Electric Field Assisted Microfluidic Platform for Generation of Tailorable Porous Microbeads as Cell Carriers for Tissue Engineering

Injection of cell‐laden scaffolds in the form of mesoscopic particles directly to the site of treatment is one of the most promising approaches to tissue regeneration. Here, a novel and highly efficient method is presented for preparation of porous microbeads of tailorable dimensions (in the range ≈300–1500 mm) and with a uniform and fully interconnected internal porous texture. The method starts with generation of a monodisperse oil‐in‐water emulsion inside a flow‐focusing microfluidic device. This emulsion is later broken‐up, with the use of electric field, into mesoscopic double droplets, that in turn serve as a template for the porous microbeads. By tuning the amplitude and frequency of the electric pulses, the template droplets and the resulting porous bead scaffolds are precisely produced. Furthermore, a model of pulsed electrodripping is proposed that predicts the size of the template droplets as a function of the applied voltage. To prove the potential of the porous microbeads as cell carries, they are tested with human mesenchymal stem cells and hepatic cells, with their viability and degree of microbead colonization being monitored. Finally, the presented porous microbeads are benchmarked against conventional microparticles with nonhomogenous internal texture, revealing their superior performance.     

Reaching law for DSMC systems with relative degree 2 switching variable

This paper presents the first attempt to design a reaching law-based discrete-time sliding mode controller with a relative degree 2 switching variable. The current value of this variable is only affected by the control signal and disturbance generated two time instants ago. It is demonstrated that the new reaching law-based strategy offers a smaller quasi-sliding mode band width when compared to a similar control scheme with relative degree 1 switching variable. This in turn leads to reduced system output error in the control system proposed in the paper.     

A multi-level approach to ubiquitous modeling and solving constraints in combinatorial optimization problems in production and distribution

Constraints, although ubiquitous in production and distribution planning, scheduling and control, often lead to inconsistencies in the decision-making process. The constraint-based modeling helps circumvent many organization-impacting issues. To address this, we developed a multi-level approach to the modeling and solving of combinatorial optimization problems. It is versatile and effective owing to the use of multi-level presolving and multiple paradigms, such as constraint programming, logic programming, mathematical programming and fuzzy logic, for their complementary strengths. The capability of this framework and its advantage over mathematical programming alone or over hybrid frameworks is shown in the illustrative example, in which combinatorial optimization is used as a benchmark to prove the effectiveness of the proposed approach. Knowledge of the problem is stored in the form of facts.     

Scaling <Emphasis Type="Italic">Ab Initio</Emphasis> Predictions of 3D Protein Structures in Microsoft Azure Cloud

Computational methods for protein structure prediction allow us to determine a three-dimensional structure of a protein based on its pure amino acid sequence. These methods are a very important alternative to costly and slow experimental methods, like X-ray crystallography or Nuclear Magnetic Resonance. However, conventional calculations of protein structure are time-consuming and require ample computational resources, especially when carried out with the use of ab initio methods that rely on physical forces and interactions between atoms in a protein. Fortunately, at the present stage of the development of computer science, such huge computational resources are available from public cloud providers on a pay-as-you-go basis. We have designed and developed a scalable and extensible system, called Cloud4PSP, which enables predictions of 3D protein structures in the Microsoft Azure commercial cloud. The system makes use of the Warecki-Znamirowski method as a sample procedure for protein structure prediction, and this prediction method was used to test the scalability of the system. The results of the efficiency tests performed proved good acceleration of predictions when scaling the system vertically and horizontally. In the paper, we show the system architecture that allowed us to achieve such good results, the Cloud4PSP processing model, and the results of the scalability tests. At the end of the paper, we try to answer which of the scaling techniques, scaling out or scaling up, is better for solving such computational problems with the use of Cloud computing.     

Tocopherols,Tocotrienols and Carotenoids in Kernel Oils Recovered from 15 Apricot (<Emphasis Type="Italic">Prunus armeniaca</Emphasis> L.) Genotypes

We studied the content of tocopherols, tocotrienols and carotenoids in oil extracted from the kernels of 15 apricot (Prunus armeniaca L.) genotypes and the associated oil yield of the studied samples. The oil yield in apricot kernels was in a wide range of 27.2–61.4% (w/w) dry weight basis. For each class of studied compounds (tocochromanols and carotenoids), a three-fold difference was found between the lowest and the highest content (78.8–258.5 and 0.15–0.53 mg/100 g of oil, respectively). γ-Tocopherol accounted for 91–94% of total tocochromanols detected in all tested samples. Lutein, zeaxanthin, β-cryptoxanthin and β-carotene were the main compounds among the eight different carotenoids detected in apricot kernel oils; they comprised 76–94% of the total carotenoids content, and compositions were characteristic for specific genotypes. The oil yield and content of lipophilic antioxidants in apricot kernel oils were significantly affected by the genotype. The oil yield was negatively correlated with the total amount of tocochromanols (r = ?0.910) and carotenoids (r = ?0.704) in apricot kernel oils.     

Electrical model of the alkaline electrolyser dedicated for SPICE

In this paper, properties of the alkaline electrolyser fed from the voltage source, producing the rectangular pulse train, were investigated. A model of this electrolyser for SPICE in the form of an electrical network was proposed. The structure of this electrical model was described, and the values of parameters of the model were presented. The correctness of this model was verified experimentally in a wide range of frequency. Using the worked out model, several characteristics of the electrolyser were calculated, and the influence of amplitude, frequency, and the duty factor of the supplying voltage on productivity and watt‐hour efficiency of the electrolysis process was discussed. The investigations were performed for 2 different concentration values of the KOH solution.     

Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability

This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution ofa workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear pro- gramming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.     

Surface roughness analysis of hardened steel after high-speed milling

The work refers to analysis of various factors affecting surface roughness after end milling of hardened steel in high-speed milling (HSM) conditions. Investigations of milling parameters (cutting speed v(c) , axial depth of cut a(p) ) and the process dynamics that influence machined surface roughness were presented, and a surface roughness model, including cutter displacements, was elaborated. The work also involved analysis of surface profile charts from the point of view of vibrations and cutting force components. The research showed that theoretic surface roughness resulting from the kinematic-geometric projection of cutting edge in the workpiece is significantly different from the reality. The dominant factor in the research was not feed per tooth f(z) (according to the theoretical model) but dynamical phenomena and feed per revolution f.