The magnetostrictive response of a Terfenol-D pellet was measured via a laboratory-based X-ray diffractometer. X-ray diffraction patterns were collected from the pellet sample with and without the presence of an applied magnetic field (~30 mT) generated by placing a large magnet under the pellet. A standard reference material, Silicon 640c, was employed as an internal standard. Magnetostriction values of 323 and 227 ppm Δl/l were determined for the (104) and (110) indexed peaks, respectively, assuming a rhombohedral structure for Terfenol-D. A threshold noise level value of ~20 to 30 ppm Δl/l was suggested based on before/after measurements in the absence of the applied field. No clear evidence of domain wall rotation was detected via changes in relative intensities of diffraction peaks in the presence of the applied magnetic field.

Real-time systems need to be built out of tasks for which the worst-case execution time is known. To enable accurate estimates of worst-case execution time, some researchers propose to build processors that simplify that analysis. These architectures are called precision-timed machines or time-predictable architectures. However, what does this term mean? This paper explores the meaning of time predictability and how it can be quantified. We show that time predictability is hard to quantify. Rather, the worst-case performance as the combination of a processor, a compiler, and a worst-case execution time analysis tool is an important property in the context of real-time systems. Note that the actual software has implications as well on the worst-case performance. We propose to define a standard set of benchmark programs that can be used to evaluate a time-predictable processor, a compiler, and a worst-case execution time analysis tool. We define worst-case performance as the geometric mean of worst-case execution time bounds on a standard set of benchmark programs.

The paper uses the material and conceptual figure of dust and matter out of place to amplify more-than-human perspectives of time, to trace the changing orientations and ethos of a site. Dust contains a complex mixture of inorganic and organic material, made up of an exuberance of microbial life such as Penicillium, Aspergillus and Cladosporium and around 20 other fungal sources. We are interested in dust as a material and metaphorical device to situate and critique temporality and the way we narrate and investigate the past and future, from a non-human, microbial point of view. Dust implies residual matter, a contradiction to order often associated with dirt. It indicates something that needs to be removed, or rearranged, something that is “out of place,” an element that does not fit. Dust also indicates time and space and signals movement and life: dust hosts a medley of non-human particles and microbial communities that engage in their own worldmaking practices. The paper brings together methods of “un-cleaning” with archival research and spatial methods of 3D scanning, modelling and mapping, as an opportunity to decentre human hubris and explore the ways in which non-humans have and continue to inhabit “our” spaces.

Molnupiravir Form I crystallizes in space group C2 (#5) with a = 6.48110(17), b = 8.71848(19), c = 27.0607(19) Å, β = 91.920(4)°, V = 1528.22(12) Å3, and Z = 4 at 295 K. The crystal structure consists of supramolecular double layers of molecules parallel to the ab-plane. The layer centers consist of hydrogen-bonded rings forming a 2D network and the outer surfaces of isopropyl groups, with van der Waals interactions between the layers. Each O atom acts as an acceptor in at least one hydrogen bond. A strong O–H⋯O hydrogen bond forms between the hydroxyl group of the oxolane ring and the carbonyl group of the oxopyrimidine ring. The other oxolane hydroxyl group forms bifurcated intra- and intermolecular hydrogen bonds. The hydroxylamino group forms an intramolecular O–H⋯N hydrogen bond with an N atom of the oxopyrimidine ring. The amino group forms an intermolecular N–H⋯N hydrogen bond to the same N atom of the ring. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

This systematic review evaluates the use of Normothermic Machine Perfusion (NMP) as a testbed for developing peripheral nerve and muscle interfaces for bionic prostheses. Our findings suggest that NMP offers a viable alternative to traditional models, with significant implications for future research and clinical applications. A literature search was performed using Ovid MEDLINE (1946 to October 2023), revealing 559 abstracts.

No studies using nerve and/or muscle electrodes for the testing or development of bionic interface technologies were identified, except for one conference abstract. NMP could serve as a test bed for future development of interface biocompatibility, selectivity, stability and data transfer, whilst complying with ethical practices and potentially offering greater relevance for human translation. Implemention of machine perfusion requires experienced personnel. Encompassing artificial intelligence alongside machine learning will provide a significant contribution to advancing interface technologies for multiple neurological disorders.

A biofilm refers to an intricate community of microorganisms firmly attached to surfaces and enveloped within a self-generated extracellular matrix. Machine learning (ML) methodologies have been harnessed across diverse facets of biofilm research, encompassing predictions of biofilm formation, identification of pivotal genes and the formulation of novel therapeutic approaches. This investigation undertook a bibliographic analysis focused on ML applications in biofilm research, aiming to present a comprehensive overview of the field’s current status. Our exploration involved searching the Web of Science database for articles incorporating the term “machine learning biofilm,” leading to the identification and analysis of 126 pertinent articles. Our findings indicate a substantial upswing in the publication count concerning ML in biofilm over the last decade, underscoring an escalating interest in deploying ML techniques for biofilm investigations. The analysis further disclosed prevalent research themes, predominantly revolving around biofilm formation, prediction and control. Notably, artificial neural networks and support vector machines emerged as the most frequently employed ML techniques in biofilm research. Overall, our study furnishes valuable insights into prevailing trends and future trajectories within the realm of ML applied to biofilm research. It underscores the significance of collaborative efforts between biofilm researchers and ML experts, advocating for interdisciplinary synergy to propel innovation in this domain.

The crystal structure of palbociclib (C24H29N7O2) used as a medication for the treatment of breast cancer has been solved and refined using synchrotron radiation after density functional theory optimization. Palbociclib crystallizes in the monoclinic system (space group P21/c, #14) at room temperature with crystal parameters: a = 11.3133(2), b = 5.62626(9), c = 35.9299(9) Å, β = 101.5071(12), V = 2241.03(8) Å3, and Z = 4. The crystal structure contains infinite N–H⋯N bonded layers. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).