bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Green Energy Applications in the Oil and Gas Industry
Authors: Temizel Cenk , Yegin Cengiz , Hosgor F. Bahar , Aydin Hakki , Kabir Shah
Published: March 2022

With the ongoing paradigm shift in the Oil and Gas industry towards greener alternatives with net-zero objectives, several developing technologies have been recently deployed or proposed as promising solutions. The overall goals are to decrease carbon footprint and improve the projects’ economics and net present value (NPV). This study outlines the latest developments with underlying principles, practices, and economics. This holistic approach encompasses the overall feasibility with the challenges and the benefits.

A comprehensive literature survey has been carried out on publicly available data to provide the theoretical background, rationale of use, screening and selection criteria, difficulties, and the workarounds. Systems to integrate the green methods with the respective oil and gas processes appear in detail, from screening to implementation. We outline economics under various scenarios with CAPEX methods and OPEX-intensive approaches to maximize the NPV. The technical details of the integration under multiple conditions that affect the system's efficiency, such as weather, seasonal temperature changes, wind, and solar exposure, have been investigated.

Efficient integration of the selected green methods with the associated oil and gas process proves to be a concrete step towards a net-zero objective. Such integration brings additional benefits of improved economics with minimal effects in terms of capital intensiveness or other burdens on the overall economics. These items range from solar thermal applications in heavy oil recovery to heat recovery from the produced fluids, biomass, geothermal, wind, and wave for offshore processes. Cases with multigreen energy methods, such as solar and heat recovery, demonstrate promising outcomes.

This article examines some of the latest green methods with various aspects corresponding to the selected oil and gas processes. We specifically focus on energy generation through standalone green methods and extracting energy from oil and gas processes in a greenway. The overall objective is to close the current gap in the literature.
Theoretical Development of a Digital-Twin Based Automation System for Oil Well Drilling Rigs
Authors: Said Mohammed Mokhtar , Pilgrim Rick , Rideout Geoff , Butt Stephen
Published: March 2022

Advancements in digital technology and digitalization of industrial process have opened new frontiers for the oil and gas industry. The amount of historical data generated from drilled wells over the past decades of operations is currently being digitized and processed to provide operators with the option to make more informed decisions based on previous experiences that current staff may not be aware of due to the constant loss of experience during industry downturns. The industry is combating this loss of experience through the innovative use of digitalization, integrated operations, and automation. Real time support centers operating under integrated operations business model are now utilizing digital twins (high fidelity models of the ongoing process being supported) to run forecasting simulations and compare results to digitalized historical data with the help of artificial intelligence and expert systems to aid with decision making and training junior staff. The existence of high-fidelity models, and digital twins is a solid foundation for automation. In this paper a review of the emergence of these technologies is used to identify where digital twins can be used as the foundation of automation solutions that would shift the focus of drilling crews from efficiency to operation and process safety.
Sand Management Practices for Extended ESP Run Life, a Study Case from a Mature Field in Ecuador
Authors: Xu Xianglin , Albuja Alexander , Izurieta Alvaro , Wan Xuepeng , Yang Hua , Wang Zhengwen
Published: March 2022

To analyze some factors that affect electrical submersible pump (ESP) performance and its reliability, focused on sand control practices (downhole devices as well as chemical sand consolidation), fluid characterization, and bending stress effects. To describe some study cases where these practices were applied over the last ten years leading to an increase in ESP reliability. To show ESP performance for 74 installations executed during the latest development stage in the Tarapoa block.

ESP performance in Tarapoa is affected by downhole devices sand production efficiency such as screens, automatic solids fallback preventer valves, and chemical sand consolidation treatments, the study starts with a summary of these experiences and related issues. Viscosity influence on ESP performance is studied since design stage sensitivity to water cut, emulsion, API gravity, and sand content is included. During the production stage, the need for single, double, or multiport capillary systems is described as well as the chemicals used. The finite element method is used to evaluate the stress effect during ESP installation and optimal setting depth for different well trajectories.

The best method for sand control in directional wells is chemical sand consolidation, this technique does not require additional downhole equipment and the effect on the productivity index is negligible. Standalone screens are recommended for horizontal wells but their effectiveness in sand production is limited. Automatic solids fallback valve provides some protection to ESP by preventing sand deposition when well is shut-in, up to 2,320 days running with this equipment. The production status of ESP under different viscosity conditions is simulated improving ESP run life. In addition, chemical injection effectively improves the physical properties of the fluid, which makes ESP relatively stable. Through the application of finite element software, a reliable stress distribution through the equipment was obtained. This analysis is used during installation operation and to define recommended setting depth. Average ESP run life extended over 1,900 days with a Mean Time Between Failure (MTBF) over 4,000 days.

This study summarizes the practices used to improve ESP performance over the last 10 years. ESP performance is affected by various factors that are described in this study from downhole sand control devices, chemical treatments, accessories installed along with ESP, and chemicals used during the production stage.
A New Insight into the Determination of True Residual Oil Saturation and Oil Relative Permeability from the Experimental Data in Heavy Oil/Water Systems
Authors: Esmaeili Sajjad , Kantzas Apostolos , Maini Brij
Published: March 2022

Determination of true residual oil saturation and oil relative permeability curve for heavy oil/water systems requires extensive effort and time as the breakthrough time occurs early in the imbibition process and the history match techniques are not able to obtain these two parameters perfectly. The aim of this work is to provide a new insight into the determination of residual oil saturation and oil relative permeability from core flooding in heavy oil/water systems at different temperatures.

Literature claimed that the ratio of water relative permeability to oil relative permeability should be considered besides the production and pressure drop data in history matching to determine the residual oil saturation more accurately. In this regard, different relative permeability curves from our previous experimental works are incorporated in a series of simulations that were run for up to 100 PV of water injection. Production and pressure drop data were generated where a normal error distribution is added to the input data. The history matching runs (considering relative permeability ratio) are carried out to examine how many pore volumes of water need to be injected to reach the true residual oil saturation accurately in different experiments.

The history matching results (with a Corey relative permeability model), employing the production data, pressure drop data, and the ratio of water relative permeability to oil relative permeability, which can be calculated fairly accurate from the Welge method, confirm that water relative permeability exponent is generally independent of the volume of injected water. Since the irreducible water saturation can be determined nicely during the oil flooding due to the inverse mobility ratio, the water relative permeability is not a function of volume of injected water. In contrast, determination of the true residual oil saturation in five experiments out of six is predicted with an error less than 5%. For several systems at different temperatures, at least 40 PV of water needs to be injected to result in accurate residual oil saturation determination. The oil exponent determination reveals a deviation of 20%-60% from the entered value to the simulation. The ratio of water relative permeability to oil relative permeability should be inserted into the simulation for acceptable history matching of relative permeability determination.

The estimation of true residual oil saturation for relative permeability determination from the experimental data cannot be achieved easily. In this study, a new technique described by in the literature has been examined and tested to determine the required pore volume of injected water in different heavy oil/water systems within a wide range of temperatures.
Machine Learning Based Integrated Approach to Estimate Total Organic Carbon in Shale Reservoirs – A Case Study from Duvernay Formation, Alberta Canada
Authors: Sharma Gaurav , Hayes Derek
Published: March 2022

Shale gas reservoirs have become prominent contributors to the world's hydrocarbon resources and production. They exhibit multiple storage mechanisms, two of which are linked to the free and adsorbed gas phase. Since the adsorbed gas may be stored as a denser phase than the free gas, the contribution of the adsorbed phase can be significant. The adsorbed volume is related to the total organic carbon (TOC) and thus, higher TOC can indicate higher hydrocarbon inplace. Furthermore, productivity can be linked to TOC through the potential for overpressure and conversion of kerogen to pore space. However, estimation of the TOC is not a trivial problem, as it depends on geological factors such as depositional environment. In this study, we propose an integrated workflow using concepts of machine learning to estimate TOC.

The workflow is divided into 3 sections which are area selection, sub-region categorization, and prediction modeling. Firstly, 3 active exploration and development areas (Kaybob, Pembina, and East shale basin) of the Duvernay Formation are highlighted and the geology of each specific area is analyzed. Thereafter, using the available core data and average properties of the attributes (Gamma Ray, resistivity, density, and distance from mean vitrinite reflectance line), each area is clustered into sub-regions using SVM, logistic regression, and k-means clustering. Finally, using Random Forest prediction, models for each sub-region are developed and ranked with average mean square errors and standard deviations.

It is observed that the Kaybob area can be clustered into 2 regions. This observation is supported by the principal component plot (PC1 vs PC2), which shows a dual cloud structure. This is further supported through clustering analysis, which also revealed the same observation. Results of the prediction modeling found random forest as the best predictor, achieving a match wiht the core data with a error less than 10% and in some cases only a 1% deviation.

Shale reservoir characterization requires estimation of the key parameters such as TOC. However, it is difficult to estimate TOC with purely physics-based or purely statistical models, as it requires limited specialized data and is impacted by subtle variations in the reservoir. This study suggests that TOC can be accurately estimated by combining geological interpretation and machine learning based algorithms without bearing cost of the specialized data.
Method for Drawdown Analysis of a Multi-Stage Hydraulically Fractured Horizontal Well That Penetrates an Unconventional Naturally Fractured Reservoir
Authors: Gutierrez Oseguera Alejandra , Aguilera Roberto
Published: March 2022

This paper examines the pressure response of a horizontal well that penetrates an unconventional, naturally fractured reservoir. The response is quite surprising. The expectation of linear flow is shattered, and only radial flow is observed. The radial flow two parallel straight lines in a semilogarithmic crossplot of flow pressure vs. time are present but they are reversed, with the last straight line showing smaller pressures as compared with the extrapolated first straight line.

Two different methods are used; the first one is a conventional approach for analyzing the first semilog straight line with a view to calculating flow capacity and permeability well as skin. The second approach involves a novel dual porosity model that permits calculating several fracture parameters of interest, and to the best of our knowledge has not been published previously in the petroleum engineering literature. In this paper, new equations with a semi-empirical component, are presented that allow matching the reversed real pressure drawdown data as well as the corresponding pressure derivatives.

The new model shows that fluid flow is dominated initially by the fractures as in the case of dual porosity conventional models. In the conventional model, flow pressure data deviate from the first straight line toward the right due to pressure support stemming from fluids that move from the matrix toward the fractures. Eventually, a pressure equilibrium is reached and a second straight line, parallel to the first one, is developed. However, in the case of the model presented in this paper the data deviates, not to the right of the first straight line, but down and below the first straight line. This pressure drop is interpreted to be the result of boundary-dominated flow. Next, a pressure equilibrium is reached between matrix and fractures, and the last line becomes parallel to the first straight line. It is shown that correct pressure and derivative matches permit estimating various parameter of interest such as size of the matrix blocks, number of fractures that intercept the well bore, storativity ratio omega, partitioning coefficient (the ratio between fracture and matrix porosity), matrix permeability, and the ratio of fracture to matrix hydraulic diffusivity.

The novelty of this study is the development of a new easy-to-use well testing model for matching an unconventional pressure response during drawdown of a horizontal well that penetrates an unconventional tight dual porosity reservoir. The new method is explained with a step-by-step example that uses real data from the giant unconventional Chicontepec paleochannel in Mexico and can be reproduced readily by the reader.
Multiscenario Development Planning by Means of the Digital Twin of the Petroleum Field
Authors: Aslanyan Artur , Popov Arkadiy , Zhdanov Ivan , Pakhomov Evgeniy , Gulyaev Danila , Farakhova Rushana , Guss Rodion , Dementeva Mariya
Published: March 2022

This paper presents a practical case of Field Development Planning (FDP) process with extensive use of petroleum asset digital twin facilities. The paper explains the process of setting up both the digital twin and the performance metrics which were used to steer the multivariate trials on redevelopment activities towards the optimal investment scenario.

The petroleum asset is represented by a block of the large oilfield in Western Siberia with ongoing waterflood project at mature stage.

The process of building FDP was performed through a series of interactive sessions with the petroleum asset digital twin which includes three major group of functionalities:

Convert redevelopment activities (drilling, workovers, production optimization and surface facilities) into the production response and basic investment indicators including NPV, PI, IRR, MIRR, ROI Provide technical performance metrics (such as formation pressure, watercut and recovery responses, potential case of integrity failures, behind casing channelings, spontaneous formation fracturing, surface pipeline pressure losses) that can help understand the results of the FDP activities Provide well and cross-well surveillance simulations (pressure tests, production and integrity logging) to help identify the candidates for future monitoring.

Two different multidisciplinary teams undertook 12 FDP iterations over two different 3D full-field model realizations to arrive at the best investment scenarios for each model.

After that the FDP team has picked up the best practices from both FDPs in the form of those field development actions which turned to be financially successful in both model realizations. All those cases were prioritized and merged into the ultimate FDP scenario and verified across both digital asset realizations.

The new FDP suggested the new drilling opportunities, few integrity workovers, few conversions and a new production target strategy for producers and injectors. Apart from investment benefits, the new FDP provides substantial accelerated oil withdrawals and increase in ultimate recovery comparing to the no-future-activity scenario.
Integrating Domain Knowledge with Machine Learning to Optimize Electrical Submersible Pump Performance
Authors: Sharma Abhishek , Songchitruksa Praprut , Sinha Rajeev Ranjan
Published: March 2022

A data-driven workflow was developed to monitor electrical submersible pump (ESP) health using an anomaly detection method with high-frequency sensor data. The workflow would help maximize the run life of ESPs while reducing the cost of maintenance. The new workflow contrasts with conventional field maintenance which is often reactive and incurs additional downtime in logistics and inventory management in diagnosing the issues and taking the recommended actions. In contrast, using machine learning (ML) concepts can save operating costs, especially in the case of the ESPs widely used for artificial lift.

Many operators augment ESPs with high-frequency (HF) sensors to monitor their performance, but much of this information remains either unused or partially used in post-failure analysis. The application of ML concepts in understanding ESP operational behavior complements the existing domain practice. The workflow we describe in this paper begins with domain knowledge and exploratory statistical analysis to find the key performance indicators (KPIs) related to ESP failure. Feature engineering and advanced ML techniques are used to build and test healthy ESP models for each selected KPI. Multiple health signals are fused to improve the performance of anomaly detection using historical ESP failure data and pullout reports as benchmarks.

In a test of the workflow, the model was trained on the data from a group of active producing wells with reported historical events, failures, and pullout reports. The data contained several well events and several reported failures. This information was used to fine-tune the alarm thresholds for the health indicators. The model was able to detect approximately 70% of failure events (true positive rate) in the data set. The false alarm rates for the configured model were approximately at 20% (false positive rate). The solution can be implemented in a dashboard to monitor ESP KPIs and show health alarms. These alarms can be further prioritized based on the failure probability and remaining useful life of the ESP. The health signal degradation patterns can be captured and learned to predict the remaining useful life of the ESPs, thus enabling operators to allocate and prioritize maintenance resources. In addition, the analysis of ESP pullout reports can provide insight into the relationship between health signals and root causes of the failure, which can be structured into a formal Bayesian network to provide automatic root cause interpretation

The data-driven approach takes advantage of the vast amount of reservoir, production, and facilities data and provides insights into nonlinear multidimensional relationships between parameters to better understand and optimize field development and to adopt a proactive approach toward equipment maintenance.
A Model Ranking Approach for Liquid Loading Onset Predictions
Authors: Jia Hao , Zhu Jianjun , Cao Guangqiang , Lu Yingda , Lu Bo , Zhu Haiwen
Published: March 2022

As a natural gas well ages, liquid loading is frequently encountered, leading to the decrease of gas production rate and many other side effects, which may in turn cease the gas production. Thus, to accurately predict liquid loading onset is of significant importance in gas wells for the sake of stable production. With years of research and development in the natural gas industry, the liquid loading onset prediction models prevail in the existing literature. Based on varying mechanisms (e.g., droplet falling back, liquid film reversal, etc.), the critical gas velocities or flow rates corresponding to flow pattern transitions in gas wells can then be calculated. However, a universally validated model, whether empirical or non-empirical, that is applicable to predict the onset of liquid loading in versatile gas wells conditions (e.g., horizontal, vertical, and inclined) is, as yet, still unavailable.

In this paper, we conduct a complete literature review and investigation of these existing liquid loading onset prediction models. First, we obtained detailed information of more than 600 gas wells, including well geometries, gas properties, operation conditions, and so on, from different gas fields. Then, we evaluate the validity of various liquid loading onset prediction models by use of a novel model ranking approach. To fully account for the effects of gas well properties (including but not limited to production, wellhead pressure, and pipe diameter) to the model prediction accuracy, the proposed method in this paper employs data clustering and normalization techniques, as well as the statistical relative error analysis, to rank and select the best suitable model for each specific gas well. Extensive comparison and verification of the ranking approach indicate that the proposed method provides a good reference for the rational production allocation and stable production of gas wells.
Experimental Investigation of Low-Concentrated Nanocomposite Polymer Gels for Water Shutoff Treatment Under Reservoir Conditions
Authors: Sheidaie Ali , Fahimpour Jalal , Sharifi Mohammad
Published: March 2022

Excessive water production is one of the main challenges in mature oil fields. Applying polymer gels has proven to be an effective chemical treatment to this problem. Because of some limitations in application of polymer gels in oil reservoirs with harsh conditions, using nanoparticles (NPs) in gel structure has been proposed recently to improve the performance of such systems. However, while main body of the previous studies has only focused on improvement of bulk properties of polymer gels in presence of NPs, there is a serious lack of data on their performance in porous media. In this study, we have conducted a comprehensive investigation on application of two new nanocomposite polymer gels through bulk-gel static tests and dynamic core displacement tests. Fe2O3 and NiO NPs at low concentrations (<100 mg·L−1) in AN125VLM/chromium acetate polymer gel system were used. The results of bulk bottle tests at 82℃ demonstrated that the nanocomposite gel systems have the same gelation time and gel strength as the NPs-free gel. Swelling ratio measurements were also performed in a high-salinity formation water (268 949 mg·L−1), and accordingly, while NiO NPs deteriorated the swelling behavior of the gel system, Fe2O3 NPs showed a positive impact. The results of the strain-sweep test demonstrated relatively similar linear viscoelastic region and thus the same gel strength for all gel samples. In addition, the results of the thermogravimetric analysis (TGA) revealed that Fe2O3 NPs had a minor positive impact on thermal resistance of the polymer gel system. Finally, high pressure-high temperature coreflooding experiments were carried out on three sandstone reservoir core samples. Although the NPs-free gel showed a desirable level of disproportionate permeability reduction, the injection of Fe2O3 nanocomposite gel showed unsatisfactory results and caused rock permeability degradation. The pressure drop trend and deposition of polymer gel particles at the upstream injection side confirmed the aggregation and plugging effect of NPs during injection. Our findings demonstrate the critical aspect of dynamic core displacement testing of nanocomposite gels before their use for field applications.
SNPs in Apolipoproteins Contribute to Sex-dependent Differences in Blood Lipids Before and After a High-fat Dietary Challenge in Healthy U.S. Adults
Authors: Wang Yining E. , Kirschke Catherine P. , Woodhouse Leslie R. , Bonnel Ellen L. , Stephensen Charles B. , Bennett Brian J. , Newman John W. , Keim Nancy L. , Huang Liping
Published: March 2022

Background: The effect of genetic polymorphisms on fasting blood lipid levels have been widely studied but the effects of these within the context of a high-fat meal challenge remain less characterized. The current study aimed to investigate the association of SNPs in lipoprotein-related genes with blood lipid profiles in healthy adults in the U.S. Methods: Subjects (n=393) between 18-66 years of age with BMIs ranging from 18.5-45 kg/m2 completed the cross-sectional Nutritional Phenotyping Study. Among them, 349 subjects (men: 48%; women: 52%) gave consent for genotyping. SNPs in APOA5, APOB, APOC3, APOE, and LDLR were assessed. The association between lipid markers and genotypes was tested separately for each SNP with analysis of variance (ANOVA), adjusted for sex, age, and BMI. We also examined two-factor interactions between SNPs and sex, age, or BMI. Results: Women carrying the C allele of rs3135506 in APOA5 or men carrying the C allele of rs429358 in APOE had reduced HDL-cholesterol levels during fasting and postprandially. The C allele in APOE was also correlated to increased LDL-C levels. The TT genotype of rs2854116 in APOC3 was associated with elevated total cholesterol. Additive effect of the risk alleles of APOA5 and APOE or APOC3 and APOE was detected. Nevertheless, the tested SNPs had little impact on the postprandial triglyceride responses to the high-fat challenge meal. We found no significant effects of SNPs in APOB (rs1042034) or LDLR (rs2228671) on triglycerides, cholesterol, or free fatty acid levels.Conclusions: In healthy adults, fasting and postprandial cholesterol levels are strongly correlated with the tested APOA5, APOE, and APOC3 genotypes. Sex attributes to the genetic impact of the tested SNPs on lipid profiles.Trial registration: ClinicalTrials.gov, NCT02367287. Registered February 20, 2015, https://clinicaltrials.gov/ct2/show/NCT02367287.
Systematic genome scans reveal common population-specific signals of balancing selection
Authors: Thorburn Doko-Miles , Sagonas Kostas , Lenz Tobias , Chain Frederic , Feulner Philine , Bornberg-Bauer Erich , Reusch Thorsten , Milinski Manfred , Eizaguirre Christophe
Published: March 2022

Balancing selection describes evolutionary processes that maintain genetic diversity. To date, the number of impacted genes and underlying biological functions remain elusive. Using 60 three-spined stickleback genomes (Gasterosteus aculeatus) from five recently diverged lake-river population-pairs, we performed genome-wide scans across two levels of organization: population-pairs and populations. We overlapped Tajima’s D and Watterson’s estimator metrics and verified signals with additional summary statistics, and evaluated alternative explanations: neutral evolution, population structure, associative overdominance, or demographic change. Candidate windows exhibiting signals of balancing selection spanned 2.31% (population-pair) and 3.10% (population) of the autosomes. These candidate windows had extended linkage disequilibrium and were enriched in intergenic and non-synonymous SNPs. We identified 715 (population-pair) and 1,010 (population) candidate genes under balancing selection. Importantly, using conservative thresholds, we found a small proportion of candidate genes overlapped with highly differentiated loci or regions of potential associative overdominance. There was little evidence of confounding effects originating from demographic change. Overall, candidate genes under balancing selection were associated with functions related to interactions with the environment (olfaction and receptor signalling pathways). Our results demonstrate selection that maintains standing genetic variation is common and evolves in response to local environmental pressures, playing an important role in adaptation.
Numerical Modeling and Performance Evaluation of SnS Based Heterojunction Solar Cell with p+ SnS BSF Layer
Authors: Bhattacharjee Payel , Garain Rajeshwari , Basak Arindam , Singh Udai P.
Published: March 2022

In this research article, the performance of the solar cell having the structure (Ag/ZnO:Al/CdS/SnS/p+-SnS/Mo/Glass) is investigated in detail using SCAPS-1D software. The main motive of this work was to study the performance parameters of SnS based solar cell having p+-SnS layer as Back Surface Field (BSF). It is found that with the addition of p+-SnS BSF layer and after performing all the optimization of the different parameters, the efficiency (η) of the solar cell improved and enhanced to 7.11%. The open circuit voltage (Voc) and the short circuit current (Jsc) of the designed solar cell enhanced up to 359 mV and 27 mA/cm2 after adding p+-SnS BSF layer. It is observed that the performance of the heterojunction solar cell is dependent on several factors like thickness of the buffer, absorber and the BSF layer, carrier concentration, defect density, series and shunt resistance of the device. Also fabricating a p+-SnS layer is of low cost and the lattice mismatch is less with the SnS layer. All these findings reveal that p+-SnS layer could be a promising BSF layer to enhance the performance of SnS based heterojunction solar cell.
Reduced Left Atrial Contractile Strain With Speckle Tracking Analysis Predicts Abnormal Plasma NTproBNP in an Asymptomatic Community Population
Authors: Liu Lin , Zhang Baowei , Yang Ying , Qi Litong , Wang Shuo , Meng Lei , Ma Wei , Huo Yong
Published: March 2022

Background: The left atrium (LA) is closely related to left ventricular diastolic function. Two-dimensional speckle tracking strain and strain rate (SR) imaging have been applied in the study of LA function. We intended to explore the relationship between global LA deformation parameters and plasma NTproBNP levels in asymptomatic community residents with normal ejection fraction and normal LA volume.Methods: A cross-sectional sample of Beijing residents underwent comprehensive Doppler echocardiography and medical record review in 2009.Global LA longitudinal strain and SR indexes were obtained in the apical four-chamber view. LA stiffness index (LASI) was calculated as the ratio of early diastolic velocity of transmitral flow/early diastolic mitral annular motion velocity (E/E') to LA reservoir strain. Results: A total of 620 individuals (mean age=65 years, left ventricular ejection fraction=70.8%, LA volume index=17.9ml/m2) were investigated in our study. 117 individuals had increased plasma NTproBNP (≥125pg/ml). LA reservoir and contractile function by LA strain and SR indexes were significantly reduced in the abnormal NTproBNP group compared with the normal NTproBNP group. Multiple regression analysis indicated that LA contractile strain was a negative predictor of plasma NTproBNP in addition to indexed LA volume and E/E'. LASI was higher in the abnormal NTproBNP group and was significantly correlated with NTproBNP (r=0.342, P<0.001). The area under ROC analysis for LASI in predicting elevated plasma NTproBNP was 0.690, higher than that for LA strain, LA volume and E/E'. The cut-off value of LASI was 0.615.Conclusions: LA reservoir and contractile functions demonstrated by LA strain and SR were significantly impaired in the community-based population with increased plasma NTproBNP levels. LA contractile strain adds incremental information in predicting abnormal NTproBNP levels. LASI was the best single index in the detection of abnormal NTproBNP.
Insights in light trapping for new generation silicon solar cell: optical characterization of plasmonic nanostructures beside anti-reflection layer
Authors: Samadi Masoumeh , Haidari Gholamhosain , Bahiraei Hamed
Published: March 2022

Designing light-trapping is one of the requirements for new generation silicon solar cells. Herein, the optical properties of front-based plasmonic nanoparticles besides the anti-reflection layer on new-generation silicon cells were investigated by the 3D-FDTD method and were compared with some experimental kinds of literature. In addition to a perfectly periodic structure, the almost periodic structure (closer to the experimental sample) was also modeled. Along with the conventional far and near-field effect, the spectral normalized enhancement parameter to the standard cell (g-curve) and its weighted to AM 1.5 and spectral integrated (G-value) were used as a suitable characterizing set. The results showed that the g-curve of the sample with the anti-reflection layer showed superiority compared to the standard cell on the ~ full wavelength range. Moreover, the thickness engineering of the anti-reflection layer can itself significantly increase cell performance (G=1.4). In contrast, the g-curve of some optimum plasmonic samples below 500 nm wavelength was not significantly effective. The best G-value among plasmonic samples was 1.3. Also, the combination of anti-reflection and plasmonic design did not lead to significant improvement not only for perfect periodic samples but also for nonperfect ones. The obtained results determined that the plasmonic system can lead to a considerable decrease in spectral reflectance, consistent with some reported experimental ones. Moreover, the simulations clarified the cause of plasmonic failure in some experimental studies that due to the lateral trap of the light, this reduction in reflection does not lead to directing near and far-field into the active layer.
Highly Sensitive Nano-Biosensor Based on a Binary Photonic Crystal for Cancer Cell Detection
Authors: Daher Malek G. , Taya Sofyan A. , Colak Ilhami , Patel Shobhit K. , Ramahi Omar M.
Published: March 2022

Today, cancer disease is a significant reason for the death of many patients. In many cases, cancer is diagnosed after metastasized during the body. Thus, the earlier detection gives a better opportunity for treatment and cure. A simple 1-D binary photonic crystal with a defect layer is proposed with the structure (Si/ SiO2)N/Defect/(Si/ SiO2)N as a detector for cancerous cells. The defect layer is taken here as the patient's blood sample. Compared with the normal blood sample, the cancerous samples lead to a considerable change in the refractive index. This index variation leads to a shift in the resonant mode position which can be used to diagnose cancer cells. The transfer matrix method is employed to analyze the structure. The number of periods, defect layer thickness and incident angle are investigated to maximize the sensitivity. The sensitivity is calculated at optimized conditions and found as 2400.08 nm/RIU. This sensitivity is extremely high when compared to the most recent biosensors. All the sensor performance parameters are calculated and discussed.
Tissue Microarrey: A potential cost-effective approach for mismatch repair testing in colorectal cancer
Authors: Farkash Shai , Schwartz Naama , Edison Natalia , Greenberg Sophia , Peled Hila Belhanes , Sindiany Wail , Krausz Judit
Published: March 2022

Background: Deficiencies in Mismatch Repair (MMR) proteins are one of the major pathways in the development of colorectal cancer (CRC). MMR status evaluation is recommended in every new CRC patient. However, this is not fully implemented due to high costs. Tissue microarray (TMA) enables allocating tissue cores from few specimens to a single paraffin block. The primary objective of this study was to evaluate the accuracy of TMA MMR immunohistochemistry (IHC) compared to whole slide. The secondary objective was to evaluate and validate automatic digital image analysis software in differentiating pathological and normal TMA cores. Methods: Pathological cores were defined if at least one MMR protein was unstained. Tumoral and normal tissue of 11 CRC patients with known MMR status was used to obtain 623 TMA cores. The MMR staining of each core was evaluated by a pathologist and compared to the whole slide result. Digital analysis software by 3DHistech Ltd. was used to identify cell nucleus and quantify nuclear staining in 323 tissue cores. To identifying pathological tissue, cores the cohort was divided into a test (N=146 cores) and validation sets (N=177 cores). A staining intensity score (SIS) was developed, and its performance compared to the pathologist review of each core and to the whole slide result.Results: Compared to the whole slide, the pathologist's assessment had 100% sensitivity (n/N= 112/112) and 100% specificity (n/N=278/278) with 95% lower limit of 97% and 99% respectively. The area under the receiver operating characteristic (ROC) curve of SIS was 77%. A cutoff of 55 was obtained from the ROC curve. By implementing the cutoff in the validation dataset, the SIS had sensitivity and specificity of 98.2%[90.1%-100%] and 58.5% [49.3%-67.4%] respectively.Conclusions: The MMR status of CRC can be evaluated in TMA tissue cores thus potentially reducing MMR testing costs. The SIS can be used as triage indicator during pathologic review.Trial registration: Institutional ethical approval was granted for the performance of this study (Emek Medical Center Ethics ID: EMC-19-0179).
Morphometric analysis of low mountains for mapping flash flood susceptibility in headwaters
Authors: Víg Balázs , Fábián Szabolcs Ákos , Czigány Szabolcs , Pirkhoffer Ervin , Halmai Ákos , Kovács István Péter , Varga Gábor , Dezső József , Nagy Gábor , Lóczy Dénes
Published: March 2022

Morphometric indices from high-resolution DEMs can contribute to the estimation of flash flood susceptibility in mountainous areas. We have screened 25 morphometric indices commonly used in literature, and based on a correlation matrix, selected those which showed the strongest relationship with flash flood generation: area (A), drainage texture (Rt), drainage density (Dd), elongation ratio (Re), form factor (Ff), lemniscate method (k), Gravelius coefficient (GC), forested area (Fa) and relief ratio (Rr). Among them Dd, Rt and Rr were in direct relationship with the probability of flash flood generation, while A, Re, Fa, Ff, k and GC are in inverse relationship with the intensity of flash floods. Our summary map shows the prioritization of the subwatersheds on a scale of 0 to 9. The flash flood risk ranking was empirically verified using 20-year water regime data obtained from 14 official stream gauges. Our conclusions only partially agree with former observations which may be explained by the particular lithology and morphology of the Mecsek Hills. Since the lower sections of the subwatersheds are urbanized, for optimal watershed management more detailed GIS analyses of anthropogenic controls on flash flood hazard are needed in the future.
Microwave Reflectometry Sensing System For Low-Cost In-Vivo Skin Cancer Diagnostics
Authors: Schiavoni Raissa , Maietta Gennaro , Filieri Elisabetta , Cataldo Andrea
Published: March 2022

Skin cancer is one of the most commonly diffused cancer in the world and its incidence rates have constantly increased in recent years. At the current state of the art, there is a lack of objective, quick and non-invasive methods for diagnosing this condition; this, combined with hospital crowding, may lead to late diagnosis. Starting from these considerations, this paper addresses the implementation of a microwave reflectometry based-system that can be used as a non-invasive method for the in-vivo diagnosis and early detection of biological abnormalities, such as skin cancer. This system relies on the dielectric contrasts existing between normal and anomalous skin tissues at microwave frequencies. In particular, a truncated open-ended coaxial probe was designed, manufactured and tested to sense (in combination with a miniaturized Vector Network Analyzer) the variations of skin dielectric properties on a group of volunteer patients. The specific data processing demonstrated the suitability of the system for discriminating malignant and benign lesions from healthy skin, ensuring simultaneously effectiveness, low cost, compactness, comfortability and high sensitivity.
Finding the Sources of Missing Heritability Using the Additive Epistatic Interaction Model: A Simulation Study
Authors: Wang Yupeng , Liu Xinyue , Joseph Paule V.
Published: March 2022

Objective
Thousands of Genome-Wide Association Studies (GWAS) have been carried out to pinpoint genetic variants associated with complex disease. However, the proportion of phenotypic variance which can be explained by the identified genetic variants is relatively low. Thus, it is desirable to propose new computational models to explain the “missing heritability” problem.
Results
Here, we propose the additive epistatic interaction model, consisting of widespread pure epistatic interactions whose effects are additive and can be summarized by a genetic risk score. Based on a simulated genotype dataset, the additive epistatic interaction model well depicted genetic risks and hereditary patterns of complex diseases. When applied to real genotypic datasets, the additive epistatic interaction model showed potential for accurately classifying human populations from the 1000 Genomes Project, and individuals with and without diabetes from the UK Biobank database. Moreover, the model’s genetic risk score can be replaced by a deep learning model which is more resistant to noises. We suggest that the additive epistatic interaction model might help to explain the “missing heritability” problem. Source code is publicly available at https://github.com/wyp1125/additive_epistasis.
Tissue Immunostaining of Candidate Prognostic Proteins in Metastatic and Non-metastatic Prostate Cancer
Authors: Pereira Érica Romão , Francelino Amanda Letícia , Pinheiro Laís Capelasso Lucas , Miqueloto Carlos Alberto , Guembarovski Alda Fiorina Maria Losi , de Oliveira Karen Brajão , Fuganti Paulo Emílio , Cólus Ilce Mara de Syllos , Guembarovski Roberta Losi
Published: March 2022

Prostate cancer (PCa) lacks specific markers capable of distinguishing aggressive tumors from those with indolent behavior. Therefore, the aim of this study was to evaluate the immunostaining of four candidate proteins (PTEN, AKT, TRPM8, and NKX3.1) through the immunohistochemistry technique (IHC), in tissues from patients with metastatic and non-metastatic PCa. Tissues from 60 patients were divided into three groups categorized according to prognostic parameters: better prognosis (n=21), worse prognosis (n=22), and metastatic (n=17). Immunostaining was analyzed by a pathologist and staining classifications were considered according to signal intensity: (0) no staining, (+) weak, and (++ and +++) intermediate to strong. AKT protein was independently associated with the presence of extraprostatic extension (p=0.012; OR=0.050; 95% CI=0.005-0.524). The immunostaining for TRPM8 (p=0.005) and NKX3.1 proteins (p<0.001) differed between malignant tumor and adjacent tissue as well as for proteins cellular location (nucleus and cytoplasm). NKX3.1 showed positive and predominantly strong immunostaining in all patients, in both tumoral and adjacent tissues. The staining in the prognostic groups showed that all metastatic samples had a positive immunostaining, with strong intensity for NKX3.1 (p=0.035). In the non-metastatic group, this strong protein staining was not observed in any patients. This study confirmed that the NKX3.1 protein is highly specific for prostate tissue and indicated that NKX3.1, AKT and TRPM8 may be prognostic markers for prostate cancer.
Comparing health service usage of different immigrant groups with Australian and New Zealanders: Evidence from the Household Income and Labour Dynamics Survey of Australia
Authors: Brown Heather , Breislin Emily
Published: March 2022

Background
We explored differences in primary and secondary health care usage across migrants from different regions in Australia.
Methods
Data comes from the Household Income and Labour Dynamics of Australia survey from waves 9, 13, and 17 (2009, 2013, and 2017). Zero inflated Poisson regressions and non-linear decompositions were estimated.
Results
Younger women from South Asia, Latin America and Eastern and Southern Europe and younger men from Eastern and Southern Europe had lower rates of GP visits compared to the host population. Older African men have higher rates of nights in hospital and younger Eastern and Southern European women, older women from the Rest of Asia, and younger African men and women have lower rates of nights in hospital compared to the host population
Discussion
Migrants are a heterogenous group and health policy needs to consider these differences to ensure the effectiveness and efficiency of service provision.
Lifestyle changes among the UAE population: a cross-sectional online survey
Authors: Hosani Mahra Khalifa Al , AlKetbi Latifa Baynouna
Published: March 2022

Introduction: The COVID-19 pandemic has brought unprecedented changes to people's daily activities. Eating and physical activity were challenged by the requirements to practice self-quarantine and isolation.Method: This cross-sectional online-based survey study investigated the effect of quarantine on different lifestyle habits. Demographic features and questions related to weight change, lifestyle habits such as physical activity, and smoking were collected. It also included the PHQ2 questionnaire. Adults and children were the targets of the study.Results: The general tendency of weight among the study population was towards weight gain, especially in children. Female participants in the housewife group were more likely to lose weight. Approximately one-third of the adult population reported high depression scores, and 21% of children reported high scores. Physical activity significantly decreased among adults (p-value 0.018) during the quarantine. Among male smokers, 8.6% reported a decrease in smoking frequency or quitting, while 6.8% reported an increased frequency.Conclusion: Weight changes were greater among females, especially housewives, and had a significant relationship with depression risk. No such relation was found between the low physical activity trends and mental wellbeing.