bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation of the Disaster Adaptation and Resilience Scale for Vulnerable Communities in Vietnam’s Coastal Regions
Authors: Nguyen Thanh Gia , Tran Binh Thang , Nguyen Minh Tu , Le Dinh Duong
Published: April 2024

Objectives: This study validated the Vietnamese version of the Disaster Adaptation and Resilience Scale (DARS) for use in vulnerable communities in Vietnam.Methods: This was a cross-sectional study involving 595 adults from 2 identified communities. The original DARS assessment tool was translated, and the validity and reliability of the Vietnamese version of DARS (V-DARS) were assessed. The internal consistency of the overall scale and its subscales was evaluated using Cronbach’s alpha and McDonald’s omega reliability coefficients. Confirmatory factor analysis (CFA) was employed to evaluate its construct validity, building upon the factor structure identified in exploratory factor analysis (EFA). Construct validity was assessed based on convergent and discriminant validity.Results: Following the established criteria for EFA, 8 items were removed, resulting in a refined V-DARS structure comprising 35 items distributed across 5 distinct factors. Both alpha and omega reliability coefficients indicated strong internal consistency for the overall scale (α=0.963, ω=0.963) and for each of the 5 sub-scales (all>0.80). The CFA model also retained the 5-factor structure with 35 items. The model fit indices showed acceptable values (RMSEA: 0.072; CFI: 0.912; TLI: 0.904; chi-square test: <0.01). Additionally, the convergent and discriminant validity of the V-DARS were deemed appropriate and satisfactory for explaining the measurement structure.Conclusions: Our findings suggest that the V-DARS is a valid and reliable scale for use within vulnerable communities in Vietnam to assess adaptive responses to natural disasters. It may also be considered for use in other populations.
El arquitecto Juan de Ochoa y la terminación del nuevo espacio litúrgico de la Catedral de Córdoba (1598-1606)
Authors: Luque Carrillo Juan
Published: April 2024

El presente trabajo reúne una serie de datos y referencias documentales extraídas del Archivo Histórico-Provincial de Córdoba, que informan sobre el proceso constructivo de la catedral, antigua mezquita cordobesa, a finales del siglo XVI.
A Study on the Barriers and Challenges for the Usage of Active Transportation in Sri Lanka
Authors: Ranawake J. , Siriwardene S.
Published: April 2024

Active transportation includes human-powered travel like walking, cycling, and skating. Active Transportation offers various benefits for users, society, and the economy in Sri Lanka. However it is not widely recognized as a primary commuting mode for daily travelers. This study mainly focuses on identifying potential barriers and potential challenges to the usage of active transportation in Sri Lanka and identifying the relationship between them especially focusing on the Colombo district. The study is limited only into the cycling because of the availability of data. The study has a quantitative approach and its cross-sectional study. A questionnaire was distributed physically and online to daily Colombo commuters for work, education and other purposes. Using convenience sampling, 384 responses were collected. The collected data were analysed by using IBM SPSS statistical tool. Most daily travelers to Colombo are males, with females fewer due to safety concerns while cycling. The majority belong to the millennial/Gen Z generation. Their trips prioritize work, followed by leisure activities. Few come for studies. The study has found four potential barriers and three potential challenges to the usage of active transportation. Identified barriers are infrastructure barriers, safety barriers, environmental barriers, and public perception about cycling. Identified challenges are costs related to cycles, air pollution, and educational aspects. It was also identified that there are negative relationships to the usage of active transportation in Sri Lanka from the above-mentioned barriers and challenges. As recommendations to future studies, it is better to look at other types of active transportation without limiting cycling focusing on the whole Sri Lankan context as this study is based only on Colombo. It will be good for future researchers can focus on the benefits and impact of potential barriers and challenges of active transportation.This study also provides some recommendations like creating new transport policies to promote cycling in Sri Lanka.
Correlation of BMI With Educational Stress By Psychometric Analysis Via Perceived Stressed Scale In Non-State University Students
Authors: Gamage B. S. , Deraniyagala N. G. I. A. , Perera K. M. D. , Aishwarya A. A. K. , Perera P. A. P. N. , Hettihewa L. M.
Published: April 2024

Overweight and Educational Stress had become two dominant problems in young population. We evaluated the effects of Educational Stress on BMI among non-state university students. Gender deference on stress and BMI, correlation between BMI and stress were determined. A google form was used for collection of data from 384 participants and stress was analyzed by Perceived Stress Scale (PSS). Majority of our study group 55.2% were female, and 44.8% were male. Mean BMI was 23.54kgm-2 (Overweight) and it was 24.70kgm-2 (overweight) in male and 22.60kgm-2 (normal) in female. As per the PSS, 52% of male participant showed moderate stress and 48% male showed high perceived stress. In female population, 50% showed moderate stress, 47% showed high perceived stress and 3% showed minimum stress. Results show that >90% of students suffer from moderate to high perceived stress and it was positively correlated with BMI (p=0.01, r=0.239) seen in both genders. Educational stress can increase the students BMI and this finding is important for an institutional intervention for better outcome of the student performances. We recommend considering the program interventions to minimize the educational stress and to reduce the high BMI related non communicable diseases in future.
On The Probabilistic Assessment of Top-Hole Casing Design
Authors: Filho C. A. F. Várady , Tenório J. K. F. , Junior E. T. Lima , Santos J. P. L. , Dias R. , Cutrim F. S.
Published: April 2024

The casing system plays a crucial role in the integrity of oil and gas wells throughout their life cycle, providing tightness, stability, and support to external loads. This paper applies reliability-based models to the design of top-hole casing sections, taking into account uncertainties associated with soil behavior and casing tubulars manufacturing. Typical load scenarios are addressed to estimate the probability of occurrence of different soil-casing system failure modes.

Reliability-based techniques stand out as powerful solutions for structural analysis and design. This work assesses soil characterization data from piezocone tests (CPTu) to statistically describe some mechanical parameters used for conductor and surface casing design. Random variables associated with the material and geometrical properties of tubulars are also considered, based on tubular manufacturing data presented in API/TR 5C3 (2018). The probabilistic models are developed by using the First Order Reliability Method (FORM), an expedited and accurate optimization-based procedure, and applied to various load scenarios to estimate failure probability in the context of top-hole casing design. Finite Element (FE) modeling is employed for the integrity analysis of the soil-casing system.

Analyses have been carried out considering the variability associated with undrained soil strength evaluated from CPTu data, as this soil strength is expected to be the most relevant random variable due to its spatial heterogeneity. Other random variables taken into account are the outer diameter and wall thickness of casing tubulars, resulting from the variability in manufacturing process. Results indicate the feasibility and relevance of the proposed FE-FORM analysis in estimating the probability of occurrence of relevant failure modes defined following the oil company’s internal regulations regarding conductor casing load capacity and surface casing triaxial stress in the non-cemented region. For the specific case studies presented, failure probabilities ranged from the order of magnitude of 10-9 to inadmissible values approaching 50%. Concerning how random variables affect the probabilistic response, it is observed that the outer diameter is not significant due to its low dispersion.

The novelty consists of considering both in-situ soil information and casing manufacturing data in a reliability-based framework that enables a more robust structural integrity analysis, supporting the decision-making process in top-hole design. This solution was implemented in the operator’s internal software and uses real data. Quantifying the soil-and-casing uncertainties by using a robust statistical-based methodology brings new information, enhancing knowledge about the variability of design parameters and its influence on the structural response.
Developing a Feasibility Assessment Strategy for Offshore Geothermal
Authors: F. Batir J. , N. Gentry E. , Boak J. , Soroush H. , Leung P. , Blacklaw D. , Zarate Luz
Published: April 2024

The author team completed a pre-feasibility study for offshore geothermal energy production. This work was performed as an exercise to build a feasibility assessment strategy focused on offshore opportunities. Presented in this paper is an overview of geothermal energy and how to convert existing oil and gas wells for geothermal energy production, review of technical considerations for use in an offshore setting, and a case study which assessed the potential for well conversion in the Gulf of Mexico to demonstrate economic viability of offshore wellbore repurposing.

We first defined an archetype well and infrastructure for assessment and then examined aspects such as infrastructure age, opportunity for well conversions on a given platform, reservoir properties, and well conversion strategies. Then, the geothermal resource potential for an offshore well and associated platform was evaluated using public data and published geologic and geothermal literature for the general Gulf Coast Basin. Next, the geothermal electrical potential was evaluated using three main variables: 1) flow rate, 2) geothermal temperature at the surface, and 3) ambient air temperature. Both temperature and flow rate estimations utilized existing public domain production data, including measured well temperatures, estimated temperature at depth, and reported production estimates. Detailed review and analysis suggest existing wells that may be or can be tied back to a common area are moderate to low temperatures, approximately 250 – 350 °F. This temperature, combined with historic production rates, is estimated to be between 300 kW and 3 MW of electricity potential, depending on repurposing methodology and total flow rate potential. Investment in the low production model (~300 kW) will most likely have an approximately flat to negative return with an average Internal Rate of Return (IRR) of 0 ±1% and Payback Period (PP) of 19 ±1.5 years. The high production model (~3 MW) will most likely have an approximately positive return with an average IRR of 10 ±2% and a PP of 14 ±2.6 years. Key assumptions that impact the economic estimates include well workover cost, power production rate, power plant cost, and operating expenses. In an offshore setting, it is important to consider long-term strategy of the existing infrastructure for developing potential financial cut-offs or maximum project timelines. The first-order power production and economic assessment example here, to our knowledge, is the first to build a systematic analysis assessment focused on a single piece of existing infrastructure for repurposing or co-production. This methodology provides sufficient information to make development decisions and can be combined with mid to long term corporate strategy, on the 5-20+ year time scale.
Generative Humane-Machine Interaction in Oil & Gas
Authors: Gonzalez Sergio
Published: April 2024

From the Acheulean stone tools in prehistory first made by the homo ergaster around 250.000 BCE (V. Smil, 2017) to the wooden plows in Mesopotamia enabling first steps to enabling machine-assisted agriculture around 4.000 BCE (V. Smil, 2017) and then the scaling of waterwheels in Greece and Rome civilizations from 100 BCE to 600 CE (V. Smil, 2017) along with following developments of windmills and blast furnaces that preceded the revolutions of steam, mechanical, combustion and electrical machines in recent history, there is one common aspect that has remained true across all civilizations: how humans interact with machines for greater efficiency, effectiveness and ultimately better quality of life.

The arrival of information technology in the 20th century transformed that interaction in many ways, starting with a dream of a small shop in Redmond, WA, which vision at its foundation in 1974 was to have a personal computer in every single household of the world and later a company from California bringing mobile phones into everyone's hands, to name a few examples out of many. This citizen-based change quickly expanded to industrial operations and machines that used to be "dumb" for ages suddenly start speaking and telling humans how they feel through basic arrangements of measurement sensors, data historians and visualization technologies.

At the moment of development of this paper we are at the end of the first quarter of the 21st century and looking back and reflecting on what has been the most significant development during this period affecting human-machine interaction, it is the author's belief that it is in the field of artificial intelligence. Artificial intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages (H.A. Simon, 1996). The goal of AI is to create machines that can perform tasks that would normally require human intelligence. (S. Russell et al., 2016) The power of cognitive and machine learning services, together with rapid advancements in robotics and autonomous systems, will significantly change the way humans interact with machines and subsequently drive impact inflection points on productivity and efficiency.

Within the artificial intelligence realm, probably one of the most exciting technological developments of today lies around generative AI, which could be briefly defined as a form of artificial intelligence in which models are trained to generate new original content based on natural language input (learn.microsoft.com). This paper focuses on how artificial intelligence in general and particularly generative AI could transform the human-machine interaction in the oil and gas industry, covering specific examples in the fields of artificial lift and power generation.
Flexible Pipe Integrity Management Strategies Applied to Lifetime Extension and Revitalization of Deepwater Brownfields
Authors: Loureiro W. C. , Vaillant A. G. , Borges B. N. , de Lemos C. D , Campello G. C. , Capella da Fonseca M. M. , Schone T. R.
Published: April 2024

The paper provides a new integrity management strategy for flexible pipes using state-of-the-art inspection and monitoring methods to gather data and a novel methodology to deal with ageing pipes that may already present some degree of damage to the tensile armor wires. The new process and integrity management methodology developed and applied to flexible pipes within Petrobras projects focused on tensile armor fatigue, enabling lifetime extension, reuse, and revitalization projects in brownfields. In order to optimize the lifetime extension process, the main failure modes for ageing flexible pipes were identified and R&D efforts were directed towards fast-tracking solutions for them. The focus was on developing tools to generate data and models to represent the end-of-life conditions of the flexible pipes. New analysis methods, based on full-scale testing of flexible pipes and years of in-field experience, were developed to better represent the behavior of ageing and damaged pipes.

Years of data from full-scale fatigue testing (tension and tension and bending) have been used, with over 60 full-scale tests in the database, along with information from historical field failures, with over 70 dissections from pipes from operation, to calibrate fatigue calculations and additional models that can predict the behavior of flexible pipes with broken armor wires. These tools use both reliability-based and novel structural analysis methodologies to identify the limit state of the pipes and estimate a time-to-failure.

Several inspection and monitoring tools were tested and developed to identify wire ruptures and provide valuable information to be used in riser integrity analysis. Various techniques have been developed to identify torsion through video imaging, however, they have limited applicability. The MODA (from the Portuguese of "Direct Optic Monitoring of the Wires") system provides information on the number of broken wires by directly measuring strain, however, retrofitting MODA may require outer sheath removal. The use of electromagnetic tools to evaluate wire strain has proven to be a reliable, albeit not as precise, method for investigating the state of wire integrity in existing pipes.

The combination of the data gathered with the new tools and the new analysis methods, allowed a more detailed risk assessment and hundreds of pipes were able to have their life extended, enabling operation of ageing assets without significant CAPEX increase. With over 740 km of pipes operating in life extension, Petrobras was already able to add over 1000 years of service life to over 400 risers that are a key part of our long-lasting assets, with a special mention to one riser that has operated for over 46 years.
Insights to Fault Displacement Hazard at an Offshore Windfarm Site
Authors: Nurminen F. , Fiorini E. , Todrani A.
Published: April 2024

Coseismic surface rupturing caused by a large earthquake has a remarkable potential for damaging the infrastructure located on or near active faults. Surface faulting hazard can be addressed by eluding the known active fault zones, but complete avoidance is not always ideal or even possible, and in these cases, probabilistic estimates of fault displacement hazard prove useful. Probabilistic analysis enables estimating the site-specific hazard that can be considered in design and cost optimization of any type of infrastructure. Here in this work, this approach is applied to a point-like structure, that can be for instance a wind farm or a platform, situated near a major fault.

Probabilistic Fault Displacement Hazard Analysis (PFDHA) allows quantification of surface rupturing hazard at possible locations of a site under development near an active fault. In this work PFDHA is applied to a site near a major fault. The scope is to see if the site-specific fault displacement analysis is aligned with the common fault zoning approaches, or if the calculations result acceptable displacement levels at minor distances to the mapped fault than the fault zoning would suggest. We apply the earthquake approach of PFDHA for estimating the fault displacement hazard at the site considering the literature-based data of the surface faulting and displacement parameters.

Different fault zoning strategies give a minimum distance that should be kept clear around active faults, but they are meant to be applied to structures for human occupancy. The results of the analysis presented here give an insight of the advantages that a probabilistic approach can provide, especially in areas of a high number of mapped surface faults or areas of major fault complexities. Considering the surface displacement hazard is essential even when the developments are not directly situated on an active fault but in their vicinity, and the probabilistic approach enables more precise accounting for fault displacement hazard than simplified safety distances. This, in some cases, may result in more efficient land use and planning.
Pile Drivability Assessment Through Rock Formations
Authors: Kadivar M. , Ghasemi P. , Christopher N. , Maynard A. , Barwise A.
Published: April 2024

This paper presents a comprehensive study on pile drivability assessment in rock formations, primarily focusing on the development and validation of an enhanced Soil Resistance to Driving (SRD) model. Utilizing a database of pile driving records from an offshore wind farm, located predominantly on Mercia Mudstone, this research introduces a novel approach in assessing pile drivability through rock formations. The SRD model, an advancement of the Stevens et al. (1982) method, incorporates modifications to better predict unit shaft and tip resistance in rock formations. The model's performance is validated using field data from several Wind Turbine locations, demonstrating its accuracy in predicting the energy required and the number of hammer blows for pile penetration in rock. This study provides valuable insights for engineers and practitioners involved in pile installation projects in similar geotechnical conditions.
CO2 Storage Complex in Shallow Waters of Campos Basin, Brazil: Capacity, Uncertainty and Risk Analysis of a Deep Saline Formation
Authors: Lira F. S. , Miranda D. , Freire E. B. , De Gasperi A. , Pinto I. A. , Cassino L. F. , Costa P. , Gonçalves M. S. , Netto P. , Pereira L. C. , Neto R. R. , da Silva P. F. , Musse A. S.
Published: April 2024

The role of Carbon Capture, Use, and Storage (CCUS) in reducing greenhouse gas emissions is crucial for achieving global climate targets. Brazil, as a significant emitter, is actively pursuing CCUS projects and the state of Rio de Janeiro, with its high emissions of industrial activities, presents an opportunity for establishing a CCUS hub. The potential storage reservoir, São Tomé Deep Saline Formation, located in the Campos Basin, offers favorable characteristics for CO2 storage. This srtdy aims to estimate the CO2 storage capacity, perform uncertainty analysis, and comprehensive risk assessments for the Sao Tome reservoir. The methodology involves mapping potential storage reservoirs, 3D geological modeling, and flow simulation to evaluate injectivity, storage potential, containment safety, and monitoring strategies. The results demonstrate a storage capacity of approximately 1 Gt of CO2, injectivity of 1.5 MMt C02/yr/well, and low leakage risk. However, further evaluation is required, including petrophysical characterization and geomechanical data. The proposal includes drilling a new well to acquire additional information and support more robust modeling. The establishment of a CCUS hub in Rio de Janeiro would bring economic, social, and environmental benefits, attracting companies seeking environmentally friendly solutions and facilitating the development of the shallow-water industry. To achieve the Net Zero Scenario by 2050, CCUS plays a crucial role in emissions reduction, and the Sao Tome reservoir presents significant potential for CO2 storage. Continued evaluation and data acquisition are necessary to ensure the feasibility of large-scale CO2 injection projects. The establishment of a CCUS hub in Rio de Janeiro would position the region as a center of environmental responsibility and attract investment and innovation in the energy sector.
Enhancing CPG-Derived CO2 Flux Measurements via a Robust Wireless Sensor Analysis Framework – A New Zealand Taranaki Basin Case Study
Authors: Katterbauer Klemens , Shehri Abdallah Al , Al-Qasim Abdulaziz , Yousef Ali
Published: April 2024

In the pursuit of more sustainable resource use, lithium has emerged as a useful resource for a range of uses, including batteries. Due to these difficulties, there is a growing need for improved field operations that preserve output levels while also making the operations more sustainable. The Fourth Industrial Revolution are greatly impacting the oil and gas sector, and as the need for lithium to be used for batteries and energy technologies grows, hydrocarbon reserves are becoming a more desirable source of these precious minerals. Lithium has been found in considerable amounts in the generated brines of a number of reservoirs worldwide. In this study, we provide a novel artificial intelligence (AI) optimization strategy for optimizing lithium recovery from reservoir operations while preserving reservoir oil production goals. A deep learning LSTM algorithm is integrated into the AI framework to estimate water injection amounts based on oil, brine, and lithium recovery. After that, a global optimization framework using the deep learning model is integrated to optimize the water injection levels in order to maximize lithium recovery while preserving reservoir oil production levels. For the purpose of recovering lithium from reservoir brine in an oil and gas reservoir, we have presented a novel AI optimization framework. The framework allows hydrocarbon recovery rates to be maintained while optimizing lithium recovery. The framework, which highlights the potential for a large improvement in lithium recovery rates from an enhanced injection procedure, was successfully demonstrated on the Volve field. This paradigm has the potential to yield substantial benefits for optimizing the use of reservoir brine, which might lead to improved sustainability in reservoir operations.
A Hybrid Physics Augmented Predictive Model for Friction Pressure Loss in Hydraulic Fracturing Process Based on Experimental and Field Data
Authors: Abdulwarith Abdulrahman , Ammar Mohamed , Kakadjian Sarkis , McLaughlin Nathan , Dindoruk Birol
Published: April 2024

During the hydraulic fracturing process, fracturing fluid leads to excessive frictional losses within the casing and formation which can be impediment to effectiveness of the operations and may result in screenout. Total pressure losses during hydraulic fracturing include wellbore pressure losses in pipe and the near wellbore pressure losses that includes both perforation and formation tortuosity losses. Wellbore friction pressure losses can be determined experimentally using flow loop test at different friction reducer concentrations and flow rates. Practically, fracturing engineers need to estimate and monitor the wellhead pressure and total friction losses during the operation and adjust the fluid, proppant, and friction reducer concentrations during the job. In this study, physics augmented data-driven models are used to predict the pressure losses in pipe using the flow loop test results and the well head pressures and total friction losses. Total friction losses used are based on field data of hydraulic fracturing stages from several wells. The flow loop test is modeled for specific friction reducer types at different concentrations and flow rates. The process of this model relies on analyzing the data obtained from the tests and establishing a correlation between the concentrations of the friction reducers, flow rate and their corresponding pressure losses. The second part is the modeling total pressure losses using field data considering fluid properties and the modeled lab results. Modeling approach incorporates all the key parameters such as fluid properties, proppant concentrations, friction reducer concentrations, slurry rates, perforation (Diameter and Density) and formation characteristics to accurately predict the frictional losses. Both models are tested using validation data. Outcome of the total friction pressure loss model and the forecasted wellhead pressure results exhibited a close agreement between the predicted and the recorded pressure values, indicating that the results can be used in actual field operations. This approach provides valuable information for optimizing hydraulic fracturing design and improving operational practices to minimize the down time. Utilization of the flow loop tests results eliminates the need for repetitive experiments, enabling parametric studies and sensitivity analysis and thus leading to more efficient operations. This unique approach, using the combination of physics-based and data-driven methodologies, represents a major step in predicting pressure losses in hydraulic fracturing operations. Integration of these models not only enhances understanding of the physical processes and the causalities, but also provides practical solutions for improving overall operational efficiency of the process.
Improving Wave Forecast Using Neural Networks
Authors: Primo B. , Achete F. , Salvio F. , Barbosa N.
Published: April 2024

Traditionally, wave forecasts are done using process-based models. These models may present inaccuracies related to their physical formulation, numerical approximations, unresolved small-scale processes, model parameterizations, or initial and boundary conditions. Recently, the combination of unprecedented large data volumes, computational power, and advances in machine learning algorithms offers opportunities to expand our knowledge of the oceanic system. In this context, the main objective of this study is to accurately predict significant wave heights, combining the results from numerical simulations and data-based models. The available data at a point in the Santos Basin, Brazil, was: 5 deterministic forecast models and 80 members from probabilistic forecast simulations and in-situ wave buoy measurement. Ensemble means and neural networks forecasts were calculated and compared to Hs forecast of individual deterministic models. Root mean square error was the chosen metric to compare the deterministic models and the data-driven forecasts. The results show ensemble means (EM) outperforming individual members for lead times higher than 24h. The neural networks forecast reduced even further the errors, especially for shorter lead times.
Data Integration Enabling Advanced Machine Learning ROP Predictions and its Applications
Authors: Wang Junzhe , Li Chengxi , Cheng Peng , Yu Jiaqing , Cheng Chris , Ozbayoglu Evren , Baldino Silvio
Published: April 2024

Predicting and optimizing the rate of penetration (ROP) is a crucial part of drilling optimization. While numerous machine learningbased ROP prediction methods exist, their practical utilization remains underexplored. This paper introduces an innovative approach, employing data integration from different sources with multiple machine learning algorithms for accurate ROP prediction. The proposed model is validated using field data and several potential applications are discussed and implemented.

The data from multiple vertical wells (real-time drilling data, insights into formation drillability obtained through coring, information related to the Bottom Hole Assembly (BHA) and PDC drill bit) are meticulously collected, screened, pre-processed, and seamlessly integrated through a data integration process. This enriched dataset then becomes the cornerstone of advanced analytics. Leveraging the strength of six machine learning-based regression models, namely the Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine Regression (SVR), Polynomial Regression (PR), and Multiple Linear Regression (MLR), a comprehensive strategy for the Rate of Penetration (ROP) prediction is meticulously implemented.

Field data from the Tarim basin in Xinjiang, China, was utilized to validate the proposed approach. Among all the methods, decision tree-based algorithms exhibit the best performance, with XGBoost standing out with a coefficient of determination (R-square) as high as 0.98, and a remarkably low mean absolute percentage error (MAPE) of 4.8%. Random Forest (RF) shows very similar results. Furthermore, both the Artificial Neural Network (ANN) and polynomial Regression demonstrate good performance, boasting R-square values around 0.93, and MAPE figures of approximately 10%. However, the Support Vector Machine Regressor (SVR) and Multiple Linear Regression (MLR) display comparatively lower scores with R-square values of 0.8 and 0.87, respectively. Regrettably, their MAPE values are considerably high at 22.39% and 19.98%, rendering them unsuitable for recommendation. Utilizing the fine-tuned ROP prediction model, practical applications such as on-the-fly ROP optimization, real-time drilling advisory systems, drill bit recommendation systems, etc. are explored and implemented. The majority of the analysis in this paper is the result of an automated data analysis pipeline, enabling a seamless deployment in future autonomous drilling operations.

This paper presents a novel machine learning approach for ROP prediction, which incorporates integrated data encompassing formation drillability information as well as information about the drilling Bottom Hole Assembly (BHA) and drill bit. This approach not only revolves around ROP prediction but also emphasizes the practical application potential of the predictive model, an aspect that received limited attention in prior research.
Data Fusion and Visualization Approaches to Achieving a Common Operating Picture During Offshore Wind Construction and Operation Works
Authors: Doolittle Daniel , Eaton Ross , Parslow Marna , Donlan Paul , Jones Craig
Published: April 2024

The offshore wind industry is promoting developments in environmental sensing, machine learning, and artificial intelligence, to better detect the presence of marine and avian species. Environmental sensing technologies (e.g., radar, video and infra-red imagery, passive acoustics, and radio telemetry) have advanced where wildlife are reliably detected and tracked, aiding their protection by minimizing conflicts with ships, other users of the ocean space, and other stressors.

Significant marine ecosystem data is collected daily offshore from a wide range of reputable sources. These disconnected sources represent, in aggregate, a trove of Domain Awareness (DA) data and if cohesively viewed, provide opportunity to better de-risk operations, protect wildlife, and avoid delays in real time. Taking care and effort to assimilate these (often disparate) data sources into common visualization platform(s) provides both more granular and macro-scale situational awareness, while advancing opportunities to apply predictive Artificial Intelligence (AI) to the data. This can result in the application of regional (or broad scale) predictions and understandings of species activities. As this data base of predictions and observations grow, additional decision making and management mitigations can be applied, such as alerting specific vessels to the presence of protected species or initiating tailored dynamic management areas (DMAs) at appropriate temporal or spatial scales.

Deployment of sensors on technically advanced host platforms, including autonomous underwater vehicles, uncrewed surface vehicles, and metocean buoys, is occurring regularly. Equally prolific are strategies to collect, analyze, and display data from each sensor, resulting in myriad data dashboards, digital twins, and immersive visualization environments offered to offshore wind developers and regulators. While accelerating technological innovation, these numerous, and often single-focus approaches can hinder the delivery of a unified picture of the worksite or regional environment, limiting conservation value of these efforts and increasing environmental and scheduling project risks.

This paper reviews some of the extant initiatives to deliver environmental data and provides a suite of best practices and recommendations for developing a DA capability or a common operating picture (COP) of developer's projects, as well as a regional view that covers multiple worksites. This work will assist developers and regulators to understand a realistic state of technical readiness and how to appropriately scope data products that support data fusion consistently across visualization platforms.
Association between allergic diseases, generalized anxiety disorder, and depressive symptoms in South Korean adolescents: a secondary data analysis of the 2022 Korea Youth Risk Behavior Survey
Authors: Lee Jaeyoung , Park So Yeon
Published: April 2024

Purpose: This study investigated the relationship between allergic diseases, general anxiety disorder, and depressive symptoms among Korean adolescents.Methods: A secondary analysis was conducted on the findings of the 18th Korea Youth Risk Behavior Survey (2022). The study included 51,850 adolescents and analyzed the relationships among allergic diseases, general anxiety disorder, and depressive symptoms using complex samples logistic regression analysis.Results: Among the Korean adolescents, 12.7% experienced general anxiety disorder, while 28.7% experienced depressive symptoms. The prevalence of allergic diseases was 5.7% for asthma, 36.3% for allergic rhinitis, and 22.2% for atopic dermatitis. General anxiety disorder was associated with asthma and allergic rhinitis but not atopic dermatitis. Depressive symptoms were associated asthma, allergic rhinitis, and atopic dermatitis.Conclusion: Examining the correlation among allergic diseases, general anxiety disorder, and depressive symptoms in adolescents underscores the need for implementing suitable strategies. Moreover, when addressing general anxiety disorder and depressive symptoms in adolescents, it becomes crucial to consider the presence of allergic diseases.
Factors influencing children’s emotional and behavioral problems perceived by North Korean refugee mothers: a descriptive study
Authors: Lee In-Sook , Jeon Jeong-Hee
Published: April 2024

Purpose: The study investigated the relationships between key variables, including the level of parenting knowledge, acculturative stress, depression, and the strengths and difficulties associated with their children’s emotions and behavior, among North Korean refugee mothers who faced the challenging task of adapting to Korean society while raising their children.Methods: A descriptive study design was used. Data were collected from 130 North Korean refugee mothers between September and November 2023.Results: The participants had very limited parenting knowledge, and 82.3% of them scored 21 or higher on the depression scale used in community epidemiological studies. Higher levels of parenting knowledge and lower levels of depression were associated with lower levels of acculturative stress. Moreover, higher levels of depression were associated with more emotional and behavioral difficulties in children. Maternal depression, age, and employment status were identified as factors that influenced emotional and behavioral problems in preschool-aged children.Conclusion: These findings underscore the importance of providing parent education and mental health support programs for North Korean refugee families in order to improve parents' parenting skills and emotional well-being.
Adjuvant Pembrolizumab in Patients with Stage IIIA/N2 Non–Small Cell Lung Cancer Completely Resected after Neoadjuvant Concurrent Chemoradiation: A Prospective, Open-Label, Single-Arm, Phase 2 Trial
Authors: Shin Junghoon , Park Sehhoon , Kim Kyung Hwan , Shin Eui-Cheol , Jung Hyun Ae , Cho Jong Ho , Sun Jong-Mu , Lee Se-Hoon , Choi Yong Soo , Ahn Jin Seok , Kim Jhingook , Park Keunchil , Shim Young Mog , Kim Hong Kwan , Noh Jae Myoung , Ahn Yong Chan , Pyo Hongryull , Ahn Myung-Ju
Published: April 2024

Purpose Optimal treatment for stage IIIA/N2 non–small cell lung cancer (NSCLC) is controversial. We aimed to assess the efficacy and safety of adjuvant pembrolizumab for stage IIIA/N2 NSCLC completely resected after neoadjuvant concurrent chemoradiation therapy (CCRT).Materials and Methods In this open-label, single-center, single-arm phase 2 trial, patients with stage IIIA/N2 NSCLC received adjuvant pembrolizumab for up to 2 years after complete resection following neoadjuvant CCRT. The primary endpoint was disease-free survival (DFS). Secondary endpoints included overall survival (OS) and safety. As an exploratory biomarker analysis, we evaluated the proliferative response of blood CD39+PD-1+CD8+ T cells using fold changes in the percentage of proliferating Ki-67+ cells from days 1 to 7 of cycle 1 (Ki-67D7/D1).Results Between October 2017 and October 2018, 37 patients were enrolled. Twelve (32%) and three (8%) patients harbored EGFR and ALK alterations, respectively. Of 34 patients with programmed cell death ligand 1 assessment, 21 (62%), nine (26%), and four (12%) had a tumor proportion score of < 1%, 1%-50%, and ≥ 50%, respectively. The median follow-up was 71 months. The median DFS was 22.4 months in the overall population, with a 5-year DFS rate of 29%. The OS rate was 86% at 2 years and 76% at 5 years. Patients with tumor recurrence within 6 months had a significantly lower Ki-67D7/D1 among CD39+PD-1+CD8+ T cells than those without (p=0.036). No new safety signals were identified.Conclusion Adjuvant pembrolizumab may offer durable disease control in a subset of stage IIIA/N2 NSCLC patients after neoadjuvant CCRT and surgery.
Open Defecation, Livestock Ownership, and Child Nutritional Status in India
Authors: Luke Nancy , Acharya Yubraj , Faytong-Haro Marco , Yang Di , Xu Hongwei , Oommen Anu Mary , Rose Winsley
Published: April 2024

South Asian children are among the most severely malnourished worldwide. One prominent hypothesis is that open defecation in the local area exposes children to human fecal pathogens that can cause diarrhea and malnutrition. Much of the existing research uses district-level measures of open defecation, which could mask important local-area variation. A second hypothesis is that animal fecal matter is a major source of exposure. This analysis tested these dual hypotheses using census data collected from 949 villages in Tamil Nadu, India, and a survey conducted in a random sample of 5,000 households in the same area. The final analytic sample consisted of 2,561 children aged 0–10 years. We estimated the association between the measures of village- and household-level open defecation, household livestock ownership, and child height-for-age Z-scores in a regression framework, controlling for potential confounders. Results revealed that village- and household-level open defecations are negatively associated with child height. There was an estimated difference of approximately 0.5 height-for-age Z-score between children living in villages with no open defecation and children in villages where all households practiced open defecation (P = 0.001) and a 0.2 Z-score difference between children living in households that practiced open defecation and those living in households that did not (P = 0.001). Livestock ownership was not associated with child height. Overall, the findings provide evidence on the centrality of open defecation in explaining persistent child malnutrition in India and the higher risk of exposure to human fecal pathogens compared with animal feces in the south Indian context.
Design and Comparative Analysis of E-Shape and H-Shape Microstrip Patch Antenna for IoT Application
Authors: Durmuş Ali , Yıldırım Zafer
Published: April 2024

In this study, H-type and E-type microstrip antenna arrays with high efficiency and management are designed using Surrogate Model Assisted Differential Evolution Algorithm (SADEA) for antenna synthesis. In order to meet the increasing wireless communication requirements of the internet of things (IoT) technology it has become very important to design new antenna designs with artificial intelligence techniques to transmit electromagnetic waves between antenna elements in the most efficient way. With a view to operate in the 2.5 GHz band, which is widely used in wireless communication technologies. H-type and E-type microstrip antennas with a 2x2 structure are analyzed and finally compared. By using the SADEA method, the amplitude and the distance between the array elements are optimized. The total amplitude of the 4-element E-type and H-type microstrip antenna array is minimized to 2.45885 V and 2.14929 V, respectively, after optimization, while it is 4V for both before optimization.
The Role of Principals in Developing Students' Life Leadership Skills from the Point of View of Principals and Teachers within the Green Line
Authors: Abu El-hija Fahima Labeb , Jubran Ali Muhammad , Tawalbeh Hadi Muhammad
Published: April 2024

The study aimed at identifying the role of principals in developing students life leadership skills from the point view of principals and teachers within the green line, as well as its differences according to demographic variables. A survey descriptive methodology using survey method was used to collect data from the research sample which was chossen randomly. The research sample consisted of (432) principals and teachers in the North Regions. The results revealed that the total level of the leadership and its kills were moderate (management skills, social skills, emotional skills, perception skills). Additionally, the results indicated that there were significant differences in life leadership skills for job title among teachers, unlike other variables such as sex and school level.
The Role of Educational Development Councils in Supporting Distance Learning During the Corona Pandemic from the Point of View of School Principals in the Directorate of Education for the Qasaba Irbid District
Authors: Abu Shehab Munther Ahmed
Published: April 2024

The study aimed to identify the role of educational development councils in supporting distance learning during the Covid – 19 from the point of view of school principals in the directorate of education of the Kasbah of Irbid district. The study used the descriptive survey methodology. A questionnaire was prepared to collect the necessary data to answer the study questions. The questionnaire was applied to the study sample, which consisted of (145) principals. The results showed that the role of educational development councils in supporting students' distance learning during the Covid–19 in both technical role and social role was medium. There were no statistically significant differences at (α=0.05) due to the effect of sex. There were no statistically significant differences at (α=0.05) due to the effect of experiences years in management, and there were statistically significant differences (α=0.05) due to the effect of school level.
The Relative Contribution of Teaching Mindfulness to Emotional Empathy among Teachers within Nazareth District in Light of some Variables
Authors: Dakwar Jawaher George , Al-Zghoul Rafe’a Aqeel
Published: April 2024

The study aimed to identify the relative contribution of teaching mindfulness to emotional empathy among teachers in the Nazareth District in the light of some variables. The descriptive correclational methodology was used. The teaching mindfulness scale, and the emotional empathy scale represented the study tools for the study, after ensuring their validity and reliability. The study sample consisted of (558) teachers who were randomly chosen. The results showed that there were no statistically significant differences in the study sample estimates to mindfulness, according to all demographic variables, and to the three dimensions of mindfulness dimensions separately, according to the (Gender, Academic qualification, and Experience) variables, and the presence of statistically significant differences in the dimension of calm due to (School level) variable, in favor of kindergarten teachers. The results also showed a statistically significant difference in the crying response dimension, due to (Gender) variable in favor of females, and the (Experience) variable (10-20 years), and the absence of statistically significant differences for the six dimensions individually, due to the (Academic qualification) variable, and the absence of a statistically significant difference for Empathy, (as a whole), due to (Gender, Academic qualification, and Experience variables), and a statistically significant difference according to the (School level) variable in favor of kindergarten teachers.