bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Pipe Fractional Flow Theory with Fully Compositional Wellbore Models
Authors: Zheng Shuang , Sharma Mukul
Published: March 2023

Multi-phase compositional wellbore flow is important in determining the flow and pressure drop in oil, gas, and geothermal wells. These effects become increasingly important in long laterals with multiple locations for fluid influx. Complex hydrocarbon phase behavior such as change in the number of phases, phase flipping, gas slippage can happen in the wellbore because of changes in pressure, temperature and inflow fluid rate and composition along the wellbore. This paper introduces a new wellbore model which integrates fully compositional fluid flow with an energy balance and pipe fractional-flow theory with multiple points of fluid entry along the wellbore.

Four sets of governing equations: component mass conservation, momentum conservation (pipe fractional flow theory), composition conservation and energy balance are solved fully implicitly along the wellbore. This is then fully implicitly coupled with the flow and energy balance equations in the reservoir and fracture domains. The primary unknowns along the wellbore (total flow rate, hydrocarbon component composition, water saturation, pressure, and temperature) can then be obtained. Flash calculations are used to calculate the hydrocarbon phase saturation, density, viscosity, etc. and the flow rate of each phase is obtained from the fractional flow theory given the local flow rate and saturations.

In the first case, we study the reservoir-wellbore flow in a gas condensate reservoir with 16 hydrocarbon components. As the pressure drops, an oil phase drops out of the single phase gas condensate, first in the wellbore and then in the reservoir. In a second case, we simulate CO2 flooding in a black-oil reservoir. Reservoir cooling is observed near the injection wellbore and an increased CO2 composition is observed in the produced oil from the production wellbore. In the third case, we study a low permeability volatile oil reservoir with 14 hydrocarbon components. Production from a hydraulically fractured horizontal wellbore is simulated considering the reservoir-fracture-wellbore flow. We observe that as the pressure drops in the wellbore, gas is liberated from the oil phase and this changes the wellbore pressure drop considerably. The lighter component compositions decrease with time while the heavier component compositions increase with time because of the liquid holdup effect. In the fourth case, we showcase a stand-alone wellbore model integrated with point sources.

This paper fully integrates a pipe-fractional flow formulation with compositional wellbore flow, and an energy balance for the first time. This allows the model to be used directly with compositional reservoir simulators. The wellbore mesh is automatically generated and coupled with the reservoir/fracture mesh to allow for an integrated and seamless simulation from the reservoir to the surface facility. This model allows engineers to accurately account for the pressure drop and phase behavior within the wellbore when simulating the production/injection of complex fluids.
Data-Space Inversion for Rapid Physics-Informed Direct Forecasting in Unconventional Reservoirs
Authors: Hui Mun-Hong , Wang Kainan , He Jincong , Tanaka Shusei
Published: March 2023

Traditionally, subsurface models are created based on reservoir characterization, then simulated and calibrated via history matching (HM) to honor data, generate forecasts, and quantify uncertainties. However, this approach is time consuming for unconventional projects with aggressive schedules. On the other hand, purely data-driven approaches such as decline curve analysis (DCA) are fast but not reliable for yet-to-be-observed flow regimes, e.g., boundaries or other effects causing late-time changes in productivity decline behaviors. We propose a physics-informed unconventional forecasting (PIUF) framework that combines simulations and data analytics for robust field applications. We apply Data-Space Inversion (DSI) to incorporate physics from a large ensemble of prior simulation models to generate posterior forecasts within a Bayesian paradigm. We also quantify the consistency of simulated physics and observed data by computing the Mahalanobis distance to ensure that the appropriate prior ensemble is employed. In lieu of history-matched models, a statistical relationship between data and forecast is learned; then posterior sampling is applied for data assimilation and direct forecasting in DSI. DSI reduces the dimensions of time-series (and other) data using parameterization like Principal Component Analysis. We implemented DSI within a tool that is connected to a vast database of observations for thousands of unconventional Permian Basin wells and a large ensemble of fracture simulations. We apply it to rapidly generate probabilistic forecasts (e.g., oil production rate, gas oil ratio) for unconventional wells and show that DSI can provide robust long-term forecasts based on early-time data when compared with DCA. We show that DSI yields robust uncertainty quantification with a manageable number of simulations compared with simple machine-learning methods like K-Nearest-Neighbors. We illustrate how data error and volume impact DSI forecasts in meaningful ways. We also introduce a DSI enhancement to generate posterior distributions for model parameters (e.g., hydraulic fracture height) to derive subsurface insights from data and understand key performance drivers. Our cloud-native implementation stores data (observed and simulated) in the cloud while the algorithm is implemented as a microservice that is efficient and elastic for the analysis of many wells. The overall framework is useful for rapid probabilistic forecasting to support development planning and de-risk new areas as an alternative to DCA or HM.
Multiscale Reservoir Simulation of High-Resolution Models
Authors: Natvig Jostein , Dias Daniel , Bratvedt Frode , Watanabe Shingo , Li Zhuoyi , Kozlova Antonina , Tomin Pavel , Jiang Jiamin , Shi Xundan
Published: March 2023

To quantify the uncertainty in reservoir performance, it is common to build ensembles of models that sample the space of possible reservoirs that are consistent with the available data. To evaluate the spread of possible outcomes, simulations experiments are run for each model in the ensemble to calculate for instance recovery factor. The geoscreening workflow is a common way to do this systematically and in a reasonable time. It can work as follows: First, run simulations with simplified physics to calculate recovery factor for every model in the ensemble. Then, use recovery factor (and other quantities) to rank and select representative models for high, medium, and low performance scenarios that can be used for full field simulations.

In this paper we present an application of the multiscale sequential fully implicit (MS SFI) framework to simulate extremely complex high-resolution models with simplified physics. This enables us to perform fast evaluations of geological uncertainty, such as in the geoscreening workflow. The multiscale SFI method computes each timestep in two steps: First, it solves a nonlinear equation for pressure (and flow). Then, it solves a nonlinear equation for saturations and mole fractions. The pressure equation is solved iteratively using a multiscale approach.

The MS SFI method has recently been made generally available in a commercial reservoir simulator and can easily be benchmarked with a state-of-the-art fully implicit (FI) method. The MS SFI method was used to successfully simulate a realistic high-resolution geological model in a practical time frame, achieving approximately 10 times speedup in CPU time compared to the FI method. This demonstrates the ability of the MS SFI method to effectively deal with extremely complex models, enabling fast quantification of geological uncertainty with a shorter turnaround time. In many instances the MS SFI method enables simulation of large models at the original geological resolutions without the need for upscaling.

Finally, we demonstrate how the MS SFI method benefits a geology screening workflow and discuss future use of the MS SFI framework to create fit-for-purpose simulation engines for other workflows.
New Fast Simulation of 4D (x, y, z, t) CO2 EOR by Fourier Neural Operator Based Deep Learning Method
Authors: Liu Jianqiao , Jing Hongbin , Pan Huanquan
Published: March 2023

The training speed is slow for the convolutional neural network (CNN)-based physics-informed neural network (PINN) in surrogate models and it is difficult to be applied to large-scale engineering problems. The Fourier Neural Operator (FNO) network can speed up 100 times faster than the PINN according to current literature. But the current FNO only handles the 3D (x, y, t) spatial-temporal domain. In this work, we developed a new framework to simulate the 4D (x, y, z, t) subsurface flow problems using the FNO network and the domain decomposition method. After numerical simulation runs, the obtained results of subsurface flow field distributions in 4D spatial-temporal domain (x, y, z, t) are decomposed into multiple 3D spatial-temporal domains (x, y, t) in the z dimension. Then, multiple FNO networks are used to train 3D spatial-temporal domain (x, y, t) in parallel to predict the distributions of the flow field in subsequent time steps. Finally, the predicted results of the 4D spatial-temporal solution in subsequent time steps are obtained by re-coupling the trained 3D (x, y, t) results in the z dimension. In this way, our new framework successfully extends FNO-network from 3D (x, y, t) to 4D (x, y, z, t) to predict field distributions in subsurface flow. The new framework was successfully applied to some very complex cases of CO2 injection for enhanced oil recovery (EOR) in compositional simulations. The predicted accuracy is enough for the method to be applied to simulate the complex CO2 EOR in fractured systems. The computational speed in 4D (x, y, z, t) can be as fast as it does in 3D (x, y, t) through parallel training. The tested results show that our new framework can efficiently simulate the EOR processes by injecting CO2 into complex fracture reservoirs. For the first time, we developed a new methodology that successfully extends the current FNO network from 3D (x, y, t) to 4D (x, y, z, t). Our framework paves way for the fast FNO network to solve the large-scale spatial-temporal domain of reservoir engineering systems.
Bridging Computational Stratigraphy and Reservoir Simulation for Geologically Realistic High-Resolution Reservoir Modeling
Authors: Li Boxiao , Li Lewis , Wen Xian-Huan , Sun Tao
Published: March 2023

Computational Stratigraphy (CompStrat) is a state-of-the-art earth-modeling method that captures the key heterogeneities in subsurface reservoirs through modeling of the detailed flow and sediment transportation processes in various depositional environments. The method is fully based on physics and generates high-resolution 3D earth models that are much more geologically realistic than those generated by traditional earth-modeling methods. It can accurately predict and preserve those spatially continuous but vertically thin and volumetrically insignificant layers, such as shale layers, thus enabling a much more accurate representation of natural reservoir connectivity.

In the past few years, CompStrat has been studied mainly within the earth science community and has yet been broadly applied in reservoir simulation research and practices. Our objective is to bridge this gap and allow this frontier technology to offer geologically realistic earth models for reservoir simulation to better understand how various geological features contribute and control subsurface flow patterns and performance, and subsequently leading to a better integration among earth modeling, flow simulation, and more reliable reservoir performance predictions.

CompStrat models often have large number of cells (hundreds of millions or more). A large proportion of them are related to thin shale layers. These thin cells can often cause convergence difficulties in reservoir simulations. We developed a grid coarsening method to drastically reduce the cell number and the simulation time with minimum altering of overall model connectivity characteristics. The method reduces the cell number by 85% to 93% and the simulation time by 94% to 99.4% with limited loss of accuracy for representative examples. Without this method, the simulation may take impractically long time to run for large models with complex multiphase flow dynamics.

The successful removal of the computational bottleneck enables the application of this frontier earth-modeling method in high-fidelity reservoir simulation. It also facilitates detailed understanding of the connection between geology and flow to offer valuable insight for reservoir modeling, production forecast uncertainty analysis, and history matching. We developed a method to label, evaluate, and rank geological features based on their influence on flow performance, with shale layers being the specific focus. The labeling is performed semi-automatically and the evaluation and ranking is done efficiently with a reduced-physics solver. The result is statistically consistent across multiple realizations.
A New Phase-Labeling Method Based on Machine Learning for CO2 Applications
Authors: Sheth Soham , Bennett James , Kachuma Dick , Heidari Mohammad Reza , Shaykhattarov Marat
Published: March 2023

Phase labeling can be very challenging for complicated compositional simulation cases. Inaccurate labeling can lead to issues ranging from incorrect resource accounting to non-convergent simulation runs. Accurate phase labeling algorithms are computationally demanding and are seldom used in commercial workflows. Instead, cheaper but inaccurate empirical methods are employed such as the Li-correlation (Reid et. el. 1966).

Phase labelling based on critical temperature alone mis-identifies fluids below the dew point pressure as liquids rather than vapour. This is a particular problem when performing surface flashes of CO2 or H2S rich fluids since both components have critical temperatures above standard temperature. This can lead to failures in the well model, for example when a well is controlled by gas rate but the produced phase is identified as a liquid. The second part of this paper therefore describes a new phase labeling method that uses both the critical temperature and saturation pressure predictions from the ML models to generate accurate labels. Results are presented for CO2 rich fluids. We show that this ML approach can result in accurate labeling and can outperform traditional methods in computational efficiency. We also show the application on simulation cases with complicated field management scenarios that require accurate phase labeling at the in-situ as well as separator conditions.

The ML workflow is based on a set of two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using different mixing strategies between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature as well as saturation pressure does not exist, and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of critical temperature and saturation pressure. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models.
Uncertainty Quantification for Transport in Porous Media Using Parameterized Physics Informed Neural Networks
Authors: Fraces Cedric G. , Tchelepi Hamdi
Published: March 2023

We present a Parametrization of the Physics Informed Neural Network (P-PINN) approach to tackle the problem of uncertainty quantification in reservoir engineering problems. We demonstrate the approach with the immiscible two phase flow displacement (Buckley-Leverett problem) in heterogeneous porous medium. The reservoir properties (porosity, permeability) are treated as random variables. The distribution of these properties can affect dynamic properties such as the fluids saturation, front propagation speed or breakthrough time. We explore and use to our advantage the ability of networks to interpolate complex high dimensional functions. We observe that the additional dimensions resulting from a stochastic treatment of the partial differential equations tend to produce smoother solutions on quantities of interest (distributions parameters) which is shown to improve the performance of PINNS. We show that provided a proper parameterization of the uncertainty space, PINN can produce solutions that match closely both the ensemble realizations and the stochastic moments. We demonstrate applications for both homogeneous and heterogeneous fields of properties. We are able to solve problems that can be challenging for classical methods. This approach gives rise to trained models that are both more robust to variations in the input space and can compete in performance with traditional stochastic sampling methods.
Salt Precipitation and Water Evaporation Modelling in a Black-Oil Reservoir Simulator
Authors: Goncalves Machado Cintia , Egberts Paul , Alvestad Jostein , Hustad Odd Steve
Published: March 2023

Salt precipitation is a major issue in gas fields as it may reduce production significantly. Salt precipitation can be triggered through evaporation of the brine into the gas leading to an increased brine salt concentration and - if reaching its solubility - precipitation will occur reducing the rock porosity and, consequently, the permeability. In this work, the open-source fully implicit black-oil reservoir simulator OPM-flow is extended to allow for salt precipitation and water evaporation modeling. In comparison to compositional approaches, the proposed formulation simplifies the model set-up and calculation procedure. An existing black oil simulation may easily be converted to include water vaporization and salt precipitation functionality. This facilitates the modeling of fields that suffer from salt precipitation and their incorporation in history matching and optimization workflows. The black-oil formulation was modified to allow the gas to contain vaporized water in addition to vaporized oil. The salt transport equation is modified to account for a solid salt precipitate, and the permeability reduction is dealt with through a mobility multiplier that depends on the change in porosity. Together, a novel fully implicit formulation was developed for salt precipitation/dissolution and water evaporation for a three-phase black-oil simulator and implemented in OPM-flow. This implementation enables simulation of salt precipitation for gas-condensate production wells in addition to gas production wells.
A Novel Heuristic Max Ratio based Workflow Scheduling Algorithm for Cloud
Authors: Naik Banavath Balaji , Kuila Pratyay , Biswas Tarun , Govil Mahesh Chandra , Singh Dhananjay
Published: March 2023

Workflow Scheduling Algorithm (WSA) for the cloud is one of the most trending issues in the research community. This is a Non-deterministic Polynomial (NP) complete problem. WSA has become more challenging due to the computation of valid sequences for dependent tasks by preserving precedence relationships. Here, a novel Heuristic Max Ratio Workflow Scheduling Algorithm (HMRWSA) is designed using the max ratio constraint as transfer delay and computation time of workflow applications. The proposed HMRWSA enhances the performance measure in terms of a number of conflicting objectives such as schedule length, energy consumption, utilization of virtual machines and speed-up ratio. Various scientific or benchmark workflow applications(WA) data sets are used to extensively simulate the algorithm. In terms of schedule length, utilisation, and spreedup ratio, the simulation results are compared to reputed heuristic approaches such as Critical-Path-on-Processor (CPOP), Heuristic Earliest Finish Time-Top (HEFT-T), Heuristic Earliest Finish Time-Button (HEFT-B), Performance Effective Task (PET), and Predict Earliest Finish Time (PEFT).
Vertical land motion reconstruction unveils non-linear effects on relative sea level
Authors: Oelsmann Julius , Marcos Marta , Passaro Marcello , Sanchez Laura , Dettmering Denise , Dangendorf Sönke , Seitz Florian
Published: March 2023

The authors have requested that this preprint be removed from Research Square.
A tradeoff between Reliability and Multipath Routing in Wireless Multimedia Sensor Networks
Authors: Abazeed Mohammed , Ali Mohammed , Qahtani Ali AL
Published: March 2023

Multipath routing is considered a promising technique to enhance multimedia transmission in wireless sensor networks, where the performance metrics such as reliability, security, and delivery ratio can be improved. On the other hand, tradeoff issues in designing multipath routing must be considered to exploit the advantages of multipath routings, such as energy consumption, delay, and packet overhead. This paper presents the effect of packet loss on reliability for video transmission in WMSN studied to investigate how multipath routing can enhance multimedia transmission. The results show that the reliability is affected based on the type of lost packets. Furthermore, based on the proposed model, the number of paths that achieve predefined reliability can be adjusted.
ROBO4 deficiency in mice cause ocular disorders via possible regulations to the splicing and translation of RNAs
Authors: Xiao Xiaoqiang , huang shaofeng , li huiping
Published: March 2023

The authors have requested that this preprint be removed from Research Square.
An Iterative Convolutional Neural Network Based Malicious Node Detection (ICNN-MND) Protocol For Internet of Things
Authors: Moka Moemedi , Serome Karabo , Selvaraj Rajalakshmi
Published: March 2023

At present Internet of Things (IoT) is widely used in various products and parts in our daily life. In the future, its uses and requirements will increase. However, there are several issues with the IoT network, one of which is secure data transmission. Also, malicious nodes in the IoT network are very difficult to identify accurately and quickly, whereas it is very important to do so. In the current context, several solutions are being proposed as there is a need to focus on setting up secure IoT frameworks, and as well as the protocols that runs on them. This research proposes a secure protocol called iterative Convolutional Neural Network based Malicious Node Detection (ICNN-MND) protocol that uses the erroneous assumption learning problematic technique. This protocol will make changes in the application layer, the network layer and the physical layer in order to achieve secure data transmission in IoT. The proposed IC-MND protocol will be able to detect attacks and eliminate them hence providing maximum data accuracy in IoT. For this, machine learning method will be used and the changes in the characteristics of the malicious nodes will be learned and their boundaries be explored. Then its error variation will be calculated and the malicious nodes be differentiated.
D-ETBMRPFS: Development of an Efficient Traffic Balanced Multicast Routing Protocol for Wireless Mobile Ad-hoc Network using Fibonacci Sequence approach
Authors: Yadav Ajay Kumar
Published: March 2023

Mobile ad hoc network (MANET) is a decentralized wireless networks being developed using a different kinds of digital devices without any fixed infrastructure. This is because resource conservation is a very important task in wireless networks. Therefore multicast Routing is an effective technique for maintaining the same state.

However, multicasting in mobile ad-hoc networks is not an easy task due to the dynamic nature of network topologies, large fluctuations in signal strength, lack of battery power, and network traffic. Traffic was considered a network issue in this document. To solve the aforementioned problem, an efficient multicast routing protocol for traffic balancing using Fibonacci sequence is proposed. This mechanism allows the sender to send data along a specific routing path using Fibonacci sequence numbers to improve network performance.
Single-use flexible bronchoscopes vs traditional reusable flexible bronchoscopes: a prospective controlled study
Authors: He Shuzhen , Xie Lihua , Liu Jianming , Zou Lijun
Published: March 2023

Background: Single-use flexible bronchoscopes（SFB）eliminate the risk of bronchoscopy-related infection compared with traditional reusable flexible bronchoscopes（RFB）. At present, there is no comparative study between SFB and RFB in the aspects of biopsy and interventional therapy. This study aims to explore whether SFB can perform complex bronchoscopic procedures such as transbronchial biopsies just like RFB.
Methods: We conducted a prospective controlled study. A total of 45 patients who required bronchoscopic biopsy in our hospital from June 2022 to December 2022 were enrolled. The patients were divided into the SFB group and the RFB group, and routine bronchoscopy, bronchoalveolar lavage, and biopsy were performed respectively. Data on the time of routine bronchoscopy， the recovery rate of bronchoalveolar lavage fluid（BALF）, biopsy time, and bleeding volume were collected. We also designed a questionnaire to compare the performance between SFB and RFB by different bronchoscope operators.
Results: The routine examination time of SFB and RFB was 3.40±0.50 min and 3.55±0.42 min, respectively. There was no significant difference between the two groups （P=0.308）. The recovery rate of BALF was （46.56±8.22）% in the SFB group and (47.00±8.07) in the RFB group, without a significant difference between the two groups（P=0.863）. The biopsy time was similar（4.67±0.51min VS 4.57±0.45 min） in both groups， with no significant difference（P=0.512）. The positive biopsy rate was 100% in both groups, with no significant difference. Overall, the bronchoscope operators were generally satisfied with SFB.
Conclusion: SFBs are non-inferior to RFBs in routine bronchoscopy, bronchoalveolar lavage, and biopsy. It is suggested that SFBs have a wider clinical application.
Delineating family needs in the transition from hospital to home for children with medical complexity: part 2, a phenomenological study.
Authors: de Riet Liz van , Alsem Mattijs W , Beijneveld Rachel SI , Woensel Job BM van , Karnebeek Clara Digna Van
Published: March 2023

Background
A systematic literature review on the transition from hospital-to-home (H2H) of families with a child with medical complexity (CMC), resulted in nine overarching themes. These demonstrated common needs and experiences despite the widely differing CMC diagnoses and family characteristics. However, none of the reported studies was conducted in the Netherlands, which hampers the creation of a tailored H2H care pathway, deemed essential for our recently established Transitional Care Unit in the Netherlands: the ‘Jeroen Pit Huis’. Therefore, the aim of this study was to gain a deeper understanding of the needs and experiences of Dutch CMC parents on H2H transition and integrate these insights with the literature review into an evidence-based H2H care pathway for CMC and their families.
Methods
A descriptive phenomenological approach was applied. Heterogeneous purposeful sampling methods were used to recruit participants according to the following criteria: parents of CMC from various regions in the Netherlands, who spoke Dutch fluently and who had been discharged home from a tertiary hospital within the previous five years. Semi-structured, open-ended interviews were conducted via video call by two researchers, who transcribed the audio recordings verbatim. Thematic analysis methods were used to identify emerging themes from the individual transcripts, involving a third and fourth researcher to reach consensus.
Results
Between March and August 2021, 14 mothers and 7 fathers participated in 14 interviews. They elaborated on the H2H transition of 14 CMC with a wide range of underlying diseases: 7 male, 7 female, aged 6 months to 10 years. Eight overarching themes, consistent with the results of the systematic review, represent CMC parental needs and experiences during the H2H process in the Netherlands: 1) autonomy, 2) division of tasks and roles, 3) family emotions, 4) impact on family life, 5) communication, 6) coordination of care, 7) support system and 8) adaptation.
Conclusions
The H2H needs and experiences reported by the CMC families in this study align with the results of our systematic review. The H2H transition process is not linear but continuous, and should extend beyond the specific medical needs of the CMC to holistic care for the family as a whole. The overarching care needs and experiences, expressed by all CMC families, regardless of underlying symptoms and diagnoses, inform the H2H care pathway and its future evaluation. Our studies highlight the necessity to focus on the family needs rather than on the specific illness of the child, as well as the value of our interdisciplinary care team partnering with parents in the ‘Jeroen Pit Huis’ towards a safe and sustainable transition home.
Perception of neurology among undergraduate medical students – what can be done to counter neurophobia during clinical studies?
Authors: Jukna Šarūnas , Puteikis Kristijonas , Mameniškienė Rūta
Published: March 2023

Background and purpose
With a global increase in the burden of neurological diseases, the aversion towards neurology (neurophobia) may challenge sufficient provision of new specialists in this field. We investigated the possible determinants of neurophobia among medical students and its influence on the intent to pursue neurology residency.
Methods
From September 2021 to March 2022, an online questionnaire was distributed to medical students in Lithuania. It included questions about knowledge, confidence, interest, and teaching quality of various medical specialties (including neurology), as well as the willingness to choose neurology for residency.

Results

Eight hundred fifty-two students responded to the survey (77.2% female) – they rated neurology as significantly more difficult than other medical areas and lacked confidence in assessing patients with neurological problems (p<0.001). However, neurology was selected as one of the most interesting subjects and was reportedly well-taught. The prevalence of neurophobia among respondents was 58.9%. Most of them (207, 87.7%) indicated that neurology professors positively affected their outlook towards this medical specialty – such experience was associated with lower odds of neurophobia (odds ratio (OR)=0.383, 95% confidence interval (CI)=0.223 to 0.658). Being less neurophobic (OR=1.785, 95% CI=1.152 to 2.767) and having conducted neurology research (OR=2.072, 95% CI=1.145 to 3.747) increased the odds of a student being willing to pursue a career in neurology.
Conclusion
Neurophobia was frequent among students in Lithuania and was inversely related to positive influence by neurology professors. Together with previous research experience in the field, low levels of neurophobia were associated with the inclination to enter neurology residency.
Water Pipeline Leak Detection and Localization With 1DCNN and Novel Adaboost Classifier
Authors: Kothandaraman Mohanaprasad , Rajasekaran Uma , Pua Chang Hong
Published: March 2023

A pipeline leak localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. Recent advancements include machine learning and deep learning techniques to detect a leak with great accuracy, and cross-correlation is the widely used tool for localization. All the existing algorithms use two architectures to detect and localize a leak. The main aim of this paper is to propose a standalone architecture for leak detection and localization with 1DCNN and novel AdaBoost architecture. The data collected from a real-time pipeline setup is redundant, very dynamic, and contains a substantial amount of surrounding noise. So an appropriate feature extraction technique helps to capture essential information from pipeline data. In the proposed method, the 1DCNN extracts the essential features from the data collected from the real-time pipeline setup located in UTAR, Malaysia, using an Acousto-optic vibration sensor. The proposed novel AdaBoost architecture localizes the leak with the help of the features extracted using 1DCNN. The performance metrics used in this paper to evaluate the proposed method are training accuracy, validation accuracy, precision, recall, F1 score, and R-squared, and the results are 99.23, 97.26, 98.18, 98.19, 98.14, and 98.55, respectively which is better compared to the implemented existing method. The results show that the proposed 1DCNN-novel AdaBoost localizes the leak with high training and validation accuracy and performs well in other metrics.
Development of a Biochar-Based Laboratory- Scale Filter for Industrial Wastewater Treatment
Authors: Jayathilake Pramod , Manage Pathmalal Marakkala , Idroos Fathima Sumaiya
Published: March 2023

Biochar (BC) has lately emerged as a viable wastewater treatment agent. The invasive aquatic plant Pistia spp. was used in the present study to produce BC. Activated and non-activated BC samples were individually incorporated into a sand and gravel filter. Chemical and physical water quality parameters were measured in the experiment before and after treatment. The control filter was primarily developed using only sand and gravel. A brine shrimp lethality assay (BSLA) was carried out to evaluate the toxicity. The effect of particle sizes of BC (65-125mm and 125-250mm) and repeated treating process on treatment efficiency were carried out as optimization studies for activated BC filter. The filter unit containing activated Pistia spp. showed a higher treatment efficiency. BSLA revealed 100% mortality in raw wastewater and no mortality in activated BC treated water. The results of the optimization study revealed that, the BC integrated filter units could be used to treat wastewater for three consecutive cycles. The BC's small particle size (65-125mm) recorded the highest treatment. This particle size showed turbidity reduction by 99.72%. It was found that reduction of Total Phosphates, Ammoniacal Nitrogen, Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Nitrate (N-NO3−), Biological Oxygen Demand (BOD5) and Total Kjeldahl Nitrogen (TKN) by 97.82%, 91.49%, 88.38%, 88.18%, 87.44%, 84.38%, and 84.38% respectively. Hence, BC derived from Pistia spp. is a promising adsorbent for wastewater treatment. Findings of the study showed a long-term green wastewater treatment method that makes effective use of invasive aquatic plants.
Molecular characterization of a novel deltaflexivirus infecting the edible fungus Pleurotus ostreatus
Authors: Xiao Junbo , Wang Xin , Zheng Ziru , Wu Yugu , Wang Zhe , Li Huaping , Li Pengfei
Published: March 2023

A novel positive single-stranded RNA virus, Pleurotus ostreatus deltaflexivirus 1 (PoDFV1), was isolated from an edible fungus Pleurotus ostreatus strain ZP6. The complete genome of PoDFV1 is 7706 nucleotides (nt) longand contains a short poly(A) tail. PoDFV1 was predicted to contain one large open reading frame (ORF1) and three small downstream ORFs (ORFs 2-4). ORF1 encodes a putative replication-associated polyprotein (RP) of 1979 amino acids (aa) containing three conserved domains, viral RNA methyltransferase (Mtr), viral RNA helicase (Hel), and RNA-dependent RNA polymerase (RdRp), common to all deltaflexiviruses. ORFs 2-4 encode three small hypothetical proteins (15-20 kDa) without conserved domains or known biological functions. Sequence alignments and phylogenetic analysis suggested that PoDFV1 is a new species belonging to the genus Deltaflexivirus(family Deltaflexiviridae, order Tymovirales). To our knowledge, this is the first report of a deltaflexivirus infecting P. ostreatus.
Joint MMSE-RISIC and Channel Revised for SC-FDE Aeronautical Mobile Communication System
Authors: Dong Pengyu , Xiang Xin
Published: March 2023

Under the condition of slow fading time-varying multipath channel with low rice factor, it is difficult to make effective use of signal energy, therefore, the traditional Minimum Mean Squared Error (MMSE) technique cannot effectively reduce the bit error rate. To solve the above problem, we proposes a new equalization scheme for aeronautical mobile communication based on MMSE andresidual intersymbol interference cancellation (MMSE-RISIC), firstly, the hard decision signal is modified to obtain the reconstruction signal of frequency domain, and then further decision is made according to the time domain error. Its essence is to modify the channel to make the estimated channel closer to the real channel. The simulation results show that the SNR can be improved by about 2-8dB with the same bit error performance, indicating that the equalization algorithm makes full use of the energy of the multipath component and obtains the signal-to-noise ratio gain.
Conservative versus Operative Treatment of FFP II Fractures in a Geriatric Cohort: A Prospective Randomized Pilot Trial
Authors: Thiesen Darius M , Althoff Gerrit , Strahl André , Rolvien Tim , Frosch Karl-Heinz , Köpke Leon , Arras Christian , Ballhause Tobias , Dalos Dimitris , Hartel Maximilian J
Published: March 2023

A clear recommendation regarding treatment strategy of fragility fractures of the pelvic ring is missing. The most typical fracture pattern is a lateral compression type injury with non-displaced fractures of the anterior pubic rami and a unilateral os sacrum fracture (FFP II). We hypothesized that operative treatment would be superior to conservative treatment. From October 2017 to April 2020, a randomized prospective non-blinded trial with n = 39 patients was carried out. Two arms with 17 operative versus 22 conservative cases were created. Inclusion criteria were a posterior pelvic ring fracture FFP type II, age over 60 years and acute fracture (< 3 weeks). Barthel index, pain level (VAS), quality of life (EQ-5D-3L), and Tinetti-Gait Test were determined on admission, at discharge, and after 3, 6, 12 and 24 months. Median follow-up was 12.9 months. The Barthel index (= 0.325), VAS (p = 0.711), quality of life (p = 0.824), and Tinetti-Gait Test (p = 0.913) showed no significant differences between the two groups after 12 months. Two patients switched from the conservative to the operative arm due to persistent immobilization and pain. The one-year mortality rate showed no significant difference (p = 0.175). Our hypothesis that surgical treatment is superior was refuted. No significant benefit was shown in terms of quality of life, mortality and pain levels. The results suggest a more differentiated treatment approach in the future, with initial conservative treatment preferred. A larger multi-center trial is required to confirm these findings.
Level of Evidence: Level I
The study was retrospectively registered with the German Clinical Trials Registry (DRKS00013703) on 10/12/2018.
Determination of the use of Ligula intestinalis as a bioindicator in malathion residues
Authors: İPEK ZEYNEP ZEHRA , Minaz Mert , Kayış Şevki
Published: March 2023

In this study, the usability of Ligula intestinalis as a bioindicator for pesticide accumulation was investigated. Two experiments were designed for the detection of pesticide residues and its withdrawal period. In the first experiment, the accumulation of malathion for 10 days was measured in the hybrid fish, Squalius orientalisxAlburnus derjugini sampled from a dam lake. In the next experiment, withdrawal was measured for 15 days. The infected and healthy fish in groups exposed malathion and not exposed malathion were sampled at the end of the first experiment. No malathion residue was found in the control group that was not exposed with malathion. For the second experiment, infected and healthy fish from malathion and without malathion groups were sampled to measure the elimination of malathion on the 1st, 4th, 5th, 8th, 12th and 15th days. At the end of the first experiment, the presence of malathion was not detected in the control group while accumulation was observed in both fish and L.intestinalis in the experimental group. At the end of the second experiment (15th day), the highest residual value was found in L. intestinalis (1.02 mg/kg) while it was determined as 0.009 mg/kg in infected fish and 0.006 mg/kg in uninfected fish. According to the correlation, malathion accumulation was linear between uninfected fish and infected fish. On the other hand, an inverse correlation was found between L. intestinalis and both malathion and control fish. As a result, it was determined that L. intestinalis can be used as a bioindicator in pesticide accumulation and the pesticide is still detectable in the parasite after withdrawal from fish.
High-Quality Chromosome-Level De Novo Assembly of the Trifolium repens
Authors: Wang Hongjie , Wu Yongqiang , He Yong , Li Guoyu , Ma Lichao , Li Shuo , Huang Jianwei , Yang Guofeng
Published: March 2023

Background: White clover (Trifolium repens L.), an excellent perennial legume forage, is a heterotetraploid native to southeastern Europe and southern Asia. It has high feeding, ecological, genetic breeding, and medicinal values and exhibits excellent resistance to cold, drought, trample, and weed infestation. Thus, white clover is widely planted in Europe, America, and China. However, the lack of reference genome limits white clover breeding and cultivation. This study generated a white clover de novo genome assembly at the chromosomal level and annotated its components.
Results: The PacBio third-generation Hi-Fi assembly and sequencing methods were used to generate a 1096 Mb genome size of T. repens, with contigs of N50 = 14 Mb, scaffolds of N50 = 65 Mb, and BUSCOs value of 98.5%. The newly assembled genome has better continuity and integrity than the previously reported white clover reference genome; thus provides important resources for the molecular breeding and evolution of white clover and other forage. Additionally, we annotated 90,128 high-confidence gene models from the genome. White clover was most closely related to Trifolium pratense and Trifolium medium but distantly related to Glycine max, Vigna radiata, Medicago truncatula, and Cicer arietinum. The expansion, contraction, and GO functional enrichment analysis of the gene families showed that T. repens gene families were associated with biological processes, molecular function, cellular components, and environmental resistance, which explained its excellent agronomic traits.
Conclusions: This study reports a high-quality de novo assembly for white clover obtained at the chromosomal level using PacBio third-generation Hi-Fi sequencing. The generated high-quality genome assembly of white clover provides a key basis for accelerating the research and molecular breeding of this important forage crop. The genome is also valuable for future studies on legume forage biology, evolution, and genome-wide mapping of quantitative trait loci associated with the relevant agronomic traits.
Investigation on Hydrogen Storage Capacity of Spherical Activated Carbons from Ion Exchange Resins
Authors: Kim Tae Gyun , Hong Yeon Soo , Lee Chang Ha , Suh Jeong Kwon
Published: March 2023

In this study, the role of spherical activated carbon from ion exchange resin as an adsorption material for hydrogen storage was researched. Spherical activated carbon samples were prepared from two types of physical activation methods using steam and CO2. The porosity induced by each method and the resulting hydrogen adsorption performance were compared and evaluated. When the samples had similar BET surface areas, steam activation induced microporous spherical activated carbon to increase hydrogen storage performance at low pressure (~1 bar) under low temperature conditions (under 77 K). CO2 activation was able to well formation of the 1-2 nm sized pore ratio in the micropores, thus enhancing hydrogen storage performance at high pressure (~200 bar) under ambient temperature (under 298 K). In conclusion, this indicated that there exists a range of spherical activated carbon pore size favorable for hydrogen adsorption, dependent on the pressure range applied.
Can we determine anterior hip coverage from pelvic anteroposterior radiographs? A study of patients with hip dysplasia
Authors: Cheng Hui , Zhang Zhendong , Sun Wei , Ren Ningtao , Luo Dianzhong , Li Yong , Zhang Jianli , Zhang Hong
Published: March 2023

Purpose: Insufficient coverage causes hip joint instability and results in hip pain. Anterior hip coverage can be determined on both pelvic anteroposterior (AP) radiographs and false profile (FP) radiographs. Four parameters are commonly used to determine the anterior coverage on pelvic AP radiographs: the crossover index, crossover sign, anterior wall index (AWI), and rule of thirds. This study aims to clarify the relationship between these 4 parameters on AP radiographs and the anterior center edge angle (ACEA) on FP radiographs.
Methods: In this study, 53 patients who underwent periacetabular osteotomy for hip dysplasia at our center between July 2020 and October 2020 were retrospectively reviewed. Four parameters on AP radiographs and the ACEA on FP radiographs before surgery and 6 months after surgery were measured and compared for each hip.
Results: Upon examining the 53 hips in this study, there was no correlation between either the crossover index and the ACEA (P=0.66) or the crossover sign before surgery. The postoperative correlation between the crossover index and the ACEA was weak (r=0.36, P=0.007), and that between the crossover sign and the ACEA was moderate (r=0.41, P=0.003). There was a weak correlation between the AWI and ACEA both before (r=0.288, P=0.036) and after (r=0.349, P=0.011) the operation. Evaluation of the anterior coverage by the rule of thirds was also not consistent when determining the anterior coverage with the ACEA.
Conclusion: Anterior coverage on AP radiographs is largely inconsistent with ACEA on FP radiographs, especially before the surgery. It is recommended to take FP radiographs routinely for determining anterior hip coverage.
Chronic low-concentration benzene exposure and hematotoxicity research trends from 1992 to 2022
Authors: 胡 娟 , Sun Qianyu , Xu Fanwei , Zhang Ludi , LI Xiuting , Zhu Baoli , Wang Boshen
Published: March 2023

Background
The purpose of this study was to analyze nearly three decades of academic research on chronic low-level benzene exposure and hematotoxicity with qualitative and quantitative analysis.
Methods
Publications from 1992 to 2022 were extracted from the Science Citation Index Expanded (SCI-E) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection (WoSCC) of Thomson Reuters. WoSCC, Microsoft Excel 2016, and Citespace were used to analyze the publication outcomes, journals, countries, institutions, authors, and research areas.
Results
As of July 13, 2022, there have been 521 papers published on chronic low-level benzene exposure and hematotoxicity. Environmental Health Perspectives has published the most articles, the United States is the leading country, Fudan University is the leading institution, Chen Y has published the most articles, Snyder R has co-cited the most articles, and Toxicology is ranked #1 in the research field.
Conclusion
Many high-quality articles have been published in the last three decades. Fudan University and the Chinese Center for Disease Control and Prevention are in the first echelon of research on long-term low-level benzene exposure and hematological toxicity. Chen Y, Ashley D, and Bertazzi P may be good candidates for collaboration in this research area. In the meantime, occupational disease researchers should pay close attention to the studies of hematological and carcinogenicity caused by occupational benzene exposure.