Wide angle near-field optical probes by reverse tube etching

We present a simple modification of the tube etching process for the fabrication of fiber probes for near-field optical microscopy. It increases the taper angle of the probe by a factor of two. The novelty is that the fiber is immersed in hydrofluoric acid and chemically etched in an upside-down geometry. The tip formation occurs inside the micrometer tube cavity formed by the polymeric jacket. By applying this approach, called reverse tube etching, to multimode fibers with 200/250 microm core/cladding diameter, we have fabricated tapered regions featuring high surface smoothness and average cone angles of approximately 30 degrees . A simple model based on the crucial role of the gravity in removing the etching products, explains the tip formation process.     

A trimethylamine–carbon dioxide draw solution for osmotic engines

This study evaluates the pressure retarded osmosis performance of TMA–CO₂ for potential use in osmotic heat engines. Power densities up to 18.6 W m^?2 were achievable at relatively low pressure (10 bar) using 5 M TMA–CO₂ draw solutions. Compared to NaCl control tests, the TMA–CO₂ exhibited 20% lower water flux due in large part to its larger molecular size and associated higher solution viscosity and lower diffusion coefficient. Compared to the ammonia‐carbon dioxide draw solution, water flux was comparable but reverse solute flux of TMA–CO₂ was nearly one order of magnitude lower. Larger solute size was found to create a performance tradeoff as reduced reverse solute flux improved water flux while higher viscosity and lower diffusion coefficient worsened water flux. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3369–3375, 2018     

Cellular neural networks for real-time DNA microarray analysis

A new methodology for real-time processing of DNA chip images is proposed. The idea developed here is to use the cellular neural network (CNN) array to analyze the DNA microarray. A CNN is an analog dynamic processor array that reflects this property: the processing elements interact directly within a finite local neighborhood. Due to its architecture, a two-dimensional CNN array is widely used to solve image processing and pattern recognition problems; moreover, the parallelism characteristic of this structure allows one to perform the most computationally expensive image analysis tasks three orders of magnitude faster than a classical CPU-based computer. This approach, thanks to the supercomputing capabilities of the CNN architecture, makes the whole DNA chip methodology fully parallel and also makes the processing phase, until now very time consuming, a real-time step. We discuss the results of testing an algorithm based on the CNN universal machine (CNN-UM) that has been designed to classify the image data. The algorithm is implemented in an analogic (analog and logic) microprocessor.     

The TAT-12/13 Cable Network

The TAT-12/13 Cable Network will provide a 10 Gb/s capacity between the United States, the United Kingdom, and France. By using ring switching equipment, this capacity is fully restorable within the network without dropping calls in the process. The undersea repeaters in this network use optical amplifier technology to transport a single 5 Gb/s optical signal on each fiber pair. With the upgrade potential already demonstrated, TAT-12/13's transport capacity may at least double before the end of its 25-year design life     

Collective behaviour in cellular neural networks to model the central pattern generator

In this paper, the synergetic aspects of locally interacting nonlinear systems, called cellular neural networks (CNNs), are considered. In particular, the reaction-diffusion dynamics are analysed in order to generate self-organizing phenomena encountered in nature, such as autonomous waves as well as morphogenetical patterns. These aspects are finally considered to model, in a very schematic and simple way, the role of the central pattern generator (CPG) for the generation and control of locomotion in animals. Following these guidelines, the CPG is modelled using CNNs. Some applications are presented to the locomotion coordination of some walking robot prototypes. The approach proposed is very efficient since it allows us to obtain complex artificial locomotion control strategies on chip.     

A Deeper Insight in Metal Binding to the hCtr1 N-terminus Fragment: Affinity,Speciation and Binding Mode of Binuclear Cu2+ and Mononuclear Ag+ Complex Species

Ctr1 regulates copper uptake and its intracellular distribution. The first 14 amino acid sequence of the Ctr1 ectodomain Ctr1_(1-14) encompasses the characteristic Amino Terminal Cu²⁺ and Ni²⁺ binding motif (ATCUN) as well as the bis-His binding motif (His5 and His6). We report a combined thermodynamic and spectroscopic (UV-vis, CD, EPR) study dealing with the formation of Cu²⁺ homobinuclear complexes with Ctr1_(1-14), the percentage of which is not negligible even in the presence of a small Cu²⁺ excess and clearly prevails at a M/L ratio of 1.9. Ascorbate fails to reduce Cu²⁺ when bound to the ATCUN motif, while it reduces Cu²⁺ when bound to the His5-His6 motif involved in the formation of binuclear species. The histidine diade characterizes the second binding site and is thought to be responsible for ascorbate oxidation. Binding constants and speciation of Ag⁺ complexes with Ctr1_(1-14), which are assumed to mimic Cu⁺ interaction with N-terminus of Ctr1_(1-14), were also determined. A preliminary immunoblot assay evidences that the anti-Ctr1 extracellular antibody recognizes Ctr1_(1-14) in a different way from the longer Ctr1_(1-25) that encompasses a second His and Met rich domain.     

Generation of n-double scrolls via cellular neural networks

N-double scrolls are chaotic attractors generated by Chua's circuit when its non-linear resistor characteristic is appropriately modified. They have recently been introduced, simulated and studied analytically by Suykens and Vandewalle. In this paper a new approach to generate n-double scroll attractors is introduced. They have been derived from a connection of three simple generalized cellular neural network cells called a state controlled CNN (SC-CNN). In this way it is established that n-double scroll attractors can be generated using an SC-CNN. The circuit implementation of the introduced system and some experimental results referring to the 2-double scroll attractor are reported.