bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Artificial Intelligence Framework for the Optimal Control of Wireless Temperature Sensors for Optimizing Oxygen Injection in Subsurface Reservoirs
Authors: Katterbauer Klemens , Al Qasim Abdulaziz , Al Shehri Abdallah , Yousif Ali
Published: March 2022

Hydrogen has become a very promising green energy source and it has the potential to be utilized in a variety of applications. Hydrogen, as a power source, has the benefits of being transportable and stored over long periods of times, and does not lead to any carbon emissions related to the utilization of the power source. Thermal EOR methods are among the most used recovery methods. They involve the introduction of thermal energy or heat into the reservoir to raise the temperature of the oil and reduce its viscosity. The heat makes the oil mobile and assists in moving it towards the producer wells. The heat can be added externally by injecting a hot fluid such as steam or hot water into the formations, or it can be generated internally through in-situ combustion by burning the oil in depleted gas or waterflooded reservoirs using air or oxygen. This method is an attractive alternative to produce cost-efficiently significant amounts of hydrogen from these depleted or waterflooded reservoirs. A major challenge is to optimize injection of air/oxygen to maximize hydrogen production via ensuring that the in-situ combustion sufficiently supports the breakdown of water into hydrogen molecules.

which can then be separated from other gases via a palladium copper alloy membrane, leaving clean blue hydrogen. A crucial challenge in this process is achieving sufficient temperature in the reservoir in order to achieve this combustion process. The temperatures typically must reach around 500 degree Celsius to break the molecules apart. Hence, accurately monitoring the temperature within the reservoir plays a crucial role in order to optimize the oxygen injection and maximize recovery from the reservoir.

Artificial intelligence (AI) practices have allowed to significantly improve optimization of reservoir production, based on observations in the near wellbore reservoir layers. This work utilizes a data-driven physics-inspired AI model for the optimal control of the high temperature wireless sensors for the optimal control of the oxygen injection in real-time.

The framework was examined on a synthetic reservoir model with various producers and injectors. Each producer and injector contain various wireless high temperature sensors that are connected to each other. The framework then utilizes the temperature sensor data, in addition to the produced hydrogen, to optimize oxygen injection.

This work represents a first and innovative approach to optimize subsurface wireless high temperature wireless sensing for maximizing hydrogen recovery from waterflooded reservoirs. The data-driven approach allows to optimize the hydrogen recovery representing a crucial element towards the drive for economical extraction of blue hydrogen.
Impact of Various Tubing Material Performance Under High CO2 Environment on Future Marginal Field Development Strategy
Authors: Syed Abd Rahim Syarifah Puteh Mariah , Azman Mohd Fairuz , Awang@Mohamed Zaidi , Ahmad Fuad Mior Yusni
Published: March 2022

It is widely known that for highly corrosive environment, corrosion-resistance alloy (CRA) is recommended as the production tubing material. This is because its alternative, carbon steel (CS) is prone to corrosion that leads to integrity issues such as tubing leak and tubing parted. However, CRA usage would increase well cost resulting in unattractive commercial implication especially for a marginal field development which tends to be very sensitive economically. This paper gathers the actual performance of various tubing materials that have been installed in a high CO2 oil field in Peninsular Malaysia, in which the findings would be used to determine a proper tubing material selection strategy for future development.

Actual data from 30 years of production was collected and to be analyzed statistically. Each well's water cut trend was evaluated to establish relationship between producing water to the corrosion rate and metal loss in the presence of CO2.

It is noted that some wells completed with CS both in single and dual completion are still producing with no leaks after 30 years. This case applies to either single string or dual string, with both strings are completed as producer. However, majority of active CS-completed wells require tubing pack off to overcome multiple leaks, especially in dual utility wells. Notably, wells that are completed with CRA e.g., 13 Cr are active with no possible leaks at all. Some wells which are completed with glass-reinforced epoxy (GRE) and higher grade of CRA; 22 & 25 Cr, also do not show any potential leaks or tubing integrity issue.

It is proven and highly recommended to complete high CO2 fields with CRA material. Nevertheless, by understanding the well and reservoir performance, particularly on the effect of water cut in relation to general corrosion due to CO2, the use of more expensive materials can be optimized. This paper is to discuss, in the context of marginal field development where production life is relatively shorter and production rate is low, the consideration of deploying a cheaper material and/or less corrosion-resistant substances as the tubing material to make the development more commercially attractive or remain status quo with CRA.
Successful Drilling of the Deepest and Hottest HPHT Carbonate Well in Central Luconia, Off the Coast of Sarawak, Offshore Malaysia
Authors: Ong Swee Hong Gary , Dejdamrongpreecha Warapong , Sookparkkit Pawan , Ungku Hamzah Ungku Syafena , Chia Boon Shin , Udin Rurizalakmal , Zulkarnain Marzuki , Manson James Hunter , Phasook Chevit , Umar Lawrence , Kongpat Nitipong
Published: March 2022

HPHT wells are typically associated with high complexity, technically challenging, long duration, high risk and high NPT as many things could go wrong especially when any of the critical nitty- gritty details are overlooked. The complexity is amplified with high risk of losses in carbonate reservoir with high level of contaminants compounded by the requirement of high mud weight above 17 ppg during monsoon season in an offshore environment.

The above sums up the challenges an operator had to manage in a groundbreaking HPHT carbonate appraisal well which had successfully pushed the historical envelope of such well category in Central Luconia area, off the coast of Sarawak where one of the new records of the deepest and hottest carbonate HPHT well had been created.

This well took almost 4 months to drill with production testing carried out in a safe and efficient manner whereby more than 4000m of vertical interval was covered by 6 hole sections.

With the seamless support from host authority, JV partners and all contractors, the well was successfully delivered within the planned duration and cost, despite the extreme challenges brought about by the COVID-19 pandemic.

This paper will share the experience of the entire cycle from pre job engineering/planning, execution and key lesson learnt for future exploitations.
Defining the Zonal Contribution in a Horizontal Well with Cased Hole ICD's Completion Using Innovative Integration of Multi Arrays Production Logging, Spectral Noise and High Resolution Temperature Log
Authors: Al-Sabea Salem , Patra Milan Kumar , Abu-Eida Abdullah , Dhafiri Jamayel Mubarak , Gobran Mahmoud Hamdy , Al Rasheed Fatemah Amir , Prosvirkin Sergey
Published: March 2022

Objectives/Scope

The Horizontal wells enhance reservoir performance by placing a long wellbore section within the reservoir. As they help in reducing water and/or gas production, increasing oil rates, reducing sand production and finally in achieving efficient drainage of the reservoir. The inflow control devices (ICDs) are used to address the issues of premature water and/or gas breakthrough, uniform flow distribution and reservoir depletion in the oilfield. They reduce the flow of unwanted fluids and balance the production distribution across the entire lateral section. The production contribution across the ICDs monitoring to take necessary remedial action is one of the challenges in this type of completion.

In this case study, the main objective was to determine the contribution profile and source of water in horizontal well with passive ICD completion installed back in 2011. The various challenges were the low production rate, heavy oil with water, erratic and inconsistent nature of the well flow, ESP design test with lack of recent flow results, and lack of some data about the ICDs. In addition, unlike most of the cases where the ICDs are installed in open hole, this case the ICDs are installed in 7in liner with nine perforation intervals.

Methods, Procedures, Process

This paper presents the use of multi arrays production logging combined with spectral noise and high precise temperature tools, to determine the contribution profile and source of water in this challenging ICD completion.

Results, Observations, Conclusions

The contribution profile across the ICDs was determined using the multi arrays production logging data and temperature simulation models assisted by noise data. The results were in contrary to the previous production logging results and helped significantly in the design of proper ICD cleaning operations. The work-over resulted in successfully restoring and attaining high oil gain.

Novel/Additive Information

The innovative combination of multi arrays production logging combined with spectral noise and high precise temperature tools to determine the contribution profile and source of water in horizontal well with ICD completion.
Microporosity Quantification and Flow Unit Prediction Using Neural Networks: A Case Study from Malaysia
Authors: Manhique Samía Janny Celeste de Ducílio , Salim Ahmed Mohamed Ahmed , Latiff Abdul Halim Abdul
Published: March 2022

This paper aims to quantify microporosity and access its impact on reservoir quality properties, to identify Hydraulic Flow Units (HFU) using core porosity and permeability data, to predict HFU and accurately estimate permeability in un-cored intervals/wells. Mercury Injection Capillary Pressure (MICP), Nuclear Magnetic Resonance (NMR) at high pressure (T2 distribution), and Water-Oil relative permeability curve data were used to quantify microporosity. The median pore-throat size was estimated through Windland's R35 equation and modified Kozeny-Carmen equations led to an accurate determination of Flow Zone Indicators (FZI) in each cored interval. Subsequently, Neural Networks were used to predict HFU in un-cored intervals using log data, this model was then extrapolated to estimate permeability in un-cored intervals. Microporosity estimations from MICP are relatively higher compared to NMR and Water-Oil Relative Permeability estimations. Estimations from MICP range from 25-69% while 11-32% is the range of estimations from the other methods. In conjunction to MICP data and clay volume assessment, two distinct zones were identified. The top intervals are characterized by high microporosity levels and steep-convex capillary pressure curves while the bottom intervals have lower microporosity levels and steep-concave capillary pressure curves. Analysis indicated that there are six (6) flow units and that the correlation coefficient (R2) between FZIcore and FZIlog is 99.89% and 91.89% for Kcore and KLog thus validating the model to be used for predictions in un-cored intervals. Through Windland's R35 equation it was concluded that there are two dominant pore throat sizes, meso and macro with ranges between 0.72 – 2.53 μm.
Interwell Connectivity Study
Authors: Shan Youngbin , Wu Yaoguang , Qin Minjun , Liu Dongming , Yao Bin
Published: March 2022

Understanding interwell connectivity is crucial for EOR decision making.

In 1990, K.N Wood et al proposed a method to evaluate the interwell Residual Oil using a reactive tracer and a non-partition tracer. A decade later in 2001 (Joseph Tang, 2001), Joseph Tang et al proposed a method to identify the single well near bore residual oil saturation by puff and huff approach in a single well carbonate reservoir. Today the interwell connectivity is still under research.

The objective of this paper is to propose latest study to evaluate interwell connectivity through two or more partitioning tracers to estimate the breakthrough, pore volume, sweeping channel geometry, high permeability channel, residual oil saturation, etc

Thanks to the new development in tracer technologies, today we can use two distinctive tracers to pump through injection well and collect tracers produced in all production wells. The different partition coefficients for two tracers can reveal the lag factor for the sweeping channel and further derive the statistical channel breakthrough time, pore volume, geometry, tortuosity and residual oil saturation. The theory, derivation and applications of the concepts are described in this paper.

Based on the analysis, sweeping channels statistical information can be calculated by a simple mathematical expression of the ratio of two distinctive tracer mass produced from production wells, the ratio of two tracer dynamic partitioning coefficients and the ratio of two injected tracer mass. With this information, operator can investigate a compartmentalization in the field to optimize flooding plan.

One 9-piont injection well grid were analyzed, and results are shown in this paper. Those results are important input to operators' reservoir model. It revealed the major sweeping channels and azimuths, the major residual oil channel and their azimuths, the possible tortuous channels and their azimuths which gives operator a direction of where the residual oil resides and how easy or difficult it can be recovered in tertiary oil production.

This new theory analyzes sweeping channel statistical information from produced masses of two distinctively partitioning tracers, which follows a rigorous mathematical derivation and setup a volume factor equation relating to produced masses of two partitioning tracers. The partitioning coefficient is also modified by a dynamic factor to better simulate the moving partition in channel rather than the static partitioning between brine and oil.
Automated Corrosion Log Quality Control and Interpretation Using Machine-Learning
Authors: Larbi Zeghlache Mohamed , Rourke Marvin , Liu Xiaotian
Published: March 2022

With the increasing focus on data mining and machine learning (ML) applications in the oil and gas industry, the substantial number of well integrity logs and variety of data types represent a suitable candidate for the implementation of automated corrosion log processing. Convolutional neural networks (CNNs) are used in many fields, especially for image processing and features recognition. On the other hand, genetic algorithms (GA) add a valuable benefit to data processing in terms of global search and optimization. This paper demonstrates the integration of ML techniques with legacy well integrity log data, improving the results and leading to a tangible time and cost savings.

A downhole well integrity evaluation triangle comprises three important services for comprehensive diagnosis: 1) cement evaluation, 2) corrosion inspection, and 3) leak detection. These services produce multiple datasets from a variety of logging tools. The types and sources of these datasets include synthetic data from simulation and modeling, tool calibration and lab testing, as well as raw, processed, and interpreted data. This paper describes the use of advanced ML techniques to scrutinize and improve well integrity evaluation. The new process resolves the recurrent challenges of well integrity evaluation in complex completion and downhole environments. It also maximizes value from existing well and field data.

Image features recognition enables major improvements in the data analysis, such as the identification of concentric casings and tubing as well as their respective collar depths and types. In addition, input parameters and well schematics promote quality control of recorded data versus the model data. The new process helps to identify casing and completion accessories and provides a reliable benchmark. Another major element is the qualitative and quantitative evaluation of corrosion using deep learning algorithms combined with the GA. This evaluation is achieved using feature extraction from the forward model (FM) data in an analogous way to collar identification in an electromagnetic decay image. The integration of big data and advanced ML enables an improved data analysis with automated data quality control (QC) and interpretation. A more pro-active well integrity management system will result.

Testing and validation using field examples demonstrate the benefits of this new methodology. The outcome is a better-quality answer product that helps depict various aspects of the acquired and interpreted data. Savings in time and cost are complemented with an improved and automated quality control.
Automated Seismic-Well Tie Using Dynamic Time Warping and Optimal Interpolation
Authors: Promrak Wisit , Uttareun Ratchadaporn
Published: March 2022

Seismic-well tie is a crucial process to correlate subsurface information from well logs and acquired seismic data. Traditionally, a manual seismic-well tie is conducted based on the interpreter's visual pattern recognition, which is subjective, time-consuming, and may lead to unrealistic velocity distortion. This paper presents a new method to automatically tie seismic to well using Dynamic Time Warping (DTW) and Optimal Interpolation (OI), to save man-hour and to obtain a more reliable time-depth relationship. To produce a better tie, we use DTW to seek the appropriate amounts of time stretching and squeezing to match the synthetic and actual seismic. Then, we balance the rigid pattern matching of DTW by using OI to smooth DTW results and constrain changed rock velocity to be within physical bound. The invented technique has been used to tie seismic to six exploration wells in the Gulf of Thailand. The results from the automated method are then compared with the manual method. For all wells, resulting synthetic-seismic correlations from the automated well tie are higher than the manual method by 1.6%-14.9%. Applied time shifts from the automated and manual methods are then compared. Notably, time adjustment correlations between the automated and manual well tie are considerably high, around 72%-85%, suggesting that both methods yield similar outcomes, yet the automated well tie gives a slightly higher match between tied synthetic and observed seismic traces.
A Technical Limits Weight Control Tool for Integrity Management of Aging Offshore Structures
Authors: Ng Sok Mooi , Khan Riaz , Lee Luong Ann , Isnadi Biramarta , Tang Horng Eng , M Salleh Fahima
Published: March 2022

This paper presents a digitized Technical Limits Weight Control (TLWC) tool for integrity management of existing fixed offshore structures. The tool is currently integrated into PETRONAS's Structural Integrity Compliance System (SICS), which is a web-based application for Structural Integrity Management (SIM) of fixed offshore structures.

The main capability of the TLWC tool is to allow a user (engineer/ manager) to determine the fitness for purpose of an offshore facility quickly and efficiently for the inclusion of additional topsides loading at various locations on the platform decks. The tool makes use of global ultimate strength analyses results which considers the acceptance criteria for each operating region in Malaysian Waters. The limits of these criteria are used to create contour boundaries, which depending on Centre of Gravity (CoG) shifts due to topsides loading patterns.

The tool can provide a high-level check on platform suitability for such increase in loading to cater for further development or operational needs. This quick check enables decision making at early engineering stage without the use of further elaborate and costly analyses. The contour plot shown in the results indicates the limiting acceptable Reserved Strength Ratio (RSR) for that platform.

The TLWC decision-making tool is ideal for practicing offshore structural integrity engineers in operations to make a quick decision with regards to addition of topside loading for platforms with marginal reserved strength. Detailed assessment is required to address potential local member overstress for the topside structural members.
Advanced Inspection Technologies for Corrosion Underneath Splash Zone Riser Hangers and Clamps
Authors: Ruangpattanatawee Kamonwan , Laemkhowthong Chatchai , Thepsriha Suthisak , Promsakulchai Sorakom , Thammachart Matina , Limsakul Chanapol , Lertthanasart Athipkiat
Published: March 2022

Corrosion underneath riser hangers and clamps in the splash zone area is historically challenging for inspectors. It is a chronic problem for offshore pipeline operators which could lead to significant failures and loss of primary containment. When the degradation of the protective coating system occurs, it will result in external severe corrosion. The splash zone riser is exposed to intermittent seawater wetting. Especially at crevice areas which can form and accelerate small concentration corrosion cells creating indiscernible localized corrosion or deep grooves.

Close visual inspection (CVI) is a conventional nondestructive examination (NDE) technique to notify a sign of corrosion. This is a very subjective and qualitative measurement. Wall loss, depth, and sizing are unknown. In order to identify the condition underneath the riser clamp without clamp removal, the company has studied the principle of advanced NDE techniques, mockup tests, and field trials. The main objective is to identify, quantify, and prioritize the severity of corrosion anomalies underneath the clamp for further maintenance and repair plans to prevent pipeline failure. The selected techniques are Computed Radiography Testing (CRT), Medium-Range Ultrasonic testing (MRUT), and Long-Range Ultrasonic testing (LRUT). The result shows that LRUT can be further developed to suit the company's purposes.
Successful Development and Deployment of a Global ROP Optimization Machine Learning Model
Authors: Robinson Timothy S. , Batruny Peter , Gomes Dalila , Hashim M. Meor Hakeem Meor , Yusoff M. Hazwan , Arriffin M. Faris , Mohamad Azlan
Published: March 2022

Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application.

A drilling dataset was gathered from exploration and development wells in both onshore and offshore operations from a variety of fields and regions. The wells were curated to have different water depths, down hole drive such as Rotary Steerable System (RSS), PDM, Standard Rotary, bit types (Mill Tooth, TCI, PDC) and inclinations (vertical or deviated). A deep neural network was used for modelling the relationship between ROP and inputs taken from real-time surface data, such as Torque, Weight-on-Bit (WOB), rotary speed (RPM), flow and pressure measurements. The performance of the ROP model was analyzed using historical data via summary statistics such as Mean Absolute Percentage Error, as well as graphical results such as residuals distributions, cumulative distribution functions of errors, and plots of ROP vs depth for independent holdout testing wells not included in the model fitting process. Analysis was done both in aggregate, and for each specific well.

The ROP model was demonstrated to generalize effectively in all cases, with only minor increases in error metrics for the holdout test wells, where the Mean Absolute Percentage Error averaged across wells was ~20%, compared to 17.5% averaged across training wells. Furthermore, residuals distributions were centered close to zero, indicating low systematic error. This work proves the case for a "global" ROP prediction model applicable "out-of-the-box" to a broad set of drilling operations.

A global ROP model has the potential to eliminate learning curves, reducing time and costs associated with having to develop a new model for every field. Furthermore, a model that effectively captures the relationships between parameters controllable by drillers and ROP can be used for automatically identifying drilling parameters that improve ROP. Preliminary field-testing of the ROP optimization system yielded positive results, with many examples of increased ROP realized after following drilling parameter recommendations provided by the software.
Sustainability Aspects for the Offshore Sector - Bridging Operations, Carbon Accounting and Esg Principles
Authors: Soultanias Ilias , Sarvaiya Jatin , Bose Aditya , Koutsourakis Panos , Plevrakis Georgios
Published: March 2022

With climate change as a transformative catalyst, the Offshore sector, which has long been a main pillar of the energy industry, is expected to undergo significant transformation in the coming years. Unique challenges associated with the fossil fuel products and their environmental impact are expected. Despite the limited and localized regulatory framework, the main drive towards sustainability is coming from the financing sector and other key external stakeholders.

Embracing sustainability pertains to a series of initiatives and actions around: Benchmarking environmental footprint of the assets and operationsDesign, Engineering, Procurement & Construction and subsequent Operations following certain sustainability criteriaIntroducing Environmental, Social and Governance reporting

This article elaborates on the structured approach of Sustainability principles and initiatives on the highly carbon intense offshore oil & gas industry.
Application of Machine Learning to Augment Wellbore Geometry-Related Stuck Pipe Risk Identification in Real Time
Authors: Othman Eswadi Bin , Gomes Dalila , Tengku Bidin Tengku Ezharuddin Bin , Meor Hashim Meor M. Hakeem , Yusoff M. Hazwan , Arriffin M. Faris , Ghazali Rohaizat
Published: March 2022

Wellbore geometry stuck pipe mechanism occurs when the string and the well are incompatible with each other. This issue is commonly related to changes in hole diameter, angle, and direction associated with symptoms such as mobile/swelling formation, undergauged hole, key seating, ledges, and high doglegs. An internal study identified that many stuck pipe incidents were associated with mechanical sticking, specifically wellbore geometry sticking with high-cost impact, which warrants proactive prevention. Throughout this paper, we provide and demonstrate how machine learning solutions can foresee the potential stuck pipe related to wellbore geometry issues based on two signs: hookload signature and dogleg severity. The application is based on the Artificial Neural Network (ANN) approach that reads the surface parameters sequence of hookload real-time data and learns with historical wells data. Machine learning (ML) then determines how the hookload behaves for each type of activity (tripping and drilling). The machine learning predictions can then be streamed on a web-based application accessible to the operations and project team. The neural network design for hookload prediction while tripping in/out considers a drag when the string moves towards a region with doglegs severity higher than the threshold chosen based on engineering judgment. This paper also discusses applications beyond real-time estimation, such as predicting the trend of the few subsequent expected hook loads up to 6 to 10 stands ahead based on case studies from previous live wells obtained from the real-time monitoring center where the product is used. The output from the machine learning solution provided a basis for risk identification and further analysis by the monitoring specialist in a proactive intervention effort to prevent stuck pipe incidents. The implementation of applications described in this paper could detect an early symptom of wellbore geometry issue; hence proactive action can be taken to avoid a potential stuck pipe event.
Do parenting stress, work-family conflict, and resilience affect retention intention in Korean nurses returning to work after parental leave?: a cross-sectional study
Authors: Jung Young-Eun , Sung Mi-Hae
Published: March 2022

Purpose: This study investigated whether parenting stress, work-family conflict, resilience affect retention intent in Korean nurses returning to work after parental leave. Methods: The participants in this study were 111 nurses recruited from 10 hospitals in Korea, who were working after formal parental leave from their hospitals. Collected data were analyzed using descriptive statistics, the t-test, analysis of variance, the Mann-Whitney U-test, Pearson correlation coefficients, and hierarchical regression with IBM SPSS for Windows version 26.0 (IBM Corp.).Results: Retention intention (33.80±7.78), parenting stress (101.70±17.57), and resilience (85.02±12.75) were at greater than moderate levels and a midpoint level of work-family conflict (29.63±7.00) was noted in this sample of mostly women nurses in their 30s. The factors affecting retention intent were parental leave duration, the number of times that participants had taken parental leave, health condition, work-family conflict, and resilience. The total explanatory power of these variables was 36.7%. Retention intent had a negative correlation with parenting stress and work-family conflict. Conversely, retention intent was positively correlated with resilienceConclusion: This study supports the need for flexible adjustment of returning nurses’ working hours and family-friendly policies to promote balance between work and family. It is also necessary to develop and apply measures that boost resilience and support health improvement for nurses returning to work.As nurses are often assigned to new areas of work upon return, training programs to aid their adjustment may also be helpful.
Spatial association between COVID-19 incidence rate and nighttime light index
Authors: Luenam Amornrat , Puttanapong Nattapong
Published: March 2022

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= −0.0078 and −0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= −0.0086 and −0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.
Glial and Vascular Cell Regulation of the Blood-Brain Barrier in Diabetes
Authors: Li Xiaolong , Cai Yan , Zhang Zuo , Zhou Jiyin
Published: March 2022

As a structural barrier, the blood-brain barrier (BBB) is located at the interface between the brain parenchyma and blood, and modulates communication between the brain and blood microenvironment to maintain homeostasis. The BBB is composed of endothelial cells, basement membrane, pericytes, and astrocytic end feet. BBB impairment is a distinguishing and pathogenic factor in diabetic encephalopathy. Diabetes causes leakage of the BBB through downregulation of tight junction proteins, resulting in impaired functioning of endothelial cells, pericytes, astrocytes, microglia, nerve/glial antigen 2-glia, and oligodendrocytes. However, the temporal regulation, mechanisms of molecular and signaling pathways, and consequences of BBB impairment in diabetes are not well understood. Consequently, the efficacy of therapies diabetes targeting BBB leakage still lags behind the requirements. This review summarizes the recent research on the effects of diabetes on BBB composition and the potential roles of glial and vascular cells as therapeutic targets for BBB disruption in diabetic encephalopathy.
A Basic Study on the Development of Essential Korean Academic Vocabulary Collection Using Meta-metering
Authors: Han Yunjung
Published: March 2022

In this study, a basic discussion was conducted on the development of the “Essential Korean Academic Vocabulary Collection” necessary for teaching and learning major academic vocabulary that foreign students must know for academic performance before entering their major. Since there was no official academic vocabulary list, six appropriate academic vocabulary lists were meta-metered to secure sufficient vocabulary, and the source redundancy and grade information of each vocabulary were scored and evaluated. A total of 4,475 vocabulary words were listed and documented by meta-metering, and the importance was evaluated with 1,154 vocabulary roots excluding 971 sources per person. As a result of the evaluation, 550 of 1,154 roots with 50 or more points were selected as the main heading, and roots with 40 or more points were suggested to be additionally collected as needed if possible. Next, a discussion was conducted on the microstructure setting and technical method of the essential academic vocabulary collection. It was suggested that the microstructure of the collection could be largely composed of a heading presentation part, as well as a word family and usage presentation part. This study is meaningful in that it drew a blueprint for the development of a vocabulary collection that can use academic vocabulary for actual teaching and learning based on existing research for the selection of academic vocabulary. In particular, it is meaningful in that it has dealt with the areas that should be considered when it comes to actual development, and has also increased the possibility of this by analyzing and proposing plans from various angles.
A Study on Actual Situations of ‘The Late Generalization’ in Japanese Universities－Focused on Cases of the University of Tokyo
Authors: Kim Kyoung Hee
Published: March 2022

In the midst of the tremendous social changes brought about by the era of the 4th Industrial Revolution as well as that of A.I. (Artifical Intelligence), a crisis in theory is now emerging within universities. As seen by the changes and reforms undertaken in education by universities to prepare their students for the future society, there have been attempts to reorganize the education system, especially within the liberal arts. Moreover, the importance of the general education at universities is receiving new attention. In this study, I will analyze and contemplate the liberal arts oriented curriculum found at the University of Tokyo. The background of the introduction of ‘The Late Generalizaton’ curriculum and its completion course will be investigated and reported on. The University of Tokyo made the liberal arts oriented education take a lead role in its reforms based on the liberal arts ideology and has been reinforcing its general education faculty to meet its goal of raising talented people to solve global issues in a rapidly changing world. In particular, by practicing ‘The Late Generalization’ not just for entry level students, but also for second, third and fourth year students, (and in fact even for graduate course students), the needs for liberal arts education to develop an inter-disciplinary way of thinking is being emphasized more and more. Also, by implementing an organic integration of liberal arts and major subjects, a new model of liberal arts education is being suggested. With such emphases being placed on liberal arts education in Japanese universities, even in the case of higher level students, liberal education has considerable implications for Korean university education. Referring to organic fusion education, which crosses the liberal arts with the natural sciences, I will attempt to identify issues on liberal arts education in Korean universities, and will also try to suggest the possibility of The Late Generalization (high level liberal arts education) for qualitative improvements of our liberal arts education.
Psychologies of Food Choice: Public views and experiences around meat and dairy consumption
Authors: Connors Caitlin , Cohen Melanie , Saint-Warrens Sam , Sissoko Fan , Allen Francesca , Cerasale Harry , Halonen Elina , Afonso Alves Calistri Nicole , Sheppard Claire
Published: March 2022

This report presents findings drawn from qualitative remote ethnography research with 24 UK participants conducted during July and August 2021, plus nine peer-to-peer interviews conducted by main sample participants with their friends and family. This research aimed to build on existing evidence in this area to fill gaps and provide an up-to-date snapshot of UK public experiences. Areas of focus included: Motivations for dietary choices Any gaps between consumer intention and behaviour Trade-offs and contextual differences (e.g. in vs. out-of home behaviours) The roles of specialist diets, substitution approaches, alternatives and ‘imitations’, locally/UK sourced meat and dairy, socio-demographics, culture and family Impact and role of food labelling and terminology The sample represented a range of variables including age, gender, nationality (England, Wales, Northern Ireland), urbanity/rurality, lifestage and household composition - and dietary profile (carnivore, ‘cutting down,’ vegetarian, vegan). This report was informed by an evidence review by the University of Bath on the factors underpinning the consumption of meat and dairy among the general public.
Modern challenges of air transport»: safety, regulations, operations and infrastructure
Authors: Bugayko Dmytro , Ierkovska Yuliya , Aliyev Fariz , Bugayko Danylo
Published: March 2022

Openness to the negative consequences of global, regional and national financial crises, the tragic events associated with the activities of terrorist organizations and, first of all, the global COVID 19 pandemic lead to the fact that the issues of efficiency and economic feasibility are among the priorities in solving the challenge of maintaining an appropriate safety level. In fact, the philosophy of aviation safety is changing from an understanding of its purely technical and technological component and extends to determining the severity of risks to property, life and health of people, the environment, financial security, and legal liability of enterprises have shocked aviation transport. At the same time, harmonization, integrity and operational interchangeability of the industry's integrated security system are achieved; the worldwide exchange of safety-related information; global systemic hazards are identified and eliminated at an early stage. The effective management of aviation safety requires a systematic approach to the development of policies, procedures and recommended practices. Safety management integrates different types of aviation activities into a single whole. The main challenges of Ukrainian air transport in the context of the globalization of the air transportation market are the annexation of Crimea, the armed conflict in the east of Ukraine, the tragedy of the Boeing-777 of Malaysian Airlines, the closure of airspace over the vast territory of Ukraine, the refusal to fly over the Russian Federation, the tragedy of the Boeing-737 of International Airlines Ukraine” in Iran and, to a greater extent, the COVID-19 pandemic. These challenges are reflected in almost all macro, meso and microeconomic indicators of the activity and infrastructure of air transport. However, air transport is not only an open system, but also an adaptive system. Modern challenges of its activities indicate the need to develop strategic scenarios and develop a mechanism for institutional support for further sustainable development, taking into account the dynamic external environment, constant changes in the structure and intensity of threats and uncertainties.
An Exact Method for a Class of Self-Similar M/Ga/1/K Queueing System of Internet Stream
Authors: Popoola Jumoke , Popoola Olusogo J. , Olaniran Oyebayo R.
Published: March 2022

In Internet stream monitoring, computing the expected parameters and distributions for the stream process under self-similarity and long-range dependency requires appropriate and exact methods queueing analyses to obtain performance of the Internet. Our method avoids fitting a non-heavy tailed distribution to Internet stream data by specifically fitting Gamma distribution to its service time process, which enhances the features of correlated events that explain self-similarity and long-range dependency compared to the classical memoryless assumptions of standard Poisson arrival and Exponential service. We propose the specific expectations of the parameters for a class of M/Ga/1/k queueing system (‘M’ represents a Markovian arrival process that follows Poisson distribution, ‘Ga’ represents a Gamma distribution service process, ‘1’ represents a single server, ‘k’ represents the buffer size) in an exact closed-form derivations using the Pollaczek Khintchine formulae alongside the Laplace transform to obtain the performance of   queue. The adequacy of the proposed model was confirmed using simulated data of Internet traffic. Keywords- Gamma distribution, Internet stream, M/Ga/1/K model, Self-similarity, heavy-tail distribution
Heavy Metal Pollution Levels of Hand-dug Wells in Close Proximity to Septic Tanks
Authors: Ojo Ochuko M.
Published: March 2022

The aim of this study is to evaluate the levels of selected heavy metal contamination in well water located in close proximity to septic tanks in the South gate area of the Federal University of Technology, Akure (FUTA). Water samples were collected from five (5) randomly selected hand-dug wells in the study area within 50ft (15.4m) distance to septic tanks. The physical attributes of the wells revealed that the wells were well constructed and all the wells sampled were lined with precast concrete pipe (PCP) rings with covers. Five heavy metals namely Cadmium (Cd), Chromium (Cr), Iron (Fe), Manganese (Mn) and Zinc (Zn) were analyzed in the laboratory using Atomic Absorption Spectroscopy. Cd, Cr and Mn were present in high concentration in some of the water samples evaluated exceeding the WHO permissible limit. However, Fe and Zn contents of the water fell within WHO standard for potable water. Secondary water treatment is recommended before human consumption. Keywords— heavy metals, potable, septic tanks, treatment, water samples.
Evaluation of Granite Production and Market Structure for the Improvement of Sales Performance in Ondo and Ogun States, Southwest Nigeria
Authors: Taiwo Blessing O. , Melodi Mbuyi M. , Ajayi Itunu O.
Published: March 2022

Poor marketing infringes the rate of production and supply of quarry end products and limits the supply rate between the producer and the industrial users. This study aimed at assessing the structure, conduct, and performance of the granite marketing system in Ogun and Ondo, recognizing and characterizing the opportunities and constraints on market participants which influence its performance. Ten granite quarries located in the southwest, Nigeria were considered in this research. The granite quarry sales and production information was derived from both primary and secondary sources. Descriptive statistics in analyzing the data collected from granite traders in the study areas. Descriptive statistics, ratios, percentages, means, and standard deviations were utilized to compare the socio-economic and institutional characteristics of the granite product traders in the two case studies. The connection between the granite sales market design, direct, and execution was analyzed utilizing the structure-conduct-performance model. The Gini coefficient and Lorenz Curve analysis indicated that the granite market in Ogun state is highly competitive for both the producers and wholesalers, and it is not controlled by a few traders. It is therefore recommend that government and private sector should promote processing which involves making financing facilities available so that the private sector will be encouraged to invest in processing activities, and that government should also provide an incentive to the private sector to continue improving the state of technology processing.Keywords— granite, Market structure, Nigeria, Quarry aggregates, production evaluation, Structure-Conduct-Performance model
Adil Samara, um capítulo à parte na reumatologia
Authors: Fajardo Estafania
Published: March 2022

Pensava em dedicar-se à anestesiologia até que um dia conheceu o professor Israel Bonomo, que lhe contou sobre a recente descoberta da cortisona. Daí em diante sua vida esteve ligada ao tratamento das enfermidades reumáticas.
REKAYASA PERANGKAT LUNAK PENGARSIPAN DIGITAL DOKUMEN ACCOUNTING FINANCIAL DENGAN PENDEKAN METODE MVC
Authors: Nasri Edy , Hendra Yul , Farezi M Nur
Published: March 2022

In the activities of document interception has an important role in the processing of document archiving in an agency or company and is used in supporting administrative processes or other activities. If the archiving of documents owned is not well managed can lead to difficulty finding the information that has been stored and ultimately can hinder the next stage of the work process. In the practice of paper-based records management, many problems are faced, thus giving space to electronic media as an alternative in archiving management. In archival terminology, electronic media is grouped as a new media archive, one form of storage of new media archives is digital media. Digital media usually requires tools in the form of computers, because it cannot be read directly, can be processed in computer programs and stored in digital data storage media. In This research discussed how to design, implement the system and maintenance and security of documents so as to avoid damage. This Software Engineering was created with MVC approach by using Framwork Codeigniter with the aim of archiving this digital document done to help and prevent a very long process in the storage and search of a document needed in a fast, precise and detailed time