Topology of megagauss magnetic fields and of heat-carrying electrons produced in a high-power laser-solid interaction

The intricate spatial and energy distribution of magnetic fields, self-generated during high power laser irradiation (at Iλ2∼1013-1014W.cm-2.μm2) of a solid target, and of the heat-carrying electron currents, is studied in inertial confinement fusion (ICF) relevant conditions. This is done by comparing proton radiography measurements of the fields to an improved magnetohydrodynamic description that fully takes into account the nonlocality of the heat transport. We show that, in these conditions, magnetic fields are rapidly advected radially along the target surface and compressed over long time scales into the dense parts of the target. As a consequence, the electrons are weakly magnetized in most parts of the plasma flow, and we observe a reemergence of nonlocality which is a crucial effect for a correct description of the energetics of ICF experiments.

Controlling Surface Topology and Functionality of Electrospun Fibers on the Nanoscale using Amphiphilic Block Copolymers To Direct Mesenchymal Progenitor Cell Adhesion

Surface patterning in three dimensions is of great importance in biomaterials design for controlling cell behavior. A facile one-step functionalization of biodegradable PDLLA fibers using amphiphilic diblock copolymers is demonstrated here to systematically vary the fiber surface composition. The copolymers comprise a hydrophilic poly[oligo(ethylene glycol) methacrylate] (POEGMA), poly[(2-methacryloyloxy)ethyl phosphorylcholine] (PMPC), or poly[2-(dimethylamino)ethyl methacrylate)] (PDMAEMA) block and a hydrophobic poly(l-lactide) (PLA) block. The block copolymer-modified fibers have increased surface hydrophilicity compared to that of PDLLA fibers. Mixtures of PLAPMPC and PLAPOEGMA copolymers are utilized to exploit microphase separation of the incompatible hydrophilic PMPC and POEGMA blocks at the fiber surface. Conjugation of an RGD cell-adhesive peptide to one hydrophilic block (POEGMA) using thiol-ene chemistry produces fibers with domains of cell-adhesive (POEGMA) and cell-inert (PMPC) sites, mimicking the adhesive properties of the extracellular matrix (ECM). Human mesenchymal progenitor cells (hES-MPs) showed much better adhesion to the fibers with surface-adhesive heterogeneity compared to that to fibers with only adhesive or only inert surface chemistries.

Burkholderia cenocepacia and Salmonella enterica ArnT proteins that transfer 4-amino-4-deoxy-L-arabinose to lipopolysaccharide share membrane topology and functional amino acids

We recently demonstrated that incorporation of 4-amino-4-deoxy-l-arabinose (l-Ara4N) to the lipid A moiety of lipopolysaccharide (LPS) is required for transport of LPS to the outer membrane and viability of the Gram-negative bacterium Burkholderia cenocepacia. ArnT is a membrane protein catalyzing the transfer of l-Ara4N to the LPS molecule at the periplasmic face of the inner membrane, but its topology and mechanism of action are not well characterized. Here, we elucidate the topology of ArnT and identify key amino acids that likely contribute to its enzymatic function. PEGylation assays using a cysteineless version of ArnT support a model of 13 transmembrane helices and a large C-terminal region exposed to the periplasm. The same topological configuration is proposed for the Salmonella enterica serovar Typhimurium ArnT. Four highly conserved periplasmic residues in B. cenocepacia ArnT, tyrosine-43, lysine-69, arginine-254 and glutamic acid-493, were required for activity. Tyrosine-43 and lysine-69 span two highly conserved motifs, 42RYA44 and 66YFEKP70, that are found in ArnT homologues from other species. The same residues in S. enterica ArnT are also needed for function. We propose these aromatic and charged amino acids participate in either undecaprenyl phosphate-l-Ara4N substrate recognition or transfer of l-Ara4N to the LPS.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Central-Tapped Node Linked Modular Fault-Tolerance Topology for SRM Applications

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) can reduce greenhouse gas emissions while switched reluctance motor (SRM) is one of the promising motor for such applications. This paper presents a novel SRM fault-diagnosis and fault-tolerance operation solution. Based on the traditional asymmetric half-bridge topology for the SRM driving, the central tapped winding of the SRM in modular half-bridge configuration are introduced to provide fault-diagnosis and fault-tolerance functions, which are set idle in normal conditions. The fault diagnosis can be achieved by detecting the characteristic of the excitation and demagnetization currents. An SRM fault-tolerance operation strategy is also realized by the proposed topology, which compensates for the missing phase torque under the open-circuit fault, and reduces the unbalanced phase current under the short-circuit fault due to the uncontrolled faulty phase. Furthermore, the current sensor placement strategy is also discussed to give two placement methods for low cost or modular structure. Simulation results in MATLAB/Simulink and experiments on a 750-W SRM validate the effectiveness of the proposed strategy, which may have significant implications and improve the reliability of EVs/HEVs.

Generating analysis topology using virtual topology operators

Virtual topology operations have been utilized to generate an analysis topology definition suitable for downstream mesh generation. Detailed descriptions are provided for virtual topology merge and split operations for all topological entities, where virtual decompositions are robustly linked to the underlying geometry. Current virtual topology technology is extended to allow the virtual partitioning of volume cells. A valid description of the topology, including relative orientations, is maintained which enables downstream interrogations to be performed on the analysis topology description, such as determining if a specific meshing strategy can be applied to the virtual volume cells. As the virtual representation is a true non-manifold description of the sub-divided domain the interfaces between cells are recorded automatically. Therefore, the advantages of non-manifold modelling are exploited within the manifold modelling environment of a major commercial CAD system without any adaptation of the underlying CAD model. A hierarchical virtual structure is maintained where virtual entities are merged or partitioned. This has a major benefit over existing solutions as the virtual dependencies here are stored in an open and accessible manner, providing the analyst with the freedom to create, modify and edit the analysis topology in any preferred sequence.

Towards Possibilistic Fuzzy Answer Set Programming

Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers.

Internet of Things. A proposed secured network topology

The myriad of technologies and protocols working at different layers pose significant security challenges in the upcoming Internet of Things (IoT) paradigm. Security features and needs vary from application to application and it is layer specific. In addition, security has to consider the constraints imposed by energy limited sensor nodes and consider the specific target application in order to provide security at different layers. This paper analyses current standardization efforts and protocols. It proposes a generic secured network topology for IoT and describes the relevant security challenges. Some exploitation examples are also provided.

Modelling of Evaporator in Waste Heat Recovery System using Finite Volume Method and Fuzzy Technique

The evaporator is an important component in the Organic Rankine Cycle (ORC)-based Waste Heat Recovery (WHR) system since the effective heat transfer of this device reflects on the efficiency of the system. When the WHR system operates under supercritical conditions, the heat transfer mechanism in the evaporator is unpredictable due to the change of thermo-physical properties of the fluid with temperature. Although the conventional finite volume model can successfully capture those changes in the evaporator of the WHR process, the computation time for this method is high. To reduce the computation time, this paper develops a new fuzzy based evaporator model and compares its performance with the finite volume method. The results show that the fuzzy technique can be applied to predict the output of the supercritical evaporator in the waste heat recovery system and can significantly reduce the required computation time. The proposed model, therefore, has the potential to be used in real time control applications.