls aged 14-19 years, in Sri Lanka
Authors: Mataraarachchi D , Pathirana T.E.A , P.K Buddhika Mahesh , Vithana P.V.S.C. 
Published: March 2023

Abstract

Introduction: School-based and community-based sexuality education has not shown a significant impact on the sexual and reproductive health (SRH) outcome of Sri Lankan adolescents. Parents as the primary educators of adolescents would be a better source of providing individualized sexuality education to their children.
Objective: To assess the existing pattern of mother-daughter communication of sexual and reproductive health (SRH) matters among Sinhala adolescent girls aged 14-19 years in Sri Lanka.
Methods: A descriptive-cross sectional study was conducted among a sample of 810 Sinhala adolescent girls, using a pre-tested, self-administered questionnaire. Descriptive statistics related to mother-daughter communication of SRH matters were presented using frequencies and percentages. Bivariate analysis was performed to assess the association of selected socio-demographic factors with mother-daughter SRH communication while Binary logistic analysis was performed to assess the independent association of selected demographic factors with mother-daughter SRH communication.
Results: The majority of the respondents (67.1%, n=540) were positive about discussing SRH matters with their mothers. For 78.2% (n=632) mother was the most preferred source of SRH information. Issues related to menstruation (88.4%, n=701), keeping body limits (94%, n=718), and preventing sexual violence (72.6%, n=574), were among the common topics discussed between mothers and daughters. Cultural barriers (46.8%, n=379),and fear of parental perception (56.3%, n=456), were the main barriers to discussing SRH topics with mothers. Adolescent girls’ age, religion, status of schooling, and family income level were significantly associated with mother-daughter SRH communication.
Conclusions and recommendations: Adolescent girls identified the mothers as a good source of SRH information. Parents' contribution as a primary source of SRH information to adolescent girls when planning adolescent sexual health interventions needs to be considered.
Optimized Federated Learning with Ensemble of Sequential Models for Detecting RPL Routing Attacks for AMI Networks
Authors: Sharma Puneet , Sakthivel T. , Alnajjar Ibrahim Ali 
Published: March 2023

Abstract
With the rapid expansion of smart applications and services, the Internet of Things (IoT) plays a vital role in enabling changes in the usage of information and communication technology. Advanced Metering Infrastructure (AMI) is the key component of the smart grid which often meets privacy and security threats from the routing layer. The Routing Protocol for Low-Power and Lossy Networks (RPL) is the prominent routing protocol for AMI networks, and securing the RPL is the cornerstone of all security controls in the AMI for successful deployment. Recently, IoT security systems have increasingly adopted privacy-preserving Federated Learning (FL) that trains a local data model across multiple decentralized edge devices and takes global decisions without the need for global data sharing. Relying on a single learning model significantly degrades the FL performance due to the frequent transmission of weights and training on heterogeneous IoT networks. This paper proposes an Optimized FL model for Securing RPL (OFL-SRPL) over AMI devices. By reducing the size of the data transferred from the multiple local models to the server, the OFL-SRPL adopted the Particle Swam Optimization (PSO) algorithm to select the best local model from the Home Area Network (HAN) gateway and build the global model. The proposed OFL-SRPL applies an ensemble of sequential classifiers with its appropriate loss function to improve the decision-making quality and reduce the FL communication burden. Instead of transferring all the local model weights to the global model, the OFL-SRPL iteratively selects the best ensemble model with sequential classifies and only sends the weight of the best model to build the global model. The sequential classifier design improves the ownership weight and handles class imbalance issues. After taking an optimized global model decision, the devices execute ensemble classifiers successfully with appropriate learning parameters. Thus, the proposed OFL-SRPL achieves 10.6% higher detection accuracy than a centralized setup of the Long Short-Term Memory (LSTM) model without incurring multiple communication rounds like traditional FL.
Improving short-duration speaker recognition by joint Bark-wavelet acoustic feature coupling and triplet dual-attention mechanism network
Authors: Zi Yunfei , Xiong Shengwu 
Published: March 2023

Abstract

Speaker recognition methods are negatively affected by the input short duration audio signal. In this paper, we tackle the problem of speaker recognition from shorter speech data by coupling proposed two acoustic features: Bark-scaled Gauss Filter Cepstral Coefficients (BGCC) and Perceptual Wavelet Packet Entropy (PWPE). Our assumption is based on the observation that BGCC and PWPE capture sufficient information on various aspects of speech that can be used to discriminate speaker, viz., speech perception and high time-frequency information representation, etc., for enhancing characteristic diversity. A triplet dual attention mechanism (Triplet-DAM) is used to couple these two features in a creative manner. The coupling method means that the feature of limited short utterance can be reused, through the dual attention mechanism, more discriminative features are enhanced for limited feature, thus improving speaker recognition performance in short duration audio signals. Extensive analysis on a variety of datasets, which speech samples of different types, diverse lengths, etc., demonstrate the superiority of the proposed feature engineering and method over existing acoustic feature extraction and speaker recognition algorithms, including those based on MFCCs, LPCCs features, and GMM-UBM, iVector-PLDA, ResCNN-triplet, respectively. The experimental results demonstrate the proposed method achieves notable improvement with the existing approach for short duration speaker recognition.
Labour outcomes among low-risk women using WHO next-generation partograph versus WHO composite partograph: A quasi-experimental study
Authors: Sospeter Phineas F. , Al-Beity Fadhlun M. Alwy , Kidanto Hussein L , Said Ali , Kikula Amani , Godfrey Samwel 
Published: March 2023

Abstract
Background: Partogram is an important tool in the management of labor and delivery. Its correct use, has been shown to decrease maternal and newborn adverse events including deaths. In Tanzania, composite partograph is used as a standard of care. Recently, the World Health Organization (WHO) released Labour Care Guide (LCG), a next generation partograph which emphases on woman centered care. This study aimed to compare maternal and newborn outcomes among low-risk pregnant women who were monitored by using WHO LCG versus those who were monitored by using composite partograph.
Methodology: This Quasi Experimental Study on LCG, was conducted at Mnazi Mmoja and Vijibweni hospitals in Dar es salaam, Tanzania. The following outcomes were compared as depicted in the WHO quality of care framework: mode of delivery, Post-Partum Hemorrhage, labour augmentation, duration of labor, maternal death, APGAR, admission to Neonatal Intensive Care Unit and perinatal death. Statistical analysis was done by using Statistical Package for Social Sciences. Stepwise logistic regression was done to identify factors associated with dichotomous outcomes. Adjusted Odds Ratio, and 95% confidence interval were used to present the results. P value <0.05 was considered significant.
Results: A total of 482 women were enrolled; 241 in the intervention and 241 in the comparison group. There was no difference regarding maternal and newborn outcomes among women in the two study groups; Post-Partum Hemorrhage (p=0.69), Perineal trauma (p=0.65) and maternal death; APGAR score at 5 minutes (p=0.41), need for NICU admission (p= 0.80) and perinatal death (p=0.33). Parity of 3 and above was associated with better maternal outcome AOR= 3.05, 95%CI:1.40- 6.65; p=0.005 while augmentation of labour was associated with less chances of better maternal outcome AOR=0.33,95% CI: 0.16- 0.68: p= 0.003. Cesarean section was associated with poor newborn outcome AOR= 0.43, 95% CI: 0.19- 0 .96; p=0.04.
Conclusion: From this study, monitoring labour in low-risk women using WHO LCG had similar maternal and newborn outcome when compared with use of a composite partograph.Large studies powered to compare maternal and newborn outcomes including high risk pregnant women in busy labour ward are highly recommended.
Low Sar Based Modified Circular Patch Antenna for Future Biomedical Applications and Body Area Networks
Authors: MERUGUMALLI RAMA KRISHNA , M Neelaveni Ammal , Chalasani Subbarao 
Published: March 2023

Abstract

In this work a Compact Circular patch-oriented bio medical antenna is proposed for low Sar applications. The proposed antenna covers the bands of both 2.4GHz and 5. 8GHz.The structural orientation and star shaped slot in the circular patch gives the given Targeted bands. The antenna is designed using flexible polyamide substrate which is having dielectric constant between 2.9-3.3 and loss tangent of 0.008. The proposed antenna is also checked its variations in radiation characteristics at different bending conditions. The obtained frequency is ranging between 2.3-3.5 and between 5.1-6.8Ghz holding fractional bandwidth of 41% and 28%. A suitable dipole and Omnidirectional patterns have been observed for proposed modal. The observed antenna a peak gain of 2.5Db at lower frequency resonance and 4.9dBi at higher frequency resonance. Validation of antenna is structured out by implementing nearby conditions of human body and observed its variation.
Efficacy and safety of Transcranial Direct Current Stimulation (tDCS) on Cognitive Function in Chronic Schizophrenia with Tardive Dyskinesia (TD): a randomized, double-blind, sham-controlled, clinical trial
Authors: Zhou Yue , Xia Xingzhi , Zhao Xueli , Yang Ruchang , Wu Yuxuan , Liu Junjun , Lyu Xiaoli , Li Zhe , Zhang Guangya , Du Xiangdong 
Published: March 2023

Abstract
Objective
Previous studies have shown that transcranial direct current stimulation(tDCS) led to an improvement of cognitive function in patinets with schizophrenia, but rare study has explored the effect of tDCS on long-term hospitalized chronic schizophrenia with tardive dyskinesia(TD). The present research explored if cognitive function in patients with long-term hospitalized chronic schizophrenia with TD could be improved through tDCS.
Methods
This study is a randomized, double-blind, sham-controlled clinical trial. Of the 52 patients, 14 dropped out and 38 completed the experiment. Thirty-eight patients on stable treatment regimens were randomly assigned to receive active tDCS(n = 21) or sham stimulation(n = 17) on weekdays of the first, third, and fifth weeks of treatment. Patients performed the Pattern Recognition Memory(PRM) and the Intra/Extradimensional Set Shift (IED) from the Cambridge Neuropsychological Test Automated Battery at baseline, at the 3-week of tDCS treatment (week3), and the end of tDCS treatment (week5). Clinical symptoms were also measured at the baseline and the fifth week using the Scale for the Assessment of Negative Symptoms (SANS) and the Positive and Negative Syndrome Scale (PANSS).Side effects of tDCS were assessed with an experimenter-administered open-ended questionnaire during the whole experiment.
Results
There were no significant differences in PRM and IED performance metrics, SANS total score and PANSS total score between active and sham tDCS groups at the end of the 5-wk treatment period (all P > 0.05).Furthermore, it was significant difference in the adverse effects of the tingling sensation between the two groups (P < 0.05), but there was no significant difference in other side effects (P > 0.05).
Conclusion
According to these findings, there is no evidence in support of the use of anodal stimulationover the left dorsolateral prefrontal cortex an approach for improving cognitive function in patients with long-term hospitalized chronic schizophrenia with TD.