bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast Evaluation of Reservoir Connectivity via a New Deep Learning Approach: Attention-Based Graph Neural Network for Fusion Model
Authors: Saihood Tariq , Saihood Ahmed , Al-Shaher Mohamed Adel , Ehlig-Economides Christine , Zargar Zeinab 
Published: September 2024

Abstract
The goal is to estimate the injector-to-producer connectivity from injection-production history data by implementing an attention-based graph neural network for fusion model (AGFM). The AGFM can identify the complex relationships between the injectors and producers, ensuring the spatially dense estimated injector-to-producer connectivity. The model is trained and tested on a dataset containing two types of injecting fluids: carbon dioxide (CO2) and water. The AGFM model correlates the relationships between every injector and all producers concerning produced oil, water, and gas. AGFM involves constructing a graph where nodes represent wells and edges represent their spatial and operational relationships. Node features include injection and production rates, while edge weights are based on physical proximity and connectivity among wells. The model can also optimize the water alternating gas ratio (WAG ratio) by alternating the injection of CO2 and water. The model can benefit from optimizing the WAG parameters to improve sweep efficiency and reduce gas channeling. We assessed the AGFM model through three scenarios of experiments. The first scenario uses CO2 as the injection fluid, the second uses water as the injection fluid, and the third uses CO2 and water alternately. We aligned the actual produced gas, oil, and water in each scenario for the methods with the model's predictions. We also compared the results with some selected state-of-the-art in terms of accuracy and mean squared error (MSE) (more quantitative and qualitative can be presented). The significant finding of the AGFM model was its ability to identify long-range dependencies between the injector and producer wells correctly. The AGFM model also correctly identified the immediate connections between each injector and all producer wells. Overall, the observational results support the conclusion that the AGFM model is a promising new approach for injector-producer connectivity estimation.
Enhancing Management Insight into Mergers & Acquisitions Using Probabilistic Financial Analysis
Authors: Hughson L. , Klimley B. J. , Orlando M. J. 
Published: September 2024

Abstract
Between 2011 and 2022 greater than 50% of global E&P merger and acquisition (M&A) transactions destroyed shareholder value with only 25% generating approximately 60% of total excess returns, per a 2024 paper from McKinsey & Co.1 Upgrading the financial analysis approach to provide better insights for M&A decision-making professionals is therefore increasingly important to enhance their probability of success. This paper analyzes the 2021 merger of Cabot Oil & Gas with Cimarex Energy (the Transaction) to understand the distinct advantages gained from applying a probabilistic approach to the financial analysis of mergers, acquisitions and divestitures. Traditional deterministic valuation and transaction analyses, which rely on a static set of assumptions, are compared to a probabilistic approach based on Monte Carlo simulation using a readily available Excel add-in. This paper reviews and utilizes the additional insights from the probabilistic output, generated from the input probability distribution functions and correlation coefficients chosen, unavailable from a traditional, deterministic financial approach. The probabilistic approach provides an enhanced transaction analysis process that produces the critical metrics company Boards and shareholders increasingly require in determining if a transaction should be undertaken. This paper highlights how a probabilistic approach to the M&A process: (i) is straightforward to implement; (ii) facilitates a greater use of real-world data; (iii) generates outcome probabilities for key transaction metrics unavailable with a deterministic approach; and (iv) provides unique risk insights into those transaction metrics. This probabilistic approach to the M&A process represents a significant, practical improvement to management's ability to evaluate specific transactions and enhance their likelihood of success.
A Producer-Based Capacitance Resistance Model (CRM) to Characterize Oil Reservoirs with Natural Water Influx: a Realistic Application
Authors: Koroglu B. , Onur M. , Li Y. , Alpak F. O. 
Published: September 2024

AbstractIn water-drive reservoirs, predicting variable water influx to individual wells poses significant challenges due to geological heterogeneity and diverse production strategies. This study introduces a novel capacitance-resistance model (CRM) that robustly incorporates natural water influx into oil reservoirs from aquifer systems, eliminating the need for average reservoir and aquifer pressure versus time data. The proposed aquifer-integrated CRM addresses the pressure change equation between the aquifer and reservoir systems using fundamental CRM parameters including aquifer pore volume and productivity index. The development process focuses on the CRM with producer representation (CRMP) and reformulates it to calculate instantaneous water influx based on reservoir and aquifer pressure differences, naming this model CRMPAQ. The history matching algorithm utilizes Sequential Least-Squares Programming (SLSQP) to manage both inequality and equality constraints, employing linear inequality constraints to ensure that the summation of aquifer-producer connectivity and well-to-well connectivity does not exceed unity. Additionally, a linear equality constraint ensures that the summation of producer pore volumes equals the total reservoir pore volume. The CRMPAQ model is designed to match total production rates and bottomhole pressures (BHPs), if available. Numerical experiments conducted using CRMPAQ on both 3D synthetic and realistic field datasets demonstrate its effectiveness. Comparisons with results from the commercial simulator ECLIPSE, which accounts for saturation dependency on well productivity, further validate the model. The realistic field dataset comprises three wells from a heterogeneous reservoir with natural water influx, frequent shut-ins, and variable skin effects, with daily records of production rates and BHPs. The results indicate that CRMPAQ yields comparable outcomes and effectively emulates the behavior of high-fidelity reservoir simulators in terms of total liquid production rates and BHP data. CRMPAQ is significantly more computationally efficient; two to three orders of magnitude faster than high-fidelity simulator history matching. Furthermore, when matching BHP data using flow rate history, CRMPAQ accurately accounts for the effects of frequent shut-ins as well as the variable productivity indices on parameter estimation. The model was tested on synthetic datasets and validated with a realistic field dataset (used in SPE-210102-PA). The realistic dataset considers three wells produced from a heterogeneous reservoir with natural water influx, frequent shut-ins, and variable skin effects. The wells' production rates and BHP data are recorded at daily time intervals.
Dynamics of the 1873 CE “Breccia De Fiore” phreatic eruption at Vulcano (Aeolian Islands, Italy) through historical chronicles, physical volcanology, and numerical modelling
Authors: Giansante Silvia , Ongaro Tomaso Esposti , Cioni Raffaello , Pistolesi Marco , Vitturi Mattia de' Michieli 
Published: September 2024

Abstract

Phreatic events may represent precursors of magmatic eruptions, but they can also occur as single or multiple episodes punctuating the activity of volcanoes characterised by active hydrothermal systems. The Breccia De Fiore deposit represents evidence of the phreatic activity preceding by fifteen years the onset of the 1888–1890 magmatic eruption at La Fossa di Vulcano. This study integrates historical chronicles, sedimentological and physical descriptions of the deposit, and 3D numerical simulations to reconstruct eruption dynamics. Results indicate that this deposit was the product of multiple, shallow explosions of low magnitude (< 5×104 m3 cumulative volume), possibly occurred during a short time interval at the beginning of the phreatic sequence in 1873. The explosions produced pyroclastic density currents and ballistic fallout, affecting the slopes of the La Fossa cone. The asymmetry of the deposit, outcropping on the north-western flank of the cone, is well reproduced by 3D simulations as the product of explosions from an inclined vent driven by shallow (i.e., < 150 m deep) pressure build-up (up to 5 MPa) from accumulating magmatic fluids. Simulations of explosion dynamics and the distribution of eruptive products allows us to put some constraints on the two main controlling parameters of phreatic scenarios: erupted total mass and specific energy. This study also highlights the potential impact of such relatively small phreatic explosions on the Vulcano island and the need for quantifying their hazards, especially given the proximity to populated sites and the growing tourism in this active volcanic area.
Interpeduncular GABAergic neuron function controls threat processing and innate defensive adaptive learning
Authors: Molas Susanna , Williams Elora , Snively Leshia , O'Meara Benjamin , Jacobs Hannah , Kolb Miranda , Zhao-Shea Rubing , Baratta Michael , Tapper Andrew 
Published: September 2024

Abstract

The selection of appropriate defensive behaviors in the face of potential threat is fundamental to survival. However, after repeated exposures to threatening stimuli that did not signal real danger, an animal must learn to adjust and optimize defensive behaviors. Despite extensive research on innate threat processing, little is known how individuals change their defensive behaviors when presented with recurrent threat exposures without evidence of a real risk. Insight into this process is critical as its dysregulation may contribute to neuropsychiatric conditions, such as anxiety disorders. Here, we used the visual looming stimulus (VLS) paradigm in mice to investigate innate threat processing and adaptive defensive learning. Repeated exposure to VLS over consecutive sessions reduced immediate freezing responses and time spent inside a sheltered area upon VLS events, leading to an increase in foraging behaviors. Fiber photometry recordings and optogenetic manipulations revealed that VLS innate adaptive defensive learning is associated with reduced recruitment of the midbrain interpeduncular nucleus (IPN), a structure associated with fear and anxiety-related behaviors. Functional circuit-mapping identified a role for select IPN projections to the laterodorsal tegmental nucleus in gating defensive learning. Finally, we uncovered a subpopulation of IPN neurons that express the neuropeptide somatostatin and encode safety- and avoidance signals in response to VLS. These results identify critical behavioral signatures of innate defensive responses and a circuit that regulates the essential features of threat processing.
How a Moment of Great Intensity Exposed Grave Structural Violence: COVID-19’s Gendered Effect on Subjective Wellbeing in MENA Countries
Authors: Barsoum Ghada , Majbouri Mahdi 
Published: September 2024

Abstract

The COVID-19 pandemic has been a critical juncture that exposed many existing inequities in facing this global threat. Building on Galtung’s conceptualization of positive peace as the absence of structural violence and institutionalized inequality, we study the gendered effect of the COVID-19 pandemic on reported subjective wellbeing (SWB) in four countries in the Middle East. Data from mobile phone panel surveys, with a total sample of 12,614 observations collected during this critical juncture, show that women consistently reported a lower level of SWB than men in all four countries. Women experienced higher increases in unemployment rates than men in all four countries, despite their already higher rates prior to the pandemic. Controlling for individual characteristics and geographic-time fixed effects, the main factor associated with lower SWB was the decline in household income, reflecting the intersectionality of poverty and gender. In addition, reported increase in the time spent on housework negatively affected women’s SWB. The pandemic has further accentuated gender inequality in all four countries and exposed the inherent structural violence experienced by women in these contexts.
Evaluation of grain yield, quality characteristics and high molecular weight glutenin subunits of bread wheat cultivars in different agro-ecological regions of Türkiye
Authors: Aktaş Bekir , GÖKDERE Halil İbrahim 
Published: September 2024

Abstract

The current study aimed to determine and compare the grain yield, some quality characteristics and high-molecular-weight glutenin subunits (HMW-GS) of bread wheat cultivars cultivated in several agro-ecological regions using a total of 46 registered bread wheat cultivars in the 2016–2017 and 2017–2018 growing seasons. The results determined that the environment had the largest share in the total variation (Genotype + Environment + Genotype×Environment Interaction). The Mediterranean region had the highest average grain yield with 8137 kg ha− 1, while the Central Anatolia region (under the rainfed conditions) had the lowest average with 4260 kg ha− 1. The average thousand kernel weight of the cultivars was 35.3–39.9 g, test weight 77.2–79.2 kg hL− 1, protein content 13.4–14.7%, Zeleny sedimentation 39.2–53.3 mL, and alveograph energy value varied between 191.2-276.4 10− 4 J. Regarding the HMW-GS, 18 of the 46 cultivars scored 10, and one scored 5. The highest mean protein content and alveograph energy value was determined in cultivars with 9 Glu-1 score. In Zeleny sedimentation, cultivars with 10 Glu-1 score showed the highest mean value. The most common subunits in loci; it is 2* in Glu-A1, 7 + 8 and 7 + 9 in Glu-B1, and 5 + 10 in Glu-D1. The fact that registered cultivars predominantly carry these subunits at Glu-1 loci, which could be the result of yield and quality-oriented selection in the breeding process. It was concluded that high quality new varieties could be developed by HMW-GS oriented crosses and selections in wheat breeding programs.
How to Solve Systematical Problem Such as Ecosystems Involving Nonlinear Dynamics and Complexity by Systematic Solution Through Modeling and Simulation
Authors: Cha Deok soo , il Kim Kyoung 
Published: September 2024

Abstract

This study is provided to third-party scientists other than physicists and mathematicians. This study revealed that nonlinear dynamics and complex systems that have not been solved for the past 300 years are systematic problems caused by dynamic systems, such as ecosystems; they also provide a systematic solution completed through [modeling-simulation-verification-return] using mathematical tools (system analysis theory) developed in the 20th century. Therefore, this is an innovative solution that no one could have imagined. Nevertheless, the systematic solution does not overlap or conflict with chaos theory in statistical physics. Therefore, we can freely use both solutions and provide them to third-party scientists without any conditions, because third-party scientists have no reason to avoid or reject them. Therefore, this study should be implemented immediately without peer review. If, at that stage, errors are found in the solution or the user wants to stick to the existing chaos theory, the existing chaos theory can be used; there is no problem with this. Therefore, this research will be established as a solution to solve the nonlinear dynamics and complexity exhibited by dynamic systems with self-regulating functions.
Fetal Kidney Transplantation into In Utero Fetuses
Authors: Yokoo Takashi , Morimoto Keita , Yamanaka Shuichiro , Matsui Kenji , Kinoshita Yoshitaka , Inage Yuka , Yamamoto Shutaro , Koda Nagisa , Matsumoto Naoto , Saito Yatsumu , Takamura Tsuyoshi , Fujimoto Toshinari , Fukunaga Shohei , Tajiri Susumu , Matsumoto Kei , Ozawa Katsusuke , Wada Seiji , Kobayashi Eiji 
Published: September 2024

Abstract

Potter sequence consists of various symptoms associated with renal dysplasia. For bilateral renal agenesis, there is no hope of survival. As a novel therapeutic approach for Potter sequence, we developed a unique approach of “transplantation of fetal kidneys from a different species during the fetal period.” In this study, we first validated the approach using allogeneic transplantation. Fetal kidneys with bladders from green fluorescent protein-expressing rats (embryonic day 14.0–16.5) were subcutaneously transplanted into allogeneic rat fetuses in utero (embryonic day 18.0–18.5). After birth, the transplanted fetal kidneys were confirmed to have urine production capability. Furthermore, long-term (up to 150 days) urine production was sustained. Next, we performed xenotransplantation. The transplantation of mouse fetal kidneys into rat fetuses in utero led to the maturation of renal tissue structures. We demonstrated organ transplantation into in utero fetuses using fetal kidneys as donor organs for fetal therapy.
Exploration of the relationship between SDGs and CSR reports with text mining techniques for stock exchange companies over Taiwan
Authors: yu Tai-Yi , Horng Jeou-Shyan , Chang I-Cheng , Yu Tai-Kuei , Liu Chih-Hsing , Chou Sheng-Fang 
Published: September 2024

Abstract

This research collects corporate social responsibility (CSR) reports from stock exchange companies in Taiwan and employs text mining technologies to analyze the relationship and document similarity between CSR reports from various industries and the Sustainable Development Goals (SDGs). The methods used include natural language processing (NLP), TF-IDF weighting, principal component analysis (PCA), and document similarity assessment. The study applies sub-items of selected SDG terms to screen feature words, build the TF-IDF matrix, analyze the CSR report contents using PCA, and utilize cosine similarity to compare the similarity between CSR reports and SDG sub-items. A total of 225 feature words were identified based on SDG sub-items, with the top 60 feature words (26.7%) accounting for 77.9% of the total TF-IDF weights, aligning with the Pareto principle. Analyzing 370 CSR reports from selected stock exchange companies (0050 ETF), unique and representative feature words and explained variations were identified. Each rotated principal component allowed the identification of corresponding SDG sub-items through specific feature words. The high diversity of feature words resulted in low and unique explained variance for each rotated principal component. Document similarity comparisons between CSR reports and SDG sub-items revealed confidence levels indicating the degree of alignment between CSR reports and SDG sub-items. For the natural language segmentation process and automatic document classification of CSR reports, the assistance of domain experts is recommended to ensure accurate and consistent segmentation and classification results.
Does institutional quality matter for renewable energy consumption and CO2 emissions? Evidence from a panel of BRICS countries
Authors: Ummalla Mallesh , Goyari Phanindra 
Published: September 2024

Abstract

The main purpose of the present study is to explore the impact of institutional quality (IQ) on renewable energy consumption (REC) and CO2 emissions (CO2e) in a panel of BRICS countries, spanning the period 1996Q4-2016Q4. To accomplish this, we use several panel econometric techniques on targeted models. The results of the Westerlund panel cointegration test show that there is a significant long-run equilibrium association among the variables. Furthermore, Augmented Mean Group (AMG) estimator outcomes reveal that IQ has a significant positive impact on REC. Non-renewable energy consumption (NREC) has a positive association with CO2e, while IQ and REC significantly reduce CO2e. Finally, we found no causal nexus between IQ, REC, and CO2e in BRICS countries. Our novel study findings not only provide new empirical evidence to the existing literature but also help policymakers strengthen the IQ to promote the uses of renewable energy and mitigate CO2e.
E-learning benefits for medical students: the longitudinal use of an online learning experience at an international medical school
Authors: Zavala-Cerna Maria G , Caballero Augusto E. , Verbeeck-Mendez Steffi , Parker Michael J. 
Published: September 2024

Abstract

The COVID-19 pandemic forced schools worldwide to change the delivery of the medical curriculum to an exclusively online format. Some important findings after this experience highlight the possibility of continuing with the online form of curriculum delivery, at least partially, to reinforce the learning experience of medical students. Our research goal was to analyze the benefits that medical students can gain after exposure to high-quality e-learning. Our primary aim was to analyze the gain of knowledge, as well as self-rated benefits and understanding, after exposure to high-quality e-learning. We conducted a retrospective study to report on the outcomes achieved after 3 years of experience with the online delivery of basic biomedical science courses. We included results from 1,546 students and 2,613 course enrollments in two different medical programs. Here, we report on students’ benefits after online courses in terms of knowledge gain and satisfaction. The information presented here could be used to make decisions about the incorporation of e-learning into the medical curriculum.
Application of a neural network for modeling and identification in a wastewater treatment plant
Authors: Carrillo Daniel Muñoz , Árcega Anilú Franco , Morales Virgilio López , García-Islas Luis Heriberto , Martines Hilario , Misses Manuel Alejandro Ojeda 
Published: September 2024

Abstract

This work presents the modeling of a wastewater treatment plant using nonlinear ordinary differential equations that allow dissolved oxygen to be monitored. The plant configuration is based on an anoxic bioreactor, an aerobic bioreactor and a secondary settler connected in cascade. The mathematical modeling of the plant based on the ASM model No. 1. On the other hand, it is possible to model the system by neural model of the plant using hierarchical neural networks. The objective of which is to identify some reactions that occur in bioreactors. The implementation of the identification of reactions is carried out through numerical simulations that allow validating the effectiveness of the proposed model and also obtaining relevant results to be able to apply them in future work in neural control.
ADHD Rating Scale Adaptation in Saudi Arabia: Factor Structure, Measurement Invariance, and Normative Data
Authors: Pierce Corey D. , Alhossein Abdulkarim , Lambert Matthew C. , Aldabas Rashed , Bakhietb Salaheldin Farah Attallah , Fitzpatrick Heather 
Published: September 2024

Abstract

The ADHD Rating Scale is widely used to assess ADHD in the United States, and psychometrically sound. However, when assessments are translated into other languages, it is necessary to re-examine psychometrics. This study aimed to analyze the factor structure, test measurement invariance, and provide normative data for an adaptation of the ADHD Rating Scale in Saudi Arabia using parent ratings of 3,127 youth and teacher ratings of 2,595 students. Outcomes corroborate the validity of the two-factor correlated model for both parent and teacher ratings, and the measurement invariance among age and gender groups. The adapted ADHD Rating Scale is suitable for assessing the ADHD symptomology of youth in Saudi Arabia using the normative standards developed in this study.