A two-phase mathematical-programming heuristic for flexible assignment of activities and tasks to work shifts

In the service industry, workers perform work shifts and are assigned to interruptible activities and uninterruptible tasks during their shifts. The work shifts of regular employees are often established several weeks in advance of the operations when the activity and task demands are still uncertain. Just a few days before the operations when these demands are unveiled with more certainty, the planned schedules can be slightly modified and on-call temporary employees can be scheduled to satisfy the demands as best as possible. As acceptable modifications, extending the planned shifts and moving workers’ meal breaks are considered. In this paper, we are interested in the scheduling problem encountered in this second step, which also involves assigning activities and tasks to the scheduled work shifts. To produce good-quality solutions in fast computational times for large-sized instances, we develop a two-phase heuristic. In the first phase, an approximate mixed-integer programming model is used to suggest temporary shifts and extensions to regular shifts and to schedule and assign tasks. In the second phase, a column-generation heuristic embedded in a rolling horizon procedure determines the final shifts and assigns activities to them. Computational results obtained on randomly generated instances are reported to evaluate the validity of the proposed solution method.     

A constrained optimization approach for an adaptive generalized subspace tracking algorithm

In this paper, we present an orthonormal version of the generalized signal subspace tracking. It is based on an interpretation of the generalized signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the CGST algorithm, guarantees the C_x-orthonormality of the estimated generalized signal subspace basis at each iteration which C_x denotes the correlation matrix of the sequence x(t). An efficient implementation of the proposed algorithm enhances applicability of it in real time applications.     

A learning-based visual saliency prediction model for stereoscopic 3D video (LBVS-3D)

Saliency prediction models provide a probabilistic map of relative likelihood of an image or video region to attract the attention of the human visual system. Over the past decade, many computational saliency prediction models have been proposed for 2D images and videos. Considering that the human visual system has evolved in a natural 3D environment, it is only natural to want to design visual attention models for 3D content. Existing monocular saliency models are not able to accurately predict the attentive regions when applied to 3D image/video content, as they do not incorporate depth information. This paper explores stereoscopic video saliency prediction by exploiting both low-level attributes such as brightness, color, texture, orientation, motion, and depth, as well as high-level cues such as face, person, vehicle, animal, text, and horizon. Our model starts with a rough segmentation and quantifies several intuitive observations such as the effects of visual discomfort level, depth abruptness, motion acceleration, elements of surprise, size and compactness of the salient regions, and emphasizing only a few salient objects in a scene. A new fovea-based model of spatial distance between the image regions is adopted for considering local and global feature calculations. To efficiently fuse the conspicuity maps generated by our method to one single saliency map that is highly correlated with the eye-fixation data, a random forest based algorithm is utilized. The performance of the proposed saliency model is evaluated against the results of an eye-tracking experiment, which involved 24 subjects and an in-house database of 61 captured stereoscopic videos. Our stereo video database as well as the eye-tracking data are publicly available along with this paper. Experiment results show that the proposed saliency prediction method achieves competitive performance compared to the state-of-the-art approaches.

     

Properties of Al2O3 nano-particle reinforced copper matrix composite coatings prepared by pulse and direct current electroplating

Cu–Al₂O₃ nano-composite coatings have high potential for use in applications in which high mechanical properties together with high corrosion resistance are required. In the present study it is intended to produce copper nano-alumina composite coatings with various nano-alumina contents in order to investigate the effect of alumina reinforcement particles on corrosion resistance and mechanical properties such as hardness and wear resistance. The composite coatings were deposited using direct current (DC) and pulse current (PC) plating. The microstructures of the coatings produced from both methods were examined via scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. The wear behaviors, micro hardness, coating thickness, corrosion rate and coating porosity were examined using appropriate methods. Compared to DC deposition, PC plating facilitated higher amounts of particle incorporation with more uniform distribution. The results indicated that the mechanical properties of the applied coatings with incorporated nano-alumina reinforcement were far more superior as compared to its own matrix as well as non-composite copper coatings. It was also found out that increasing the amount of nano-alumina content in the coating, led to enhanced general properties of the coatings.     

Nafion/histidine functionalized carbon nanotube: High-performance fuel cell membranes

Here we show preparation and characterization of a new type of composite membrane based on Nafion^®/histidine modified carbon nanotube by imidazole groups (Im-CNT), for direct methanol fuel cell (DMFC) applications. Due to the presence of this imidazole-based amino acid on the surface of CNT, new electrostatic interactions can be formed in the interface of Nafion^® and Im-CNT. Physical characteristics of these nanocomposite membranes are investigated by water uptake, methanol permeability, ion exchange capacity, proton conductivity, as well as fuel cell performance results.     

Decision support in pre-hospital stroke care operations: A case of using simulation to improve eligibility of acute stroke patients for thrombolysis treatment

Stroke is the third most common cause of death and the sixth most common cause of disability worldwide. Treating acute ischemic stroke with thrombolytic therapy within 4.5 hours from symptom onset is effective in improving patient outcomes. The time from stroke onset to arrival to hospital has been identified as the single most important issue in determining patients' eligibility for stroke thrombolysis. There is a need for simultaneous systemic evaluation of multi-factorial interventions in pre-hospital acute care systems, aimed at increasing patients' eligibility for stroke thrombolysis. In this paper an OR solution is proposed in the form of a simulation model that provides clear measure of the relative benefit of alternative potential interventions, demonstrating how OR modeling can be used for providing decision support in pre-hospital stroke care operations and contributing to health OR literature.     

Photobiomodulation Therapy and the Glymphatic System: Promising Applications for Augmenting the Brain Lymphatic Drainage System

The glymphatic system is a glial-dependent waste clearance pathway in the central nervous system, devoted to drain away waste metabolic products and soluble proteins such as amyloid-beta. An impaired brain glymphatic system can increase the incidence of neurovascular, neuroinflammatory, and neurodegenerative diseases. Photobiomodulation (PBM) therapy can serve as a non-invasive neuroprotective strategy for maintaining and optimizing effective brain waste clearance. In this review, we discuss the crucial role of the glymphatic drainage system in removing toxins and waste metabolites from the brain. We review recent animal research on the neurotherapeutic benefits of PBM therapy on glymphatic drainage and clearance. We also highlight cellular mechanisms of PBM on the cerebral glymphatic system. Animal research has shed light on the beneficial effects of PBM on the cerebral drainage system through the clearance of amyloid-beta via meningeal lymphatic vessels. Finally, PBM-mediated increase in the blood–brain barrier permeability with a subsequent rise in Aβ clearance from PBM-induced relaxation of lymphatic vessels via a vasodilation process will be discussed. We conclude that PBM promotion of cranial and extracranial lymphatic system function might be a promising strategy for the treatment of brain diseases associated with cerebrospinal fluid outflow abnormality.     

Crude oil price forecasting: a biogeography-based optimization approach

The importance of crude oil in the world economy has made it imperative for efficient models to be designed for predicting future prices. This paper proposes an alternative approach based on a time series and biogeography-based optimization (BMMR–BBO) for the estimation of the West Texas Intermediate (WTI) crude oil price. To evaluate the forecasting ability of the presented model, we compared its performance with those of time series functions. The results of the experiment showed that BMMR-BBO performed better than the other methods and is a fairly good option for crude oil price prediction. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans, and industrial production.