bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining the congruence hypothesis in vocational interest research: the case of teacher students
Authors: Leichner Nikolas , Ottenstein Charlotte , Eckhard Jan , Matheis Svenja , Weis Susanne , Schmitt Manfred , Lischetzke Tanja 
Published: August 2022

AbstractAccording to the congruence hypothesis, job and study satisfaction will be higher when individual interests and the respective environment (both conceptualised according to Holland’s RIASEC model) are congruent. As our target group were teacher students, all participants who did not intend to become a teacher or did not meet other inclusion criteria (e.g., no missing data on relevant variables) were removed from the sample, resulting in a final sample of N = 1171. Teacher students completed questionnaires on their vocational interests and their satisfaction with course content. To obtain an assessment of the environment (study majors), N = 166 lecturers were asked to rate their courses with respect to Holland’s RIASEC model. As previous findings have indicated that conclusions are influenced by the congruence measure that is used, we applied two different approaches. First, we computed the profile correlation between the individual interest profile and the environmental profile for each individual to measure congruence. Profile correlation scores were then correlated with satisfaction with course content scores. This correlation was significant (r = .21, p < .001), offering support for the congruence hypothesis. Second, Response Surface Analysis (RSA) was used to predict satisfaction with course content scores from the individual interest and environmental assessment variables and their interaction separately for each interest dimension. Results showed that the relationships between these three constructs were complex, but evidence for the congruence hypothesis could not be found. This makes this study the first study to investigate this hypothesis using RSA methodology.
Common Ultrasound Applications for Pediatric Musculoskeletal Conditions
Authors: de Borja Celina , Watkins Rhonda , Woolridge Tiana 
Published: August 2022

Abstract
Purpose of Review
To discuss the use of ultrasound for diagnosis and management of common pediatric musculoskeletal conditions through a case-based approach.

Recent Findings
Ultrasound is an essential diagnostic modality in the early detection of developmental dysplasia of the hips and can be used as early as 6 weeks of age when the ossific nucleus has not developed yet. Ultrasound is helpful in diagnosing traumatic injuries such as fractures and intramuscular hematomas, can visualize fracture healing at early stages, and can also be used to guide aspiration of hematomas that can help with decreasing pain and faster recovery. Ultrasound is superior to radiographs in evaluating joint effusions and soft tissue infections or masses and is better tolerated by children compared to other imaging modalities such as magnetic resonance imaging (MRI).

Summary
Ultrasound is an easily accessible, affordable, non-invasive, and radiation-free imaging modality that is well tolerated by children and their families. It can aid in the diagnosis and management of a wide variety of musculoskeletal conditions including developmental, traumatic, and infectious etiologies, as well as in the evaluation of superficial soft tissue masses.
The Child and Adolescent Thriving Index 1.0: Developing a Measure of the Outcome Indicators of Well-Being for Population Health Assessment
Authors: Anderson Nathaniel W. , Markowitz Anna J. , Eisenberg Daniel , Halfon Neal , Moore Kristin Anderson , Zimmerman Frederick J. 
Published: August 2022

AbstractThe well-being of children and adolescents is emerging as an area of interest for population health measurement. Previous approaches assessing national and state trends in well-being have relied on composite indices. However, these methodologies suffer from several weaknesses. This paper develops an improved index for the United States that is measurable with existing population-data resources. It derives the appropriate weights for items in this index using a longitudinal panel of 2,942 children in the Panel Study of Income Dynamics. Candidate component measures are selected for the index based on their demonstrated association with several subjective scales assessed during young adulthood. The final index demonstrates that a broad range of measures indicate higher levels of population-level well-being. The predictive validity of the index for outcomes during young adulthood is also assessed: a one-standard-deviation increase in the index score is associated with a 7.9-percentage-point decrease [95% CI: 5.9 – 9.8] in ever reporting fair or poor health, a 6.3-percentage-point decrease [95% CI: 4.6 – 8.0] in ever reporting depression, and a 17.2% [95% CI: 13.7% – 20.5%] increase in peak earnings. These values for predictive validity are slightly higher than those of existing methodologies. We also find that incorporating contextual indicators from childhood and adolescence does not substantively improve predictive validity. Policy-makers and government agencies interested in population-level well-being of children and adolescents can continue to use existing indices as reasonable proxies, but should also commit to upgrading data systems to make them more child-centric in the future.
The risk and prognostic influence of caspase 9 promotor polymorphisms in Egyptian patients with acute myeloid leukemia
Authors: Makhlouf Manal Mohamed , Ayoub Mahmoud Aly , Mourad Dalia Farag 
Published: August 2022

AbstractAcute myeloid leukemia (AML) is a genetic disorder of hematopoietic stem cells (HSCs) followed by clonal selection and uncontrolled proliferation leading to malignant neoplasm. Inappropriate regulation of apoptosis contributes to many human disorders including cancer. Caspase 9 (CASP9) is associated with the intrinsic pathway of apoptosis. Functional single-nucleotide polymorphisms (SNPs) in CASP9 might influence gene expression leading to altered apoptosis and increased AML risk. Previously, two CASP9 promoter polymorphisms (CASP9 1263 rs4645978A > G and CASP9 712 rs4645981C > T) were shown to be associated with increased risk of developing AML and inferior AML survival in South Indian subjects. This study was to evaluate these polymorphisms in an independent cohort of AML patients and controls in Egypt. PCR–RFLP for CASP9 1263 rs4645978 A > G and CASP9 712 rs4645981 C > T genotypes were done in 60 de novo AML cases and 40 healthy control subjects. Our study showed that CASP9 712 rs4645981 C > T gene polymorphism is associated with increased risk of developing AML and poor disease outcome (p value = 0.006, < 0.001; OR = 3.644, 26; and 95% CI = 1.39–9.528, 6.5–103.5, respectively). In contrast, CASP9 1263 rs4645978 A > G showed no significant difference between AML patients and the controls regarding the risk of developing AML or disease outcome (p value = 0.301, 0.573, respectively). CASP9 712 rs4645981 C > T could be involved in the pathophysiology and development of AML in Egypt and may be useful as a predictive molecular markers for inferior prognosis in AML. Notably, risk was highest and outcomes worst in patients with both the 712C > T and 1263A > G alleles.
A multi-criteria approach to drinking and irrigation water assessment of spring water in Igbo-Etiti, Nigeria
Authors: Ezea Valentine Chinakwugwo , Ihedioha Janefrances Ngozi , Abugu Hillary Onyeka , Ekere Nwachukwu Romanus 
Published: August 2022

AbstractThe study aimed at assessing the spring water within Igbo-Etiti, Nigeria, for drinking and irrigation applications using physicochemical parameters, water quality index, mineral and trace elements, pollution indices, and risk assessment. Standard methods were used to determine the physicochemical properties of the spring water in rainy and dry seasons. Trace metals such as Pb, Cd, Zn and Cu were determined with atomic absorption spectrophotometer. The results showed that most of the physicochemical properties studied were within the guideline values set by Nigeria Standard for Drinking Water Quality (NSDWQ), WHO and US EPA for drinking water purposes. However, pH of all the spring water (4.27–4.73; and 4.95–5.73) for dry and rainy seasons, respectively, were slightly acidic. Lead (Pb) (0.01–1.08 mg/L) and cadmium (Cd) (0.01–0.15 mg/L) concentrations were above the guideline values in both seasons. This could be attributed to the lithology of the study area which is Nsukka FORMATION. Leaching of lead and sulphides from the embedded coal deposits could be responsible for the increased lead levels and made the water acidic. The mineral and trace elements examined were within WHO and NSDWQ limits for drinking water except Cd and Pb. The permeability index and other irrigation indices showed that all the spring water was suitable for irrigation purposes. The pollution index suggested that the pollution level of all the samples in the dry season was insignificant (PIS < 1). Two-way ANOVA showed significant differences in most of the parameters studied in the dry and rainy seasons. Pearson correlation analysis and cluster analysis showed strong significant positive and negative correlations in some of the parameters studied in both seasons. The water quality index showed that none of the spring water had excellent water status. However, one spring (Iyi Ase) had poor water status in dry season and is considered unsafe for drinking. Iyi Ase was also considered not suitable for irrigation application as predicted by most of the pollution indices, while others were generally considered suitable for irrigation application. Probable cancer and non-cancer risk assessment revealed a probable risk associated with the consumption of the spring in Igbo-Ettiti area, Nigeria.
Modelling of liquid loading in gas wells using a software-based approach
Authors: Abhulimen Kingsley Eromoses , Abhulimen K. E. , Oladipupo A. D. 
Published: August 2022

AbstractLiquid loading is the most common operational problem influencing gas well productivity for the petroleum operator. Liquid loading is defined as an operational constraint that is associated with gas wells where the major driving mechanism for hydrocarbon production is by the associated gas-driven mechanisms. Liquid loading occurs when liquid accumulated in the tubing or casing results in the gas velocity lower than the critical value (the minimum velocity required for gas to push the liquid out of the gas well), which overtime leads to a hydrostatic back pressure greater than the formation pressure of the well, thereby limiting the flow of gas into the well. The continuous build-up of pressure from liquid loading eventually minimizes well productivity and expensive work over operations. However, current mathematical models to predict liquid loading are flawed with varying inaccuracies and depending on the models deployed will ultimately lead to loss of production time and well productivity. In our work we present prediction of liquid loading using a software-based model incorporating the particle swarm optimization algorithm, genetic algorithm, and artificial neural network and Bayesian neural network algorithms applications. The results of our research findings show that artificial neural network software-based model with a simulated accuracy of 93% and 92% for test and trained data, respectively, outperformed the particle swarm optimization data-driven model with a simulated sensitivity accuracy of 92% and 83%, and genetic algorithm data-driven models with a simulated accuracy of 89% and 83%. The Bayesian neural network was postulated as a robust model because of its simplicity shown to have simulated accuracy of 77% and 73% for train and test data, respectively. Thus software-based code environment and data-driven model developed and presented in this paper may resolve many of current deficiencies and gaps in the current technical literature to predict liquid loading with high precision offering saving in millions of dollars to the operators.
Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt
Authors: Abdelghany Wael K. , Hammed M. S. , Radwan Ahmed E. , T. Nassar 
Published: August 2022

AbstractSand production is one of the major challenges in the oil and gas industry, so a comprehensive geomechanical analysis is necessary to mitigate sand production in mature fields. As the pore pressure drastically decline in depleted reservoirs, the sand production risk becomes more critical and needs to be studied. However, the absence of key logs in many wells is a big challenge in the petroleum industry, and most geologists and engineers use empirical equations to predict missed log intervals. We conducted a comprehensive geomechanical modeling study on a full set of logs from two wells from the Hilal field, Gulf of Suez, Egypt, to infer the geomechanical elements and predict sand production. We have used the multi-arm calipers to calculate the actual depth of damage ratio to validate the geomechanical parameters in the prognosis model and confirm the stress orientations. We used machine learning approach to infer key sonic log in X-10 well to replace the empirical equations. The multi-arm calipers analysis showed an observed anisotropy in the hole diameter size with more enlargement in the ENE direction and fits with the minimum horizontal stress direction in the direction of N 60oE. The later also deduced the maximum horizontal stress direction in N150 ° based on the induced fractures from borehole image data in a nearby field. We developed and compared two sand management models: one using empirical equation and the other using machine learning. The model driven by the Gardner equation suggests sand production from day one, which is not matched with the production data, while the model driven by machine learning suggests no sand production risk, which is matched with the actual production data. Our results demonstrate the advantage of using machine learning technique in geomechanical studies on the classical empirical equations in the area of study that can be applied in other basins. The findings of this study can help with a better understanding of the implications of machine learning on geomechanical characterization and sand management.
Building 1D and 3D static reservoir geomechanical properties models in the oil field
Authors: Sanei Manouchehr , Ramezanzadeh Ahmad , Asgari Amin 
Published: August 2022

AbstractReservoir geomechanical models provide valuable information for various applications ranging from the prediction of surface subsidence to the determination of pore pressure and induced stress changes, wellbore stability, fault reactivation, and caprock integrity. Three-dimensional geological modeling of reservoir geomechanics is an essential tool to predict reservoir performance by considering the geomechanics effects. Thus, this study focuses on the application of 3D static reservoir geomechanical model workflow by using 3-D seismic and well log data for proper optimization in the Volve oil field, Norway. 3D Seismic data are applied to generate the interpreted horizon grids and fault polygons. The horizon which cut across the nine wells is used for the detailed topographic analysis. The workflow includes 1D geomechanical and petrophysical models which are calculated at well locations by using log data. Structural and property modeling (pore pressure, vertical and horizontal stresses, elastic properties, porosity, permeability, and hydrocarbon saturation) is distributed by geostatistical methods such as Kriging and Gaussian. This study indicates the effectiveness of the three-dimensional static modeling technique as a tool for better understanding of the spatial distribution of reservoir geomechanical properties, hence, providing a framework for analyzing future activities in the reservoir such as proposal position and trajectory of new wells for future field development and assessing arbitrary injection-production schedules.
Coalition Formation in Games with Externalities
Authors: Montero Maria 
Published: August 2022

AbstractThis paper studies an extensive form game of coalition formation with random proposers in games with externalities. It is shown that an agreement will be reached without delay if any set of coalitions profits from merging. Even under this strong condition, the equilibrium coalition structure is not necessarily efficient. There may be multiple equilibria even in the absence of externalities, and symmetric players are not necessarily treated symmetrically in equilibrium. If the grand coalition forms without delay in equilibrium, expected payoffs must be in the core of the characteristic function game that assigns to each coalition its equilibrium payoff. Compared with the rule of order process of Ray and Vohra (Games Econ Behav 26:286–336, 1999), the bargaining procedure with random proposers tends to give a large advantage to the proposer, whereas the bargaining procedure with a rule of order tends to favor the responders. The equilibria of the two procedures cannot be ranked in general in terms of efficiency.
Integrating knowledge on green infrastructure, health and well-being in ageing populations: Principles for research and practice
Authors: Dennis Matthew , Barker Adam , Anderson Jamie , Ashton Jenna C. , Cavan Gina , Cook Penny A. , French David , Gilchrist Anna , James Philip , Phillipson Christopher , Tzoulas Konstantinos , Wheater C. Philip , Wossink Ada , Lindley Sarah 
Published: August 2022

AbstractAgeing and urbanisation pose significant challenges for public health and urban planning. Ageing populations are at particular risk from hazards arising from urbanisation processes, some of which are in turn exacerbated by climate change. One approach for mitigating the negative effects of urbanisation on ageing populations is the leveraging of the beneficial effects of urban green infrastructure as a public health intervention in the planning process. We assessed the potential of available theoretical frameworks to provide the context for such leverage. This involved active engagement with academics and practitioners specialising in ageing, green infrastructure and health and well-being through a knowledge-brokering approach. We concluded that an integrated and comprehensive framework on the socio-cultural-ecological determinants of health is lacking. To address this, we present a set of principles for overcoming challenges to knowledge integration when working at the intersection of green infrastructure, ageing, health and well-being. Our findings—and the co-production process used to generate them—have wider significance for trans-disciplinary research into the benefits of the natural environment to human health and well-being as well as other complex and interconnected topics associated with global grand challenges.
Conservation status and cultural values of sea turtles leading to (un)written parallel management systems in Fiji
Authors: Kitolelei Salanieta , Soderberg Alisi , Qaqara Nemillie , Prakash Shritika S. , Tuiono Malakai , Veitayaki Joeli , Piovano Susanna 
Published: August 2022

AbstractGlobally and locally, conservationists and scientists work to inform policy makers to help recovery of endangered sea turtle populations. In Fiji, in the South Pacific, sea turtles are protected by the national legislation because of their conservation status, and are also a customary iTaukei resource. Centered on our interview-based study at Qoma and Denimanu villages, parallel management systems coexist, where both the (written) national legislation and the (unwritten) customary iTaukei rules determine the time and the quantity of sea turtle harvest. In addition, non-governmental organizations and academic institutions may influence local sea turtle management by providing scientific awareness and helping divert the economic values from the meat to the living animal. We suggest that the government and non-governmental organizations emphasize community management of sea turtles, and work alongside the customary chiefs and their fishing clans to understand the real harvest (eventually by allowing quotas) and to monitor the recovery of South Pacific sea turtles in Fijian waters.
Identification of boundary conditions of railway bridges using artificial neural networks
Authors: Salehi Mohammadreza , Erduran Emrah 
Published: August 2022

AbstractThis article presents a study that aims to identify the boundary conditions of a railway bridge using system identification and artificial neural networks. Vibrations generated by three different train types recorded during a 24-h long measurement campaign is used to identify the modal frequencies and mode shapes of a single-span 50 m long railway bridge. Frequency Domain Decomposition and Stochastic Subspace Identification with Covariance methods were used to identify the modal properties from the recorded vibrations and the effect of the used Operational Modal Analysis on the identified modal properties was evaluated. An initial finite-element (FE) model based on the design drawings was not able to replicate the observed dynamic behavior of the bridge. Using a sensitivity analysis, the key parameters of the finite-element model that impact the vibration frequencies of the bridge was determined. 300 finite-element models were created by changing the values of these key parameters within their effective range and were used to identify the relationship between these parameters and the vibration frequencies using Artificial neural networks (ANNs). Leveraging this relationship, the values of the FE model parameters that minimizes the error between the measured and computed frequencies was determined. As a result, the mean error between the computed and the identified vibration frequencies was reduced from 27.3% for the initial model to 3.0% for the updated model. The study indicates that boundary conditions are among the most influential parameter on the dynamic behavior of bridges and can deviate significantly from the simplistic models generally used in the FE models.
Families of faces and the normal cycle of a convex semi-algebraic set
Authors: Plaumann Daniel , Sinn Rainer , Wesner Jannik Lennart 
Published: August 2022

AbstractWe study families of faces for convex semi-algebraic sets via the normal cycle which is a semi-algebraic set similar to the conormal variety in projective duality theory. We propose a convex algebraic notion of a patch—a term recently coined by Ciripoi, Kaihnsa, Löhne, and Sturmfels as a tool for approximating the convex hull of a semi-algebraic set. We discuss geometric consequences, both for the semi-algebraic and convex geometry of the families of faces, as well as variations of our definition and their consequences.
Extremal structure in ultrapowers of Banach spaces
Authors: García-Lirola Luis C. , Grelier Guillaume , Rueda Zoca Abraham 
Published: August 2022

AbstractGiven a bounded convex subset C of a Banach space X and a free ultrafilter $${\mathcal {U}}$$
U
, we study which points $$(x_i)_{\mathcal {U}}$$

(

x
i

)

U

are extreme points of the ultrapower $$C_{\mathcal {U}}$$

C
U

in $$X_{\mathcal {U}}$$

X
U

. In general, we obtain that when $$\{x_i\}$$

{

x
i

}

is made of extreme points (respectively denting points, strongly exposed points) and they satisfy some kind of uniformity, then $$(x_i)_{\mathcal {U}}$$

(

x
i

)

U

is an extreme point (respectively denting point, strongly exposed point) of $$C_\mathcal U$$

C
U

. We also show that every extreme point of $$C_{{\mathcal {U}}}$$

C
U

is strongly extreme, and that every point exposed by a functional in $$(X^*)_{{\mathcal {U}}}$$

(

X
∗

)

U

is strongly exposed, provided that $$\mathcal U$$
U
is a countably incomplete ultrafilter. Finally, we analyse the extremal structure of $$C_{\mathcal {U}}$$

C
U

in the case that C is a super weakly compact or uniformly convex set.