Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

Accuracy of absorbed dose in external photon beam radiotherapy : what level is sufficient and how to approach it?

Radiation therapy (RT) plays currently significant role in curative treatments of several cancers. External beam RT is carried out mostly by using megavoltage beams of linear accelerators. Tumor eradication and normal tissue complications correlate to dose absorbed in tissues. Normally this dependence is steep and it is crucial that actual dose within patient accurately correspond to the planned dose. All factors in a RT procedure contain uncertainties requiring strict quality assurance. From hospital physicist´s point of a view, technical quality control (QC), dose calculations and methods for verification of correct treatment location are the most important subjects. Most important factor in technical QC is the verification that radiation production of an accelerator, called output, is within narrow acceptable limits. The output measurements are carried out according to a locally chosen dosimetric QC program defining measurement time interval and action levels. Dose calculation algorithms need to be configured for the accelerators by using measured beam data. The uncertainty of such data sets limits for best achievable calculation accuracy. All these dosimetric measurements require good experience, are workful, take up resources needed for treatments and are prone to several random and systematic sources of errors. Appropriate verification of treatment location is more important in intensity modulated radiation therapy (IMRT) than in conventional RT. This is due to steep dose gradients produced within or close to healthy tissues locating only a few millimetres from the targeted volume. The thesis was concentrated in investigation of the quality of dosimetric measurements, the efficacy of dosimetric QC programs, the verification of measured beam data and the effect of positional errors on the dose received by the major salivary glands in head and neck IMRT. A method was developed for the estimation of the effect of the use of different dosimetric QC programs on the overall uncertainty of dose. Data were provided to facilitate the choice of a sufficient QC program. The method takes into account local output stability and reproducibility of the dosimetric QC measurements. A method based on the model fitting of the results of the QC measurements was proposed for the estimation of both of these factors. The reduction of random measurement errors and optimization of QC procedure were also investigated. A method and suggestions were presented for these purposes. The accuracy of beam data was evaluated in Finnish RT centres. Sufficient accuracy level was estimated for the beam data. A method based on the use of reference beam data was developed for the QC of beam data. Dosimetric and geometric accuracy requirements were evaluated for head and neck IMRT when function of the major salivary glands is intended to be spared. These criteria are based on the dose response obtained for the glands. Random measurement errors could be reduced enabling lowering of action levels and prolongation of measurement time interval from 1 month to even 6 months simultaneously maintaining dose accuracy. The combined effect of the proposed methods, suggestions and criteria was found to facilitate the avoidance of maximal dose errors of up to even about 8 %. In addition, their use may make the strictest recommended overall dose accuracy level of 3 % (1SD) achievable.

Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors

To mitigate the effects of climate change, countries worldwide are advancing technologies to reduce greenhouse gas emissions. This paper proposes and measures optimal production resource reallocation using data envelopment analysis. This research attempts to clarify the effect of optimal production resource reallocation on CO2 emissions reduction, focusing on regional and industrial characteristics. We use finance, energy, and CO2 emissions data from 13 industrial sectors in 39 countries from 1995 to 2009. The resulting emissions reduction potential is 2.54 Gt-CO2 in the year 2009, with former communist countries having the largest potential to reduce CO2 emissions in the manufacturing sectors. In particular, basic material industry including chemical and steel sectors has a lot of potential to reduce CO2 emissions.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Optimal configurations of active fiber composites based on asymptotic torsional analysis - art. no. 641413

Active Fiber Composites (AFC) possess desirable characteristics over a wide range of smart structure applications, such as vibration, shape and flow control as well as structural health monitoring. This type of material, capable of collocated actuation and sensing, call be used in smart structures with self-sensing circuits. This paper proposes four novel applications of AFC structures undergoing torsion: sensors and actuators shaped as strips and tubes; and concludes with a preliminary failure analysis. To enable this, a powerful mathematical technique, the Variational Asymptotic Method (VAM) was used to perform cross-sectional analyses of thin generally anisotropic AFC beams. The resulting closed form expressions have been utilized in the applications presented herein.

Optimal auctions for multi-unit procurement with volume discount bids

Our attention, is focused on designing an optimal procurement mechanism which a buyer can use for procuring multiple units of a homogeneous item based on bids submitted by autonomous, rational, and intelligent suppliers. We design elegant optimal procurement mechanisms for two different situations. In the first situation, each supplier specifies the maximum quantity that can be supplied together with a per unit price. For this situation, we design an optimal mechanism S-OPT (Optimal with Simple bids). In the more generalized case, each supplier specifies discounts based on the volume of supply. In this case, we design an optimal mechanism VD-OPT (Optimal with Volume Discount, bids). The VD-OPT mechanism uses the S-OPT mechanism as a building block. The proposed mechanisms minimize the cost to the buyer, satisfying at the same time, (a) Bayesian, incentive compatibility and (b) interim individual rationality.

Translational fusion of two beta-subunits of human chorionic gonadotropin results in production of a novel antagonist of

The strategy of translationally fusing the alpha-and beta-subunits of human chorionic gonadotropin (hCG) into a single-chain molecule has been used to produce novel analogs of hCG. Previously we reported expression of a biologically active singlechain analog hCG alpha beta expressed using Pichia expression system. Using the same expression system, another analog, in which the alpha-subunit was replaced with the second beta-subunit, was expressed (hCG beta beta) and purified. hCG beta beta could bind to LH receptor with an affinity three times lower than that of hCG but failed to elicit any response. However, it could inhibit response to the hormone in vitro in a dose- dependent manner. Furthermore, it inhibited response to hCG in vivo indicating the antagonistic nature of the analog. However, it was unable inhibit human FSH binding or response to human FSH, indicating the specificity of the effect. Characterization of hCG alpha beta and hCG beta beta using immunological tools showed alterations in the conformation of some of the epitopes, whereas others were unaltered. Unlike hCG, hCG beta beta interacts with two LH receptor molecules. These studies demonstrate that the presence of the second beta-subunit in the single-chain molecule generated a structure that can be recognized by the receptor. However, due to the absence of alpha-subunit, the molecule is unable to elicit response. The strategy of fusing two beta-subunits of glycoprotein hormones can be used to produce antagonists of these hormones.

Diffusion of Pure CH4 and Its Binary Mixture with CO2 in Faujasite NaY: A Combination of Neutron Scattering Experiments and Molecular Dynamics Simulations

The self-diffusion properties of pure CH4 and its binary mixture with CO2 within MY zeolite have been investigated by combining an experimental quasi-elastic neutron scattering (QENS) technique and classical Molecular dynamics simulations. The QENS measurements carried out at 200 K led to an unexpected self-diffusivity profile for Pure CH4 with the presence of a maximum for a loading of 32 CH4/unit cell, which was never observed before for the diffusion of apolar species in azeolite system With large windows. Molecular dynamics simulations were performed using two distinct microscopic models for representing the CH4/NaY interactions. Depending on the model, we are able to fairly reproduce either the magnitude or the profile of the self-diffusivity.Further analysis allowed LIS to provide some molecular insight into the diffusion mechanism in play. The QENS measurements report only a slight decrease of the self-diffusivity of CH4 in the presence of CO2 when the CO2 loading increases. Molecular dynamics simulations successfully capture this experimental trend and suggest a plausible microscopic diffusion mechanism in the case of this binary mixture.

Metabolomic and proteomic analysis of breast cancer patient samples suggests that glutamate and 12-HETE in combination with CA15-3 may be useful biomarkers reflecting tumour burden

Evaluation of protein and metabolite expression patterns in blood using mass spectrometry and high-throughput antibody-based screening platforms has potential for the discovery of new biomarkers for managing breast cancer patient treatment. Previously identified blood-based breast cancer biomarkers, including cancer antigen 15.3 (CA15-3) are useful in combination with imaging (computed tomography scans, magnetic resonance imaging, X-rays) and physical examination for monitoring tumour burden in advanced breast cancer patients. However, these biomarkers suffer from insufficient levels of accuracy and with new therapies available for the treatment of breast cancer, there is an urgent need for reliable, non-invasive biomarkers that measure tumour burden with high sensitivity and specificity so as to provide early warning of the need to switch to an alternative treatment. The aim of this study was to identify a biomarker signature of tumour burden using cancer and non-cancer (healthy controls/non-malignant breast disease) patient samples. Results demonstrate that combinations of three candidate biomarkers from Glutamate, 12-Hydroxyeicosatetraenoic acid, Beta-hydroxybutyrate, Factor V and Matrix metalloproteinase-1 with CA15-3, an established biomarker for breast cancer, were found to mirror tumour burden, with AUC values ranging from 0.71 to 0.98 when comparing non-malignant breast disease to the different stages of breast cancer. Further validation of these biomarker panels could potentially facilitate the management of breast cancer patients, especially to assess changes in tumour burden in combination with imaging and physical examination.

A unified construction of space-time codes with optimal rate-diversity tradeoff

The problem of constructing space-time (ST) block codes over a fixed, desired signal constellation is considered. In this situation, there is a tradeoff between the transmission rate as measured in constellation symbols per channel use and the transmit diversity gain achieved by the code. The transmit diversity is a measure of the rate of polynomial decay of pairwise error probability of the code with increase in the signal-to-noise ratio (SNR). In the setting of a quasi-static channel model, let n(t) denote the number of transmit antennas and T the block interval. For any n(t) <= T, a unified construction of (n(t) x T) ST codes is provided here, for a class of signal constellations that includes the familiar pulse-amplitude (PAM), quadrature-amplitude (QAM), and 2(K)-ary phase-shift-keying (PSK) modulations as special cases. The construction is optimal as measured by the rate-diversity tradeoff and can achieve any given integer point on the rate-diversity tradeoff curve. An estimate of the coding gain realized is given. Other results presented here include i) an extension of the optimal unified construction to the multiple fading block case, ii) a version of the optimal unified construction in which the underlying binary block codes are replaced by trellis codes, iii) the providing of a linear dispersion form for the underlying binary block codes, iv) a Gray-mapped version of the unified construction, and v) a generalization of construction of the S-ary case corresponding to constellations of size S-K. Items ii) and iii) are aimed at simplifying the decoding of this class of ST codes.

Optimal multicommodity flow through the complete graph with random edge capacities

We consider a multicommodity flow problem on a complete graph whose edges have random, independent, and identically distributed capacities. We show that, as the number of nodes tends to infinity, the maximumutility, given by the average of a concave function of each commodity How, has an almost-sure limit. Furthermore, the asymptotically optimal flow uses only direct and two-hop paths, and can be obtained in a distributed manner.

Interactions of linear dicationic molecules with lipid A: structural requisites for optimal binding affinity

The structural determinants of the binding affinity of linear dicationic molecules toward lipid A have been examined with respect to the distance between the terminal cationic functions, the basicity, and the type of cationic moieties using a series of spermidine derivatives and pentamidine analogs by fluorescence spectroscopic methods, The presence of two terminal cationic groups corresponds to enhanced affinity, A distinct sigmoidal relationship between the intercationic distance and affinity was observed with a sharp increase at 11 Angstrom, levelling off at about 13 Angstrom. The basicity (pK) and nature of the cationic functions are poor correlates of binding potency, since molecules bearing primary amino, imidazolino, or guanido termini are equipotent, The interaction of pentamidine, a bisamidine drug, with lipid A, characterized in considerable detail employing the putative intermolecular excimerization of the drug, suggests a stoichiometry of 1:1 in the resultant complex, The binding is driven almost exclusively by electrostatic forces, and is dependent on the ionization states of both lipid A and the drug, Under conditions when lipid A is highly disaggregated, pentamidine binds specifically to bis-phosphoryl- but not to monophosphoryl-lipid A indicating that both phosphate groups of lipid A are necessary for electrostatic interactions by the terminal amidininium groups of the drug, Based on these data, a structural model is proposed for the pentamidine-lipid A complex, which may be of value in designing endotoxin antagonists from first principles.

Optimal Resource Partitioning in a Military Conflict based on Lanchester Attrition Models

This paper develops a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts. The Lanchester attrition model is used to develop the dynamical equations governing the variation in force strength. Three different allocation schemes - Time-Zero-Allocation (TZA), Allocate-Assess-Reallocate (AAR), and Continuous Constant Allocation (CCA) - are considered and the optimal solutions are obtained in each case. Numerical examples are given to support the analytical results.