Healing the psyche through music, myth, and ritual.

According to Carl Jung, a person's symptoms occur as a result of the psyche's creative attempt to self-regulate. These symptoms are viewed as the symbol-making function of the psyche. Music therapy as a therapeutic tool is also viewed as having a symbol-making function in that the physical act of making music involves conceptualizing one's symptoms into sounds. The sound serves as a musical symbol of the symptom. The myth of Orpheus is offered as an example of how early man amplified the creative potential contained in music to heal from life's woes. The Orphic archetype illustrates that music serves as a medium for healing and as an expression of Self. That psyche can be healed by music is a reminder of the connection between music therapy and the myth of Orpheus. In using the therapeutic tool of music one can transcend, through the symbol making process inherent in music therapy, the tension of opposites that are created at the crossroads of disease and wellness. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Developing Cost Response Models for Company-Level Cost Flow Forecasting of Project-Based Corporations

The difficulty in applying the standard curve (S-curve) and cost-schedule integration (CSI) techniques for company-level cost flow forecasting in a project-based industry is the prerequisite of forecasting future unknown individual projects and contract classifications. By analyzing cost flows at the company level through a pool of macroeconomic and internal financial data, this paper proposes an innovative approach to firm-specific model estimation. First, a series of data transformations introduce linear relationships between cost, macroeconomic, and internal financial variables. Second, multivariate regression analysis is employed for initial model building. Third, for the purposes of model restructuring, a subsequent application of Yule–Walker estimates and incomplete principal component analysis is used. This paper uses a sample of four project-based construction firms to demonstrate model performance. Using this methodology, mean absolute percentage error (MAPE) values of the forecasting models range from 0.27 to 0.60%. As such, the transformed cost, macroeconomic, internal financial data could strongly predict company-level cost flow forecasting. While converting the predicted cumulative cost data to periodic cost flows, the MAPE values were augmented, ranging from 7.04 to 17.55%, thus, requiring future research.     

From the Editors.

We could not imagine a more appropriate inauguration of the Psychology of Aesthetics, Creativity, and the Arts (PACA) going to four issues per year than with a special issue honoring the life and work of the great aesthetician and psychologist, Rudolf Arnheim. PACA is off to a great start. With this move to four issues per year, we are restarting our numbering system, so this special issue is Volume 1, Issue 1. As you read the articles in this issue, we hope that you enjoy reflecting on how much Rudolf Arnheim has contributed to the field of psychology of aesthetics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique

A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.     

Free-Edge and Ply Cracking Effect in Angle-Ply Laminated Composites Subjected to In-Plane Loads

This paper presents a semianalytical method for the prediction of interlaminar stresses and displacements near the free edges and ply cracks in general angle-ply laminates subjected to biaxial extensions and/or in plane shear deformation. The method is based on a state space representation of the three-dimensional equations of elasticity. Numerical solutions are obtained by using layer refinement in the through thickness direction and Fourier series expansion in the other directions. By this approach, an angle-ply laminate may be composed of an arbitrary number of monoclinic layers and each layer may have different material property and thickness. This method guarantees continuous fields of all interlaminar stresses across interfaces between material layers. Numerical results are compared with those obtained from other methods. It is found that the theory provides a satisfactory approximation to the stress singularities near the free edges and ply cracks. Numerical solutions for antisymmetric laminates under extension and general laminates under shearing are new in the literature and can be used as benchmarks for validating new models.     

Model Selection in Applied Science and Engineering: A Decision-Theoretic Approach

Mathematical models are developed and used to study the properties of complex systems in just about every area of applied science and engineering. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. A decision-theoretic method is developed for selecting the optimal member from the collection. The optimal model depends on the available information, the class of candidate models, and the model use. The candidate models may be deterministic or random. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, are briefly reviewed. These methods ignore model use and require data to be available. In addition, examples are used to show that classical methods for model selection can be unreliable in the sense that they can deliver unsatisfactory models when data is limited. The proposed decision-theoretic method for model selection does not have these limitations. The method accounts for model use via a utility function. This feature is especially important when modeling high-risk systems where the consequences of using an inappropriate model for the system can be disastrous.     

Case Study-Based Challenges of Quality Concrete Finishing for Architecturally Complex Structures

This paper focuses on the procedure utilized in the construction of tilt-up irregular concrete panels that are constructed on-site using concrete slabs and wooden formwork. The case study required high-quality concrete finishing. The erection and installation procedure called for a maximum panel-to-panel joint tolerance of 1.27?cm (0.5?in.), often 90° joints between panels. To meet precision requirements, the casting slabs were leveled and flattened with laser screed technology and smoothed with chemical solutions. To ensure that the final result met expectations, a mock-up model was built using different types of materials and to simulate site constraints. The architectural design is a composition of precast concrete panels like “Lego” pieces assembled similarly to a jigsaw puzzle. The unique construction process required a state-of-the-art analysis to produce the set quality. Quality conditions as set by the owner included creating a smooth concrete surface on all panels while avoiding damages and reducing equipment and material costs. The proposed methodology is described through its implementation on the case study, which is also described in this paper.     

A fuzzy clustering algorithm enhancing local model interpretability

In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in literature. Some examples illustrate the proposed approach.