An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.     

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Trend Assessment by the Innovative-Şen Method

Hydro-meteorological time series may include trend components mostly due to climate change since about three to four decades. Trend detection and identification in a better and refined manner are among the major current research topics in water resources domain. Even though different methodologies can be found for trend detection in literature, two well-known procedures are the Mann-Kendall (MK) trend test and recently innovative-?en trend method, which provides different aspects of the trend. The theoretical basis and application of these two methods are completely different. The MK test gives a holistic monotonic trend without any categorization of the time series into a set of clusters, but the innovative-?en method is based on cluster and provides categorical trend behavior in a given time series. The main purpose of this paper is to provide important differences between these two approaches and their possible similarities. The applications of the two approaches are given for hydro-meteorological records including relative humidity, temperature, precipitation and runoff from Ergene drainage basin in the north-western part of Turkey. It is observed that although MK trend test does not show significant trend almost in all the cases, the innovative-?en approach yields trend categorizations as “very low”, “low”, “medium” “high” and “very high”, which should be taken into consideration in future flood (“very high”) and drought (“very low”) studies.     

Reordering for improving global Arnoldi–Tikhonov method in image restoration problems

This paper discusses the solution of large-scale linear discrete ill-posed problems arising from image restoration problems. Since the scale of the problem is usually very large, the computations with the blurring matrix can be very expensive. In this regard, we consider problems in which the coefficient matrix is the sum of Kronecker products of matrices to benefit the computation. Here, we present an alternative approach based on reordering of the image approximations obtained with the global Arnoldi–Tikhonov method. The ordering of the intensities is such that it makes the image approximation monotonic and thus minimizes the finite differences norm. We present theoretical properties of the method and numerical experiments on image restoration.     

Effects of different agro-industrial waste as substrates on proximate composition,metals, and mineral contents of oyster mushroom (Pleurotus ostreatus)

The use of agro-industrial waste as substrates for mushroom cultivation is considered a promising management strategy for reducing and valorising these wastes, simultaneously reducing the cost of mushroom cultivation. In this study, oyster mushroom Pleurotus ostreatus were cultivated on twelve substrates composed of either tea waste, lime sawdust, alder sawdust, hornbeam sawdust/shaving, wheat stalk-straw, wheat bran or their composite to determine the proximate composition and accumulation of thirteen elements in their fruiting bodies. The proximate composition of P. ostreatus did not show a significant difference, regardless of the employed substrate. (one-way manova ; F_{(66, 107)} = 1.329, Wilk’s λ = 0.041, P > 0.05). However, their chemical element contents show a statistically significant difference (one-way manova ; F_{(132, 418)} = 32.163, Wilk’s λ = 0.000, P < 0.05). These results were supported by discriminant function and principal component analyses. The highest mean concentrations of six of twelve elements (i.e., Ca, Mg, Na, Zn, Cd and Cr) were recorded in P. ostreatus cultivated on the lime-sawdust substrate. Three health indices viz., estimated daily intake, target hazard quotient (THQ) and total THQ were applied to determine the risk to human health via the consumption of P. ostreatus, suggesting that they are safe for human consumption.     

Quality assessment of surface and groundwater in Solaklı Basin (Trabzon, Turkey)

The paper reports a study of the surface and ground water in Solakl? Basin, Trabzon, from an assessment of 33 sampling points. Many of the parameters analysed are only marginally acceptable as potable water, even though the samples were collected during the wet season. The highest concentrations of contaminants were found downstream of the centres of population. The results indicate regular monitoring is required and some measures should be taken to protect the limited resources.     

Enhancing Effect of Calix[4]arene Amide Derivatives on Lipase Performance in Enantioselective Hydrolysis of Racemic Arylpropionic Acid Methyl Esters

Calix[4]arene amide derivatives were employed as new additives within the sol-gel encapsulation of lipase from Candida rugosa (CRL) to improve its catalytic properties. Evaluation of catalytic activity of the encapsulated lipases was acheived by enantioselective hydrolysis of both racemates, Naproxen methyl ester and 2-phenoxypropionic acid methyl ester, in aqueous buffer solution/isooctane reaction system. Results show that enantioselectivity was improved by using calix[4]arene amide derivatives-based encapsulated lipases. The reaction of naproxen methyl ester resulted in 47.6% conversion (x) in 24 h with 88.9% enantiomeric excess of substrate (ee_s), analogous to an enantioselectivity (E) value of 297 (E = 137 for the encapsulated free enzyme). The conversion of 2-phenoxypropionic acid methyl ester, obtained was 48.4% with E value of 327, enantiomeric excess of substrate (ee_s) of 92% for the reaction time of 1 h (E = 211 for the encapsulated free enzyme).     

The effects of xanthan and guar gums on staling of gluten-free rice cakes baked in different ovens

The objective of this study was to determine the effects of different concentrations of xanthan and guar gums and their blends on staling of gluten-free rice cakes baked in microwave-infrared combination oven (MW–IR) and to compare the cakes with conventionally baked ones. Gums were added at concentrations of 0.3% and 1.0%. For preparation of gum blend, 0.5% xanthan gum was mixed with 0.5% guar gum. In order to understand the staling behaviour of cakes, cakes were stored at 22 ± 2 °C for 120 h. Xanthan-guar gum blend decreased hardness, weight loss, retrogradation enthalpy and the change in setback viscosity values of cakes during storage for both types of ovens as compared to control formulation. It was found that oven type was a significant factor in affecting staling parameters of gluten-free cakes.