Binary Black Hole Merger

SpaceX's Starship Super Heavy is the most powerful launch vehicle ever flown, intended to return humans to the moon and reach Mars. After measurements of three test flights (Flights 5, 6, and 9), this paper summarizes the measurements and briefly discusses launch noise and booster flyback boom characteristics. With a planned launch cadence to rival that of the Falcon 9, Starship's noise characterization is critical to determining its impacts and its place relative to other launch vehicles and noise sources. This paper accompanies an Acoustics 2025 plenary talk.

We describe a novel variation of the mirror twin Higgs model in which the color gauge group in both sectors is extended to SU(4)c and spontaneously broken to SU(3)c exclusively in the visible sector. Through this process, the mirror Z2 symmetry is spontaneously broken, allowing for a phenomenologically viable electroweak vacuum alignment. This structure produces interesting collider signatures, including heavy vectors and fermions with fractional electric charges. The twin sector, with unbroken SU(4)c, produces interesting cosmological characteristics, such as the possibility to reduce ∆Neff and stable spin-0 baryons. The enlarged top quark sector required by the extended color gauge symmetry preserves naturalness, with even less tuning than the original twin Higgs in many circumstances.

The use of audible sound for acoustic excitation is commonly employed to assess and monitor structural health, as well as to replicate the acoustic environmental conditions that a structure might experience in use. Achieving the required amplitude and specified spectral shape is essential to meet industry standards. This study aims to implement a sound focusing method called time reversal (TR) to achieve higher amplitude levels compared to simply broadcasting noise. The paper seeks to understand the spatial dependence of focusing long-duration noise signals using TR to increase the spatial extent of the focus. Both one- and two-dimensional measurements are performed and analyzed using TR with noise, alongside traditional noise broadcasting without TR. The variables explored include the density of foci for a given length/area, the density of foci for varying length with a fixed number of foci, and the frequency content and bandwidth of the noise. A use case scenario is presented that utilizes a single-point focus with an upper frequency limit to maintain the desired spectral shape while achieving higher focusing amplitudes.

A general method for designing proteins with high conformational specificity is desirable for a variety of applications, including enzyme design and drug target redesign. To assess the ability of algorithms to design for conformational specificity, we introduce MotifDiv, a benchmark dataset of 200 conformational specificity design challenges. We also introduce CSDesign, an algorithm for designing proteins with high preference for a target conformation over an alternate conformation. On the MotifDiv benchmark, CSDesign designs protein sequences that are predicted to prefer the target conformation. We apply this method in vitro to redesign human MAP kinase ERK2, an enzyme with active and inactive conformations. Out of two designs for the active conformation, one increased activity sufficiently to retain activity in the absence of activating phosphorylations, a property not present in the wild type protein.

Undergraduate students on track for medical school are often required to take general physics lab courses. Many of these students carry an attitude of obligation into these courses which can make it challenging for instructors to engage students in course material. We address the question: How does engaging with medically based models in introductory physics labs affect pre-med undergraduate perceptions of the modeling process and their perceptions of science? We redesigned an electricity and magnetism lab in an introductory physics lab course, where approximately 70% of the undergraduates reported plans to attend medical school. We situated the lab in the mechanics of MRI magnetic resonance and collected data on the participants’ experiences through surveys and lab submissions. As a part of the analysis, we modified a rubric to evaluate engagement in modeling and applied grounded coding theory to the survey responses to develop themes of the participants’ understanding of scientific modeling. The participants’ understanding and engagement in scientific modeling increased during the newly developed lab and remained high for subsequent labs. We recommend that instructors of undergraduate nonmajor labs consider the demographic of their student population and design lab experiences situated within their interests and focus on central science practices like modeling.

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples. 

It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.