bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementing a Hardware Agnostic Commercial Black-Oil Reservoir Simulator
Authors: Szyndel Matthew , Lemon Christopher , de Brito Dias Daniel , Dodds Eamon , Khramchenkov Eduard , Rinco Simone , Sheth Soham , Tene Matei , Han Choongyong , Shi Xundan , Wolfsteiner Christian , Cao Hui , Liao Terrence , Sekachev Michael , Zaydullin Rustem
Published: March 2023

Commercial reservoir simulators have traditionally been optimized for parallel computations on central processing units (CPUs). The recent advances in general-purpose graphics processing units (GPUs) have provided a powerful alternative to CPU, presenting an opportunity to significantly reduce run times for simulations. Realizing peak performance on GPU requires that GPU-specific code be written, and also requires that data are laid out sympathetically to the hardware. The cost of copying data between the CPU memory and GPU memory at the time of this writing is egregious. Peak performance will only be realized if this is minimized.

In paper Cao et al., 2021, the authors establish approaches to enable a simulator to give excellent performance on a CPU or GPU, with the same simulation result using either hardware. We discuss how their prototype was generalized into high-quality, maintainable code with applicability across a wide range of models.

Different parts of a reservoir simulator benefit from different approaches. A modern, object-oriented simulator requires components to handle initialization, property calculation, linearization, linear solver, well and aquifer calculations, field management, and reporting. Each of these areas will present architectural challenges when broadening the scope of the simulator from CPU only to supporting CPU or GPU. We outline these challenges and present the approaches taken to address them. In particular, we discuss the importance of abstracting compute scheduling, testing methods, data storage classes, and associated memory management to a generic framework layer.

We have created a high-quality reservoir simulator with the capacity to run on a CPU or GPU with results that match to within a very small tolerance. We present software engineering approaches that enable the team to achieve and maintain this in the future. In addition, we present test outcomes and discuss how to achieve excellent performance.

To our knowledge, no simulator capable of both CPU simulation and full GPU simulation (meaning simulation with no copies of full grid-size data for purposes other than reporting) has been presented. We will present novel software approaches used to implement the first such commercial simulator.
A Quasi-Newton Method for Well Location Optimization Under Uncertainty
Authors: Eltahan Esmail , Alpak Faruk Omer , Sepehrnoori Kamy
Published: March 2023

Subsurface development involves well-placement decisions considering the highly uncertain understanding of the reservoir in the subsurface. The simultaneous optimization of a large number of well locations is a challenging problem. Conventional gradient-based methods are known to perform efficiently for well-placement optimization problems when such problems are translated into real-valued representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients of the objective function may be too expensive to compute for realistic applications in the absence of the implementation of the adjoint method. In this paper, we develop a quasi-Newton method based on the stochastic simplex approximate gradient (StoSAG), which requires only objective-function values.

We have implemented the BFGS quasi-Newton updating algorithm together with line-search and trust-region optimization strategies. We have developed a novel approach to enhance the accuracy of StoSAG gradients by modifying their formulations to enable exploiting the objective-function structure. The objective function is treated as a summation of element functions, each representing the contribution from an individual well at distinct time steps. Instead of working with a single value for the gradient, we treat it as a sum of sub-gradients. We then utilize problem-specific prior knowledge to form a matrix W that acts on the sub-gradients. The entries of W vary from 0 to 1 and are proportional to the interference effects the neighbouring wells have on each other. We define those entries (or weights) based on the radii of investigation around the wells. The BFGS-StoSAG variants are demonstrated on a realistic synthetic case with 26 wells while varying the average reservoir permeability.

We first show that the BFGS algorithm delivers promising performance as in many cases it results in the most rapid improvement for the objective-function values (especially in early iterations). Further testing results confirm that the trust-region protocol is more effective than the line-search protocol for accelerating convergence with BFGS. Although the objective function is not always continuously differentiable with respect to well locations, the StoSAG variants overcome this challenge owing to their smoothing properties of approximate gradients. Moreover, we show that using our gradient correction procedures on the well-location optimization problem results in drastic acceleration in convergence indicating enhancement in the StoSAG gradient approximation quality.
Coupled CO2 Injection Well Flow Model to Assess Thermal Stresses under Geomechanical Uncertainty
Authors: Andrianov Nikolai , Amour Frédéric , Hajiabadi Mohammad Reza , Nick Hamidreza M. , Haspang Martin Patrong
Published: March 2023

We develop a two-phase transient non-isothermal wellbore flow model, augmented with a radial heat conduction in the annulus, casing, and the reservoir. Using the available data for a saline aquifer in Denmark, we build a one-dimensional geomechanical well model and assess the stresses at the wellbore wall using the analytical Kirsch formula. Using the temperature at the wellbore wall, we calculate the corresponding thermal stresses. Furthermore, we assess the impact of the uncertainty in thermal expansion coefficients on the magnitudes of thermal stresses. For the cases considered, the magnitude of the changes in the critical pressure and in the fracture pressure with and without thermal stresses does not exceed 3%.
High Performance Integrated Asset Modeling: A Giant Gas Field Case Study
Authors: Rosa Andrea , Wiegand Klaus , Mukundakrishnan Karthik , Pizzolato Alberto , Panfili Paola , Cominelli Alberto , Picone Silvia , Ruffino Rosario , Patacchini Leonardo
Published: March 2023

We present the development of a field-scale simulation tool coupling the model of Zohr, a super giant deepwater gas reservoir requiring a multi-million active cells grid with dual porosity/dual permeability formulation, to its gathering network. Deepwater field development often relies on a complex subsea gathering infrastructure, possibly evolving over time and leading to a complex topology. Here, facilities route the multiphase production stream to an onshore compression plant throughout a network with bifurcations.

To perform integrated modeling of such assets, we have developed a Facility Network Solver (FNS) with flexible topology, whose formulation relies on a graph representation with continuity equations at nodes and tabulated constitutive equations at edges. FNS was designed to be integrated with the industrial grade GPU-based reservoir simulator used in the company, both jointly developed by Eni and Stone Ridge Technology, with emphasis on preserving usability and simulation performance.

The correctness of FNS results in the presence of bifurcations was thoroughly assessed in standalone mode, through benchmarking against what is today considered a reference commercial network solver. FNS integration with the reservoir simulator enables forecasts where the back-pressure is taken into account. Engineers can better assess the viability of different development scenarios, including dynamic upgrades to the network topology, using reservoir simulation workflows they are well accustomed to. In particular, a single tool replaces often heterogeneous associations of third-party software, without impacting simulation time.

Sensitivity analyses were performed on both coupling frequency and location. It was concluded that periodic coupling at the well-head was a satisfactory setting, yielding negligible performance overhead with respect to standalone reservoir simulations, thus enabling the integrated model to be used routinely as the sole simulation model.
An Adaptive Coloring Scheme for Graphics Processing Unit Preconditioners
Authors: Lemon Christopher , Cao Hui , Szyndel Matthew , Khramchenkov Eduard
Published: March 2023

A single modern graphics processing unit (GPU) typically has the memory bandwidth equivalent to many central processing unit (CPU) nodes. This makes GPU hardware appealing for linear solvers that tend to require high memory bandwidth and fast inter-core communication. Reservoir simulators are designed to handle a wide range of simulation models, and to obtain peak performance the linear solver must be well suited to the resulting linear systems. This fact can lead to disappointing performance when shifting the linear solver from CPU to GPU. To fully utilize the capabilities of the latest GPU devices, we must transition from coarse-grained to fine-grained parallel preconditioners. To enable such high levels of parallelism in the linear solver a common approach is to employ a multicolor reordering of the linear system. Depending on the specific properties of the simulation model, this process can cause a significant weakening of the parallel preconditioner, resulting in much slower convergence. In some situations, this slow convergence can cause an order of magnitude increase in the linear iteration count, and result in the GPU linear solver performing worse than the CPU version. In this paper we analyze the impact on performance of employing different coloring schemes for different simulation models and we identify how the coloring can be automatically adapted for the properties of each simulation model. In this way, the performance improvements expected on the GPU can be realized for a wider range of simulations.
Phase-Field Simulation of Near-Wellbore Nucleation and Propagation of Hydraulic Fractures in Enhanced Geothermal Systems (EGS)
Authors: Fei Fan , Costa Andre , Dolbow John E. , Settgast Randolph R. , Cusini Matteo
Published: March 2023

Enhanced geothermal systems (EGS) rely on the artificial creation of fractures (i.e., hydraulic fractures) to enhance the permeability of the formation which would, otherwise, be too low to allow for fluid circulation. Hydraulic fracturing involves complex nucleation and propagation processes, which are key to the analysis and prediction of well productivity. Numerical simulations are commonly employed to understand the specific mechanisms behind nucleation and propagation of hydraulic fractures. However, most numerical approaches face tremendous challenges in tracking and accommodating the evolving fracture geometry, especially when curved and branched fractures occur. The phase-field method can overcome this obstacle, as it can model fracture propagation without the need for tracking the fracture tip nor for remeshing. However, the most common phase-field formulation is unable to accurately capture fracture nucleation. In this work, we develop a new phase-field approach for hydraulic fracturing that accounts for fracture nucleation due to the strengths of geologic material and the existence of small defects. Verification examples show that the proposed formulation can accurately predict near-wellbore nucleation and propagation of hydraulic fractures and the wellbore breakdown pressure. Simulation of a three-dimensional wellbore problem further demonstrates the efficiency of the proposed phase-field method in handling fracture nucleation and propagation.
Water Injection Optimization to Prevent Reservoir Souring
Authors: Izgec Bulent
Published: March 2023

This paper presents a three-stage integrated workflow using thermal/ geomechanical and thermal/ chemical simulation models to prevent reservoir souring during high concentration sulphate water injection. Reservoir souring takes place when sulphate reducing bacteria (SRB) is activated by cold injection water and gains access to high concentrations of sulphate along with volatile fatty acids (VFA) and/ or hydrocarbons (BTEX) as nutrients.

In the first stage a thermal/ chemical reservoir simulation model is history matched to quantify the changes in nutrient distribution around the injector. Earlier in the life of the well high volumes of low sulphate content water injection washes away the VFA/ BTEX in the near wellbore area creating a nutrient depleted zone for SRB.

In the second stage, a detailed wellbore model is coupled with a thermal/ geomechanical reservoir simulator and history matched to predict the downhole injection water temperature. Because the size of the cooled region changes with injection induced fractures extending from the wellbore into the formation, a thermal/ geomechanical simulation model is preferred.

The final stage of the workflow takes place later in the life of the injector when injection water has high sulphate concentrations. During this stage, the size of the formation cooling around the wellbore is constrained within the nutrient depleted zone with dynamic injection rate optimization to stop bacterial activity and accordingly reservoir souring.

Case studies from deepwater Gulf of Mexico reservoirs demonstrate how the workflow has been successfully operationalized to eliminate reservoir souring during high concentration sulphate water injection. Four years after the initiation of high sulphate concentration water injection, lack of sour gas production from the fields points to the robustness of the method. As suggested by the simulation models SRB activity is not promoted if cooling is only limited to the nutrient depleted zone.

This method helps quantify an upper limit for the sulphate concentration in injection water that will not cause reservoir souring. Chemical injections and/ or adjustments to the existing facility design is not required.
Smooth Formulation for Three-Phase Black-Oil Simulation with Superior Nonlinear Convergence
Authors: Jiang Jiamin , Wen Xian-Huan
Published: March 2023

Black-oil simulations with phase changes are challenging, because of the complex interactions between the different components and the equilibrium behavior of the phases. The common method for solving this type of nonlinear problem is to use a fully-implicit approach. However, the conventional black-oil model can lead to difficulties with converging using Newton's method. Discontinuities in discrete system can occur when a phase transition happens, which can lead to oscillations or even failure of the Newton iterations. The goal is to design a smoothing formulation that eliminates any sudden changes in properties or discontinuities that occur during phase transitions.

We first employ a compositional formulation based on K-values to describe the standard black-oil model. Next, the coupled system is reformulated such that the discontinuities are carried over to the phase equilibrium model. In this manner, a single, succinct non-smooth equation is obtained, which allows for deriving a smoothing approximation. A mixed complementarity problem (MCP) for phase-equilibrium in the area of chemical process modeling served as the foundation for the reformulation. The new formulation is non-intrusive and simple to implement, requiring minor changes to current black-oil simulator frameworks.

We analyze and demonstrate that phase changes lead to the changes of fluid-properties and discrete system, under the conventional black-oil formulation. By comparison, the newly proposed formulation uses a smoothing parameter to ensure smooth transitions of variables between the phase regimes. It also generates unique solutions that are valid for all three phases. Several complex heterogeneous problems are tested. The conventional black-oil model experiences many time-step cuttings and wasting nonlinear iterates. On the contrary, the smoothing model exhibits excellent convergence behaviors. Overall, the new formulation addresses the issues with convergence caused by phase-changes, while barely affecting solution results.
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

Log 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

TOC 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

Fine-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

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

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

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

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

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

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

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

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

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

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.
Application of machine learning techniques for identifying soil-dwelling fungi and Chromista
Authors: Struniawski Karol , Kozera Ryszard , Trzciński Paweł , Lisek Anna , Sas-Paszt Lidia
Published: March 2023

The main goal of this paper is to construct automated system for accurate identification of soil microorganisms on a genera level based on microscopic images of the monocultural colonies. The microorganisms in question belong to one of the following genera: Fusarium, Trichoderma, Verticillium or Phytophthora. Proposed classification system is fully automated and works on raw microscopic images without extra preprocessing such as marking indicators or coloration applied. The implemented scheme comprises of the following steps: image preprocessing to enhance microorganisms characteristic traits, image segmentation to distinguish background from region of interest, features calculation focused on color and texture of image, feature selection and lastly classification using machine learning methods. Due to a differentiation of analyzed microorganism structures a novel method of segmentation, consisting of several steps and combining various machine graphics methods, that maximizes classification accuracy is developed. This work reports on research examining the impact of selected classifiers on final microorganisms’ identification. The highest noted mean classification accuracy renders 91.73% across 50runs of 10% cross-validation for Regularized Extreme Machine Learning classifier tested on a full set of features with parameter values set to 3900 neurons with Leaky Rectified Linear activation function in hidden layer and regularization constant C = 2.8.
Synergistic effects of silicon and phosphorus co-application on rice (Oryza sativa L.) growth, yield and nutrient use efficiency in saline soil
Authors: Kamruzzaman Md. , Akter Shova , Khan Md. Zulfikar , Amin Md. Sadiqul
Published: March 2023

Despite the important role of silicon (Si) for the better utilization of plant nutrients, it is not well understood how the combinations of Si and P (phosphorus) will behave as a co-fertilizer in saline soil for better yield performance and nutrient uptake. Hence, the goal of this study was to identify the effects of different combinations of Si and P applications on the growth, yield, nutrient uptake including nutrient use efficiency of rice (Oryza sativaL.) in saline soil. Combinedly, 3 levels of silicon (Si), i.e., Ca2SiO4: 0, 300 and 600 kg Si ha− 1and, 4 levels of phosphorus (P), i.e., TSP: 0, 13.2, 26.4, and 39.6 kg P ha− 1were applied to salt-tolerant rice (Binadhan-8) variety. The results revealed that the significant effect of the Si and P combination improves the growth, and yield parameters (tiller number, filled grain, total grain, grain yield, biological yield, straw yield). We also found a strong positive correlation between the Si and P combination and other essential nutrients (N, P, K and S) implying that the interactive effects of Si and P fertilization improve nutrient uptake and increase nutrient use efficiency. Comparing all results, the combination of 300 kg Si ha− 1and 26 .4 kg P ha− 1is potentially optimum to maximize grain yield by 41.66% and straw yield by 55% and N uptake by 216%, P uptake by 193%, K uptake by 263% and S uptake by 130% over control in saline soil.
Elimination of FKBP51 attenuates CCl4-induced liver injury via enhancement of mitochondrial function by increased Parkin activity
Authors: Qiu Bin , Zhong Zhaohui , Dou Longyu , Xu Yuxue , Zou Yi , Weldon Korri , Wang Jun , Zhang Lingling , Liu Ming , Williams Kent E. , Spence John Paul , Bell Richard L. , Lai Zhao , Yong Weidong , Liang Tiebing
Published: March 2023

Background & AimsLiver injury is a common feature of most chronic liver diseases. Previously, we found thatFkbp51knockout (KO) mice resist high fat diet-induced fatty liver and alcohol-induced liver injury. The aim of this research is to identify the mechanism by whichFkbp51affects liver injury using the carbon tetrachloride (CCl4) injection model.MethodsCCl4-induced liver injury was compared betweenFkbp51KO and wild type (WT) mice. Step-wise and in-depth analyses were applied, including liver histology, biochemistry, RNA-Seq, mitochondrial respiration, electron microscopy, and molecular assessments. The selective FKBP51 inhibitor (SAFit2) was tested as a potential treatment to ameliorate liver injury.ResultsFkbp51knockout mice exhibited protection against liver injury, as evidenced by liver histology, reduced fibrosis-associated markers (Collagen I, α-SAM, CTGF, and TIMP1), and lower serum AST and ALT levels. RNA-seq identified differentially expressed genes between KO and WT after liver injury. Pathway and STRING analysis revealed that gene hubs involved in fibrogenesis, inflammation, mitochondria, and oxidative metabolism pathways are significantly altered and predicted the interaction of FKBP51, Parkin, and HSP90. Cellular studies supported co-localization of Parkin and FKBP51 in the mitochondrial network, and Parkin was shown to be expressed higher in the liver of KO mice at baseline and after liver injury relative to WT. Further functional analysis identified that KO mice exhibited increased ATP production and enhanced mitochondrial respiration. KO mice have increased mitochondrial size, increased autophagy/mitophagy and mitochondrial-derived vesicles (MDV), and reduced reactive oxygen species (ROS) production, which supports enhancement of mitochondrial quality control (MQC). Application of SAFit2, an FKBP51 inhibitor, reduced the effects of CCl4-induced liver injury and was associated with increased Parkin and ATP production.ConclusionsDownregulation of FKBP51 represents a promising therapeutic target for the treatment of liver disease.
Factors related to post-traumatic stress disorder among nurses after directly caring for patients with COVID-19: a cross-sectional study
Authors: Yoon Hyo-Jeong , Bae Soon Yeung , Baek Jihyun
Published: March 2023

Background Nurses are particularly at risk of suffering from post-traumatic stress disorder (PTSD) owing to their overwhelming workload, risk of infection, and lack of knowledge about the coronavirus disease 2019 (COVID-19). PTSD negatively affects an individual’s health, work performance, and patient safety. This study aims to assess factors related to PTSD among nurses after directly caring for patients with COVID-19. Methods This study is a secondary analysis aimed at identifying factors influencing PTSD among nurses who directly cared for COVID-19 patients. Data from 168 nurses, collected between October and November 2020, were analyzed. The independent variables were personal, interpersonal, and organizational and COVID-19-related factors (experience of quarantine and direct care of patients with COVID-19), and the dependent variables were PTSD symptoms evaluated based on the PTSD Checklist-5. The nurses’ experience of direct care for COVID-19 patients in the designated COVID-19 isolation wards during the first wave of the pandemic (February 2020 to May 2020) were included. Results Among the nurses, 18.5% exhibited symptoms of PTSD. When directly caring for a patient in the designated COVID-19 isolation ward, nurses witnessing the death of a patient (p = .001), low level of nurse staffing (p = .008), and inconvenience of electronic health (p = .034) were associated with PTSD symptoms. The experience of quarantine owing to COVID-19 was also associated with PTSD symptoms (p = .034). Additionally, the higher the nurse managers’ ability, leadership, and support of nurses in the current ward, the higher the possibility of lowering nurses’ PTSD symptoms (p = .006). Conclusions Governments and hospitals should prepare and implement organizational intervention programs to improve nurse manager leadership, nurse staffing levels, and electronic health records programs. Additionally, because nurses who have witnessed the death of a COVID-19 patient or who are self-isolating are vulnerable to PTSD, psychological support should be provided.