12 - Methodology: the data analysis

Research suggests that being unable to generate solutions to problems in times of distress may contribute to suicidal thoughts and behavior, and that depression is associated with problem-solving deficits. This study examined active and passive problem solving as moderators of the association between depressive symptoms and future suicidal ideation among suicide attempters and nonattempters. Young adults (n = 324, 73% female, mean age = 19, standard deviation = 2.22) with (n = 78) and without (n = 246) a suicide attempt history completed a problem-solving task, self-report measures of hopelessness, depression, and suicidal ideation at baseline, and a self-report measure of suicidal ideation at 6-month follow-up. Passive problem solving was higher among suicide attempters but did not moderate the association between depressive symptoms and future suicidal ideation. Among attempters, active problem solving buffered against depressive symptoms in predicting future suicidal ideation. Suicide prevention should foster active problem solving, especially among suicide attempters.

This study examined whether active problem solving would buffer against, whereas passive problem solving would exacerbate, the association of negative life stress with suicidal ideation. Young adult college students (73 females, Mage = 19.0) from a diverse urban public university, with (n = 37) and without (n = 59) a suicide attempt history completed measures of life stress, problem solving, hopelessness, depression, and suicidal ideation. Hierarchical linear regressions were conducted to test moderating roles of active and passive problem solving, along with suicide attempt history, on the relation between negative life event stress and suicidal ideation. There was a weaker relation between life stress and suicidal ideation at high and average levels of relevant problem solving than at low levels, and this was the case primarily for suicide attempters but not for non-attempters. Individuals with a past attempt produced more passive solutions than non-attempters, but among attempters, even passive problem solving buffered the association of life stress with suicidal ideation. Relevant problem solving in the face of life stress may be especially important for individuals vulnerable to suicidal ideation due to an attempt history. Among such at-risk individuals, generating even passive solutions in the face of life stress may be more adaptive than generating few solutions. Thus, clinical interventions with suicide attempters that focus on generating solutions to problems, even if these are initially passive, may help mitigate the effect of life stress on suicidal ideation.

The risk of sand production must be evaluated from very early stages of field development planning to select the optimum well completion, and if required, identify the appropriate sand control options. However, during the appraisal or early development stages, sand production predictions often cannot be verified against field data because the lack of sanding observations. The analysis of predicting the onset and severity of sand production generally consists of using analytical or numerical rock mechanical models and ideally calibration with field sand production data. In this paper we show how in the absence of field sanding data, reliable sanding predictions can still be achieved by combining commonly used analytical and numerical prediction methods. This approach has been validated using a brown field dataset from South East Asia with several sand producing wells and multiple pressure depleted reservoirs. The analytical method uses a poro-elastic model and core-calibrated log-derived rock strength profiles with an empirical effective rock strength factor (ESF). The ESF should be calibrated against documented field sanding observations from wells with credible formation and drawdown pressures. The numerical method uses a poro-elasto-plastic model defined from triaxial core tests. The rock failure criterion in the numerical method is based on a critical strain limit (CSL) corresponding to the failure of the inner wall of thick-walled cylinder core tests or if existing field sanding data. In the brown oil field example, both analytical and numerical methods accurately predict the onset of sanding in the sand-prone wells. The core-based CSL defined by the numerical method match very well with the field sanding data with a very small margin of error. In contrast, the default and non-calibrated ESF values commonly used in the analytical method required considerable tuning to match with the field sanding data. This suggests that the core-based CSL can be used with a reasonable confidence for sand production prediction purposes in the field and the prediction can be as reliable as field-calibrated models. After the field validation, the predictions for planned infill wells by both analytical and numerical methods were consistent and similar. The paper will show how this approach is used in two other fields from the Asia Pacific region both at appraisal stages and with no field sanding data. Both cases show that different ESF values are required to match with the numerical simulations. In the absence of field sanding observation, particularly in the early stages of field development, the use of non-calibrated default ESF values in the analytical method could lead to erroneous sanding assessment and poor sand management decisions. Application of a combined analytical and numerical sanding evaluation enhances the reliability of sand production predictions. After a calibration is obtained, either with field sanding data or calibration against numerical simulation and core-based CSL criteria, the analytical method can be used with confidence and has the added benefits of simplicity and quick realizations of various scenarios and output plotting capabilities and inputs for sand control and well completion decisions.

Medical diagnostic reasoning is ill-defined and complex, requiring novice physicians to monitor and control their problem-solving efforts. Self-regulation is critical for effective medical problem-solving, helping individuals progress towards a correct diagnosis through a series of actions that informs subsequent ones. BioWorld is a computer-based learning environment designed to support novices in developing medical diagnostic reasoning as they receive feedback in the context of solving virtual cases. The system provides tools that scaffold learners in their requisite cognitive and metacognitive activities. Novices attain higher levels of competence as the system dynamically assesses their performance against expert solution paths. Dynamic assessment in this system relies on a novice-expert overlay and it is used to develop feedback when novices request help. When help-seeking occurs, help is provided by the tutoring module which applies a set of pre-defined rules based on the context of the learner’s activity. The system also provides cumulative feedback by comparing the novice solution with an expert solution following completion of the case. This chapter covers the essential design guidelines of this scaffolding approach to metacognitive activities in problem-solving within the domain of medical education. Specifically, we review recent advances in modeling metacognition through online measures, including concurrent think-aloud protocols, video-screen captures, and log-file entries. Educational data mining techniques are outlined with the goals of capturing metacognitive activities as they unfold throughout problem solving, and guiding the design of scaffolding tools in order to promote higher levels of competence in novices.

This paper presents an efficient numerical method for the harmonic analysis of viscoelastically damped structural systems characterized by a frequency‐dependent structural damping matrix, making use of the complex mode superposition method. Departing from the undamped eigensolution, the proposed numerical method updates the complex and frequency‐dependent eigenpair avoiding the solution of a complex eigenproblem for each computational frequency. The complex eigenvalues and eigenvectors are updated within the desired tolerance by an adaptive step‐size control scheme using the first‐ and higher order eigenderivatives. The influence on the computation time of the considered number of eigenderivatives and the tolerance is discussed, and the efficiency of the proposed numerical method for the harmonic analysis of viscoelastically damped large‐ordered structural systems is proved. Finally, a practical application is presented where the harmonic response of a constrained layer damping cantilever beam subjected to a base motion is analyzed. The complex and frequency‐dependent eigenvalues and eigenvectors are computed, the modal contributions to the total response are determined, and the total response is approximated by the complex mode superposition method. Finally, the approximated response is validated with the exact one computed by the direct frequency method and with that experimentally measured. Copyright © 2011 John Wiley & Sons, Ltd.

Computational costs limit large scale reservoir simulations to coarse grid systems. Determination of an effective permeability for a simulation grid block requires a proper scale-up of small scale permeability heterogeneities within that grid block. Conventional scale-up techniques are limited to a diagonal tensor representation of effective permeability. Therefore, such techniques cannot handle general permeability anisotropy (full tensor) exemplified by cross-bedded permeability structures that may be present on a smaller scale. An analytical method is developed to calculate an effective permeability tensor for a grid block by accounting for small scale heterogeneities within the grid block. The method honors both the location and the orientation of the small scale heterogeneities. Effective permeability tensors calculated using the analytical method and a numerical method show excellent agreement. Miscible displacement simulations show that the effective permeability tensor method outperforms conventional scale-up techniques in predicting flood front locations in cases of general permeability anisotropy.

Prefabricated circular tunnel linings are used in the construction of tunnels using the shield method, which significantly increases the speed and reduces the cost of building highways. Many methods have been developed for designing seismic resistant tunnel linings. Due to simplicity, analytical methods are usually used for preliminary design. In this case, the presence of joints between the blocks of the ring and between the rings is not taken into account. Compared to analytical methods, numerical methods, especially three-dimensional (3D), allow the most explicit consideration of the problem being solved. However, due to their complexity and time-consuming nature, 3D numerical models are only rarely used. In most reinforced concrete prefabricated linings, the deformation at the joints has a significant effect on the deformation of the rings. Thus, the magnitude and distribution of internal forces largely depend on the distribution and characteristics of the joints. Therefore, one of the most important factors in the design of a circular tunnel lining is the effect of segment connections on its overall behavior. This paper compares the internal forces that occur in the tunnel lining, determined using analytical analysis and numerical simulation in a three-dimensional formulation. When using numerical analysis methods, seismic loads are considered as quasi-static. However, almost all quasi-static analyzes presented in the literature include the same assumptions as closed form solutions and therefore have the same limited applicability. The main disadvantage of quasi-static models is that they do not take into account changes in the behavior of the structure over time.

Modern long-span floor system typically possesses low damping and low natural frequency, presenting a potential vibration sensitivity problem induced by human activities. Field test and numerical analysis methods are available to study this kind of problems, but would be inconvenient for design engineers. This paper proposes a simplified method to determine the acceleration amplitudes of long-span floor system subjected to walking or running load, which can be carried out manually. To theoretically analyze the acceleration response, the floor system is simplified as an anisotropic rectangular plate and the mode decomposition method is used. To facilitate the calculation of acceleration amplitude aP, a coefficient ɑwmn or ɑRmn is introduced, with the former depending on the geometry and support condition of floor system and the latter on the contact duration tR and natural frequency. The proposed simplified method is easy for practical use and gives safe structural designs.

The primary objective of this study is to investigate the rolling noises made by passenger car radial (PCR) tires with special pitch angles using the numerical analysis method, and compare the numerical analysis results with those of physical tire experiments. By using the numerical simulation features, we can understand the noise performances of the different designs through the numerical analysis results before creating the actual molds and thereby reduce the development costs. In this study, the researcher adopted the finite volume (FV) numerical simulation method because the rapid solving speed feature of the FV method enabled it to be widely adopted by the various major fields such as automotive, aerospace, or shipbuilding where the simulation results are time-sensitive and analytically correct. To achieve the experimental results, the researcher of this study commissioned Kenda Rubber Ind., Co., Ltd. to design the tire pitches and manufacture the tires, and deliver the tires to the Automotive Research & Testing Center (ARTC) for the experiments. When the numerical simulation and experiment results are compared, the characteristic frequency positions are consistent with the error rate of only 3.98%. As such, the analysis method proposed in this study can serve as references for tire manufacturing designs as well as mobile object noise analyses in the future.

Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring during CCUS. We have demonstrated this method by deploying strainmeters at shallow depths (30 to 40m) and then conducting injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 ne/d over a few days (1 nanostrain = 1 part per billion strain). We then used the strain data to estimate reservoir properties, geometry and pressure through inversion of poroelastic forward models using both numerical and novel analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed fast, closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. This approach markedly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. Bayesian inversion is important because it provides predictions with uncertainties, which makes the results useful for decision making. We have shown with field tests and simulations that the strain tensor in the caprock is sensitive to pressure in the reservoir, reservoir properties and boundaries, and pressure in the caprock caused by leaks. These results indicate that measuring and interpreting the shallow strain tensor could be a valuable tool for both initial reservoir characterization efforts and long-term monitoring during CCUS. Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and our objective was to evaluate opportunities for strain monitoring during characterization and monitoring for CCUS. Our approach was to deploy strainmeters at shallow depths (30 to 40m) and then conduct injection well tests in an underlying reservoir at 530m depth. The results indicate that the horizontal strain at shallow strainmeters was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 ne/d over a few days (1 nanostrain = 1 part per billion strain). We then used the strain data to estimate reservoir properties, geometry and pressure through inversion of poroelastic forward models using both numerical and novel analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed fast, closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. This approach markedly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. Bayesian inversion is important because it provides predictions with uncertainties, which makes the results useful for decision making. In conclusion, we have shown with field tests and simulations that the strain tensor in the caprock is sensitive to pressure in the reservoir, reservoir properties and boundaries, and pressure in the caprock caused by leaks. These results indicate that measuring and interpreting the shallow strain tensor could be a valuable tool for both initial reservoir characterization efforts and long-term monitoring during CCUS.

Numerical simulation method of thermal analysis for bridges without using field measurements

We consider the one-parameter conjugated analogs of Sylvester type matrix equations. On the basis of simple transformations we obtainthe equivalent matrix equation containing only the unknown matrix which should be determined. Next, using the apparatus of Kroneckerproducts of matrices, the analytical solution of the problem was obtained, which is limited in practical applications, but serves as a basis for the development of numerical-analytical methods for solving the original problem. Serial and parallel numerical and analytical solution methods are proposed based on the differential transformations G. E. Pukhov. With sequential numerical-analytical method we operate with numeric recursive procedures in the first stage of the calculation, and analytical relations – in the second stage. In parallel numerical-analytical method we operate with linear hypersystem of numerical equations in the first stage of the calculation, and analytical relations – in the second stage. For all the methods the relevant conditions for the unique solvability of the problem were obtained. A model example was considered for which using numerical and analytical methods an exact solution of the Taylor was obtained. The proposed numerical-analytical techniques can be efficiently implemented by means of modern information technology.

In the context of science, engineering, and design, reflection has been identified as a key practice that helps one to improve their problem-solving process and adapt it in the face of new challenges. The purpose of this study is to further examine the role of reflection its triggers, and its various types in the context of puzzle-based problem-solving as players engaged in solving complex puzzles within an online puzzle-based game entitled, Baba is You.® Within each level of Baba is You®, the player is tasked with solving a puzzle by interacting with various objects, and movable word blocks. Through interacting with these word blocks, players can change the rules of the game. 25 players, 13 middle school, 12 college students, from the United States were recruited via online flyers for this study and participated in video-recorded interviews over Zoom to solve several levels. None of the players had played Baba is You® before the study. A thematic qualitative analysis of two levels attempted by all players, Float and Changeless, were conducted to identify different types and triggers for reflection during problem solving, and its impacts on the following problem-solving actions. The findings revealed that players engage in three different main types of reflection during game play: reflection on problem orientation, reflection on observed deviation, and reflection on action-revision. Overall, findings show that there were three main types of reflection during these puzzles play sessions and they played a critical role in improving players’ problem-solving actions.

In order to evaluate the failure probability of a complicated structure, the structural responses usually need to be estimated by some numerical analysis methods such as finite element method (FEM). The response surface method (RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However, the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network (LONN)-based RSM to estimate the structural reliability. In this method, the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method, i.e. first-order reliability methods (FORM) to calculate the failure probability of the structure. Numerical examples show that the proposed approach is applicable to structural reliability analysis, as well as the structure with implicit performance functions.

In the previous papers, the numerical analysis method was proposed for estimating the mechanical behavior of braided composites, and its validity was confirmed. An analytical model can be developed for considering the continuity and crimp of fibers, and can be faithfully expressed in reinforcement configuration. The purpose of this study is to extend this analysis method to estimation of the mechanical properties of textile composites such as woven, braided and knitted fabrics. In this paper, the tensile properties and fracture behavior of weft-knitted fabric composite were estimated. The validity of this analysis method was examined by comparing predicted results with experiment data. Consequently, deformation properties and the stress state of weft-knitted fabric composite could be expressed using this analytical model. The predicted tensile modulus agreed well with the experimental results. Accordingly, it was clear that the tensile properties of weft-knitted fabric composite could be estimated using this analysis method.