introduction quality control is an important phenomenon in nuclear medicine imaging. a jaszczak spect phantom provides consistent performance information for any spect or pet system. this article describes the simulation of a jaszczak phantom and creating an executable phantom file for comparing assessment of spect cameras using simind monte carlo simulation program which is well-established fo...

As a trilogy, this first paper along with the authors' two companion articles introduces alternate approaches to standard $R$-matrix parametrization of nuclear cross sections. The authors present evidence so-called shadow poles in theory for ${}^{134}$Xe. number and location depends upon how one continues operators complex wave numbers. also establish new pole sections, called $w\phantom{\rule{...

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Tunnel coupling is a key parameter in coupled semiconductor quantum dots, and crucial ingredient various device applications, such as exchange gates spin shuttling information processing. A widely used charge-sensing technique to extract the tunnel of double dot accounts for only ground orbital state each dot, but authors show that Si valley-orbit must be included analysis. With their more comp...

Key applications of quantum computing, including error correction, rely critically on the capability to measure (read out) state selected bits, without disturbing other qubits or terminating computation. Such $m\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{...

As was suggested twenty years ago, double-vortex condensation can drive a superconductor into fractionalized insulator with \ensuremath{\mathbb{Z}}${}_{2}$ topological order. With the advent of symmetry-protected phases, authors explore here effect strong correlation in weak superconductor, showing that gives rise to translation symmetry-enriched This $t\phantom{\rule{0}{0ex}}o\phantom{\rule{0}...

A data-driven wall model for turbulent flows over periodic hills is developed using a feedforward neural network and wall-resolved large-eddy simulation data. The employs wall-normal distance, near-wall velocities, pressure gradients as input features, the shear stresses output labels, respectively. In $a$ $p\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{...