Exploring the Belief Systems of Software Development Professionals

It is commonly accepted that an individual's beliefs and actions are based on his or her assessment and perceptions of the world. In order to determine what practices an individual is likely to follow at any given time, it is necessary to understand the individual's behavioral intention in a given circumstance. From an Information Technology perspective, a software development professional's belief systems are potentially the basis for the adoption and implementation of new and innovative work practices and processes. In this article, we explore the belief systems of software development professionals in order to understand the beliefs underlying intention and practice, and we seek answers about how they adopt or reject new and innovative software development processes and practices. The results point out a strong influence of past experiences, personality types, and repeated behavior on current software development processes and practices in industrial settings.     

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.     

Taguchi optimization approach for the polypropylene fiber reinforced concrete strengthening with polymer after high temperature

In this study, the strengthening with polymer the polypropylene fiber reinforced concrete exposed to high temperature was examined. Taguchi L₉ (3³) orthogonal array was used for the design of experiments. Three different parameters were used in the study; polypropylene fiber percentage (0 %, 1 % and 2 %), high temperature degree (300 °C, 600 °C and 900 °C) and curing period (3, 7 and 28 days). Cube samples of 100x100x100 mm sizes were produced for the compressive strength and ultrasonic pulse velocity tests. The samples were removed from the water and dried at 105?±?5 °C, and then they were exposed to temperatures of 300 °C, 600 °C and 900 °C. Then, the polymerization of monomer and the vinyl acetate monomer impregnation on the samples were carried out. The compressive strength and ultrasonic pulse velocity tests were made. Taguchi analysis showed that the largest compressive strength and ultrasonic pulse velocity were obtained at a rate of 0 % from the samples with polypropylene fiber exposed to 600 °C and kept for 28 days as cure period. It was determined as the result of Anova analysis that high temperature had made biggest effect on the compressive strength and ultrasonic pulse velocity of the concrete reinforced with polymer.     

BiLSTM and dynamic fuzzy AHP-GA method for procedural game level generation

Neural Computing and Applications - The use of games in daily life, especially in education, has been in an incline during the COVID-2019 pandemic. Thus, game-based learning environments have...     

Single image super resolution based on sparse representation using discrete wavelet transform

Multimedia Tools and Applications - Single image super resolution (SR) based on sparse representation is a promising technique where the SR problem is solved by searching for the most robust...     

Improved assignment with ant colony optimization for multi-target tracking

Detecting and tracking ground targets is crucial in military intelligence in battlefield surveillance. Once targets have been detected, the system used can proceed to track them where tracking can be done using Ground Moving Target Indicator (GMTI) type indicators that can observe objects moving in the area of interest. However, when targets move close to each other in formation as a convoy, then the problem of assigning measurements to targets has to be addressed first, as it is an important step in target tracking. With the increasing computational power, it became possible to use more complex association logic in tracking algorithms. Although its optimal solution can be proved to be an NP hard problem, the multidimensional assignment enjoyed a renewed interest mostly due to Lagrangian relaxation approaches to its solution. Recently, it has been reported that randomized heuristic approaches surpassed the performance of Lagrangian relaxation algorithm especially in dense problems. In this paper, impelled from the success of randomized heuristic methods, we investigate a different stochastic approach, namely, the biologically inspired ant colony optimization to solve the NP hard multidimensional assignment problem for tracking multiple ground targets.     

Feedback tuning of bifurcations

The present paper studies a feedback regulation problem that arises in at least two different biological applications. The feedback regulation problem under consideration may be interpreted as an adaptive control problem for tuning bifurcation parameters, and it has not been studied in the control literature. The goal of the paper is to formulate this problem and to present some preliminary results.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.