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The evolution of the hominin hand has been widely linked to the use and production of flaked stone tool technologies. After the earliest handheld flake tools emerged, shifts in hominin hand anatomy allowing for greater force during precision gripping and ease when manipulating objects in-hand are observed in the fossil record. Previous research has demonstrated how biometric traits, such as hand and digit lengths and precision grip strength, impact functional performance and ergonomic relationships when using flake and core technologies. These studies are consistent with the idea that evolutionary selective pressures would have favoured individuals better able to efficiently and effectively produce and use flaked stone tools. After the advent of composite technologies during the Middle Stone Age and Middle Palaeolithic, fossil evidence reveals differences in hand anatomy between populations, but there is minimal evidence for an increase in precision gripping capabilities. Furthermore, there is little research investigating the selective pressures, if any, impacting manual anatomy after the introduction of hafted composite stone technologies (‘handles’). Here we investigated the possible influence of tool-user biometric variation on the functional performance of 420 hafted Clovis knife replicas. Our results suggest there to be no statistical relationships between biometric variables and cutting performance. Therefore, we argue that the advent of hafted stone technologies may have acted as a ‘performance equaliser’ within populations and removed (or reduced) selective pressures favouring forceful precision gripping capabilities, which in turn could have increased the relative importance of cultural evolutionary selective pressures in the determination of a stone tool’s performance.

 

Spin Hall oscillators (SHOs) based on bilayers of a ferromagnet (FM) and a non-magnetic heavy metal (HM) are electrically tunable nanoscale microwave signal generators. Achieving high output power in SHOs requires driving large-amplitude magnetization dynamics by a direct spin Hall current. Here we present an SHO engineered to have easy-plane magnetic anisotropy oriented normal to the bilayer plane, enabling large-amplitude easy-plane dynamics driven by spin Hall current. Our experiments and micromagnetic simulations demonstrate that the easy-plane anisotropy can be achieved by tuning the magnetic shape anisotropy and perpendicular magnetic anisotropy in a nanowire SHO, leading to a significant enhancement of the generated microwave power. The easy-plane SHO experimentally demonstrated here is an ideal candidate for realization of a spintronic spiking neuron. Our results provide an approach to design of high-power SHOs for wireless communications, neuromorphic computing, and microwave assisted magnetic recording.

 

Routinely assessing the stability of molecular crystals with high accuracy remains an open challenge in the computational sciences. The many-body expansion decomposes computation of the crystal lattice energy into an embarrassingly parallel collection of computations over molecular dimers, trimers, and so forth, making quantum chemistry techniques tractable for many crystals of small organic molecules. By examining the range-dependence of different types of energetic contributions to the crystal lattice energy, we can glean qualitative understanding of solid-state intermolecular interactions as well as practical, exploitable reductions in the number of computations required for accurate energies. Here, we assess the range-dependent character of two-body interactions of 24 small organic molecular crystals by using the physically interpretable components from symmetry-adapted perturbation theory (electrostatics, exchange-repulsion, induction/polarization, and London dispersion). We also examine correlations between the convergence rates of electrostatics and London dispersion terms with molecular dipole moments and polarizabilities, to provide guidance for estimating convergence rates in other molecular crystals. 

The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions. 

We analyze the rotational dynamics of six magnetic dipoles of identical strength at the vertices of a regular hexagon with a variable-strength dipole in the center. The seven dipoles spin freely about fixed axes that are perpendicular to the plane of the hexagon, with their dipole moments directed parallel to the plane. Equilibrium dipole orientations are calculated as a function of the relative strength of the central dipole. Small-amplitude perturbations about these equilibrium states are calculated in the absence of friction and are compared with analytical results in the limit of zero and infinite central dipole strength. Normal modes and frequencies are presented. Bifurcations are seen at two critical values of the central dipole strength, with bistability between these values.