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Fare allocation for legs and O&D pairs plays a crucial role in airline revenue management. Despite a large number of dynamic pricing studies, there are only a few widely adopted studies in which assumptions affect most tactical decisions with potentially large impacts on airline profitability. These decisions involve approximating future pricing schemes, allocation of fare classes, and setting booking limits. We propose a fare allocation model for a single leg in the presence of a realistic nonhomogeneous Poisson demand with an increasing rate. We aim to compute when and how to markup the price for an airfare product (switch to an upper fare class) to maximize the expected revenue. We study a fluid approximation of the underlying stochastic problem considering independent demand from each customer segment and examine different properties that lead to several important insights. Finally, we propose a dynamic look-ahead pricing scheme to compare our fluid approximation results against the well-known EMSRb heuristic and a dynamic programming solution on randomly generated booking request data. Numerical examples illustrate the effectiveness of our proposed approach.

We study a workforce scheduling problem faced in contact centers with considerations on a fair distribution of shifts in compliance with agent preferences. We develop a mathematical model that aims to minimize operating costs associated with labor, transportation of agents, and lost customers. Aside from typical work hour-related constraints, we also try to conform with agents' preferences for shifts, as a measure of fairness. We plot the trade-off between agent satisfaction and total operating costs for Vestel, one of Turkey's largest consumer electronics companies. We present insights on the increased cost to have content and a fair environment on several agent availability scenarios.

In this paper, we present a review and analysis of studies that focus on humanitarian inventory planning and management. Specifically, we focus on papers which develop policies and models to determine how much to stock, where to stock, and when to stock throughout the humanitarian supply chain. We categorize papers according to the disaster management cycle addressed; specifically, we focus on pre-disaster and post-disaster inventory management. We evaluate existing literature in terms of problem aspects addressed such as decision makers, stakeholders, disaster types, commodities, facility types, performance measures as well as methodological aspects (i.e., types of policies, models, and solution approaches). We identify current gaps in the literature and propose directions for future research. TÜBA ; TÜBİTAK

We consider perishable products under shelf and backroom storage capacity constraints.We propose an order quantity model with assortment, pricing and shelf space allocation decisions.We propose a metaheuristic approach that would maximize the retailer's profit.We compare the performance of our algorithm with well-known MINLP solvers. We introduce an economic order quantity model that incorporates product assortment, pricing and space-allocation decisions for a group of perishable products. The goal is to maximize the retailer's profit under shelf-space and backroom storage capacity constraints. We assume that the demand rate of a product is a function of the selling prices and the displayed stock levels of all the products in the assortment. We propose a Tabu Search based heuristic method to solve this complex problem.

Background Sustained efforts at preventing diabetic foot ulcers (DFUs) and subsequent leg amputations are sporadic in most health care systems despite the high costs associated with such complications. We sought to estimate effectiveness targets at which cost-savings (i.e. improved health outcomes at decreased total costs) might occur. Methods A Markov model with probabilistic sensitivity analyses was used to simulate the five-year survival, incidence of foot complications, and total health care costs in a hypothetical population of 100,000 people with diabetes. Clinical event and cost estimates were obtained from previously-published trials and studies. A population without previous DFU but with 17% neuropathy and 11% peripheral artery disease (PAD) prevalence was assumed. Primary prevention (PP) was defined as reducing initial DFU incidence. Results PP was more than 90% likely to provide cost-savings when annual prevention costs are less than $50/person and/or annual DFU incidence is reduced by at least 25%. Efforts directed at patients with diabetes who were at moderate or high risk for DFUs were very likely to provide cost-savings if DFU incidence was decreased by at least 10% and/or the cost was less than $150 per person per year. Conclusions Low-cost DFU primary prevention efforts producing even small decreases in DFU incidence may provide the best opportunity for cost-savings, especially if focused on patients with neuropathy and/or PAD. Mobile phone-based reminders, self-identification of risk factors (ex. Ipswich touch test), and written brochures may be among such low-cost interventions that should be investigated for cost-savings potential.

Emphasis on effective demand management is becoming increasingly recognized as an important factor in operations performance. Operations models that account for supply costs and constraints as well as a supplier’s ability to influence demand characteristics can lead to an improved match between supply and demand. This paper presents a class of optimization models that allow a supplier to select, from a set of potential markets, those markets that provide maximum profit when production/procurement economies of scale exist in the supply process. The resulting optimization problem we study possesses an interesting structure and we show that although the general problem is $${\mathcal{NP}}$$ -complete, a number of relevant and practical special cases can be solved in polynomial time. We also provide a computationally very efficient and intuitively attractive heuristic solution procedure that performs extremely well on a large number of test instances.

With the rapid development of nanotechnology products, there is a significant concern on the adverse effects that might be associated with them. Traditional biological assays are typically used to asses the toxicity in vitro. There are, however, questions regarding the suitability of these assays for this purpose, mainly due to the potential interaction of the particles with the utilized dyes. In addition, this process can be costly and time consuming, as a large number of different assays have to be used. To address some of these issues, Raman spectroscopy is used in this study to investigate the particle-cell interactions. The spectrum of a living cell is a very complex and rich collection of data directly related to its chemical composition. To enhance the data resolution and make the detection of toxicity more robust, data-mining techniques have been deployed. Furthermore, data-mining techniques enable full automation of the entire process, minimizing user input. The Raman spectroscopy successfully evaluated the toxicity of TiO2 nanoparticles by both the peak-by-peak analysis and with the implementation of support vector machines. The particles were found to display cytotoxicity after 36 h of exposure. The results were confirmed by MTT (3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyltetrazolium Bromide) assay and are in agreement with the existing literature on the subject. Overall, Raman spectroscopy appears to be among the very few techniques that exhibit low levels of interferences (obscuration, fluorescence, emission, etc.) from the particle addition. Since it does not rely on biomarkers, it can be used in situ for an extended period with minimal effects on the cellular biochemistry. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we consider the classification problem within the multiple instance learning (MIL) context. Training data is composed of labeled bags of instances. Despite the large number of margin maximization based classification methods, there are only a few methods that consider the margin for MIL problems in the literature. We first formulate a combinatorial margin maximization problem for multiple instance classification and prove that it is NP-hard. We present a way to apply the kernel trick in this formulation for classifying nonlinear multiple instance data. We also propose a branch and bound algorithm and present computational results on publicly available benchmark data sets. Our approach outperforms a leading commercial solver in terms of the best integer solution and optimality gap in the majority of image annotation and molecular activity prediction test cases.

In the present study, Raman spectroscopy is employed to assess the potential toxicity of chemical substances. Having several advantages compared to other traditional methods, Raman spectroscopy is an ideal solution for investigating cells in their natural environment. In the present work, we combine the power of spectral resolution of Raman with one of the most widely used machine learning techniques. Support vector machines (SVMs) are used in the context of classification on a well established database. The database is constructed on three different classes: healthy cells, Triton X-100 (necrotic death), and etoposide (apoptotic death). SVM classifiers successfully assess the potential effect of the test toxins (Triton X-100, etoposide). The cells that are exposed to heat (45 degrees C) are tested using the classification rules obtained. It is shown that the heat effect results in apoptotic death, which is in agreement with existing literature.

The underlying optimization problem for the maximal margin classifier is only feasible if the two classes of pattern vectors are linearly separable. However, most of the real life classification problems are not linearly separable. Nevertheless, the maximal margin classifier encompasses the fundamental methods used in standard SVM classifiers. The solution to the optimization problem in the maximal margin classifier minimizes the bound on the generalization error (Vapnik, 1998). The basic premise of this method lies in the minimization of a convex optimization problem with linear inequality constraints, which can be solved efficiently by many alternative methods (Bennett & Campbell, 2000).

We present a pilot study of an application of consistent biclustering to analyze scalp EEG data obtained from epileptic patients undergoing treatment with a vagus nerve stimulator (VNS). The ultimate goal of this study is to develop a physiologic marker for optimal VNS parameters (e.g. output current, signal frequency, etc.) using measures of scalp EEG signals. A time series of STLmax values was computed for each scalp EEG channel recorded from two epileptic patients and used as a feature of the two datasets. The averaged samples from stimulation periods were then separated from averaged samples from non‐stimulation periods by feature selection performed within the consistent biclustering routine.The obtained biclustering results allow us to assume that signals from certain parts of the brain consistently change their characteristics when VNS is switched on and could provide a basis for desirable VNS stimulation parameters. A physiologic marker of optimal VNS effect could greatly reduce the cost, time, an...

In this chapter a potential problem with application of the Granger‐causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

Many studies have documented abnormal horizontal and vertical eye movements in human neurodegenerative disease as well as during altered states of consciousness (including drowsiness and intoxication) in healthy adults. Eye movement measurement may play an important role measuring the progress of neurodegenerative diseases and state of alertness in healthy individuals. There are several techniques for measuring eye movement, Infrared detection technique (IR). Video‐oculography (VOG), Scleral eye coil and EOG. Among those available recording techniques, EOG is a major source for monitoring the abnormal eye movement. In this real‐time quantitative analysis study, the methods which can capture the characteristic of the eye movement were proposed to accurately categorize the state of neurodegenerative subjects. The EOG recordings were taken while 5 tested subjects were watching a short (>120 s) animation clip. In response to the animated clip the participants executed a number of eye movements, including vert...

We suggest that dark energy has a quantum informational origin. Landauer's principle associated with the erasure of quantum information at a cosmic horizon implies the non-zero vacuum energy having effective negative pressure. Assuming the holographic principle, the minimum free energy condition, and the Gibbons-Hawking temperature for the cosmic event horizon we obtain the holographic dark energy with the parameter $d\simeq 1$, which is consistent with the current observational data. It is also shown that both the entanglement energy and the horizon energy can be related to Landauer's principle.

This study investigated the model‐based design, fabrication and in vitro and in vivo experimental characterization of a pancreatic substitute consisting of mouse insulinoma cells encapsulated in agarose in a disk‐shaped construct. Two construct prototypes were examined: (i) a single disk construct comprised of agarose and β5TC3 cells; and (ii) a buffered disk construct, consisting of agarose and βTC3 cells, coated with an additional layer of pure agarose. Diffusional studies of glucose and insulin were performed to characterize the transport properties of the material. Three dimensional oxygen diffusion‐reaction models were used to predict the appropriate cell loadings for the two construct prototypes under varying external oxygen tensions. In vitro and in vivo experiments found the overall viable cell number for each construct prototype plateaued to the same value, regardless of the initial cell seeding number, when constructs were placed under identical environmental conditions. Furthermore, mathematical model calculations correlated well with experimental in vitro and in vivo results of cell viability, indicating oxygen tension to be the dominating factor in establishing total viable cell number in these constructs. These results indicate that modeling is useful for the development of tissue engineered constructs when permissive matrices and continuous cell lines are used. The applicability of this modeling and experimental methodology in the development of agarose‐based constructs for use as a bioartificial pancreas is discussed.

During the last several decades, the Bayesian networks have turned into a dynamic area of research. This great interest is owning to the advantages offered by special structure of Bayesian networks, which allows them to be very efficient in modeling domains with inherent uncertainty. Bayesian networks techniques can be successfully applied to mining various types of biomedical data.This chapter demonstrates how various complex research problems in biology, biomedicine and other fields can be solved by means of the combination of methods from Bayesian networks and data mining.

We propose a novel metaheuristic search for global optimization inspired by the behavior of a monkey climbing trees looking for food. The tree branches are represented as perturbations between two neighboring feasible solutions of the considered global optimization problem. The monkey mark and update these branches leading to good solutions as it climbs up and down the tree. A wide selection of perturbations can be applied based on other metaheuristic methods for global optimization. We show that Monkey Search is competitive compared to the other metaheuristic methods for optimizing Lennard‐Jones and Morse clusters, and for simulating protein molecules based on a geometric model for protein folding.

In this communication, we performed nonlinearity analysis in the EEG signals recorded from patients with temporal lobe epilepsy (TLE). The largest Lyapunov exponent (Lmax) and phase randomization surrogate data technique were employed to form the statistical test. EEG recordings were acquired invasively from three patients in six brain regions (left and right temporal depth, sub‐temporal and orbitofrontal) with 28–32 depth electrodes placed in depth and subdural of the brain. All three patients in this study have unilateral epileptic focus region on the right hippocampus(RH). Nonlinearity was detected by comparing the Lmax profiles of the EEG recordings to its surrogates. The nonlinearity was seen in all different states of the patient with the highest found in post‐ictal state. Further our results for all patients exhibited higher degree of differences, quantified by paired t‐test, in Lmax values between original and its surrogate from EEG signals recorded from epileptic focus regions. The results of this study demonstrated the Lmax is capable to capture spatio‐temporal dynamics that may not be able to detect by linear measurements in the intracranial EEG recordings.

Cellular stress activates transcription of various genes that mediate stress‐driven proliferation and differentiation in many cells including osteoblasts, endothelial cells, and fibroblasts. In response to mechanical stress, expression of some genes is altered regardless of cell types and that of others in specific cell types. Using the microarray‐based expression data for primary fibroblasts isolated from fetal mouse cornea, skin and tendon, we conducted a model‐based transcription analysis and predicted transcription‐factor binding motifs (TFBMs) responsible for the observed gene alteration. The computational procedure was formulated as a combinatorial optimization problem, and the AntModeler using an ant algorithm was employed to select TFBMs for each of the three fibroblast types. The results indicate that the stress responses are regulated mostly through cell type specific TFBMs together with a limited number of common TFBMs. The predicted role of those TFBMs should be evaluated experimentally.

Epilepsy is one of the most common chronic neurological disorders that affects close to 50 million people worldwide. Antiepilepsy drugs (AEDs), the main stay of epilepsy treatment, control seizures in two thirds of patients only. Other therapies include the ketogenic diet, ablative surgery, hormonal treatments and neurostimulation. While other approaches to stimulation of the brain are currently in the experimental phase vagus nerve stimulation (VNS) has been approved by the FDA since July 1997 for the adjunctive treatment of intractable partial onset epilepsy with and without secondary generalization in patients twelve years of age or older. The safety and efficacy of VNS have been proven and duplicated in two subsequent double‐blinded controlled studies after two pilot studies demonstrated the feasibility of VNS in man. Long term observational studies confirmed the safety of VNS and that its effectiveness is sustained over time. While AEDs influence seizure thresholds via blockade or modulation of ionic channels, inhibit excitatory neurotransmitters or enhance inhibitory neurotransmitters the exact mechanism of action of VNS is not known. Neuroimaging studies revealed that VNS increases blood flow in certain regions of the brain such as the thalamus. Chemical lesions in the rat brains showed that norepinephrine is an important link in the anticonvulsant effect of VNS. Analysis of cerebrospinal fluid obtained from patients before and after treatment with VNS showed modest decreases in excitatory neurotransmitters. Although Hammond et al. reported no effect of VNS on scalp EEG by visual analysis and Salinsky et al. found no effect of VNS on scalp EEG by spectral analysis, Kuba et al. suggested that VNS reduces interictal epileptiform activity. Further, nonlinear dynamical analysis of the electroencephalogram in the rat and man have reportedly shown predictable changes (decrease in the short term Lyapunov exponent STLmax and T‐index) more than an hour prior to the clinical or electroencephalographic seizure onset. It is possible that intermittent VNS maintains chaoticity of brain activity in patients with epilepsy that respond to this therapy. The most optimal stimulation parameters of VNS are not known and further study of nonlinear dynamics of brain activity may shed some light on more effective interception or prevention of seizures. Online real time analysis may allow on‐demand stimulation rather than hit‐or‐miss approach

Advances in cytogenetics and molecular biology has established that chromosomal alterations are critical in the pathogenesis of human cancer. Recurrent chromosomal alterations provide cytological and molecular markers for the diagnosis and prognosis of disease. They also facilitate the identification of genes that are important in carcinogenesis, which in the future may help in the development of targeted therapy.A large amount of publicly available cancer genetic data is now available and it is growing. There is a need for public domain tools that allow users to analyze their data and visualize the results. This chapter describes a web based software tool that will allow researchers to analyze and visualize Comparative Genomic Hybridization (CGH) datasets. It employs novel data mining methodologies for clustering and classification of CGH datasets as well as algorithms for identifying important markers (small set of genomic intervals with aberrations) that are potentially cancer signatures. The developed...

Adverse drag reactions (ADRs) are estimated to be one of the leading causes of death. Many national and international agencies have set up databases of ADR reports for the express purpose of determining the relationship between drugs and adverse reactions that they cause. We formulate the drug‐reaction relationship problem as a continuous optimization problem and utilize C‐GRASP, a new continuous global optimization heuristic, to approximately determine the relationship between drugs and adverse reactions. Our approach is compared against others in the literature and is shown to find better solutions.

Glioblastoma multiforme (GBM) is a malignant brain cancer with poor patient prognosis (i.e. time to survival, time to tumor progression). A number of clinical trials are underway evaluating novel therapeutic strategies and magnetic resonance imaging is the most routinely performed procedure for accurate serial monitoring of patients. The electronic availability of the comprehensive data collected as part of the clinical trials provides an unprecedented opportunity to discover new relationships in complex diseases such as GBM. However, imaging data, which is the most accurate non‐invasive assessment of GBMs, is not directly amenable for data mining. The focus of this chapter is on image analysis techniques including image spatial and intensity standardization, novel methods for robust tumor and edema segmentation, and quantification of tumor intensity, texture, and shape characteristics. The chapter concludes with an application of discovering the relationship between these quantitative image‐derived features and time to survival in GBM patients; the data is part of a comprehensive large electronically accessible archive at UCLA (UCLA Neuro‐oncology database).

Non‐unique probes are used to identify the targets, i.e., viruses, present in a given sample. Since the number of selected non‐unique probes is equal to the number of hybridization experiments, it is important to find a minimum set of non‐unique probes, which is NP‐complete. Using d‐disjunct matrix, we present two (1+(d+1)logn)‐approximation algorithms to identify at most d targets. Based on our selected non‐unique probes, we also present the decoding algorithms with linear time complexity. In addition, our solutions are fault tolerant. The proposed algorithms can identify at most d targets in the presence of experimental errors.

This study presents an application of support vector machines (SVMs) to the analysis of electroencephalograms (EEG) obtained from the scalp of patients with epilepsy implanted with the vagus nerve stimulator (VNS) used in VNS Therapy®. The purpose of this study is to devise a physiologic marker using scalp EEG for determining optimal VNS parameters. Scalp EEG recordings were obtained from six patients with history of intractable partial onset epilepsy treated with VNS as adjunctive therapy to medicines. Averaged scalp EEG samples were used as features for separation. SVM classification accuracy was used as a measure of EEG similarity to separate a time segment during the beginning of stimulation from all the successive non‐overlapping time segments within a full VNS on/off cycle. This analysis was performed for all the automated VNS cycles occurring during approximately twenty‐four hours of 25 channels of scalp EEG. The patient that resulted in the lowest degree of EEG pattern similarity had the highest V...

The performance of five seizure detection schemes, i.e., Nonlinear embedding delay, Hurst scaling, Wavelet Scale, autocorrelation and gradient of accumulated energy, in their ability to detect EEG seizures close to the seizure onset time were evaluated to determine the feasibility of their application in the development of a real time closed loop seizure intervention program (RCLSIP). The criteria chosen for the performance evaluation were, high statistical robustness as determined through the predictability index, the sensitivity and the specificity of a given measure to detect an EEG seizure, the lag in seizure detection with respect to the EEG seizure onset time, as determined through visual inspection and the computational efficiency for each detection measure. An optimality function was designed to evaluate the overall performance of each measure dependent on the criteria chosen. While each of the above measures analyzed for seizure detection performed very well in terms of the statistical parameters, the nonlinear embedding delay measure was found to have the highest optimality index due to its ability to detect seizure very close to the EEG seizure onset time, thereby making it the most suitable dynamical measure in the development of RCLSIP in rat model with chronic limbic epilepsy.