Theoretical Engineering and Satellite Comlink of a PTVD-SHAM System

This paper focuses on super helical memory system's design, 'Engineering, Architectural and Satellite Communications' as a theoretical approach of an invention-model to 'store time-data'. The current release entails three concepts: 1- an in-depth theoretical physics engineering of the chip including its, 2- architectural concept based on VLSI methods, and 3- the time-data versus data-time algorithm. The 'Parallel Time Varying & Data Super-helical Access Memory' (PTVD-SHAM), possesses a waterfall effect in its architecture dealing with the process of voltage output-switch into diverse logic and quantum states described as 'Boolean logic & image-logic', respectively. Quantum dot computational methods are explained by utilizing coiled carbon nanotubes (CCNTs) and CNT field effect transistors (CNFETs) in the chip's architecture. Quantum confinement, categorized quantum well substrate, and B-field flux involvements are discussed in theory. Multi-access of coherent sequences of 'qubit addressing' in any magnitude, gained as pre-defined, here e.g., the 'big O notation' asymptotically confined into singularity while possessing a magnitude of 'infinity' for the orientation of array displacement. Gaussian curvature of k(k<0) is debated in aim of specifying the 2D electron gas characteristics, data storage system for defining short and long time cycles for different CCNT diameters where space-time continuum is folded by chance for the particle. Precise pre/post data timing for, e.g., seismic waves before earthquake mantle-reach event occurrence, including time varying self-clocking devices in diverse geographic locations for radar systems is illustrated in the Subsections of the paper. The theoretical fabrication process, electromigration between chip's components is discussed as well.Comment: 50 pages, 10 figures (3 multi-figures), 2 tables. v.1: 1 postulate entailing hypothetical ideas, design and model on future technological advances of PTVD-SHAM. The results of the previous paper [arXiv:0707.1151v6], are extended in order to prove some introductory conjectures in theoretical engineering advanced to architectural analysi

Comparison of spinal cord stimulation profiles from intra- and extradural electrode arrangements by finite element modelling

Spinal cord stimulation currently relies on extradural electrode arrays that are separated from the spinal cord surface by a highly conducting layer of cerebrospinal fluid. It has recently been suggested that intradural placement of the electrodes in direct contact with the pial surface could greatly enhance the specificity and efficiency of stimulation. The present computational study aims at quantifying and comparing the electrical current distributions as well as the spatial recruitment profiles resulting from extra- and intra-dural electrode arrangements. The electrical potential distribution is calculated using a 3D finite element model of the human thoracic spinal canal. The likely recruitment areas are then obtained using the potential as input to an equivalent circuit model of the pre-threshold axonal response. The results show that the current threshold to recruitment of axons in the dorsal column is more than an order of magnitude smaller for intradural than extradural stimulation. Intradural placement of the electrodes also leads to much higher contrast between the stimulation thresholds for the dorsal root entry zone and the dorsal column, allowing better focusing of the stimulus

Personalizing Simulations of the Human Atria : Intracardiac Measurements, Tissue Conductivities, and Cellular Electrophysiology

This work addresses major challenges of heart model personalization. Analysis techniques for clinical intracardiac electrograms determine wave direction and conduction velocity from single beats. Electrophysiological measurements are simulated to validate the models. Uncertainties in tissue conductivities impact on simulated ECGs. A minimal model of cardiac myocytes is adapted to the atria. This makes personalized cardiac models a promising technique to improve treatment of atrial arrhythmias

A Probabilistic Model for Estimating the Depth and Threshold Temperature of C-fiber Nociceptors

The subjective experience of thermal pain follows the detection and encoding of noxious stimuli by primary afferent neurons called nociceptors. However, nociceptor morphology has been hard to access and the mechanisms of signal transduction remain unresolved. In order to understand how heat transducers in nociceptors are activated in vivo, it is important to estimate the temperatures that directly activate the skin-embedded nociceptor membrane. Hence, the nociceptor’s temperature threshold must be estimated, which in turn will depend on the depth at which transduction happens in the skin. Since the temperature at the receptor cannot be accessed experimentally, such an estimation can currently only be achieved through modeling. However, the current state-of-the-art model to estimate temperature at the receptor suffers from the fact that it cannot account for the natural stochastic variability of neuronal responses. We improve this model using a probabilistic approach which accounts for uncertainties and potential noise in system. Using a data set of 24 C-fibers recorded in vitro, we show that, even without detailed knowledge of the bio-thermal properties of the system, the probabilistic model that we propose here is capable of providing estimates of threshold and depth in cases where the classical method fails

Interacting gases of ultracold polar molecules

Ultracold quantum gases are versatile model systems for exploring quantum physics or for the simulation of solid state materials. Meanwhile, they have been created from various atomic species - from alkali metals over alkaline earths to rare earth elements. The latest addition are quantum gases of different kinds of polar molecules. Expectations for quantum gases of heteronuclear molecules are high: Due to their large electric dipole moments, these molecules can interact with each other via long-range interactions - not just via contact interactions as is the case for most atoms. Additionally, they have vibrational and rotational degrees of freedom, with open up new possibilities for quantum simulation. But the same degrees of freedom also pose some challenges. For example, they make the preparation of the quantum gas more difficult, which is typically produced with a combination of laser cooling and evaporative cooling in the atomic case. Molecules mostly lack closed transitions in their spectra, which are required for laser cooling. Therefore, we create our molecular quantum gas from a mixture of two atomic quantum gases. In this work, such an experimental method was developed for fermionic NaK molecules, which is based on the two-photon process Stimulated Raman Adiabatic Passage (STIRAP). Within STIRAP, the hyperfine structure of the chosen intermediate state plays an important role. Experimentally, and with the help of a theoretical model describing the whole process, we find that we produce the most molecules when we use a large one-photon detuning, if the hyperfine structure of the intermediate state is unresolved. In another project, we explored the rotational level structure of the molecular ground state populated by STIRAP. Rotation is closely linked to the electric dipole moment. The superposition of the ground state with the first excited rotational state, for example, has a transition dipole moment of almost 60% of the permanent electric dipole moment. Unfortunately, coherence times of such superpositions are typically short, as the different polarizabilities of the rotational states lead to dephasing in optical traps. However, using a special polarization angle and a small, dc electric field, we can compensate these differences and realize a spin-decoupled magic trap. With this new technique we obtain record coherence times, at least for small molecular densities. For larger densities we observe first indications for dipolar interactions in a bulk gas of polar molecules, which we also model using the moving-average cluster expansion (MACE)