An optimal control method for aggregate production planning in large-scale manufacturing systems with capacity expansion and deterioration

An optimal control approach to continuous-time aggregate production planning problems is presented. The proposed approach describes the production and capacity evolution (expansion, sell and deterioration) processes in the form of differential equations with regular production, subcontracting and capacity change rates controllable on one hierarchical level. In this way, the traditional disadvantages of the two-level problem consideration (one level for strategic capacity planning and the other for production smoothing) are avoided. Analytical properties for optimal production and capacity control regimes and conditions for their changeover are derived by the maximum principle. Based on these results, an insight into the optimal behaviour of the production system is gained and a fast numerical method is developed to identify and sequence the optimal regimes for arbitrary demand profiles. A computational example illustrates the effectiveness of the approach.     

A corpus of Schlieren photography of speech production: potential methodology to study aerodynamics of labial,nasal and vocalic processes

Language Resources and Evaluation - This report presents a corpus of articulations recorded with Schlieren photography, a recording technique to visualize aeroflow dynamics for two purposes. First,...     

DEEP—differential evolution entirely parallel method for gene regulatory networks

The Differential Evolution Entirely Parallel (DEEP) method is applied to the biological data fitting problem. We introduce a new migration scheme, in which the best member of the branch substitutes the oldest member of the next branch that provides a high speed of the algorithm convergence. We analyze the performance and efficiency of the developed algorithm on a test problem of finding the regulatory interactions within the network of gap genes that control the development of early Drosophila embryo. The parameters of a set of nonlinear differential equations are determined by minimizing the total error between the model behavior and experimental observations. The age of the individuum is defined by the number of iterations this individuum survived without changes. We used a ring topology for the network of computational nodes. The computer codes are available upon request.     

Utilizing national public-key infrastructure in mobile payment systems

Payments are the locomotive behind any business domain. It has been predicted that mobile payments will become one of the most successful mobile services, and the security of payments is an important requirement. However, it is difficult to strongly authenticate mobile users remotely and provide an adequate level of non-repudiation of transactions. In this article, we argue that a nationwide public-key infrastructure supported by governmental bodies can be used in a mobile payment system. Not only does it provide strong security, but it also makes the system open to any mobile user, merchant, or financial service provider. Two payment protocols are described: one for virtual point-of-sale payments, and one for vending machine payments. We argue that such a system can be implemented using open development platforms, and its performance is adequate for enabling swift transactions. A prototype of a system which accepts virtual point-of-sale payments is implemented, and its performance and usability are evaluated.     

Cross-Tie Domain Wall Ground State in Thin Films

No Heading Cross-tie domain wall is a complex two dimensional magnetic domain wall structure, often found experimentally in thin soft ferromagnetic films with thicknesses of about several exchange lengths. The structure of such a wall can roughly be imagined as a sequence of magnetic vortices and anti-vortices, arranged along a straight line. In this work, equilibrium energies of different one-dimensional magnetic domain wall configurations, existing in thin soft ferromagnetic films, are calculated (fully taking into account the exchange and long range dipolar interaction), based on the various classical Ritz models. They are compared to the energy of two-dimensional cross-tie domain wall, thus, estimating the region of the film thicknesses, where the cross-tie structure is the lowest energy domain wall configuration. The upper boundary of this region is estimated here for the first time. Convenient approximate analytical energy expressions in terms of elementary functions are given here both for classical and for cross-tie domain walls.PACS numbers: 75.75.+a, 75.25.+z, 75.60.–d.     

Protein Misfolding and Aggregation in the Brain: Common Pathogenetic Pathways in Neurodegenerative and Mental Disorders

Mental disorders represent common brain diseases characterized by substantial impairments of social and cognitive functions. The neurobiological causes and mechanisms of psychopathologies still have not been definitively determined. Various forms of brain proteinopathies, which include a disruption of protein conformations and the formation of protein aggregates in brain tissues, may be a possible cause behind the development of psychiatric disorders. Proteinopathies are known to be the main cause of neurodegeneration, but much less attention is given to the role of protein impairments in psychiatric disorders’ pathogenesis, such as depression and schizophrenia. For this reason, the aim of this review was to discuss the potential contribution of protein illnesses in the development of psychopathologies. The first part of the review describes the possible mechanisms of disruption to protein folding and aggregation in the cell: endoplasmic reticulum stress, dysfunction of chaperone proteins, altered mitochondrial function, and impaired autophagy processes. The second part of the review addresses the known proteins whose aggregation in brain tissue has been observed in psychiatric disorders (amyloid, tau protein, α-synuclein, DISC-1, disbindin-1, CRMP1, SNAP25, TRIOBP, NPAS3, GluA1, FABP, and ankyrin-G).     

Differences in Synovial Cytokine Profile Associated with Long-Term Clinical Outcomes in Patients with Knee Osteoarthritis Undergoing Corrective Osteotomy with Platelet-Rich Plasma or Stromal Vascular Fraction Post-Treatments

Functional outcomes and synovial fluid (SF) cytokine concentrations in response to platelet-rich plasma (PRP) or stromal vascular fraction (SVF) post-treatments following open wedge high tibial osteotomy (HTO) in 20 patients with knee osteoarthritis (OA) were examined. Six weeks after surgery, the knees of 10 patients were injected with autologous PRP (PRP subgroup), while another 10 patients were injected with autologous SVF (SVF subgroup) and monitored for 1.5 years. Pain assessment (VAS score) and functional activity (KOOS, KSS, Outerbridge, and Koshino scores) were applied. PRP subgroup performed better compared with the SVF subgroup according to KOOS, KSS, and VAS scores, while the SVF subgroup demonstrated better results according to Outerbridge and Koshino testing and produced more pronounced cartilage regeneration in the medial condyle and slowed down cartilage destruction in its lateral counterpart. SF was collected before and one week after PRP or SVF injections and tested for concentrations of 41 cytokines (Multiplex Assay). In the PRP subgroup, a significant decrease in IL-6 and CXCL10 synovial concentrations was accompanied by an increase in IL-15, sCD40L, and PDGF-AB/BB amounts. The SVF subgroup demonstrated a significant decrease in synovial TNFα, FLT-3L, MIP-1β, RANTES, and VEGF concentrations while SF concentrations of MCP-1 and FGF2 increased. Both post-treatments have a potential for increased tissue regeneration, presumably due to the downregulation of inflammation and augmentation of synovial growth factor concentrations.     

Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks

Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with “good” and “bad” morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classifier on cropped images of various sizes, we showed that the spatial scale of ~144 μm was the most informative in terms of classification quality, which was an intermediate size between the characteristic diameters of a single cell (~15 μm) and the entire colony (~540 μm). We additionally performed a proteomic analysis of several H9 cell samples used in the computational analysis and showed that cells of different phenotypes differentiated at the molecular level. Our results indicated that the proposed approach could be used as an effective method of non-invasive automated analysis to identify undesirable developmental anomalies during the propagation of pluripotent stem cells.