bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Indoor Air Quality and Tackling COVID - 19 Virus
Authors: Al Awadhi Ibrahim , Sharma Ashok , Karim Twana 
Published: December 2021

Abstract

Objective/Scope
One of the main concerns of Oil & Gas Plants and associated Buildings is how to improve indoor air quality (IAQ) and tackling viruses. IAQ can be affected, or may become under high risk by some of nearby gases, microbial contaminates or energy stressor that affect the HSE condition. This paper presents the main factors that been considered to provide practical solutions to achieve high IAQ and tackling viruses (such as COVID-19).

Methods, Procedures, Process
IAQ refers to the air quality within and around the plants/buildings. IAQ can usually be affected, or may become under high risk by nearby gases, particulates, microbial contaminates or any mass that affect 100% HSE. Inadequate air quality in building will increase the risk and impact on transferring viruses to people (such as COVID and Flue) and equipment performance (such as equipment failure, components corrosion and short circuits on control board). Survey and data was recorded to evaluate air quality performance in atmosphere instead of assuming it. Accordingly, the impact of inadequate IAQ was studied and evaluated.

Results, Observations & Conclusions
The international standard set a good IAQ in respect of gas concentration and human who works inside buildings in a way that less than 50% people should not detect any odor, 25% should not experience discomfort, 10% should not suffer from mucosal irritation and 5% should not experience annoyance. Study concluded that inadequate IAQ inside the building will affect people performance/health and installed equipment performance. In addition, improper HVAC system operation will be become breeding site for odor causing mold and bacteria, specifically on cooling coil. Hence, several technics were studied to improve IAQ, by installing Ultraviolet (UV) light to stop growing bacterial inside the HVAC system, installing chemical filter in air intakes to remove atmospheric dust, gases and bacteria by 100%, upgrading filtration efficiency to MERV-13 or highest achievable to capture at least 75 – 95% of airborne particles between 0.3 and 1.0 micron, increase outdoor air ventilation and temperature/humidity control.
The performance of HVAC system and quality of air inside building were monitored by simulating IAQ based on ISO 16890, filters life cycle, energy consumption, and the results were found 100% satisfactory and provided solutions that are now successfully implemented in all new and some of the existing buildings.

Novel/Additive Information
There are several buildings with similar issues and these approach/technics now being adopted in new constructed/existing buildings to protect human and asset integrity, which will support ADNOC Way by sustaining safe environment operation, lower health risk, reduce of equipment failure, reduce maintenance cost and 100% HSE. There are numbers of occupied buildings across the world were surrounded by aggressive gases/pollution with poor IAQ and above approaches it can be followed to realize larger benefits.
Predicting the Productivity Enhancement After Applying Acid Fracturing Treatments in Naturally Fractured Reservoirs Utilizing Artificial Neural Network
Authors: Hassan Amjed Mohamed , Aljawad Murtada Saleh , Mahmoud Mohamed Ahmed 
Published: December 2021

Abstract
Acid fracturing treatments are conducted to increase the productivity of naturally fractured reservoirs. The treatment performance depends on several parameters such as reservoir properties and treatment conditions. Different approaches are available to estimate the efficacy of acid fracturing stimulations. However, a limited number of models were developed considering the presence of natural fractures (NFs) in the hydrocarbon reservoirs. This work aims to develop an efficient model to estimate the effectiveness of acid fracturing treatment in naturally fractured reservoirs utilizing an artificial neural network (ANN) technique.
In this study, the improvement in hydrocarbon productivity due to applying acid fracturing treatment is estimated, and the interactions between the natural fractures and the induced ones are considered. More than 3000 scenarios of reservoir properties and treatment parameters were used to build and validate the ANN model. The developed model considers reservoir and treatment parameters such as formation permeability, injection rate, natural fracture spacing, and treatment volume. Furthermore, percentage error and correlation coefficient were determined to assess the model prediction performance. The proposed model shows very effective performance in predicting the performance of acid fracturing treatments. A percentage error of 6.3 % and a correlation coefficient of 0.94 were obtained for the testing datasets. Furthermore, a new correlation was developed based on the optimized AI model. The developed correlation provides an accurate and quick prediction for productivity improvement. Validation data were used to evaluate the reliability of the new equation, where a 6.8% average absolute error and 0.93 correlation coefficient were achieved, indicating the high reliability of the proposed correlation.
The novelty of this work is developing a robust and reliable model for predicting the productivity improvement for acid fracturing treatment in naturally fractured reservoirs. The new correlation can be utilized in improving the treatment design for naturally fractured reservoirs by providing quick and reliable estimations.
A Customized High Performance Water-Based Mud Solved the Challenge to Drill Reactive Shale Formation and Simplify the Logistic Challenge in an Exploration Well in Offshore East Kalimantan, Indonesia
Authors: Pinartjojo Djoko , Hutahaean Edison Tamba Tua , McManus Ian , Nerwan Aphrizal S. I. N. , Hansen Rudiny 
Published: December 2021

AbstractExploration drilling obviously requires a robust drilling fluid system to be a key factor in overcoming both the known and unexpected challenges of a structure that consists of reactive clay and lost circulation zones. Extra consideration has to be given to regulatory environmental requirements and complications resulting from regional politics. A High-Performance Water Based Mud (HPWBM) system was selected to address the aforementioned issues.The HPWBM was customized to respond to the subsurface conditions with the main requirement to provide maximum shale inhibition through a non-dispersed environment. Polyamine was utilized to stabilize all types of clay; an encapsulation polymer and a non-ionic polymer were included to prevent dispersion and to seal micro-fractures. A complete shale study was performed to determine the optimum concentration of the base fluid and each shale inhibitor. Then hydraulic behaviour of the mud was simulated with contractor proprietary software to understand the parameters for optimal hole cleaning as well as Equivalent Circulating Density (ECD) simulation.The HPWBM system successfully facilitated the execution of the exploration well and provided highly effective clay stabilization. No Non-Productive Time (NPT) was recorded as a result of reactive clay issues. The mud system also facilitated a good rate of penetration (ROP), formation stability, and lubricity. Waste cuttings transportation was not required. In addition, there is also no requirement for costly base oil including its associated transportation costs. The successful implementation of the HPWBM yielded an estimating saving of 25% compared to invert emulsion fluids, prior to considering costs associated with an expensive Liquid Mud Plant (LMP), environmental, and freight costs. Significant cost savings were achieved by eliminating the need for LMP rental, mobilization and demobilization. Another notable saving was realized from the reduced system maintenance of the HPWBM as less dilution was required compared to a regular Water Based Mud.Thinking outside of the box and embracing the departure from the default consideration of an invert system with a thorough risk assessment augmented value to wellbore construction. A smartly designed HPWBM system provided performance comparable to an invert emulsion system but with superior benefits with respect to environmental protection, simplified logistics and lower costs.
Polymer Outage Strategy for On-Going Polymer Injection Operation in the Sultanate of Oman
Authors: Svec Yi , Kindi Osama , Sawafi Marwan , Farajzadeh Rouhi , Al Sulaimani Hanaa , Van Berkel Jasper , Al-Hinai Suleiman , Al Abri Moh’d , Nofli Sami , Al-Yahyai Ahmed , Al-Mahrooqi Majid 
Published: December 2021

Abstract
Polymer outage (or polymer injection unavailability) is undesirable but also inevitable. When it happens, the question is how to respond to it to minimize its adverse impact on the production. This paper presents the rationale for generating a polymer outage strategy to operate a polymer flood field in the southern area of the Sultanate of Oman. The work presented here is based on field performance and analytical analysis. The diagnostic plots were created from 10 years of polymer flood field response and were used for this operating decision. The pros and cons of two scenarios were discussed. The selected operational strategy is to minimize the short falls of polymer outage. The strategy was implemented in the field.
Simultaneous injection and production pause (SIPP) is recommended for the full field polymer outage. It minimizes the impact on polymer incremental oil and hence less deferment. Calibrated with the actual results, analytical method is used to determine when to shut down and whether a short of buffer period of water can be tolerated before SIPP is carried out.
The polymer literature focus on polymer mechanisms, modeling, project initiation and implementation but no paper discusses the operational strategy on how to respond to field polymer outages. This paper shares our operational learnings and the field results of various polymer operation modes on polymer incremental oil. The learning from this field may be of interest to other operators who are planning or currently implementing polymer flood in their fields.
Temporal Dynamics of Microcystins In Two Reservoirs With Different Trophic Status During The Early Growth Stage of Cyanobacteria
Authors: Xue Qingju , Kong Ming , Xie Liqiang , Li Tong , Liao Mengna , Yan Zebin , Zhao Yanyan 
Published: December 2021

Abstract
Harmful cyanobacterial blooms are increasing in frequency and severity, which makes their toxic secondary metabolites of microcystins (MCs) have been widely studied, especially in their distribution and influence factors in different habitats. However, the distribution of MCs on the early growth stage of harmful cyanobacteria and its influence factors and risks are still largely unknown. Thus, in the present study, two reservoirs (Lutian Reservoir and Lake Haitang) with different trophic status in China have been studied weekly from March to May in 2018, when the cyanobacteria communities were just in the early growth stage, to investigate the variation of MCs concentration and the relationships between MCs and environmental parameters. During the investigation, Lutian Reservoir and Lake Haitang were found to be mesotrophic and light eutrophic, respectively. In Lutian Reservoir, the concentration of EMCs was obviously higher than that of IMCs with a mean value of 0.323 and 0.264 μg/L, respectively. Meanwhile, the concentration of EMCs also fluctuated more sharply than that of IMCs. Congeners of IMC-YR and EMC-LR were respectively dominant in total concentrations of IMCs and EMCs. Unsurprisingly, in Lake Haitang, the concentrations of IMC and EMC were both significantly higher than that in Lutian Reservoir with a mean concentration of 0.482 and 0.472 μg/L, respectively. Differently, the concentration of MC-YR was dominant in both IMCs and EMCs, followed by MC-LR. In correlation analysis, the IMCs were significantly and positively correlated with the density and biomass of phytoplankton phyla and potential MCs-producing cyanobacteria and the parameters of water temperature (WT), nutrients and organic matters. Similar results were also observed for EMCs. While the different variation of MCs in two reservoirs might be primarily caused by the differences in WT, nutrients (especially phosphorus), organic matters and the composition of MCs-producing cyanobacteria. In addition, the coexistence of the dominant species of Pseudoanabaena sp., which can produce taste-and-odor compound of 2-methylisoborneol (2-MIB), might have significant impacts on the concentration and toxicity of MCs. Our results suggested that the risks posed by MCs at the early growth stage of cyanobacteria should also deserve our attentions, especially in mesotrophic water bodies.
Harmonizing Surface Reflectance Between Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI
Authors: Cao Hongye , Han Ling , Li Liangzhi 
Published: December 2021

Abstract
Remote sensing dynamic monitoring methods often benefit from a dense time series of observations. To enhance these time series, it is sometimes necessary to integrate data from multiple satellite systems. For more than 40 years, Landsat has provided the longest time record of space-based land surface observations, and the successful launch of the Landsat-8 Operational Land Imager (OLI) sensor in 2013 continues this tradition. However, the 16-day observation period of Landsat images has challenged the ability to measure subtle and transient changes like never before. The European Space Agency (ESA) launched the Sentinel-2A satellite in 2015. The satellite carries a Multispectral Instrument (MSI) sensor that provides a 10-20m spatial resolution data source providing an opportunity to complement the Landsat data record. The collection of Sentinel-2A MSI, Landsat-7 ETM+, and Landsat-8 OLI data provide multispectral global coverage from 10m to 30m with further reduced data revisit intervals. There are many differences between sensor data that need to be taken into account to use these data together reliably. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8 and Sentinel-2 archived in the Google Earth Engine (GEE) cloud platform. To test and quantify the differences between these sensors, hundreds of thousands of surface reflectance data from sensor pairs were collected over China. In this study, some differences in the surface reflectance of the sensor pairs were identified, based upon which a cross-sensor conversion model was proposed, i.e., a suitable adjustment equation was fitted using an ordinary least squares (OLS) linear regression method to convert the Sentinel-2 reflectance values closer to the Landsat-7 or Landsat-8 values. The regression results show that the Sentinel MSI data are spectrally comparable to both types of Landsat image data, just as the Landsat sensors are comparable to each other. The root mean square error (RMSE) values between MSI and Landsat spectral values before coordinating the sensors ranged from 0.014 to 0.037, and the RMSE values between OLI and ETM + ranged from 0.019 to 0.039. After coordination, RMSE values between MSI and Landsat spectral values ranged from 0.011 to 0.026, and RMSD values between OLI and ETM + ranged from 0.013 to 0.034. The fitted adjustment equations were also compared to the HLS (Harmonized Landsat-8 Sentinel-2) global fitted equations (Sentinel-2 to Landsat-8) published by the National Aeronautics and Space Administration (NASA) and were found to be significantly different, increasing the likelihood that such adjustments would need to be fitted on a regional basis. This study believes that despite the differences in these datasets, it appears feasible to integrate these datasets by applying a linear regression correction between the bands.
Comparative Study of Ensemble Models of Deep Convolutional Neural Networks for Crop Pests Classification
Authors: su zhongbin , luo jiaqi , wang yue , kong qingming , dai baisheng 
Published: December 2021

Abstract
Pest infestations on wheat, corn, soybean, and other crops can cause substantial losses to their yield. Early diagnosis and automatic classification of various insect pest categories are of considerable importance for accurate and intelligent pest control. However, given the wide variety of crop pests and the high degree of resemblance between certain pest species, the automatic classification of pests can be very challenging. To improve the classification accuracy on publicly available D0 dataset with 40 classes, this paper compares studies on the use of ensemble models for crop pests classification. First, six basic learning models as Xception, InceptionV3, Vgg16, Vgg19, Resnet50, MobileNetV2 are trained on D0 dataset. Then, three models with the best classification performance are selected. Finally, the ensemble models, i.e, linear ensemble named SAEnsemble and nonlinear ensemble SBPEnsemble, are designed to combine the basic learning models for crop pests classification. The accuracies of SAEnsemble and SBPEnsemble improved by 0.85% and 1.49% respectively compared to basic learning model with the highest accuracy. Comparison of the two proposed ensemble models show that they have different performance under different condition. In terms of performance metrics, SBPEnsemble giving accuracy of classification at 96.18%, is more competitive than SAEnsemble.
Disruption of Proprioceptive Information During Electrical Stimulation of the Cutaneous Afferents
Authors: Katic Natalija , Balaguer Josep-Maria , Gorskii Oleg , Pavlova Natalia , Karal-ogly Dzhina , Bulgin Dmitry , Orlov Sergei , Musienko Pavel , Raspopovic Stanisa , Capogrosso Marco 
Published: December 2021

Abstract
Restoration of proprioception with neurotechnology is critical to improve effectiveness of robotic neuro-prostheses. Unfortunately, after initial enthusiasm clinical results showed that unlike touch, proprioception could not be reliably induced. Here we show that concurrent activation of multiple sensory modalities may trigger unwanted sensory regulation mechanisms that disrupt proprioception. We recorded intra-spinal neural activity induced by stimulation of proprioceptive afferents from the radial nerve in three monkeys. Then, we superimposed stimulation of the radial nerve cutaneous branch and quantified its impact on spinal neural activity via population analysis. Proprioceptive pulses produced robust neural trajectories in the neural manifold that were disrupted by concurrent stimulation of cutaneous afferents. This disruption correlated with a reduction of afferent volleys and multi-unit activity both in the spinal cord and somatosensory cortex. Our results suggest that limited specificity not only impacts localization of artificial percepts, but also their nature to an extent that was never considered.
Lycium Barbarum Polysaccharides Regulate the Gut Microbiota To Modulate Metabolites in High Fat Diet-Induced Obese Rats
Authors: Fan Yanna , Yan Lu , Li Mengyao , Pu Zhiyu , Zhang Yannan , Yang Jianjun 
Published: December 2021

Abstract
Previous studies had indicated that the gut microbiota was a main internal factor leading to obesity through energy storage and metabolic disorders. Lycium Barbarum Polysaccharides (LBP) have been discovered with a more protective effect on intestinal flora. But it is unclear whether LBP could regulate the gut microbiota to modulate metabolites, finally relieving obesity. A related study of high-throughput 16S rRNA sequencing and serum metabolomics profiling in LBP intervention on high fat diet-induced obese rats then explored the beneficial effects of LBP and the underlying mechanism. LBP affected lipid parameters such as total cholesterol, Triglyceride, and High-density lipoprotein. The gut microbiota result detected 16 types of the phylum of bacteria in total, while four of them (Bacteroidetes, Firmicutes, Proteobacteria, Deferribacteres) were significantly different. LBP upregulated the level of Firmicutes of obese rats. LBP might associate with the gut microbiota that participates in the membrane transport and metabolism of amino acid, carbohydrate, energy, and lipid. The serum metabolomics profiling of high-fat diet-induced obesity rats found over 30 differential metabolites between model and intervention groups. Primary metabolites include cortisol, glycohyocholic acid, homo-L-arginine, ursodeoxycholic acid, isoursodeoxycholic acid, glycoursocholic acid, 4-ethylphenylsulfate, deoxycholic acid, 7-hydroxy-3-oxocholanoic acid isomers, gly-phe, pipecolic acid, proline betaine, and pyrocatechol sulfate. Pathway analysis in serum found four disorder pathways: glycerophospholipid metabolism, glycine-serine-threonine metabolism, biosynthesis of unsaturated fatty acids, and linoleic acid metabolism. The studies revealed that LBP treatment increased the diversity of fecal microorganisms and reduced metabolic disorders in obese rats. LBP ameliorated metabolic disorders and rebalanced the gut microbiome.
Working Mechanism of Remdesivir and Discovering New Potential Inhibitors to SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) using High-Throughput Virtual Screening and Molecular Dynamics Simulations .
Authors: Brunt Dylan , Lakernick Phillip , WU CHUN 
Published: December 2021

Abstract
RNA-dependent RNA polymerase (RdRp), is an enzyme essential component in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to RdRp over adenosine-triphosphate (ATP) and active-form remdesivir-triphosphate (RTP) using high-throughput virtual screening (HTVS) coupled with extensive (total 4800 ns) molecular dynamics (MD) simulations with using the ZINC compounds database against SARS-CoV-2 RdRp (PDB: 7BV2). We found that the simulations with both ATP and RTP remained stable for the duration of their trajectories, and it was revealed that the phosphate tail of RTP was stabilized by a positive amino acid pocket near the entry channel of RTP and magnesium ions containing residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.