Correction to: Buckling of magneto-electro-hygro-thermal piezoelectric nanoplates system embedded in a visco-Pasternak medium based on nonlocal theory

Microsystem Technologies - The original version of this article unfortunately contained a mistake. Farzad Ebrahimi was not listed among the authors.     

Postbuckling analysis of piezoelectric multiscale sandwich composite doubly curved porous shallow shells via Homotopy Perturbation Method

In this study postbuckling behaviors of multiscale composite sandwich doubly curved piezoelectric shell with a flexible core and MR layers by employing Homotopy Perturbation Method in hygrothermal environment has been investigated. By using Reddy third shear deformable theory the face sheets and third-order polynomial theory of the flexible core the strains and stresses are obtained. A mathematical model for the multiscale composite layered shell with a flexible core and magnetorheological layer (MR) that incorporates the nonlinearity of the in-plane and the vertical displacements of the core is assumed. Three-phase composite shells with polymer/Carbon nanotube/fiber and polymer/Graphene platelet/fiber either uniformly or non-uniformly based on different patterns according to Halpin–Tsai model have been considered. The governing equations of multiscale shell have been derived by implementing Hamilton’s principle. Meanwhile, simply supported boundary conditions are employed to the shell. For investigating correctness and accuracy, this paper is validated by other previous researches. Finally, different parameters such as temperature rise, various distribution patterns, magnetic fields and curvature ratio are considered in this article. It is found these parameters have significant effect on the frequency–amplitude curves.

     

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.     

Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.     

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.

     

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.