Screening physicochemical,microbiological and bioactive properties of fruit vinegars produced from various raw materials

Food Science and Biotechnology - In the present study, variety of fruit vinegars were investigated in terms of their physicochemical, microbiological and bioactive properties. Total phenolic and...     

Automatic detection of building roofs from point clouds produced by the dense image matching technique

Three-dimensional (3D) spatial information of object points is a vital requirement for many disciplines. Laser scanning technology and techniques based on image matching have been used extensively to produce 3D dense point clouds. These data are used frequently in various applications, such as the generation of digital surface model (DSM)/digital terrain model (DTM), extracting objects (e.g., buildings, trees, and roads), 3D modelling, and detecting changes. The aim of this study was to extract the building roof points automatically from the 3D point cloud data created via the image matching techniques with optical aerial images (with red, green, and blue band (RGB) and infrared (IR)). In the first stage of the study, as an alternative to laser scanning technology, which is more expensive than optical imaging systems, the 3D point clouds were produced by matching high-resolution images using a Semi Global Matching algorithm. The normalized difference vegetation index (NDVI) values for each point were calculated using the spectral information (RGB + IR) in the 3D point cloud data, and the points that represented the vegetation cover were determined using these values. In the second stage, existing ground and non-ground points that were free of vegetation cover were determined within the point cloud. Subsequently, only the points on the roof of the building were detected automatically using the proposed algorithm. Thus, points of the roofs of buildings located in areas with different topographic characteristics were detected automatically detected using only images. It was determined that the average values of correctness (Corr), completeness (Comp), and quality (Q) of the pixel-based accuracy analysis metrics were 95%, 98%, and 93%, respectively, in the selected test areas. According to the results of the accuracy analysis, it is clear that the proposed algorithm is very successful in automatic extraction of building roof points.     

A sharp augmented Lagrangian-based method in constrained non-convex optimization

In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for solving constrained non-convex optimization problems. The algorithm consists of outer and inner loops. At each inner iteration, the discrete gradient method is applied to minimize the sharp augmented Lagrangian function. Depending on the solution found the algorithm stops or updates the dual variables in the inner loop, or updates the upper or lower bounds by going to the outer loop. The convergence results for the proposed method are presented. The performance of the method is demonstrated using a wide range of nonlinear smooth and non-smooth constrained optimization test problems from the literature.     

Theoretical and experimental approaches to measuring mechanical properties of Zn<Subscript>1−x</Subscript>Co<Subscript>x</Subscript>O binary tetrahedral bulk semiconductors

We prepared a \({{\text{Zn}}_{1 - {\text{x}}}}{{\text{Co}}_{\text{x}}}{\text{O}}\) system as polycrystalline nanoparticles with various compositions \((x=0.01, 0.02, 0.03, 0.04, 0.05, {\text{and}} \; 0.10)\) using sol–gel techniques and use zinc acetate dihydrate and cobalt acetate tetrahydrate as precursors. Nanoparticles were pressed under a pressure of 4 tons for 5 min into 2 mm thick disk shaped compacts 10 mm in diameter, which were then annealed at 500 °C for 30 min under a 5B Ar atmoshpere. We carried out X-ray diffraction, scanning electron microscopy, and Vickers microhardness analyses of Co doped \({\text{Zno}}\)-based nano bulk materials in detail, focusing especially on theoretical and experimental mechanical analyses. We found that calculated values were higher than the Vickers microhardness experimental results. Doping ZnO with Co did not lead to significant changes in the a and c axes. The calculated hardness values are larger than those from the experiments. Acoording to the SEM and EDS images grain size decreases as Co doping increases and the amount of Zn decreases with Co doping, respectively.     

Analysis of AC Transport Loss in Conductor on Round Core Cables

Journal of Superconductivity and Novel Magnetism - The conductor on round core (CORC) cable wound with second-generation high-temperature superconducting (HTS) tapes is a promising cable candidate...     

Economic and cross-media effect analyses of best available techniques for caustic recovery from mercerization textile wastewater

Clean Technologies and Environmental Policy - Caustic can be recovered from textile mercerization wastewaters by evaporation or membrane filtration. The main objective of this study was to evaluate...     

Nonlinear Short-term Prediction of Aluminum Foil Thickness via Global Regressor Combination

In this study, short-term prediction of aluminum foil thickness time-series data recorded during cold-rolling process was investigated. The locally projective nonlinear noise reduction was applied in order to improve the predictability of the time series. The higher-order statistics methods (bispectrum and bicoherence) were used to detect the nonlinearity. The embedding vectors with appropriate embedding dimension and time delay were obtained via the false nearest neighbors and mutual information methods, respectively. The maximum prediction horizon was determined depending on the maximal Lyapunov exponent. For various prediction horizons, the embedding vector and corresponding thickness value pairs were used as the dataset to assess the prediction performance of various machine learning algorithms (i.e., multilayer perceptron neural network, support vector machines with Pearson VII function-based kernel, and radial basis function network). The n-step ahead prediction outputs of the machine learning algorithms were globally combined with simple voting in favor of the one having minimum absolute error. The accuracy of our proposed method was compared with nonlinear autoregressive exogenous model for various thickness time-series data using mean absolute percentage error measure.     

Productivity and costs in the course of timber transportation with the Koller K300 cable system in Turkey

This study was carried out on very steep and difficult terrain. Whole trees were logged uphill to the forest road by a Koller K300 cable crane. Investigations were carried out by time studies for which the repetition time measurement method was selected. Through time studies, the productivity and cost of the Koller cable system were evaluated.     

A comparative study of linear and non-linear regression analysis for ammonium exchange by clinoptilolite zeolite

Ammonium ion exchange from aqueous solution using clinoptilolite zeolite was investigated at laboratory scale. Batch experimental studies were conducted to evaluate the effect of various parameters such as pH, zeolite dosage, contact time, initial ammonium concentration and temperature. Freundlich and Langmuir isotherm models and pseudo-second-order model were fitted to experimental data. Linear and non-linear regression methods were compared to determine the best fitting of isotherm and kinetic model to experimental data. The rate limiting mechanism of ammonium uptake by zeolite was determined as chemical exchange. Non-linear regression has better performance for analyzing experimental data and Freundlich model was better than Langmuir to represent equilibrium data.     

Analysis and performance assessment of a combined geothermal power-based hydrogen production and liquefaction system

In this paper, the thermodynamic study of a combined geothermal power-based hydrogen generation and liquefaction system is investigated for performance assessment. Because hydrogen is the energy of future, the purpose of this study is to produce hydrogen in a clear way. The results of study can be helpful for decision makers in terms of the integrated system efficiency. The presented integrated hydrogen production and liquefaction system consists of a combined geothermal power system, a PEM electrolyzer, and a hydrogen liquefaction and storage system. The exergy destruction rates, exergy destruction ratios and exergetic performance values of presented integrated system and its subsystems are determined by using the balance equations for mass, energy, entropy, energy and exergy and evaluated their performances by means of energetic and exergetic efficiencies. In this regard, the impact of some design parameters and operating conditions on the hydrogen production and liquefaction and its exergy destruction rates and exergetic performances are investigated parametrically. According to these parametric analysis results, the most influential parameter affecting system exergy efficiency is found to be geothermal source temperature in such a way that as geothermal fluid temperature increases from 130 °C to 200 °C which results in an increase of exergy efficiency from 38% to 64%. Results also show that, PEM electrolyzer temperature is more effective than reference temperature. As PEM electrolyzer temperature increases from 60 °C to 85 °C, the hydrogen production efficiency increases from nearly 39% to 44%.