QuePED: Revisiting queueing networks for the performance evaluation of database designs

Databases form the common component of many software systems. However, performance models specifically targeted at the database design have not been extensively studied. In this paper, we propose QuePED – a queueing network performance evaluation model for database designs. QuePED provides for the performance modelling of database design constructs, including active database rules; thus, deviating from current methods that consider database designs as processing demands on the hardware architecture. We present a formal specification of QuePED and describe its application to database designs. Experimental evaluation has shown the ability of QuePED to capture the steady state performance of an implementation of the TPC-C benchmark.     

The design of next generation in-vehicle navigation systems for the older driver

It has been proposed that the current design of in-vehicle displays may not be appropriate for the older driver. This paper describes an empirical, road-based investigation of the benefits to older and younger drivers of providing landmarks within the instructions presented by an in-vehicle navigation system. Thirty two participants navigated a challenging urban route using either landmarks or distance information to identify the location of forthcoming manoeuvres. A range of driver behaviour measures were collected, including visual glance data, driving errors, driver workload, navigation errors, navigation confidence, and pre and post-trial driver attitudinal responses. Results show that, for older and younger drivers, landmarks reduced the time spent glancing to a visual display, reduced navigation and driving errors, and influenced driver confidence. There were some key differences between the older and younger drivers. The wider implications for the design of in-car interfaces for the older driver are discussed.     

Automatic recognition of vigilance state by using a wavelet-based artificial neural network

In this study, 5-s long sequences of full-spectrum electroencephalogram (EEG) recordings were used for classifying alert versus drowsy states in an arbitrary subject. EEG signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron (MLP), was used for the classification of EEG signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg–Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the MLPNN inputs, spectral analysis of EEG signals was performed using the discrete wavelet transform (DWT) technique. The MLPNN was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3% alert, 96.6% drowsy, and 90% sleep. The classification results showed that the MLPNN trained with the Levenberg–Marquardt algorithm was effective for discriminating the vigilance state of the subject.     

A rule‐based approach to detect and prevent inconsistency in the domain‐engineering process

A medium‐sized domain‐engineering process can contain thousands of features that all have constraint dependency rules between them. Therefore, the validation of the content of domain‐engineering process is vital to produce high‐quality software products. However, it is not feasible to do this manually. This paper aims to improve the quality of the software products generated by the domain‐engineering process by ensuring the validity of the results of that process. We propose rules for two operations: inconsistency detection and inconsistency prevention. We introduce first‐order logic (FOL) rules to detect three types of inconsistency and prevent the direct inconsistency in the domain‐engineering process. Developing FOL rules to detect and prevent inconsistency in the domain‐engineering process directly without the need to the configuration process is our main contribution. We performed some experiments to test the scalability and applicability of our approach on domain‐engineered software product lines containing 1000 assets to 20000 assets. The results show that our approach is scalable and could be utilized to improve the domain‐engineering process.     

Modeling Storm-Water Runoff Quantity and Quality from Marine Drydocks

Storm-water runoff from a floating marine drydock can be a major source of pollution for the waterway in which the drydock is located. Significant amounts of pollutants build up over drydock surfaces because of intensive industrial activity. During periods of rainfall these pollutants can be washed off and quickly transported into the receiving water. In this study, a mathematical model has been developed to simulate the quantity and quality of storm-water runoff from marine drydocks. The mathematical model is based on the two-dimensional kinematic-wave and convective transport equations for total suspended solids. An empirical formula is used to model the wash-off process. An implicit finite-difference scheme is employed to solve the governing equations of the model numerically. In support of the modeling effort, the quantity and quality of storm-water runoff from a private drydock was monitored under actual rainfall-runoff conditions. The samples collected were analyzed in the laboratory to determine the pollutant loading of the runoff, and these data were subsequently used to calibrate and verify the mathematical model.     

On multi-soliton solutions for the (2+1)-dimensional breaking soliton equation with variable coefficients in a graded-index waveguide

In this work, we construct multi-soliton solutions of the $(2+1)$-dimensional breaking soliton equation with variable coefficients by using the generalized unified method. We employ this method to obtain double- and triple-soliton solutions. Furthermore, we study the nonlinear interactions between these solutions in a graded-index waveguide. The physical insight and the movement role of the waves are discussed and analyzed graphically for different choices of the arbitrary functions in the obtained solutions. The interactions between the solitons are elastic whether the coefficients of the equation are constants or variables.     

Complexity and performance of an Augmented Lagrangian algorithm

Algencan is a well established safeguarded Augmented Lagrangian algorithm introduced in [R. Andreani, E. G. Birgin, J. M. Martínez, and M. L. Schuverdt, On Augmented Lagrangian methods with general lower-level constraints, SIAM J. Optim. 18 (2008), pp. 1286–1309]. Complexity results that report its worst-case behaviour in terms of iterations and evaluations of functions and derivatives that are necessary to obtain suitable stopping criteria are presented in this work. In addition, its computational performance considering all problems from the CUTEst collection is presented, which shows that it is a useful tool for solving large-scale constrained optimization problems.     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

An intelligent based-model role to simulate the factor of safe slope by support vector regression

An infrastructure development in landscape and clearing of more vegetated areas have provided huge changes in Malaysia gradually leading to slope instabilities accompanied by enormous environmental effects such as properties and destructions. Thus, prudent practices through vegetation incorporating to use slope stability is an option to the general stabilized technique. Few researches have investigated the effectiveness of vegetative coverings related to slope and soil parameters. The main goal of this study is to provide an intelligent soft computing model to predict the safety factor (FOS) of a slope using support vector regression (SVR). In the other words, SVR has investigated the surface eco-protection techniques for cohesive soil slopes in Guthrie Corridor Expressway stretch through the probabilistic models analysis to highlight the main parameters. The aforementioned analysis has been performed to predict the FOS of a slope, also the estimator’s function has been confirmed by the simulative outcome compared to artificial neural network and genetic programing resulting in a drastic accurate estimation by SVR. Using new analyzing methods like SVR are more purposeful than achieving a starting point by trial and error embedding multiple factors into one in ordinary low-technique software.