Multi-H phase-coded modulations with asymmetric modulationindexes

Multi-H phase-coded modulation (MHPM) is a bandwidth-efficient modulation scheme which offers substantial coding gain over conventional digital modulation schemes. MHPM with asymmetric modulation indices corresponding to the bipolar data +1 and -1 is considered, and numerical results for the minimum Euclidean distances are provided. It is shown that performance improvements on the error probability over conventional MHPM are gained with essentially the same bandwidth and a very slight modification in implementation. The upper bounds on the error probabilities as functions of observation intervals and received E_b/N₀ are also investigated in detail. It is concluded that the concept of asymmetric modulation indices for MHPM is attractive for bandwidth and power-efficient modulation     

Two-level optimal output feedback stabilizer design

Design of two-level system stabilizers is considered using an optimal reduced-order model whose state variables are torque angles and speeds. The reduced-order model retains their physical meaning and is used to design a two-level linear feedback controller that takes into account the realities and constraints of the electrical power systems. The two-level control strategy is used, and a global control signal is generated from the output variables to minimize the effect of interaction. The effectiveness of this controller is evaluated, and an example, the multimachine system, is given to illustrate the advantages of the proposed method. The responses of the system with the two-level scheme and optimal reduced order scheme are included for comparative analyses     

Scaling laws and internal structure for characterizing electrospun poly[(R)-3-hydroxybutyrate] fibers

Using chloroform/dimethylformamide (CF/DMF) co-solvent, electrospinning of poly[(R)-3-hydroxybutyrate] (PHB) solutions was carried out at ambient temperature. The effects of the applied voltage (V), flow-rate (Q), and solution viscoelastic properties on the Taylor cone, electrified jet, and fiber morphology were investigated. In addition, the electric field developed by the needle-plate electrode configuration was calculated using a finite element analysis to reveal the tip-to-collector (H) effect. Among the processing parameters (V, Q and H), it was found that Q played a key role in determining the jet diameter (d_j) and electrospun fiber diameter (d_f), and scaling laws existed between them, i.e., d_j-Q^0.61 and d_f-Q^0.33. The diameter reduction ratios of D_o/d_j (D_o is the needle diameter) and d_j/d_f were measured as 50-120 and 5-10, respectively; it suggested that major jet stretching took place in the straight electrified jet region, and further chain orientation could be gained by the subsequent process of jet whipping. By changing PHB concentrations from 5 to 15 wt%, the solution viscosity (η_o) was increased from 100 to 4900 cP, whereas the surface tension and solution conductivity remained unchanged; it provided a good model solution to exclusively reveal the η_o effect on the electrospinning process. Our results showed that the η_o-dependence of d_j and d_f also followed simple scaling laws: d_j-η_o^0.06, and d_f-η_o^0.39, with a prefactor depending on the processing variables, mainly the flow-rate. Regardless of the PHB concentrations used, the obtained PHB fibers showed a similar crystallinity fraction of ca. 0.63 and possession of major α-crystals together with a small amount of β-crystals with zigzag chain conformation.     

Pioneering e-supply chain integration in semiconductor industry: a case study

Taiwan Semiconductor Manufacturing Company (TSMC) is the largest semiconductor foundry in the world. Advanced Semiconductor Engineering Inc. (ASE) is the world’s leader in semiconductor assembly and testing. From 1998 to 2004, the two companies completed electronic integration of 11 key business processes through the Internet. The result is a seamless interface between TSMC, ASE and their joint customers. They can now obtain accurate, timely information on their product status and respond appropriately when needed. While the direct economic benefits are estimated to be around US$ 10 million through productivity increase over a total investment of about US$ 2 million, the indirect benefits of this initiative could be on the order of US $100 million if the joint customers’ benefits are considered. In collaboration with the RosettaNet organization, TSMC and ASE leveraged their pioneering experiences to define three data exchange standards which can then be widely adopted in the semiconductor industry. This case study is a demonstration of how two leading companies in their respective fields can join forces to make a difference in creating value for the entire semiconductor industry, which in turn benefits society at large. With the momentum continuing to build and the sphere of influence continuing to expand, it is anticipated that TSMC, ASE and the entire sector will upgrade their competitiveness in terms of cost, quality, responsiveness and customer orientation.     

Construction of a prediction model for the structural stability of a surface grinder using backpropagation neural network

Learning and prediction capability of the backpropagation neural network (BPNN) have been used to build the prediction model for the structural stability of a surface grinder. The Lagrange energy method is applied to derive the dynamic equation of the lumped parameter model of the surface grinder. The major factors influencing the structural stability of the system can be determined after the ratio of kinetic energy of the sub-structure and the ratio of potential energy of the sub-structure interface are obtained. An orthogonal rotatable central composite design is adopted to dispose the treatment combinations of the major factors. The BPNN model is constructed by the treatment combinations of the training patterns and verified by the treatment combinations of the test patterns. In this paper, a 3-layer BPNN model with a 10-neuron hidden layer which converged after 4,072 learning cycles is selected to predict the structural stability of a surface grinder within the planned ranges. The percentage residuals of both training patterns and test patterns are all within 3.41%, thus the prediction accuracy of the BPNN model is excellent so that the engineering demands are well satisfied.     

Extrahepatic metabolism of 4-methylumbelliferone and lidocaine in the anhepatic rabbit

OBJECTIVE: The initially well-fixed implants of total hip replacement (THR) are in the long-term subject to aseptic loosening. Many cytokines can contribute to osteolysis due to osteoclast recruitment and/or activation. However, in this respect tumor necrosis factor-alpha (TNF-alpha) plays a pivotal role, because it upregulates interleukin-1 and 6 and granulocyte-macrophage colony stimulating factor. The aim of this study was to assess the eventual presence, cellular localization and extent of expression of TNF-alpha in the synovial-like membrane at the implant or at the cement to bone interface compared to control synovial membrane. METHODS: Twenty samples from the synovial-like membrane of the periprosthetic tissues were compared to control samples. TNF-alpha containing cells were visualized using an avidin-biotin-peroxidase complex (ABC) method and analyzed by light microscopy, double labelling and image analysis. RESULTS: TNF-alpha was found in the periprosthetic tissues in fibroblasts and vascular endothelial cells, but mainly in the macrophages was it found to coincide with areas containing implant-derived debris. TNF-alpha containing cells were more numerous in the synovial-like membrane in the interface tissue from the proximal stem area (2816 +/- 318 cells) than in the control synovial membrane (565 +/- 93 cells, p < 0.01). Interestingly, similarly high TNF-alpha expression (3452 +/- 582 cells) was also seen in the synovial-like membrane of the pseudocapsule. CONCLUSION: These findings suggest that the foreign body-type host reaction caused by THR is characterized by the high expression of TNF-alpha. Because such expression occurred in the interface tissue between the implant and surrounding bone, TNF-alpha, due to its pivotal direct and indirect role in the activation and recruitment of osteoclasts, may contribute to periprosthetic osteolysis and to the loosening of THR.     

Evidence for McKusick''s hypothesis of deficient collagen cross-linking in patients with homocystinuria

Fractionation of the venom of the spider Phoneutria nigriventer revealed that it was a mixture of several neurotoxic peptides. The peptides so far characterized either inhibited or induced neurotransmitter release. These effects were mediated by Ca2+ channels or increasing Na+ permeability through voltage sensitive Na(+)-channels, respectively. The pooled toxic components (fraction P4) showed stimulatory effects on acetylcholine release from brain cortical slices. In addition, a component of the observed effects resembling that of alpha-latrotoxin was identified, which was characterized by the ability to provoke release of acetylcholine (ACh) at low temperature and in a manner independent of extracellular Ca2+ and of voltage sensitive Na(+)-channels.     

Spray deposition process study of SnO2silicon solar cells using orthogonal experimental design

With the “orthogonal experimental design” (OED), the dependence of conductivity and transparency of tin oxide films and open circuit voltage V_oc of SIS solar cells on the spray deposition process factors was studied. The OED technique was found to be a powerful method for realizing the best factor combination. Using OED, curves were obtained which clearly depict the effects of each factor on the SIS characteristics. The results of the experiments also illustrate exactly which variation in fabrication technique most affects the sheet resistance, V_oc, and film transparency. Under the optimal combination of conditions, a thin film doped with NH₄F was obtained, with about 90% transmission and a sheet resistance of about 80 Ω/□, along with an SIS structure with a V_oc of about 0.61 V.     

Construction and characterization of a set of E. coli strains deficient in all known loci affecting the proteolytic stability of secreted recombinant proteins

Even though secretion offers numerous advantages for the production of proteins in Escherichia coli, the expression of many heterologous proteins is severely limited by degradation in the periplasmic space. We found that mutations in rpoH, the RNA polymerase sigma factor responsible for heat shock protein synthesis, affect the stability of heterologous secreted proteins. A particularly dramatic increase in expression was further observed in rpoH degP double mutants. To minimize proteolytic degradation, we constructed a family of 25 isogenic strains deficient in all known cell envelope proteases (DegP, Protease III, Tsp(Prc), and OmpT), as well as the rpoH15 mutant allele, and characterized their growth in both shake flasks and fermentors. The availability of this set of strains permits the selection of a suitable host based on the optimal combination between the optimum reduction in protease activity and acceptable growth properties.     

Symmetry breaking and training from incomplete data with Radial Basis Boltzmann Machines

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.