Polymer‐derived SiCN cellular structures from replica of 3D printed lattices

In comparison with metals and polymers, ceramics and/or carbon are more difficult to process into well‐defined cellular architectures (e.g., cubic, tetrakaidecadehron, etc.) using Additive Manufacturing techniques. The present work reports a simple method for generating complex and precise SiCN ceramic lattices using a preceramic polymer and applying the replica approach to structures fabricated using stereolithography of plastic materials, with the associated ease of fabrication. Three‐dimensional printed plastic lattices impregnated with a polysilazane were converted to SiCN by pyrolysis at 1000°C in inert atmosphere. In spite of the high amount of mass loss (~58%) and volume shrinkage (~65%), the impregnated structures did not collapse during pyrolysis, leading to highly porous (total porosity ~93 vol%) components possessing suitable strength for handling and potential use as lightweight components.     

Hyperuricemia is Associated with Increased Apo AI Fractional Catabolic Rates and Dysfunctional HDL in New Zealand Rabbits

The potential cause–effect relationship between uric acid plasma concentrations and HDL functionality remains elusive. Therefore, this study aimed to explore the effect of oxonic acid (OA)-induced hyperuricemia on the HDL size distribution, lipid content of HDL subclasses, and apo AI turnover, as well as HDL functionality in New Zealand white rabbits. Experimental animals received OA 750 mg/kg/day by oral gavage during 21 days. The HDL-apo AI fractional catabolic rate (FCR) was determined by exogenous labeling with ¹²⁵I, and HDL subclasses were determined by sequential ultracentrifugation and PAGE. Paraoxonase-1 activity (PON-1) and the effect of HDL on relaxation of aorta rings in vitro were determined as an indication of HDL functionality. Oxonic acid induced a sixfold increase of uricemia (0.84 ± 0.06 vs. 5.24 ± 0.12 mg/dL, P < 0.001), and significant decreases of triglycerides and phospholipids of HDL subclasses, whereas HDL size distribution and HDL-cholesterol remained unchanged. In addition, HDL-apo AI FCR was significantly higher in hyperuricemic rabbits than in the control group (0.03697 ± 0.0038 vs. 0.02605 ± 0.0017 h^?1 respectively, P < 0.05). Such structural and metabolic changes were associated with lower levels of PON-1 activities and deleterious effects of HDL particles on endothelium-mediated vasodilation. In conclusion, hyperuricemia is associated with structural and metabolic modifications of HDL that result in impaired functionality of these lipoproteins. Our data strongly suggest that uric acid per se exerts deleterious effects on HDL that contribute to increase the risk of atherosclerosis.     

Effectiveness for detecting faults within and outside the scope of testing techniques: an independent replication

The verification and validation activity plays a fundamental role in improving software quality. Determining which the most effective techniques for carrying out this activity are has been an aspiration of experimental software engineering researchers for years. This paper reports a controlled experiment evaluating the effectiveness of two unit testing techniques (the functional testing technique known as equivalence partitioning (EP) and the control-flow structural testing technique known as branch testing (BT)). This experiment is a literal replication of Juristo et al. (2013). Both experiments serve the purpose of determining whether the effectiveness of BT and EP varies depending on whether or not the faults are visible for the technique (InScope or OutScope, respectively). We have used the materials, design and procedures of the original experiment, but in order to adapt the experiment to the context we have: (1) reduced the number of studied techniques from 3 to 2; (2) assigned subjects to experimental groups by means of stratified randomization to balance the influence of programming experience; (3) localized the experimental materials and (4) adapted the training duration. We ran the replication at the Escuela Politécnica del Ejército Sede Latacunga (ESPEL) as part of a software verification & validation course. The experimental subjects were 23 master’s degree students. EP is more effective than BT at detecting InScope faults. The session/program and group variables are found to have significant effects. BT is more effective than EP at detecting OutScope faults. The session/program and group variables have no effect in this case. The results of the replication and the original experiment are similar with respect to testing techniques. There are some inconsistencies with respect to the group factor. They can be explained by small sample effects. The results for the session/program factor are inconsistent for InScope faults. We believe that these differences are due to a combination of the fatigue effect and a technique x program interaction. Although we were able to reproduce the main effects, the changes to the design of the original experiment make it impossible to identify the causes of the discrepancies for sure. We believe that further replications closely resembling the original experiment should be conducted to improve our understanding of the phenomena under study.     

Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction

Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be important methods in real world applications, which range from pattern recognition, time series prediction, to intelligent control. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect or incomplete information. In this paper we are presenting several models of interval type-2 fuzzy neural networks (IT2FNNs) that use a set of rules and interval type-2 membership functions for that purpose. Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey–Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory

We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.     

Proteomic expression analysis and comparison of protein and mRNA expression profiles in human malignant gliomas

Gliomas are highly heterogeneous and therapy resistant tumors with a poor prognosis. Novel experimental therapeutic approaches have shown some promising results, but often target specific molecular mechanisms or antigens, and careful characterization of the molecular subgroup of the tumors will therefore likely be important. Thorough investigations of gene and protein alterations are also important to better understand the tumorigenic mechanisms. We have undertaken a proteomic approach, using 2-D DIGE and LC-MS/MS protein identification, to investigate 38 human gliomas and normal brains. We show that the proteome profile can discriminate between normal brain and tumors, and between tumors of varying grade by a supervised classifier. Furthermore, an analysis of the identified proteins shows an enrichment of proteins associated to pathways known to be central in gliomas, such as MEK/Erk signaling and actin cytoskeleton. It also shows a shift between different glial fibrillary acidic protein (GFAP) representatives in different grades. In a previous study the gene expression profile was characterized in an almost identical set of tumors, which enabled a paired analysis of the gene and protein expression profiles. We show that there is often a weak correlation between the mRNA and protein level. This, together with the ability of proteomics to identify PTMs, emphasizes the benefit of characterization on a protein level.     

Motion pattern analysis of parallel kinematic machines: A case study

This paper looks at the “five-axis machines” for machining operations. These machines have five (or six at maximum) degrees of freedom (dof). The output motion must have at least three translational and two rotational dof. This output motion pattern can be obtained with different structural topologies: serial, parallel, hybrid and with serial and/or parallel manipulators working in co-operation. The latter allows higher motion ranges in rotational and translational dof. Moreover, it provides a good stiffness, a highly valued requisite in applications like machining. Manipulators of this type are characterized by their kinematics to be integrated in the CNC, which requires the study of the relative motion between their modules. This motion is usually complex, and in certain cases presents kinematic relations not evident. This work presents a methodology to solve the motion pattern out of the direct and inverse kinematics of the relative motion between the modules of which the manipulator is composed. On the one hand, the spindle is mounted on a parallel module. On the other hand, the working table is mounted on a serial module. This methodology was applied to the Hermes parallel manipulator in combination with rotary and linear tables. As a result, a series of considerations regarding other possible combinations between modules was made and a new machine is proposed.     

Generating a catalog of unanticipated schemas in class hierarchies using Formal Concept Analysis

ContextInheritance is the cornerstone of object-oriented development, supporting conceptual modeling, subtype polymorphism and software reuse. But inheritance can be used in subtle ways that make complex systems hard to understand and extend, due to the presence of implicit dependencies in the inheritance hierarchy.ObjectiveAlthough these dependencies often specify well-known schemas (i.e., recurrent design or coding patterns, such as hook and template methods), new unanticipated dependency schemas arise in practice, and can consequently be hard to recognize and detect. Thus, a developer making changes or extensions to an object-oriented system needs to understand these implicit contracts defined by the dependencies between a class and its subclasses, or risk that seemingly innocuous changes break them.MethodTo tackle this problem, we have developed an approach based on Formal Concept Analysis. Our Formal Concept Analysis based-Reverse Engineering methodology (FoCARE) identifies undocumented hierarchical dependencies in a hierarchy by taking into account the existing structure and behavior of classes and subclasses.ResultsWe validate our approach by applying it to a large and non-trivial case study, yielding a catalog of hierarchy schemas, each one composed of a set of dependencies over methods and attributes in a class hierarchy. We show how the discovered dependency schemas can be used not only to identify good design practices, but also to expose bad smells in design, thereby helping developers in initial reengineering phases to develop a first mental model of a system. Although some of the identified schemas are already documented in existing literature, with our approach based on Formal Concept Analysis (FCA), we are also able to identify previously unidentified schemas.ConclusionsFCA is an effective tool because it is an ideal classification mining tool to identify commonalities between software artifacts, and usually these commonalities reveal known and unknown characteristics of the software artifacts. We also show that once a catalog of useful schemas stabilizes after several runs of FoCARE, the added cost of FCA is no longer needed.