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Metallic/intermetalic materials with BCC structures hold an intrinsic instability due to phonon softening along [110] dirrection, causing BCC to lower-symmetry phases transformation when the BCC structures are thermally or mechanically stressed. Fe50Rh50 binary system is one of the exceptional BCC structures (ordered-B2) that has not been yet showing such transformation upon application of thermal stress, although mechanical deformation results in B2 to disordered FCC (gamma) and L10 phases transformation. Here, a comprehensive transmission electron microscopy (TEM) study is conducted on thermally-stressed samples of Fe50Rh50 aged at water and liquid nitrogen from 1150 degree C and 1250 degree C. The results show that, samples quenched from 1150 degree C into water and liquid nitrogen show presence of 1/4{110} and 1/2{110} satellite reflections, the latter of which is expected from phonon dispersion curves obtained by density functional theory calculation. Therefore, it is believed that Fe50Rh50 maintains the B2 structure that is in premartensite state. Once Fe50Rh50 is quenched from 1250 degree C into liquid nitrogen, formation of two short-range ordered tetragonal phases with various c/a ratios (~1.15 and 1.4) is observed in line with phases formed from mechanically deformed (30%) sample. According to our observations, an accurate atomistic shear model ({110}<1-10>) is presented that describes the martensitic transformation of B2 to these tetragonal phases. These findings offer implications useful for understanding of magnetic and physical characteristics of metallic/intermetallic materials.

Nanoelectromechanical systems (NEMS) have proven outstanding performance in the detection of small masses down to single proton sensitivity. To obtain a high enough throughput for the application in practical mass spectrometry, NEMS resonators have to be arranged in two-dimensional (2D) arrays. However, all state-of-the-art electromechanical transduction methods rely on electrical lines placed close to the mechanical resonators, which drastically restricts the density of 2D resonator arrays. An exception is the transduction by surface acoustic waves (SAWs), which has so far only been shown for the transduction of single nanomechanical pillar resonators. Here, we demonstrate the transduction of pillar pairs by surface acoustic waves. The pillars have a diameter of 700 nm and show a mass responsivity of -588$\pm$98 ng$^{-1}$. The distances between the pillar pairs are 70 nm and 14.3 $\mu$m. SAW transduction enabled us to measure both pillars of each pair with electrical lines no closer than 300 $\mu$m, illustrating the potential of SAWs to transduce dense arrays of pillar resonators, a crucial step towards practical mass spectrometry with NEMS., Comment: 6 pages, 4 figures

One of the challenges of nanoelectromechanical systems (NEMS) is the effective transduction of the tiny resonators. Vertical structures, such as nanomechanical pillar resonators, which are exploited in a wide range of fields, such as optomechanics, acoustic metamaterials, and nanomechanical sensing, are particularly challenging to transduce. Existing electromechanical transduction methods are ill-suited as they complicate the pillars' fabrication process, put constraints on the pillars' material, and do not enable a transduction of freestanding pillars. Here, we present an electromechanical transduction method for single nanomechanical pillar resonators based on surface acoustic waves (SAWs). We demonstrate the transduction of freestanding nanomechanical platinum-carbon pillars in the first-order bending and compression mode. Since the principle of the transduction method is based on resonant scattering of a SAW by a nanomechanical resonator, our transduction method is independent of the pillar's material and not limited to pillar-shaped geometries. It represents a general method to transduce vertical mechanical resonators with nanoscale lateral dimensions., Comment: 24 pages, 6 figures

Nanogranular material systems are promising for a variety of applications in research and development. Their physical properties are often determined by grain sizes, shapes, mutual distances and by the chemistry of the embedding matrix With focused electron beam induced deposition arbitrarily shaped nanocomposite materials can be designed, where metallic, nanogranular structures are embedded in a carbonaceous matrix. Using "post-growth" electron beam curing, these materials can be tuned for improved electric transport or mechanical behavior. Such an optimization necessitates a thorough understanding and characterization of the internal changes in chemistry and morphology, which is where conventional projection based imaging techniques fall short. Here, we apply scanning transmission electron tomography to get a comprehensive picture of the distribution and morphology degree of embedded Pt nanograins after initial fabrication, and we demonstrate the impact of electron beam curing, which leads to condensed regions of interconnected metal nanograins.

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Building nanotechnological analogues of naturally occurring magnetic structures has proven to be a powerful approach to studying topics like geometry-induced magnetic frustration and to provide model systems for statistical physics. Moreover, it practically allows to engineer novel physical properties by realizing artificial lattice geometries that are not accessible via natural crystallization or chemical synthesis. This has been accomplished with great success in the field of two-dimensional artificial spin ice systems with important branches reaching into the field of magnetic logic devices. Although first proposals have been made to advance into three dimensions (3D), established nanofabrication pathways based on electron beam lithography have not been adapted to obtain free-form 3D nanostructures. Here we demonstrate the direct-write fabrication of freestanding ferromagnetic 3D nano-architectures with full control over the degree of magnetic frustration. By employing micro-Hall sensing, we have determined the magnetic stray field generated by our free-form structures in an externally applied magnetic field and we have performed micromagnetic and macro-spin simulations to deduce the spatial magnetization profiles in the structures and analyze their switching behavior. Furthermore we show that the magnetic 3D elements can be combined with other 3D elements of different chemical composition and intrinsic material properties.

Many organizations face critical decisions that rely on forecasts of binary events. In these situations, organizations often gather forecasts from multiple experts or models and average those forecasts to produce a single aggregate forecast. Because the average forecast is known to be underconfident, methods have been proposed that create an aggregate forecast more extreme than the average forecast. But is it always appropriate to extremize the average forecast? And if not, when is it appropriate to anti-extremize (i.e., to make the aggregate forecast less extreme)? To answer these questions, we introduce a class of optimal aggregators. These aggregators are Bayesian ensembles because they follow from a Bayesian model of the underlying information experts have. Each ensemble is a generalized additive model of experts' probabilities that first transforms the experts' probabilities into their corresponding information states, then linearly combines these information states, and finally transforms the combined information states back into the probability space. Analytically, we find that these optimal aggregators do not always extremize the average forecast, and when they do, they can run counter to existing methods. On two publicly available datasets, we demonstrate that these new ensembles are easily fit to real forecast data and are more accurate than existing methods.