Structure Processing of Web-Based Menus

Grounded in cognitive load theory, levels of processing theory, modes of learning research, and the facets of working memory (WM) framework, the primary goal of this investigation was to seek empirical evidence for a structure processing mechanism in WM that facilitates the coordination of associations made into coherent structures and the role this mechanism might play in performance and retention. This goal was motivated by a gap in the understanding of mental model production and encoding processes in WM. To carry out this goal, two usability studies were conducted to investigate retention, error, and time performance of Web-based menus created with structured or unstructured design. Relationships between these measures and coordination ability in WM, as well as storage and supervision ability, were also assessed. In Study 1, which tested simple Web-based menus, correlations of .28, ?.56, and ?.54 were detected between coordination (CC) and retention scores, CC and errors, and CC and time; in Study 2, which tested commercial Web-based menus, correlations of .46, ?.45, and ?.25 were detected. For Studies 1 and 2, differences in favor of structured design for retention, error, and time performance were highly significant (p < .001).     

Adiabatic quantum optimization with qudits

Most realistic solid state devices considered as qubits are not true two-state systems. If the energy separation of the upper energy levels from the lowest two levels is not large, then these upper states may affect the evolution of the ground state over time and therefore cannot be neglected. In this work, we study the effect of energy levels beyond the lowest two energy levels on adiabatic quantum optimization in a device with a double-well potential as the basic logical element. We show that the extra levels can be modeled by adding additional ancilla qubits coupled to the original logical qubits, and that the presence of upper levels has no effect on the final ground state. We also study the influence of upper energy levels on the minimum gap for a set of 8-qubit spin glass instances.     

Direct 5-axis tool-path generation from point cloud input using 3D biarc fitting

In reverse engineering, geometrical information of a product is obtained directly from a physical shape by a digitizing device. To fabricate the product, manufacturing information (usually tool-path) must be generated from a CAD model. The data digitized must be processed and in most cases, a surface model is constructed from them using some of the surface fitting technologies. However, these technologies are usually complicated and the process for constructing a surface patch from a massive digitizing data is time-consuming. To simplify the process for getting tool-path information, a simple algorithm is proposed in this paper. The algorithm is used to generate a 5-axis machining tool-path. Instead of implementing any complicated surface fitting techniques, a direct method is proposed for constructing three-dimensional (3D) triangular mesh from the digitizing data with the mesh points considered as the tool contact locations. Depending on the locations of the points digitized, a decimation procedure is applied such that some of the digitizing data will be filtered out. Then, the tool axis orientations which must be determined in 5-axis tool-path are calculated and the tool center locations are determined accordingly. A 3D biarc fitting technique is applied for all the tool center locations so that a complete 5-axis tool-path is obtained.     

Finding Your True IT Transformation

If you want to transform your business and cause a dramatic improvement for the enterprise, you'll need to start by truly transforming your IT life cycle. It's easy to believe that IT is truly reinventing itself by hiring new people, installing new tools, or preaching a new methodology. However, providing transformational leadership starts with the leadership itself along with the development of the three core competencies of strategy, solutions, and implementation. Your job in IT leadership is to develop all three competencies in your organization by cultivating the necessary skills and techniques, and then to cause each of them to happen continuously and concurrently. When these changes become the organization's new habits, you're on the path to true transformation.     

过程分析仪表

当需要监视一个不属于"四大类(压力、流量.温度、液位)"的过程变量时,可以通过一些过程分析仪表完成。这类仪表同样也有自己的"四大类":成分-探测计量过程流体中的某种化学成分;电化学-测量某种离子浓度,大多数是氢(pH);分光光度法-利用光的吸收特性测量某种成分。物理特性-测量重力、密度、粘度等物     

Correlated light scattering by a dense distribution of condensation droplets on a window pane

An analytical model of the scattering structure factor for an assembly of noninteracting hard disks has recently appeared in the literature [Phys. Rev. A 42, 5978-5989 (1990)]. We employ this model to calculate correlated light scattering by monodispersions and binary mixtures of condensation droplets on a window pane. We find that an area fraction of ? ≥ 0.6 is required for producing the near-forward direction scattering suppression and that a moderately wide polydispersion of droplet sizes is capable of producing the experimentally observed bright ring of colored light.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

Regional patterns of affirmative action compliance costs

In this paper we estimate the amount by which firms' labor costs are increased by compliance with affirmative action regulations imposed on federal contractors for 160 SMSAs in 1980. The paper focuses on determining the cause(s) for variation in these compliance costs across SMSAs and in identifying areas of high and low compliance cost. We find that compliance costs are lower in larger SMSAs, more rapidly growing SMSAs, and SMSAs that have relatively large proportions of minorities and females in the labor market.The authors would like to thank Fred Wildes and Long Gen Ying, both of San Diego State University, for their assistance on this project. In addition, support for Prof. Getis' work was provided by the National Science Foundation, Grant No. SES-9123832.This paper was presented at the annual conference of the Western Regional Science Association in Tucson, Arizona.     

Fuzzy grey relational analysis for software effort estimation

Accurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends on the characteristics of the dataset, which is subject to considerable uncertainty. The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise. To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA). Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the k ^th continuous feature ( | ( x_o(k) - x_i(k) | ) \left( {\left| {\left( {{x_o}(k) - {x_i}(k)} \right.} \right|} \right) .GRA is a problem solving method that is used to assess the similarity between two tuples with M features. Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree. Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features. Both techniques are suitable when relationship between effort and other effort drivers is complex. Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple Linear Regression and Artificial Neural Networks methods.     

Parameter sensitivity study of the Nelder-Mead Simplex Method

This paper presents a parameter sensitivity study of the Nelder-Mead Simplex Method for unconstrained optimization. Nelder-Mead Simplex Method is very easy to implement in practice, because it does not require gradient computation; however, it is very sensitive to the choice of initial points selected. Fan-Zahara conducted a sensitivity study using a select set of test cases and suggested the best values for the parameters based on the highest percentage rate of successful minimization. Begambre-Laier used a strategy to control the Particle Swarm Optimization parameters based on the Nelder Mead Simplex Method in identifying structural damage. The main purpose of the paper is to extend their parameter sensitivity study to better understand the parameter’s behavior. The comprehensive parameter sensitivity study was conducted on seven test functions: B2, Beale, Booth, Wood, Rastrigin, Rosenbrock and Sphere Functions to search for common patterns and relationships each parameter has in producing the optimum solution. The results show important relations of the Nelder-Mead Simplex parameters: reflection, expansion, contraction, and Simplex size and how they impact the optimum solutions. This study is crucial, because better understanding of the parameters behavior can motivate current and future research using Nelder-Mead Simplex in creating an intelligent algorithm, which can be more effective, efficient, and save computational time.