The effect of few historical data on the performance of sample average approximation method for operating room scheduling

We model the scheduling problem of a single operating room for outpatient surgery, with uncertain case durations and an objective function comprising waiting time, idle time, and overtime costs. This stochastic scheduling problem has been studied in diverse forms. One of the most common approaches used is the sample average approximation (SAA). Our contribution is to study the use of SAA to solve this problem under few historical data using families of log t distributions with varying degrees of freedom. We analyze the results of the SAA method in terms of optimality convergence, the effect of the number of scenarios, and average computational time. Given the case sequence, computational results demonstrate that SAA with an adequate number of scenarios performs close to the exact method. For example, we find that the optimality gap, in units of proportional weighted time, is relatively small when 500 scenarios are used: 99% of the instances have an optimality gap of less than 2.6 7% (1.74%, 1.23%) when there are 3 (9, many) historical samples. Increasing the number of SAA scenarios improves performance, but is not critical when the case sequence is given. However, choosing the number of SAA scenarios becomes critical when the same method is used to choose among sequencing heuristics when there are few historical data. For example, when there are only three (nine, many) historical samples, 99% of the instances have less than 25.38% (13.15%, 6.87%) penalty in using SAA with 500 scenarios to choose the best sequencing heuristic.     

CORN GLUTEN HYDROLYSIS BY ALCALASE: KINETICS OF HYDROLYSIS

In the present study, the reaction kinetics of corn gluten hydrolysis by Alcalase, a bacterial protease produced by Bacillus licheniformis, was investigated. The reactions were carried out for 10 min in 0.1 L of aqueous solutions containing 10, 20, 30, 40, and 50 g protein L^?1 corn gluten at various temperature and pH values. The amount of enzyme added to the reaction solution was 0.25% (v/v). Also, to determine decay and product inhibition effects for Alcalase, a series of inhibition experiments were conducted with the addition of various amounts of hydrolysate. For each experimental run, both the amount of hydrolysis (meqv L^?1) and the soluble protein amount (g L^?1) were investigated with respect to time, and the initial reaction rates were determined from the slopes of the linear models that fitted to these experimental data. The kinetic parameters, K_m and V_max were estimated as 53.77 g L^?1 and 5.94 meqv L^?1min^?1. The type of inhibition for Alcalase was determined as uncompetitive, and the inhibition constant, K_i, was estimated as 44.68% (hydrolysate/substrate mixture).     

EFFECT OF IMPELLER SPEED AND VISCOSITY ON WHEY LACTOSE HYDROLYSIS AND β-GALACTOSIDASE STABILITY

In the present work, the effects of impeller speed and viscosity on the enzymatic hydrolysis of whey lactose and enzyme inactivation were studied. The experiments were carried out in 250 mL of 25 mM phosphate buffer solution containing 50 g/L whey lactose by using a commercial β-galactosidase produced from Kluyveromyces marxianus in a batch reactor system. The degree of lactose hydrolysis (%) and residual enzyme activity (%) was investigated versus impeller speeds from 100 to 600 rpm and viscosities from 1.005 to 13.43 cp for 30 minutes of processing time. The mathematical models depending on these process parameters were derived using the experimental data of residual lactose concentration and residual β-galactosidase activity. The predicted models have been confirmed with the experimental results.     

Corn gluten hydrolysis by Alcalase: Calibration of pH-stat

In the present study, pH-stat method was calibrated by TNBS and OPA reactions for control of hydrolysis of corn gluten by Alcalase. Hydrolysis experiments were conducted in a batch reactor at the temperature of 50 °C for various pH values, and at pH 7 for 40 and 60 °C. The calibration was characterized by good linear correlations for both methods. The mean pK values of corn gluten hydrolysates were obtained as 6.83, 6.59 and 6.36 for 40, 50 and 60 °C, respectively.     

Evaluation of sericin/collagen membranes as prospective wound dressing biomaterial

Sericin, a silk protein, has high potential for use in biomedical applications. In this study, wound dressing membranes of Sericin (S) and Collagen (C) were prepared by glutaraldehyde cross-linking at S/C; 2:1, 1:1, 1:2, and 0:1 weight ratios. They were stable in water for 4 weeks. However, increasing the proportion of sericin had decreasing effect on the membrane stability. Water swelling property of membranes was enhanced with sericin. The highest water swelling was obtained in 1:1 group (9.06 g/g), but increasing collagen or sericin content in the membranes had a diminishing effect. Highest water vapor transmission rate was obtained with 1:2 group (1013.80 g/m²/day). Oxygen permeability results showed that 1:2 (7.67 mg/L) and 2:1 (7.85 mg/L) S/C groups were better than the other groups. While sericin decreased the tensile strength and elongation of membranes, it increased modulus. Sericin also increased brittleness of membranes, but their UTS range (24.93–44.92 MPa) was still suitable for a wound dressing. Membranes were not penetrable to microorganisms. Cytotoxicity studies showed that fibroblasts and keratinocytes attached and gained their characteristic morphologies. They also proliferated on membranes significantly. After 1 week of subcutaneous implantation, a fibrous capsule formed around all membranes with an acute inflammation. Sericin containing membranes showed signs of degradation (at 2nd week), while collagen only membranes remained largely intact. Eventually, sericin containing membranes degraded in 3 weeks with moderate inflammatory response. Overall results suggest that sericin/collagen membranes would be favorable as wound dressing material when sericin ratio is less than or equal to the collagen component.     

Examination of 2-methacryloyloxyethyl phosphorylcholine polymer coated acrylic resin denture base material: surface characteristics and <Emphasis Type="BoldItalic">Candida albicans</Emphasis> adhesion

The aim of this study is to evaluate the effects of 2-methacryloyloxyethyl phosphorylcholine (MPC) polymer coating with various concentrations onto acrylic resin denture base material on surface characteristics such as contact angle and surface roughness and on Candida albicans adhesion which is the major factor of denture stomatitis. Specimens, prepared from heat-polymerized acrylic denture base material, were divided into control and three test groups, randomly. Surfaces of the specimens in test groups were coated with poly(MPC) (PMPC) by graft polymerization of MPC in different concentrations (0.25?mol/L; 0.50?mol/L and 0.75?mol/L), while no surface treatment was applied to the control group. Contact angles and surface roughness were examined, and chemical composition of the surfaces was analyzed by Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (FTIR) to verify the presence of PMPC coatings. Then, specimens were incubated with C. albicans for 18?h and the number of adhered cells was determined. Upon PMPC coating, the contact angle values statistically decreased, but no difference was found in surface roughness values. A statistically significant decrease was observed in C. albicans adhesion in parallel with the increase in the MPC polymer concentration. There was no significant difference between 0.50?mol/L and 0.75?mol/L groups in terms of adhesion. These findings indicated that graft polymerization of MPC on acrylic denture base material reduces the adhesion of C. albicans, and may be evaluated as a coating for prevention of denture stomatitis.     

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg–Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.     

Adaptive backstepping sliding mode control with tuning functions for nonlinear uncertain systems

An adaptive backstepping tuning functions sliding mode controller is proposed for a class of strict-feedback nonlinear uncertain systems. In this control design, adaptive backstepping is used to deal with unknown or uncertain parameters and the matching condition restricting the Lyapunov based design. The main drawback of the Lyapunov based adaptive backstepping which is the overparametrisation is eliminated by the tuning functions. The adaptive backstepping tuning functions design is combined with the sliding mode control in order to overcome quickly varying parametric and unstructured uncertainties, and to obtain chattering free control. The proposed controller not only provides robustness property against uncertainty but also copes with the overparametrisation problem. Experimental results of the proposed controller are compared with those of the standard sliding mode controller. The proposed controller exhibits satisfactory transient performance, good estimates of the uncertain parameters, and less chattering.     

A hybrid named entity recognizer for Turkish

Named entity recognition is an important subfield of the broader research area of information extraction from textual data. Yet, named entity recognition research conducted on Turkish texts is still rare as compared to related research carried out on other languages such as English, Spanish, Chinese, and Japanese. In this study, we present a hybrid named entity recognizer for Turkish, which is based on a manually engineered rule based recognizer that we have proposed. Since rule based systems for specific domains require their knowledge sources to be manually revised when ported to other domains, we enrich our rule based recognizer and turn it into a hybrid recognizer so that it learns from annotated data when available and improves its knowledge sources accordingly. The hybrid recognizer is originally engineered for generic news texts, but with its learning capability, it is improved to be applicable to that of financial news texts, historical texts, and child stories as well, without human intervention. Both the hybrid recognizer and its rule based predecessor are evaluated on the same corpora and the hybrid recognizer achieves better results as compared to its predecessor. The proposed hybrid named entity recognizer is significant since it is the first hybrid recognizer proposal for Turkish addressing the above porting problem considering that Turkish possesses different structural properties compared to widely studied languages such as English and there is very limited information extraction research conducted on Turkish texts. Moreover, the employment of the proposed hybrid recognizer for semantic video indexing is shown as a case study on Turkish news videos. The genuine textual and video corpora utilized throughout the paper are compiled and annotated by the authors due to the lack of publicly available annotated corpora for information extraction research on Turkish texts.     

Investigation into some tribological properties of plasma nitrided hot-worked tool steel AISIH11

Interest in the tribological properties of plasma nitriding has increased substantially over the past years because plasma nitriding provides a high nitride depth and improved hard facing. The present study examines the tribological properties of AISI Hll plasma nitrided, hot- worked steel. Different nitriding temperatures and durations were considered. Characterization of the composite structures was investigated with wear tests, x-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and microhardness tests. The depth profile of the nitrided zone was measured using the nuclear reaction analysis (NRA) technique. Plasma nitriding affected the microhardness, wear properties, and morphology considerably. Increase in process temperature increased the nitride zone depth.