scholarly journals [NO TITLE AVAILABLE]

2001 ◽  
Vol 21 (2) ◽  
pp. 199-218 ◽  
Author(s):  
Fernando Chiyoshi ◽  
Roberto D. Galvão ◽  
Reinaldo Morabito
Keyword(s):  
Regression Model ◽  
Emergency Medical Services ◽  
Real World ◽  
Specific Problem ◽  
Computing Time ◽  
Total System ◽  
Homogeneous Case ◽  
Real World Applications ◽  
Hypercube Model ◽  
Time Required

The objective of the present paper is to analyze the use and solution of the hypercube model for the case of non-homogeneous servers (servers with different mean service times). Systems with non-homogeneous servers can be found in several real world applications, such as for example in the provision of Emergency Medical Services (EMS) in some Brazilian cities. The importance of explicitly considering non-homogeneous servers in the hypercube model is initially demonstrated through an illustrative example. It is then shown that the solution for the non-homogeneous case can be advantageously obtained by the method of Gauss-Siedel. This method was tested for a network of 55 nodes, in models with between 10 and 17 servers, with the total system workload varying between 0.1 and 0.9. Finally, a regression model is proposed to estimate the computing time required to solve a specific problem

Rich-Media Interactive Simulations

2016 ◽  
pp. 261-283
Author(s):  
Suzanne Tsacoumis
Keyword(s):  
Real World ◽  
High Fidelity ◽  
Rich Media ◽  
Job Candidates ◽  
Real World Applications ◽  
Fidelity Measures ◽  
Time Required ◽  
The Cost ◽  
Interactive Simulations

High fidelity measures have proven to be powerful tools for measuring a broad range of competencies and their validity is well documented. However, their high-touch nature is often a deterrent to their use due to the cost and time required to develop and implement them. In addition, given the increased reliance on technology to screen and evaluate job candidates, organizations are continuing to search for more efficient ways to gather the information they need about one's capabilities. This chapter describes how innovative, interactive rich-media simulations that incorporate branching technology have been used in several real-world applications. The main focus is on describing the nature of these assessments and highlighting potential solutions to the unique measurement challenges associated with these types of assessments.

scholarly journals Tree-structured scale effects in binary and ordinal regression

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Gerhard Tutz ◽  
Moritz Berger
Keyword(s):  
Regression Model ◽  
Real World ◽  
Scale Effects ◽  
Recursive Partitioning ◽  
Ordinal Regression ◽  
Scaling Effects ◽  
Variance Heterogeneity ◽  
Significance Level ◽  
Ordinal Response ◽  
Real World Applications

AbstractIn binary and ordinal regression one can distinguish between a location component and a scaling component. While the former determines the location within the range of the response categories, the scaling indicates variance heterogeneity. In particular since it has been demonstrated that misleading effects can occur if one ignores the presence of a scaling component, it is important to account for potential scaling effects in the regression model, which is not possible in available recursive partitioning methods. The proposed recursive partitioning method yields two trees: one for the location and one for the scaling. They show in a simple interpretable way how variables interact to determine the binary or ordinal response. The developed algorithm controls for the global significance level and automatically selects the variables that have an impact on the response. The modeling approach is illustrated by several real-world applications.

scholarly journals Learning Discriminative Recommendation Systems with Side Information

2017 ◽  
Author(s):  
Feipeng Zhao ◽  
Yuhong Guo
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Real World ◽  
Linear Regression Model ◽  
Side Information ◽  
Recommendation Systems ◽  
Joint Model ◽  
Partially Observed ◽  
Real World Applications

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.

scholarly journals Electronic Phenomena of Transition Metal Oxides

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 256
Author(s):  
Christian Rodenbücher ◽  
Kristof Szot
Keyword(s):  
Solid State ◽  
Transition Metal ◽  
Metal Oxides ◽  
Real World ◽  
Transition Metal Oxides ◽  
Major Research ◽  
Research Fields ◽  
Real World Applications

Transition metal oxides with ABO3 or BO2 structures have become one of the major research fields in solid state science, as they exhibit an impressive variety of unusual and exotic phenomena with potential for their exploitation in real-world applications [...]

scholarly journals Applications of Distributed-Order Fractional Operators: A Review

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 110
Author(s):  
Wei Ding ◽  
Sansit Patnaik ◽  
Sai Sidhardh ◽  
Fabio Semperlotti
Keyword(s):  
Fractional Calculus ◽  
Real World ◽  
Transport Processes ◽  
Model Systems ◽  
Variable Order ◽  
Fractional Operators ◽  
Research Activity ◽  
Road Map ◽  
Real World Applications ◽  
Distributed Order

Distributed-order fractional calculus (DOFC) is a rapidly emerging branch of the broader area of fractional calculus that has important and far-reaching applications for the modeling of complex systems. DOFC generalizes the intrinsic multiscale nature of constant and variable-order fractional operators opening significant opportunities to model systems whose behavior stems from the complex interplay and superposition of nonlocal and memory effects occurring over a multitude of scales. In recent years, a significant amount of studies focusing on mathematical aspects and real-world applications of DOFC have been produced. However, a systematic review of the available literature and of the state-of-the-art of DOFC as it pertains, specifically, to real-world applications is still lacking. This review article is intended to provide the reader a road map to understand the early development of DOFC and the progressive evolution and application to the modeling of complex real-world problems. The review starts by offering a brief introduction to the mathematics of DOFC, including analytical and numerical methods, and it continues providing an extensive overview of the applications of DOFC to fields like viscoelasticity, transport processes, and control theory that have seen most of the research activity to date.

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