Increasing penetration of variable and intermittent renewable energy resources on the energy grid poses a challenge for reliable and efficient grid operation, necessitating the development of algorithms that are robust to this uncertainty. However, standard algorithms incorporating uncertainty for generation dispatch are computationally intractable when costs are nonconvex, and machine learning-based approaches lack worst-case guarantees on their performance. In this work, we propose a learning-augmented algorithm, RobustML, that exploits the good average-case performance of a machine-learned algorithm for minimizing dispatch and ramping costs of dispatchable generation resources while providing provable worst-case guarantees on cost. We evaluate the algorithm on a realistic model of a combined cycle cogeneration plant, where it exhibits robustness to distribution shift while enabling improved efficiency as renewables penetration increases.

Objectives/Goals: Transmission-blocking vaccines hold promise for malaria elimination by reducing community transmission. But a major challenge that limits the development of efficacious vaccines is the vast parasite’s genetic diversity. This work aims to assess the genetic diversity of the Pfs25 vaccine candidate in complex infections across African countries. Methods/Study Population: We employed next-generation amplicon deep sequencing to identify nonsynonymous single nucleotide polymorphisms (SNPs) in 194 Plasmodium falciparum samples from four endemic African countries: Senegal, Tanzania, Ghana, and Burkina Faso. The individuals aged between 1 and 74 years, but most of them ranged from 1 to 19 years, and all presented symptomatic P. falciparum infection. The genome amplicon sequencing was analyzed using Geneious software and P. falciparum 3D7 as a reference. The SPNs were called with a minimum coverage of 500bp, and for this work, we used a very sensitive threshold of 1% variant frequency to determine the frequency of SNPs. The identified SNPs were threaded to the crystal structure of the Pfs25 protein, which allowed us to predict the impact of the novel SNP in the protein or antibody binding. Results/Anticipated Results: We identified 26 SNPs including 24 novel variants, and assessed their population prevalence and variant frequency in complex infections. Notably, five variants were detected in multiple samples (L63V, V143I, S39G, L63P, and E59G), while the remaining 21 were rare variants found in individual samples. Analysis of country-specific prevalence showed varying proportions of mutant alleles, with Ghana exhibiting the highest prevalence (44.6%), followed by Tanzania (12%), Senegal (11.8%), and Burkina Faso (2.7%). Moreover, we categorized SNPs based on their frequency, identifying dominant variants (>25%), and rare variants (Discussion/Significance of Impact: We identified additional SNPs in the Pfs25 gene beyond those previously reported. However, the majority of these newly discovered display low variant frequency and population prevalence. Further research exploring the functional implications of these variations will be important to elucidate their role in malaria transmission.

This research explores a phenomenon that we see nearly every day and has implications for how we view people in other nations: Different media outlets may report the same international events either in terms of the nation (e.g., “Russia invades Ukraine”) or in terms of the leader (e.g., “Putin invades Ukraine”). Five studies, conducted during the 2022 Russia-Ukraine Conflict and involving both field and experimental data, find that readers of nation-framed news about the conflict had worse impressions of the people in the associated nation (Russians) than readers of the corresponding leader-framed version. We explain the psychology behind this framing effect and identify its moderators. Our research underscores the importance of responsible media practices in shaping global perceptions.

Prior to the No Surprises Act (NSA), numerous states passed laws protecting patients from surprise medical bills from out-of-network (OON) hospital-based physicians supporting elective treatment in in-network hospitals. Even in non-emergency situations, patients have little ability to choose physicians such as anaesthesiologists, pathologists or radiologists. Using a comprehensive, multi-payer claims database, we estimated the effect of these laws on hospital-based physician reimbursement, charges, network participation and potential surprise billing episodes. Overall, the state laws were associated with a reduction in anaesthesiology prices and charges, but an increase in pathology and radiology prices. The price effects for each state exhibit substantial heterogeneity. California and New Jersey experienced increases in network participation by anaesthesiologists and pathologists and reductions in potential surprise billing episodes, but, overall, we find little evidence of changes in network participation across all of the states implementing surprise billing laws. Our results suggest that the effects of the NSA may vary across states.

Cells of magnetotactic bacteria are used as model systems for studying the magnetic properties of ferrimagnetic nanocrystals. Each individual bacterial strain produces magnetosomes (membrane-bounded magnetic crystals) that have distinct sizes, shapes, crystallographic orientations and spatial arrangements, thereby providing nanoparticle systems whose unique magnetic properties are unmatched by synthetic chemically-produced crystals. Here, we use off-axis electron holography in the transmission electron microscope to study the magnetic properties of isolated and closely-spaced bullet-shaped magnetite (Fe3O4) magnetosomes biomineralized by the following magnetotactic bacterial strains: the cultured Desulfovibrio magneticus RS-1 and the uncultured strains LO-1 and HSMV-1. These bacteria biomineralize magnetite crystals whose crystallographic axes of elongation are parallel to <100> (RS-1 and LO-1) or <110> (HSMV-1). We show that the individual magnetosome crystals are single magnetic domains and measure their projected in-plane magnetization distributions and magnetic dipole moments. We use analytical modelling to assess the interplay between shape anisotropy and the magnetically preferred <111> magneto-crystalline easy axis of magnetite.

This study documents several correlations observed during the first run of the plasma wakefield acceleration experiment E300 conducted at FACET-II, using a single drive electron bunch. The established correlations include those between the measured maximum energy loss of the drive electron beam and the integrated betatron X-ray signal, the calculated total beam energy deposited in the plasma and the integrated X-ray signal, among three visible light emission measuring cameras and between the visible plasma light and X-ray signal. The integrated X-ray signal correlates almost linearly with both the maximum energy loss of the drive beam and the energy deposited into the plasma, demonstrating its usability as a measure of energy transfer from the drive beam to the plasma. Visible plasma light is found to be a useful indicator of the presence of a wake at three locations that overall are two metres apart. Despite the complex dynamics and vastly different time scales, the X-ray radiation from the drive bunch and visible light emission from the plasma may prove to be effective non-invasive diagnostics for monitoring the energy transfer from the beam to the plasma in future high-repetition-rate experiments.

This paper presents a comprehensive technical overview of the Linac Coherent Light Source II (LCLS-II) photoinjector laser system, its first and foremost component. The LCLS-II photoinjector laser system serves as an upgrade to the original LCLS at SLAC National Accelerator Laboratory. This advanced laser system generates high-quality laser beams for the LCLS-II, contributing to the instrument’s unprecedented brightness, precision and flexibility. Our discussion extends to the various subsystems that comprise the photoinjector, including the photocathode laser, laser heater and beam transport systems. Lastly, we draw attention to the ongoing research and development infrastructure underway to enhance the functionality and efficiency of the LCLS-II, and similar X-ray free-electron laser facilities around the world, thereby contributing to the future of laser technology and its applications.

In Global Navigation Satellite Systems (GNSS)-denied environments, aiding a vehicle's inertial navigation system (INS) is crucial to reducing the accumulated navigation drift caused by sensor errors (e.g. bias and noise). One potential solution is to use measurements of gravity as an aiding source. The measurements are matched to a geo-referenced map of Earth's gravity to estimate the vehicle's position. In this paper, we propose a novel formulation of the map matching problem using a hidden Markov model (HMM). Specifically, we treat the spatial cells of the map as the hidden states of the HMM and present a Viterbi style algorithm to estimate the most likely sequence of states, i.e. most likely sequence of vehicle positions, that results in the sequence of observed gravity measurements. Using a realistic gravity map, we demonstrate the accuracy of our Viterbi map matching algorithm in a navigation scenario and illustrate its robustness compared with existing methods.

Dense suspensions of solid particles in viscous liquid are ubiquitous in both industry and nature, and there is a clear need for efficient numerical routines to simulate their rheology and microstructure. Particles of micron size present a particular challenge: at low shear rates, colloidal interactions control their dynamics while at high rates, granular-like contacts dominate. While there are established particle-based simulation schemes for large-scale non-Brownian suspensions using only pairwise lubrication and contact forces, common schemes for colloidal suspensions generally are more computationally costly and thus restricted to relatively small system sizes. Here, we present a minimal particle-based numerical model for dense colloidal suspensions that incorporates Brownian forces in pairwise form alongside contact and lubrication forces. We show that this scheme reproduces key features of dense suspension rheology near the colloidal-to-granular transition, including both shear thinning due to entropic forces at low rates and shear thickening at high rates due to contact formation. This scheme is implemented in LAMMPS, a widely used open source code for parallelised particle-based simulations, with a runtime that scales linearly with the number of particles, making it amenable for large-scale simulations.

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

The early use of Robust Design (RD) supports the development of product concepts with low sensitivity to variation, which offers advantages for reducing the risk of costly iterations. Due to the lack of approaches for early evaluation of product robustness, the embodiment-function-relation and tolerance (EFRT-) model was developed, which combines the contact and channel approach and tolerance graphs. The information exchange of both approaches offers a high potential for reliable robustness evaluation results. However, that potential currently relies unused, since the link between applicable robustness criteria and the extended information is missing. To solve this problem, four research steps were determined: (1) understanding of robustness, (2) collection of RD principles, (3) identification of EFRT-model information and (4) mapping of RD principles and information. The results show nine adapted RD principles, the identified model information for the robustness evaluation, the evaluation criteria as well as their mapping. Utilizing the mapping and the proposed criteria in this contribution, a more comprehensive robustness evaluation in early stages is enabled.

This paper uses vector autoregression model analysis to identify monetary policy shocks on UK data using surprise changes in the policy rate as external instruments and imposing block exogeneity restrictions on domestic variables to estimate parameters from the viewpoint of the domestic economy. The results show large and persistent effects of monetary policy shocks on the domestic economy and point to the critical role of exchange rates and term premia. The analysis resolves important empirical puzzles of traditional recursive identification methods.