Urban expansion modeling using an enhanced decision tree algorithm

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.

     

Applying multiple decomposition methods and optimization techniques for achieving optimal cost in mixed materials heat exchanger networks

The optimization of the total annual cost in heat exchanger networks has been one of the overarching goals when synthesizing these networks. Several methodologies and techniques have been developed to achieve optimal costs in mixed material heat exchanger networks. This paper demonstrates the application of two decomposition methodologies (total decomposition and partial decomposition) for typical cost rules. The objective function was defined as the optimization and minimization of the total annual cost in mixed materials heat exchanger network. Three optimization algorithms, hybrid genetic‐particle swarm optimization (GA‐PSO), shuffled frog leaping algorithm (SFLA) techniques, and ant colony optimization (ACO), were used to further optimize the total cost in mixed materials heat exchanger network. The results indicate that the total annual cost in partial decomposition method was smaller than that in full integration method and total decomposition method. The reduction of the total annual cost was about 27% for GA‐PSO algorithm, 24% for SFLA and 10% for ACO relative to the results reported in this work. In partial decomposition method, at least one mixed material of heat exchanger was used to reduce the hot and cold utility for decreasing the total annual cost. Partial decomposition method resulted in the highest reduction of the total annual cost compared with other methods. Percentage of difference of the total annual cost were 0.36%, 1.92%, and 5.05% for full integration, total decomposition, and partial decomposition methods, respectively, in comparison with the previous studies. Results have been compared with the results of other studies to demonstrate the accuracy of the applied algorithms.     

Auto-recognition and part model complexity quantification of regular-freeform revolved surfaces through delta volume generations

Engineering with Computers - Vast research works implementing feature-based technology have successfully been devoted. However, work on recognition of revolved regular-freeform surfaces is still...     

Influence of fine ceramic aggregates on the residual properties of concrete subjected to elevated temperature

The residual properties of concrete subjected to elevated temperature are of importance to assess the stability of the structure. This paper investigates the performance of concrete containing white ware ceramic sand exposed to elevated temperature. Concrete mixes containing 0%, 50%, and 100% ceramic sand were prepared. The specimen were exposed to elevated temperatures of 200°C, 500°C, and 800°C for a duration of 60 minutes. Their residual mechanical properties (compressive strength, split tensile strength), ultra sonic pulse velocity, and mass change for different cooling regimes were investigated and compared among specimen. The results showed that incorporation of ceramic sand in concrete mixes improved the resistance against elevated temperature of hardened concrete.     

Analyzing readers behavior in downloading articles from IEEE digital library: a study of two selected journals in the field of education

Scientometrics - In this study, we investigate the downloads behavior of readers for two well-known IEEE journals in the field of education, i.e., IEEE Transactions on Learning Technologies (TLT)...     

Martensitic phase transformations in Ni–Ti-based shape memory alloys: The Landau theory

We present a simple Landau free energy functional for cubic-to-orthorhombic and cubic-to-monoclinic martensitic phase transformations. The functional is derived following group–subgroup relations between different martensitic phases – tetragonal, trigonal, orthorhombic and monoclinic – in order to fully capture the symmetry properties of the free energy of the austenite and martensite phases. The derived free energy functional is fitted to the elastic and thermodynamic properties of NiTi and NiTiCu shape memory alloys which exhibit cubic-to-monoclinic and cubic-to-orthorhombic martensitic phase transformations, respectively.     

Insight into the binding of copper(II) by non-toxic biodegradable material (Oryza sativa): effect of modification and interfering ions

The present studies are focused on the use of non-toxic biodegradable straw from Oryza sativa in its simple and modified forms for the binding of copper(II) ions. A relatively new “green” method was adopted for modification with urea under microwaves. The studies have been performed by using the aqueous solution of Cu(II) ions with and without the presence of Cd(II) and Pb(II) as interfering ions. FTIR analysis showed the presence of oxygen- and nitrogen-containing functional groups in simple and modified materials. The emergence of new bands and shifts in the peaks confirmed the modification. The kinetics of the process was studied using the commonly employed mathematical models. Although Elovich model seemed to fit yet coefficient of determination did not reinforce it. Pseudo-second-order model was found to explain the kinetics of the binding of metal ions by simple and modified straw. The equilibrium was studied using the non-linear approach. Based on root mean square error values, it was found that Langmuir model was the most suitable model, followed by Temkin model. Surface areas were compared for single and multi-metal systems. The effect of pH was also studied. Under the studied set of conditions, the modification of straw caused a decrease in the equilibrium time of contact and increase in the biosorption capacities. The presence of other ions decreased the capacities drastically due the competition to bind with the materials.     

Synthesis,Characterization, and Gas Sensing Properties of Pure and Mn-doped ZnO Nanocrystalline Particles

Nanocrystalline ZnO and Mn (1 wt.%)-doped ZnO particles have been synthesized via reverse micelle method. The structural, particulate, and optical properties of the synthesized nanoparticles have been studied by XRD, TEM, UV-Vis, and PL spectroscopy. The obtained data indicate the synthesis of the pure nanoparticles structure with wurtzite structure, average particle size of 18-21 nm, and high optical quality. Gas sensing properties of the nanocrystalline ZnO and Mn-doped ZnO particles toward gasoline and ethanol vapors have been investigated at different temperatures and concentrations. The results show that the optimum working temperature of the gas sensors based on ZnO and Mn-doped ZnO particles are about 633 and 620 K toward ethanol vapor and about 560 and 608 K toward gasoline vapor, respectively. Based on the results, although Mn impurities reduce the sensitivity of the ZnO gas sensor, they cause sensor to saturate at much higher gas concentration.