bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling the Pressure Transverse for Foam Drilling Operation in Vertical Well
Authors: Fadairo Adesina , Adeyemi Gbadegesin , Ling Kegang , Rasouli Vamegh , Iroko Adedayo , Olayinka Samuel Babatunde , Gbadamosi Olumide
Published: August 2021

Pressure transverse in foam drilling operation is sensitive and difficult to predict particular at the start of flow that follows the unavoidable shut in due to inevitable procedure of stop and proceed arising from re-connection of additional drilling pipe to further drill depth. The practice in drilling may not enable the flow to attain steadiness flow region before running in the length of drill pipe. Most existing models in the literature for predicting pressure transverse in foam drilling operation only captured the steadiness flow region of the foam drilling operation by keeping out restriction terms induced by accumulation and kinetic for simplicity sake, hence unsteadiness flow region experienced during foam drilling operation was rarely modelled. It is highly expedient to derive a model that evident the unsteadiness region in order to accurately predict pressure transverse, hence sufficiently analyses the well stability during foam drilling operation.

In this study, a model for forecasting pressure transverse in foam drilling operation was established considering restriction term caused by accumulation and kinetic that constitute for accurate formulation of hydraulic model that govern flow of foam during underbalanced drilling. By applying the proposed model to a case study reported in literature, pressure transverse at unsteadiness flow region for foam drilling operation can be quantitatively estimated and analyzed. The result obtained in a case study carried out indicates high variance in pressure as function time at the beginning of flow in foam drilling where unsteadiness is promoted before matching up closely with the results obtained from the existing Guo et al 2003 model at the steadiness flow region. The new model has a better accuracy with a percentage error of 0.74% and 6.4% as compared to previous models by Guo et al 2003. The proposed model make possible for drilling engineer to take decision with larger precision during hydraulic design of foam drilling operation and guaranteeing well stability in complex drilling system.
Assessment of Conflict Management in Niger Delta and Implications for Sustainable Development of Oil and Gas in Nigeria
Authors: Oruwari Humphrey Otombosoba
Published: August 2021

Nigerian oil and gas industry have over the years witnessed incessant conflicts between the stakeholders, particularly the host communities in Niger Delta region and the oil and gas companies in partnership with the Federal Government. Conflict which is here defined as manifestation of disagreement between individual and groups arising from differing and mutually incompatible interests has both positive and negative effects depending on how it was managed. Managing conflicts is all about limiting the negative aspects. The study examined conflicts management in Nigeria oil and gas industry and how best the positive elements of conflicts can be maximally exploited for the mutual benefit of both oil and gas company and the host communities in Niger Delta. The study adopted the multidisciplinary approach, literature review, case study and relied on secondary sources using analytical method of data analysis. The study findings revealed that the major factors that precipitate conflicts between the oil and gas industry and host communities in Niger Delta include economic, social, political, and ecological factors. There are available strategies that can be used in conflict management. These include avoiding, accommodating, or smoothing, competing, or forcing, compromising, and collaborating. Any of these strategies can be used to manage conflict depending on the situation, the environment factor, and the nature of the conflict. The problem is that the oil and gas companies in partnership with the Nigerian government often adopted the wrong approach in dealing with the conflict with host communities, using avoiding or forcing strategies. The study recommends collaboration strategy which ensues long term-term solution to mutual benefits.
Impact of Public Health on Oil Production Operation Expenditure – Case Study: Covid-19 Era Expenses in Nigeria Oil & Gas Industry
Authors: Ugolo Jerry Obaro
Published: August 2021

Oil price is primarily determined by global supply and demand forces as well as governments policies and action or inaction of institutions like OPEC. However, in recent times, it has become evident that public health is a vital factor influencing demand and in turn oil price. In US, oil price reached a negative value for the first time in history by April 2020.

Personnel and public health have been shown to have profound effect on operational expenditure (OPEX) of organizations, this in turn affecting the profitability of such organizations. Extra measures involving cost, had to be taken by organizations all over the world to ensure health and safety of their personnel in their sites. In Nigeria, effect of covid-19 measures for companies were, shut in of production, declaration of force majeure on ongoing contracts, slashing of costs, suspension on evaluation of future projects, profile assets for sale, remote/tele working, etc. Huge costs were also incurred as a part of corporate social responsibility for host communities/states where they operate. The consequential outcome is that there are reports of lower than planned profitability and liquidity positions.

This paper examines action taken during this covid crisis and their impact on the financial status of their organizations. Using a quantitative and descriptive research design, an online survey has been used to gather information from respondents from different oil and gas companies of cost incurred by them. Secondary data was also obtained from quarterly reports of some companies of the oil majors to show their profitability comparing Q1-Q4 of 2019 and 2020. The paper also appraises action and inaction by corporate/government bodies to stimulate economic growth and help its personnel/citizenry. An attempt is also made to glean experience and lessons from organization that lived through the periods being examined.
Control to Assurance – A Re-Imagined Health Safety and Environment HSE Leadership in the Oil and Gas Industry
Authors: Okenyi Kingsley , Ogbodu Ejiro , Apena Abayomi , Olagunju Olalekan
Published: August 2021

The importance of HSE in oil and gas activities especially in drilling operations cannot be overemphasized. Over the years, many systems of how to reduce and eliminate HSE challenges in our operations have been implemented. These systems keep changing to meet goal zero target.

Despite the implementation and the published advances in HSE approaches and tools, HSE performance continues to be at least one incident above target which is Goal Zero. This paper is focused on identifying if the Best Value Approach (BVA) can be modified into a HSE leadership model to help HSE leaders, frontline barrier leaders with delivering GOAL ZERO.

This paper specifically focuses on identifying the unique practice of the BVA that has generated a significant amount of documented high-performance results in the procurement of services and project management. This paper will translate this proven performance to HSE leadership. The paper will match this theory with a current HSE leadership practice of Assist and Assure in Shell. The aim, match theory to practice and then to theory and then produce a concept that will help HSE leaders optimise the implementation of the Assist and Assure Process.
Integrated Approach to Produced Water Disposal Management in a Brown Field: Safeguarding Production, Reducing Cost, Managing Deferment & Reducing HSSE Exposure
Authors: Ogbunude Basil , Obot Aniekan , Sa'ad Abdul-Wahab , Maxwell-Amgbaduba Sunday , Agbor Etta , Tafida Maryam , Okereke Onyedikachi , Mude Jonathan , Gogo Oforiokuma
Published: August 2021

Often, the production of oil and gas from underground reservoirs is accompanied by produced water which generally increases with time for a matured field, attributable to natural water encroachment, bottom water ingress, coning effect due to higher production rates, channeling effects, etc. This trend poses a production challenge with respect to increased OPEX cost and environmental considerations of treatment/handling and disposal of the produced water considering the late life performance characterized by low reward margins. Hence, produced water management solutions that reduce OPEX cost is key to extending the field life whilst ensuring a positive cash flow for the asset.

SK field is located in the Swamp Area of the Niger Delta, with a capacity of 1.1Bcf gas plant supplying gas to a nearby LNG plant. Oil and gas production from the field is evacuated via the liquid and gas trunk lines respectively. Due to the incessant tampering with oil delivery lines and environmental impact of spillage, the condensate is spiked through the gas trunk line to the LNG plant. Largely, the water/effluent contained in the tank is evacuated through the liquid line. Based on the availability of the liquid line (ca. 40%-60%), the produced water is a constraint to gas production with estimated tank endurance time (ca. 8 days at 500MMscfd). This leads to creaming of gas production and indeed gas deferments due to produced water management, making it difficult to meet the contractual supply obligation to the LNG plant.

An interim solution adopted was to barge the produced water to the oil and gas export terminal, with an associated OPEX cost of ca. US$2Mln/month. Upon further review of an alternate barging option, this option was considered too expensive, inefficient and unsustainable with inherent HSSE exposure. Therefore, a produced water re-injection project was scoped and executed as a viable alternative to produced water management. This option was supported by the Regulators as a preferred option for produced water management for the industry.
Data-Driven Insights from Nigeria's Natural Gas Data Using PowerBI
Authors: Adejola Adenike , Iledare Omowumi , Nnadili Paraclete
Published: August 2021

Each year, the Nigerian gas industry churns out big data on all channels of its value chain. The data is collated, analyzed, and reported by government agencies, corporate companies, institutions, and even academia. Some of these reports are the NNPC and DPR annual oil and gas reports. The annual oil and gas reports contain data tables, charts, and data driven insights. Considering the growing uncertainty in business intelligence triggered by the COVID-19 pandemic and the fast-paced 4th industrial revolution, the future of data reporting, analyzing, and presentation is also experiencing a new normal. Oil and gas stakeholders desire quick data-driven and actionable insights to reduce business risks caused by the impacts of these key drivers. This article explores and presents the use of Power BI on Nigerian gas data from 2000 to 2018. It extracts data on demand, production, utilization, gas flare volumes, export, current infrastructure capacity, domestic gas supply, and other relevant data categories. The collated data is developed into a dataset by appending and merging tables from the different reports. This data is prepared, and model relationships are created to answers questions on demand, production, infrastructure, and sustainability of the Nigerian Gas market. Empirical results show that new insights can be obtained from the dataset using new tools and a thoughtful data design process. These insights are presented on a dashboard where key takeaways for quick business decisions and policy implementations are easily assessed. The method is proposed as the future of annual energy reporting. It is also a continuous improvement process that can be applied by all oil and gas stakeholders in their data architecture.
Emotional Intelligence: A Modern Approach to Health and Safety in the Oil and Gas Industry, Effects of Covid-19 Pandemic in Work Environments, and Solutions to Enhance Work Deliverability - A Case Study of Nigeria
Authors: Osuala Justice , Obuh Matilda
Published: August 2021

Assurance of reliable public health and safety of/in work environments patently depends on the Emotional Intelligence (EI) of workers. EI, which is the ability to understand and regulate emotions of one-self and others, is one of the uncommonly utilized strategies to optimizing deliverability in workplaces. Work pressure can never be overemphasized, as when not managed effectively, can retard compliance to industry set rules, integrity, and values, as well as distort deliverability of workers. The oil and gas sector were not left out in the impact of the COVID-19 pandemic which did not only affect the economy of the global oil market, but also, jeopardized the health and safety of its workers and the environment at large. This paper highlights Emotional Intelligence (EI) as a method to curb the negative effects which the pandemic has caused. It further positions Affability as a must-have skill for oil and gas workers for the purpose of assuring health and safety in work environments. Conceptual and critical thinking processes were utilized to introduce and prioritize Affability as an aspect of EI to proffering solutions to threats regarding safe and workable environment in the industry. Using SPSS, a comprehensive analytical result was generated for 102 oil and gas workers in Nigeria, who took an Emotional Intelligence test compiled by Global Leadership Foundation. A mean of 45.35% was calculated with 57% of 102 scoring below %50. A critical observation indicates that, EI among oil and gas workers was unreliable. This is due to unawareness to effectiveness of developing affable skills to combat safety and health threats in work environments. In an informative way, this paper introduces Affability as a core aspect of Emotional Intelligence and its effectual contribution to promoting a healthy and safe oil and gas work environment.
An Approach for Exploring the Application of the Flow Zone Index Concept in Describing Petrophysical Properties Using Available SCAL Data Relative Permeability
Authors: Enaworu Efeoghene , Pritchard Tim , Davies Sarah J.
Published: August 2021

This paper describes a unique approach for exploring the Flow Zone Index (FZI) concept using available relative permeability data. It proposes an innovative routine for relating the FZI parameter to saturation end-points of relative permeability data and produces a better model for relative permeability curves. In addition, this paper shows distinct wettabilities for various core samples and validated functions between FZI and residual oil saturation (Sor), irreducible water saturation (Swi), maximum oil allowed to flow (Kro, max), maximum water allowed to flow (Krw, max),and mobile/recoverable oil (100-Swi-Sor).

The wettability of the core samples were defined using cross-plots of relative permeability of oil (Kro), relative permeability of water (Krw), and water saturation (Sw). After classifying the data sets into their respective wettabilities based on these criteria, a stepwise non-linear regression analysis was undertaken to develop realistic correlations between the FZI parameter, initial water saturation and end-point relative permeability parameters. In addition, a correlation using Corey's type generalised model was developed using relative permeability data, with new power law constants and well defined curves. Other parameters, including Sor, Swi, Kro, max, Krw,max and mobile oil, were plotted against FZI and correlations developed for them showed unique well behaved plots with the exception of the Sor plot. A possible theory to explain this unexpected behaviour of the FZI Vs Sor cross plot was noted and discussed.

These derived functions and established relationships between the FZI term and other petrophysical parameters such as permeability, porosity, water saturation, relative permeability and residual oil saturation can be applied to other wells or reservoir models where these key parameters are already known or unknown.

These distinctive established correlations could be employed in the proper characterization of a reservoir as well as predicting and ground truthing petrophysical properties.
Rock Property Cross-Plot Analysis and Post-Stack Acoustic Impedance Inversion for Optimal Reservoir Characterization in Aplha Field, Onshore Niger Delta Basin
Authors: Azuoko George-Best , Ekwe Amobi , Emmanuel Amulu , Usman Ayatu , Ndidiamaka Eluwa , Victor Omonona , Kufre Udo
Published: August 2021

In the quest to recover by-passed hydrocarbons, extend the life of mature fields, increase hydrocarbon reserves and satiate the increasing global demand for energy, the need for robust reservoir characterization using acoustic impedance inversion continues to grow. In this study, petrophysical parameters were evaluated for two sand intervals RX2 and RX5. Detailed cross-plot analysis of robust petrophysical properties, (density, water Saturation, Lambda-rho and Mu-rho and Porosity) facilitated fluid and lithology discrimination. Well to seismic correlations and acoustic-Impedance model-based, 3-D seismic inversion was done using Hampson Russell software, while petrophysical attribute slices and event-time structure maps were extracted at two horizons - H1 and H2. Results show that RX2 is 100ft thick in Well A, ranging from 5860ft to 5960ft, and 141ft thick in Well B, ranging from 5794ft to 5935ft. Interval RX5, 71ft thick, ranges from 6447ft to 6518ft in Well A, and 88ft thick in Well B, ranging from 6447ft to 6535ft. These intervals had average densities of 2.20g/cc for RX2 and 2.23g/cc for RX5 in Well A. In well B, density values are 1.95g/cc in RX2 and 2.06g/cc for RX5. Average porosities of 25.5% and 27.5% in RX2 and RX5 respectively for Well A; 29% and 19% in RX2 and RX5 respectively for Well B were observed. Respectively, average water and hydrocarbon saturation values of 0.31Swand 0.69Shfor Well A; 0.51Swand 0.49Shfor Well B, was recorded in both intervals. From the results, the thicknesses of RX2 and RX5 conform to the standard thickness of hydrocarbon reservoirs in the study area. Furthermore, the discrimination of the reservoir contents into fluid and lithology by the cross plots, and the observations in the attribute slices indicate that the selected intervals RX2 and RX5 are viable conventional hydrocarbon reservoirs.
Predicting Oil Production Flow Rate Using Artificial Neural Networks - The Volve Field Case
Authors: George Alexander
Published: August 2021

Accurate prediction of oil production flow rates helps production engineers to detect anomalous values which in turn will provide insights about any flaws in huge oil well systems. To aid this, oil flow rate is commonly estimated using empirical correlations. However, in some cases, significant error is inherent in application of this empirical correlation and will often yield inaccurate results.

This present work aims to develop a machine learning algorithm based on an Artificial Neural Network to predict with (high accuracy) the oil production flow rate, using an open source data obtained from Volve production field in Norway. The Downhole Pressure and Temperature, Average Tubing and Annular Pressure Details, Onstream Hours, and Choke Details are used as the input parameters to the algorithm. The procedure can be considered a valid approach for its high accuracy and due to the wide acceptance of data-driven analytics in the industry today.

To develop the model structure, 70% of the data was used the training dataset, and to further evaluate the performance, 30% of the data was used to derive the mean square error and determination coefficient. An error distribution histogram and the cross-plot between simulation data and verification data were drawn.

These results show high predictability of the model and affirmed that ANN has the ability to handle large dataset and also will give a better prediction of oil flow rate when compared to the empirical correlations method. Therefore, equipping production engineers with the capacity to accurately predict oil flow rates from upstream pressure, choke size, and producing gas to oil ratio of a producing well rather than the use of empirical correlations.
Determination of Hydrocarbon Dew Point Hdp of Natural Gas – Experimental and Theoretical
Authors: Mmata Bella , Okeke Hilary , Onyekonwu Mike , Joshua Jesse
Published: August 2021

Hydrocarbon dew point (HDP) temperature is defined as the temperature at which the first hydrocarbon liquid begins to condense in a natural gas cooled at constant pressure, which is an important qualitative parameter for pipeline operators. Hydrocarbon liquid drops-out along the gas pipeline will decrease the effective cross-sectional area of the pipes causing increased pressure drop, reduced line capacity as well as system shutdowns. It is therefore imperative that the gas pipelines be operated above the hydrocarbon dew point (HDP) temperature to ensure a trouble free line; this of course requires the knowledge of the HDP for the pipeline. In this work, onsite measurement of the HDP of 5 different natural gases from different flow stations and gas processing plants in the Niger-Delta region was done using an automatic optical condensation dew point meter. Subsequently, each of the gases was collected in stainless steel Proserv bottles and taken to the laboratory for compositional analysis using gas chromatographic technique with reference to GPA 2286. In addition, the Peng Robinson (PR) and Soave Redlich Kwong (SRK) Equations of state (EOS) were used to predict the hydrocarbon dew point temperature of the gases at the flow line pressures. The Average Absolute Error (AAE) for EOS PR was 9.33% while that for EOS SRK was 14.68%. Obviously, it showed that PR EOS gave better predictions than SRK EOS. The result of this work also showed that even a 50% variation in molar contributions of the non-hydrocarbon components had negligible effects on the predicted hydrocarbon dew point temperatures for all the gas samples tested.
Design and Construction of an Air Quality Monitoring System to Mitigate Virus Spread
Authors: Ochie Samuel , Ochie Karen
Published: August 2021

Air pollution is one of the most dangerous problems we face in the world today. It causes many illnesses and diseases that affect the immune system of humans and non-human animals and is also a means of propagating the novel COVID-19 or corona virus. Temperature, humidity, and the level of carbon dioxide characterize the air quality in an environment. In addition to abnormal temperatures and humidity causing direct problems to humans like headaches, heatstroke, hypothermia and hyperthermia, death and so on, it could also cause complicated problems like the acceleration of the growth and lifespan of harmful viruses like the corona virus especially in closed spaces like on a drilling rig or a processing facility. Multiple studies show that the influenza virus, coronavirus, and many others spread from host to host faster in areas with low humidity and high temperatures and upon infection, mortality rate is higher in low humidity regions. Inhalation of toxic levels of carbon dioxide has adverse effects ranging from drowsiness to coma and even death. Despite safety measures put in place in offshore facilities, there are still cases of corona virus outbreaks, hence this study aims to combat the facilitated spread of viruses and enhancement of good air quality via the design and construction of a device that measures the temperature, humidity, and carbon dioxide levels, using a DHT11 and MQ135 sensor. The temperature, humidity and carbon dioxide levels of the living area, bedroom, kitchen, and balcony in a facility was captured by the device to determine the quality of air and characterized. The values were then compared with the expected values from a trusted website to determine the accuracy of the device. The device showed a 99.8% accuracy and passed quality check making it a recommendation to enhance air quality in facilities, houses, or offices.
Managing the Impact of in Situ Fines Migration in Production Optimization – A Case History from a Niger Delta Field
Authors: Oruambo Tamunomiete , Arochukwu Elias , Okoro Felix , Dennar Linda , Otubu Olalekan
Published: August 2021

In the oil and gas business, a key strategy of well management is the deployment of the right tools and knowledge to enable continuous and optimized production. One of such tools is Matrix acidizing - A stimulation activity designed to remove wellbore damage and improve well inflow. The ability to sustain optimal production from most wells after acidization is often hampered with further fines migration problems and this requires specialized treatment to mitigate.

WELL-001 quit production and was re-entered for a workover in 2018, to recomplete shallower on the same reservoir sand and restore production, however, post workover and subsequent clean up, the well failed to sustain flow. Two additional stimulation operations were also unsuccessful despite gas lift assistance. An Integrated review was held which identified key damage mechanisms impeding flow; deep fines migration which are not well handled by conventional stimulation recipes, emulsion and impairment from Loss Circulation Material (CaCO3 + XCD Polymer).

A Novel solution was identified which included an Ultra-Thin Tackifying Agent (UTTA) as part of the stimulation cocktail with the primary purpose of stabilizing the fines at source and preventing further migration with the flowing fluids. The treatment was deployed successfully and the well lifted immediately, achieving a rate of 800 bopd vs a planned potential of 650 bopd. The impact of this success is not only evident in production but also in resource volume estimation and booking.
Investigating the Use of Machine Learning Models for the Prediction of Pressure Gradient and Flow Regimes in Multiphase Flow in Horizontal Pipes
Authors: Isemin Isemin , Nkundu King-Akanimo
Published: August 2021

During multiphase flow, there is a variation of the physical distribution of the phases within the conduit leading to different flow regimes and consequently variation in the pressure gradient along with the flow regime, hence flow parameter is of vital importance in the prediction of flow regime and pressure gradient in multiphase flow. Analytical solutions and empirical correlations have been developed to predict the flow regime and the pressure gradient respectively. However, in this study, we seek to use supervised machine learning to make predictions taking parameters such as relative phase volume, bulk fluid flow rates, individual phase flow rates, conduit diameters, inclination, phase densities and temperature as input to the model. The data representing these parameters can be regularly updated to reflect the flow conditions in the well. The flow is composed of water, oil and air at different temperatures. The machine learning models used are Logistic Regression, Decision Trees and Principal Component Analysis. The first two is supervised and are tuned for accuracy dependent on pressure gauge readings while the third seeks to determine the parameters of greatest influence on the predicted output, the flow regime and pressure gradient.

The model is constrained to learning and making predictions for fluid production through the tubing only. The trained model shows promise for application in the industry as it allows for automation of systems used to control flow and affords a more comprehensive approach to mitigating flow problems in pipeline systems and flow systems in oilfields.
Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction
Authors: Adetifa Ololade , Iyalla Ibiye , Amadi Kingsley
Published: August 2021

Rate of penetration is an important parameter in drilling performance analysis. The accurate prediction of rate of penetration during well planning leads to a reduction in capital and operating costs which is vital given the recent downturn in oil prices. The industry is seen to embrace the use of novel technologies and artificial intelligence in its bid to be sustainable which is why this study focuses on the use of artificial intelligent models in predicting the rate of penetration. The predictive performance of three data-driven models [artificial neural network (ANN), extreme learning machine (ELM) and least-square support vector machine (LS-SVM)] were evaluated using actual drilling data based on three performance evaluation criteria [mean square error (MSE), coefficient of determination (R2) and average absolute percentage error (AAPE)]. The models were validated using the physics based Bourgoyne and Young's model. The results show that all three models performed to an acceptable level of accuracy based on the range of the actual drilling data because, although the ELM had the least MSE (1317.44) and the highest R2 (0.52 i.e. 52% prediction capability) the LS-SVM model had a smaller spread of predicted ROP when compared with the actual ROP and the ANN had the least AAPE (38.14). The results can be improved upon by optimizing the controllable predictors. Validation of the model's performance with the Bourgoyne and Young's model resulted in R2 of 0.29 or 29% prediction capability confirming that artificial intelligent models outperformed the physics-based model.
Understanding the Factors Influencing Pedestrian Walking Speed over Elevated Facilities using Tree-Based Ensembles and Shapley Additive Explanations
Authors: Banerjee Arunabha , Raoniar Rahul , Maurya Akhilesh Kumar
Published: August 2021

Accurate estimation of factors affecting pedestrian walking speed is of paramount importance for efficient operation and management of at-grade and grade-separated infrastructures (such as foot over bridges or skywalks). Understanding such factors helps in planning for better circulation of pedestrians within confined elevated passageways as well as evacuation preparedness during emergencies. The walking speed on elevated infrastructure generally depends on the microscopic factors (demographics characteristics), macroscopic factors (average flow and density), and geometric factors (obstruction, land use type, length, connectivity, and effective width). The wide variability of these factors and their impact on walking speed makes the speed prediction modeling complex. Therefore, accuracy of such models depends on accurate field data collection, identification of pertinent variables, and implementation of appropriate modeling approaches. With the increase in computational capabilities, tree-based ensembles have gained immense popularity due to their high prediction accuracy in comparison to traditional regression models. The tree-based ensembles provide better interpretable results without a huge data requirement and are able to capture the complex non-linear relationships. These properties make tree-based ensemble models better candidates for modeling pedestrian walking speed, however, exploration on the tree-based ensemble in pedestrian related research is limited. In the current study, an attempt is made to model and compare seven tree-based models (including ensembles) to suggest the best modeling approach to identify the dominating factors and accurate prediction of pedestrian walking speeds over elevated walkways. The result of the present study showed that Gradient Boosted Trees (MAE 9.27) and Light Gradient Boosted Trees (MAE: 9.96) were best in predicting walking speed over the skywalk and foot over bridge facilities, as these boosting based methods improved the weak trees (on the basis of accuracy) sequentially. The variable importance of final models was estimated using SHapley Additive exPlanations (SHAP) which revealed that walking speed was dependent on the average flow, average density, and length of the facility. Moreover, other features such as gender, age, height, and width of the facility also play a significant role in determining the pedestrian walking speeds. The identification of important variables not only provides better insight on factors that affect walking speed over elevated facilities but also provides a valuable source of information to researchers, planners, and policymakers for better designing, operation, and management of the elevated pedestrian infrastructures.
CDK10 Regulates Gastric Cancer Metastasis By Inhibiting lncRNA-C5ORF42-5 Via Phosphorylation Level of AKT/ERK
Authors: Deng Zi-Jian , Chen Dong-Wen , Chen Xi-Jie , Fang Jia-Ming , Xv Liang , Peng Jun-Sheng , Chen Shi
Published: August 2021

Background: Gastric cancer is the fourth most common malignant disease. Both CDK10 and long noncoding RNAs (lncRNAs) have been found to exert biological functions in multiple cancers. However, it is still unclear whether CDK10 represses tumor progression in gastric cancer by reducing potential targeting lncRNAs. Methods: The functions of CDK10 and lncRNA-C5ORF42-5 in proliferation, invasion and migration were assessed by MTS assays, colony formation assays, cell cycle and apoptosis assays, Transwell assays, wound healing assays and animal experiments. We used high-throughput sequencing to confirm the existence of lncRNA-C5ORF42-5 and quantitative real-time PCR was used to evaluate lncRNA expression. Then, with RNA-seq sequencing as well as GO function and KEGG enrichment analysis, we identified the signaling pathways in which lncRNA-C5ORF42-5 was involved in gastric cancer. Finally, western blotting was used to identify the genes regulated by lncRNA-C5ORF42-5. Results: Our results showed that CDK10 is expressed at relatively low levels in gastric cancer cell lines and inhibits the progression of gastric cancer cells both in vitro and in vivo. Next, based on high-throughput sequencing, we identified a novel lncRNA, lncRNA-C5ORF42-5, in the stable CDK10-overexpressing cell line compared with the CDK-knockdown cell line and their controls. Additionally, we confirmed that lncRNA-C5ORF42-5 acts as an oncogene to promote metastasis in gastric cancer in vitro and in vivo. We then ascertained that lncRNA-C5ORF42-5 is a major contributor to the function of CDK10 in gastric cancer metastasis by upregulating lncRNA-C5ORF42-5 to reverse the effects of CDK10 overexpression. Finally, we explored the mechanism by which lncRNA-C5ORF42-5 overexpression affects gastric cancer cells to elucidate whether lncRNA-C5ORF42-5 may increase the activity of the SMAD pathway of BMP signaling and promote the expression of EMT-related proteins, such as E-cadherin. Additionally, overexpression of lncRNA-C5ORF42-5 affected the phosphorylation levels of AKT and ERK. Conclusion: Our findings suggest that CDK10 overexpression represses gastric cancer tumor progression by reducing lncRNA-C5ORF42-5 and hindering activation of the related proteins in metastatic signaling pathways, which provides new insight into developing effective therapeutic strategies in the treatment of metastatic gastric cancer.
Organic and Inorganic Template Assisted Synthesis of Silica Nanotubes and Evaluation of Its Properties for Drug Targeting
Authors: Mahendran Logesh , Ravichandran Abimanyu , Ballamuraugan A.M.
Published: August 2021

Mesoporous silica network nanotubes were fabricated using both organic and inorganic templates such as citric acid (CA), cetyltrimethylammonium bromide (CTAB), and sodium bicarbonate (SBC). The phase analysis of synthesized silica network was confirmed by X-ray diffractometer (XRD) analysis, and the present functional groups were revealed by Fourier Transformer Infrared Spectroscopy (FTIR) and the formation of tubular morphology was analyzed by Transmission Electron Microscopy (TEM). The mesoporous nature of each template sample was studied using Brunauer–Emmett–Teller (BET) instrument. The surface area and porous size were calculated successfully for fabricated silica network nanotubes.
A Novel Nomogram for Predicting Cancer-Specific Survival in Women with Uterine Sarcoma: A Large Population-Based Study
Authors: Li Yuan-jie , Lyu Jun , Li Chen , He Hai-rong , Wang Jin-feng , Wang Yue-ling , Fang Jing , Ji Jing
Published: August 2021

Background: Uterine Sarcoma (US) is a rare malignant uterine tumor in women with aggressive behavior and rapid progression. The purpose of this study was to perform a comprehensive nomogram to predict the cancer-specific survival (CSS) for US based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods: Retrospetive population-based study was conducted using the data of patients with US between 2010 and 2015 from SEER database. They were randomly divided into a training cohort and a validation cohort in a 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, nomogram was established to predict the patients’ CSS. The discrimination and calibration of the nomogram were evaluated by concordance index (C-index) and the area under the curve (AUC). Finally, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.Results: A total of 3861 patients with US were included in our study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognositic factors. In our nomogram, pathology grade has the highest risk on CSS in US, followed by age at diagnosis, then surgery status. Comparing to the AJCC staging system, the new nomogram showed better predictive discrimination with higher C-index in both training and validation cohort (0.796 and 0.767 vs0.706 and 0.713, respectively) . Furthermore, AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.Conclusion: Our study validated the first comprehensive nomogram for US which could provide more accurately and individualized survival predictions for US patients in clinical practice.
Avoiding CFRP Delamination During Abrasive Water Jet Piercing: A New Piercing Method
Authors: Popan Ioan Alexandru , Bâlc Nicolae , Popan Alina Ioana
Published: August 2021

Carbon Fibre Reinforced Polymer (CFRP) is used in top industries like aerospace, automotive or medicine. Abrasive water jet (AWJ) technology has demonstrated its capacity in machining CFRP parts with a high dimensional accuracy due to its low mechanical loading, reduced machining temperature, high productivity, reduced tooling, and environmental friendliness. An important challenge when machining composite materials with AWJ is material delamination, determined by the high-speed water jet hitting the material during the piercing process. It is the ideal tool for cutting complex CFRP parts, in cases where the piercing point is outside of the workpiece. The challenge lies in machining features where material piercing is required, like holes, slots or internal contours. This paper presents a method of piercing the composite materials with abrasive water jet, that can avoid delamination. The method requires adding the abrasive particles in the water jet at the very beginning of jet formation, thus obtaining a mixed abrasive water jet during the first impact with the composite workpiece. A new cutting system was designed and set up based on the proposed piercing method and was compared with a conventional AWJ cutting system. The insertion of the abrasive particles into the water jet was monitored by using acoustic emission (AE). An analysis of the influence of piercing parameters (water pressure, standoff distance, abrasive inlet angle and abrasive delay time) on the delamination was conducted. The process outcomes such as hole surface integrity, delamination, particles embedment, uncut fibers and dimensional characteristics, were evaluated. The results show that the method is promising in reducing delamination.
Hypoxia-mediated YTHDF2 expression and activation of the mTOR/AKT axis in lung squamous cell carcinoma
Authors: Xu Peng , Hu Kang , Sun Zhi-Gang , Zhang Nan
Published: August 2021

Background

N6-methyladenosine (m6A) is a dynamic and reversible internal RNA structure of eukaryotic mRNA. YTH domain family 2 (YTHDF2), an m6A-specific reader YTH domain family, plays fundamental roles in several types of cancer. However, the function of YTHDF2 in lung squamous cell carcinoma (LUSC) remains elusive.
Methods

Functionally, NCI-H226 and SK-MES-1 cells were exposed to hypoxia to detect the protein levels of hypoxia-inducible factor-1α (HIF-1α), endogenous YTHDF2, and phospho-AKT (Ser473) analyzed by western blotting and then the association of these proteins with LUSC was analyzed with a bioinformatics database. Next, we established stable YTHDF2 upregulation models in NCI-H226 and SK-MES-1 cells to explore the function of YTHDF2 in LUSC cells by performing in vitro and in vivo assays. Finally, we affirmed that YTHDF2 overexpression was involved in activating the mTOR/AKT signaling and inducing the EMT process in LUSC using western blotting. Clinically, immunohistochemical staining revealed the relationship between YTHDF2 expression levels and the clinicopathological characteristics of lung squamous cell carcinoma patients.
Results

The results showed that hypoxia-mediated YTHDF2, a tumor promoter, promoted cell proliferation and invasion by activating the mTOR/AKT axis and inducing the EMT process in LUSC. Moreover, YTHDF2 was closely associated with pN (pN– 37.0%, pN + 73.9%; P = 0.002) and pTNM stage (pI 50.0%, PII 43.3%, pIIIa 80.6%; P = 0.007), ultimately resulting in poor survival for LUSC patients.
Conclusion

In brief, the results highlight the critical role of YTHDF2 in both hypoxia exposure and the pathogenesis of LUSC.
Performance Analysis and Optimization of RIS-Assisted Networks in Nakagami-m Environment
Authors: Samuh Monjed H. ,   Anas Salhab1, El-Malek Ahmed H. Abd
Published: August 2021

This work studies and optimizes the performance of reconfigurable intelligent surface (RIS)-aided networks in Nakagami- m fading environment. First, accurate closed-form approximations for the channel distributions are derived. Then, closed-form formulas for the system outage probability, average symbol error probability (ASEP), and the channel capacity are obtained. Furthermore, we provide three different optimization approaches for finding the optimum number of reflecting elements to achieve a target outage probability. At the high signal-to-noise ratio (SNR) regime, a closed-form expression for the asymptotic outage probability is obtained to give more insights into the system performance. Results show that the considered system can achieve a diversity order of, where a is a function of the Nakagami- m fading parameter m and the number of reflecting elements N . Moreover, findings show that m is more influential on the diversity order than N . Finally, the achieved expressions are applicable to non-integer values of m and any number of meta-surface elements N .
LncRNA RHPN1-AS1 Inhibition Induces Autophagy and Apoptosis in Prostate Cancer Cells via the miR-7-5p/EGFR/PI3K/AKT/mTOR Signaling Pathway
Authors: Ma Xiu-Long , Ren Hong-Tao , Zhang Yang , Wang Bao-Feng , Ma Hong-Bing
Published: August 2021

lncRNA RHPN1-AS1 (RHPN1-AS1) has been confirmed to promote tumor progression in multiple cancers and is upregulated in prostate cancer (PCa), but whether it has an effect on PCa progression remains unclear. In this study, we found that PCa patients with high RHPN1-AS1 expression had a shorter survival time, and RHPN1-AS1 was significantly upregulated in PCa tissues and cells. Based on informatics analysis we predicted that miR-7-5p binds to 3’UTR of RHPN1-AS1 and epidermal growth factor receptor (EGFR) and verified it by luciferase reporter gene assay. Subsequently, we transfected PCa cells with RHPN1-AS1 overexpression vector (RHPN1-AS1), knockdown plasmids (sh-RHPN1-AS1) and/or miR-7-5p mimics or inhibitor and/or overexpression vector (EGFR) or small interfering RNA of EGFR (si-EGFR) or its control, and found that overexpression of RHPN1-AS1 inhibited miR-7-5p expression and promoted EGFR expression, silencing RHPN1-AS1 inhibited proliferation and invasion and induced G2/M arrest, apoptosis and autophagy in PCa cells. 3MA (an inhibitor of autophagy)-mediated autophagy inhibition attenuated RHPN1-AS1 inhibition-induced apoptosis. Overexpression miR-7-5p or silencing EGFR promoted LC3-I to LC3-II conversion, enhanced autophagy activity, induced cleaved-caspase-3 expression and apoptosis in PCa cells. Furthermore, we found that overexpression of RHPN1-AS1 promoted phosphorylation of phosphatidylinositol 3-kinase (PI3K), AKT and mTOR, inhibited LC3-I to LC3-II conversion and reduced apoptosis in PCa cells, while GSK2126458 (an inhibitor of PI3K) reversed the effect of RHPN1-AS1 on PCa cells. In summary, RHPN1-AS1 acted as a ceRNA of miR-7-5p to upregulate EGFR expression, silencing RHPN1-AS1 suppressed PCa tumor progression by inducing autophagy and apoptosis in PCa cells through the miR-7-5p/EGFR/PI3K/AKT/mTOR pathway.
Symmetry Breaking of PT-symmetric Solitons in Self-defocusing Saturable Nonlinear Schrödinger Equation
Authors: WenBo Bo , Dai Chao-Qing , Wang Yue-Yue , Li Peng-Fei
Published: August 2021

The symmetry breaking phenomenon of the parity-time (PT) symmetric solitons in self-defocusing saturable nonlinear Schrödinger equation is studied. As the soliton power increases, branches of asymmetric solitons are separated from antisymmetric solitons, and they coexist with both symmetric and antisymmetric solitons. The anti-symmetric solitons require different power thresholds when they are under different saturable nonlinear strength. The stronger the saturable nonlinearity is, the larger the power threshold is. The saturable nonlinear strength has obvious modulation effect on the symmetry breaking of antisymmetric solitons and the bifurcation of the power curve. However, when the modulation strength of PT- symmetric potential increases, the effect of this modulation effect weakens. The antisymmetric solitons are only stable in the low power region, and the stability of symmetric and asymmetric solitons is less affected by the soliton power. The increase of the saturable nonlinear strength leads to the increase of the critical power of the symmetry breaking. When a beam propagates in a PT-symmetric optical waveguide, the symmetry breaking of antisymmetric solitons can be controlled by changing the saturable nonlinear strength.