Search

Understanding the physics of strongly correlated materials is one of the grand challenge problems for physics today. A large class of scientifically interesting materials, from high-$T_c$ superconductors to spin liquids, involve medium to strong correlations, and building a holistic understanding of these materials is critical. Doing so is hindered by the competition between the kinetic energy and Coulomb repulsion, which renders both analytic and numerical methods unsatisfactory for describing interacting materials. Fault-tolerant quantum computers have been proposed as a path forward to overcome these difficulties, but this potential capability has not yet been fully assessed. Here, using the multi-orbital Fermi-Hubbard model as a representative model and a source of scalable problem specifications, we estimate the resource costs needed to use fault-tolerant quantum computers for obtaining experimentally relevant quantities such as correlation function estimation. We find that advances in quantum algorithms and hardware will be needed in order to reduce quantum resources and feasibly address utility-scale problem instances.

The development of fault-tolerant quantum computers (FTQCs) is gaining increased attention within the quantum computing community. Like their digital counterparts, FTQCs, equipped with error correction and large qubit numbers, promise to solve some of humanity's grand challenges. Estimates of the resource requirements for future FTQC systems are essential to making design choices and prioritizing R&D efforts to develop critical technologies. Here, we present a resource estimation framework and software tool that estimates the physical resources required to execute specific quantum algorithms, compiled into their graph-state form, and laid out onto a modular superconducting hardware architecture. This tool can predict the size, power consumption, and execution time of these algorithms at as they approach utility-scale according to explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We use this tool to study the total resources on a proposed modular architecture and the impact of tradeoffs between and inter-module connectivity, latency and resource requirements.

We present a new analysis of the KELT-24 system, comprising a well-aligned hot Jupiter, KELT-24~b, and a bright ($V=8.3$), nearby ($d=96.9~\mathrm{pc}$) F-type host star. KELT-24~b was independently discovered by two groups in 2019, with each reporting best-fit stellar parameters that were notably inconsistent. Here, we present three independent analyses of the KELT-24 system, each incorporating a broad range of photometric and spectroscopic data, including eight sectors of TESS photometry and more than 200 new radial velocities (RVs) from MINERVA. Two of these analyses use KELT-24's observed spectral energy distribution (SED) through a direct comparison to stellar evolutionary models, while our third analysis assumes an unknown additional body contributing to the observed broadband photometry and excludes the SED. Ultimately, we find that the models that include the SED are a poor fit to the available data, so we adopt the system parameters derived without it. We also highlight a single transit-like event observed by TESS, deemed likely to be an eclipsing binary bound to KELT-24, that will require follow-up observations to confirm. We discuss the potential of these additional bodies in the KELT-24 system as a possible explanation for the discrepancies between the results of the different modeling approaches, and explore the system for longer-period planets that may be weakly evident in the RV observations. The comprehensive investigations that we present not only increase the fidelity of our understanding of the KELT-24 system, but also serve as a blueprint for future stellar modeling in global analyses of exoplanet systems., Comment: 27 pages, 18 figures

The spin-orbit splitting between neutron 1$p$ orbitals at $^{33}$Si has been deduced using the single-neutron-adding ($d$,$p$) reaction in inverse kinematics with a beam of $^{32}$Si, a long-lived radioisotope. Reaction products were analyzed by the newly implemented SOLARIS spectrometer at the reaccelerated-beam facility at the National Superconducting Cyclotron Laboratory. The measurements show reasonable agreement with shell-model calculations that incorporate modern cross-shell interactions, but they contradict the prediction of proton density depletion based on relativistic mean-field theory. The evolution of the neutron 1$p$-shell orbitals is systematically studied using the present and existing data in the isotonic chains of $N=17$, 19, and 21. In each case, a smooth decrease in the separation of the $1p_{3/2}$-$1p_{1/2}$ orbitals is seen as the respective $p$-orbitals approach zero binding, suggesting that the finite nuclear potential strongly influences the evolution of nuclear structure in this region., Comment: 10 pages, 5 figures

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error., Comment: Accepted at Medical Image Analysis Journal

Graphical models and likelihood ratios can be used by forensic scientists to compare support given by evidence to propositions put forward by competing parties during court proceedings. Such models can also be used to evaluate support for activity-level propositions, i.e. propositions that refer to the nature of activities associated with evidence and how this evidence came to be at a crime scene. Graphical methods can be used to show explicitly different scenarios that might explain the evidence in a case and to distinguish between evidence requiring evaluation by a jury and quantifiable evidence from the crime scene. Such visual representations can be helpful for forensic practitioners, the police and lawyers who may need to assess the value that different pieces of evidence make to their arguments in a case. In this paper we demonstrate for the first time how chain event graphs can be applied to a criminal case involving drug trafficking. We show how different types of evidence (i.e. expert judgement and data collected from a crime scene) can be combined using a chain event graph and show how the hierarchical model deriving from the graph can be used to evaluate the degree of support for different activity-level propositions in the case. We also develop a modification of the standard chain event graph to simplify their use in forensic applications.

We present the results of a search for ammonia maser emission in 119 Galactic high-mass star-forming regions (HMSFRs) known to host 22 GHz H$_2$O maser emission. Our survey has led to the discovery of non-metastable NH$_3$ inversion line masers toward 14 of these sources. This doubles the number of known non-metastable ammonia masers in our Galaxy, including nine new very high excitation ($J,K$)~=~(9,6) maser sources. These maser lines, including NH$_3$ (5,4), (6,4), (6,5), (7,6), (8,6), (9,6), (9,8), (10,8), and (11,9), arise from energy levels of 342 K, 513 K, 465 K, 606 K, 834 K, 1090 K, 942 K, 1226 K, and 1449 K above the ground state. Additionally, we tentatively report a new metastable NH$_3$ (3,3) maser in G048.49 and an NH$_3$ (7,7) maser in G029.95. Our observations reveal that all of the newly detected NH$_3$ maser lines exhibit either blueshifted or redshifted velocities with respect to the source systemic velocities. Among the non-metastable ammonia maser lines, larger velocity distributions, offset from the source systemic velocities, are found in the ortho-NH$_3$ ($K=3n$) than in the para-NH$_3$ ($K\neq3n$) transitions., Comment: 14 pages, 4 tables, 9 figures. Accepted for publication in A&A

We compare three graphical methods for displaying evidence in a legal case: Wigmore Charts, Bayesian Networks and Chain Event Graphs. We find that these methods are aimed at three distinct audiences, respectively lawyers, forensic scientists and the police. The methods are illustrated using part of the evidence in the case of the murder of Meredith Kercher. More specifically, we focus on representing the list of propositions, evidence, testimony and facts given in the first trial against Raffaele Sollecito and Amanda Knox with these graphical methodologies.

Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geographic constraints, and fairness. First, we address a matching problem where the goal is to maximize a welfare function in two-sided markets where buyers' requirements and sellers' supplies are represented by value functions that assign prices (or costs) to specified volumes of water. For the setting where the value functions satisfy certain monotonicity properties, we present an efficient algorithm that maximizes a social welfare function. When there are minimum water requirement constraints, we present a randomized algorithm which ensures that the constraints are satisfied in expectation. For a single seller--multiple buyers setting with fairness constraints, we design an efficient algorithm that maximizes the minimum level of satisfaction of any buyer. We also present computational complexity results that highlight the limits on the generalizability of our results. We evaluate the algorithms developed in our work with experiments on both real-world and synthetic data sets with respect to drought severity, value functions, and seniority of agents.

A fast and wide Coronal Mass Ejection (CME) erupted from the Sun on 2012-03-13. Its interplanetary counterpart was detected in situ two days later by STEREO-A and near-Earth spacecraft. We suggest that at 1 au the CME extended at least 110$^\circ$ in longitude, with Earth crossing its east flank and STEREO-A crossing its west flank. Despite their separation, measurements from both positions showed very similar in situ CME signatures. The solar source region where the CME erupted was surrounded by three coronal holes (CHs). Their locations with respect to the CME launch site were east (negative polarity), southwest (positive polarity) and west (positive polarity). The solar magnetic field polarity of the area covered by each CH matches that observed at 1 au in situ. Suprathermal electrons at each location showed mixed signatures with only some intervals presenting clear counterstreaming flows as the CME transits both locations. The strahl population coming from the shortest magnetic connection of the structure to the Sun showed more intensity. The study presents important findings regarding the in situ measured CME on 2012-03-15, detected at a longitudinal separation of 110$^\circ$ in the ecliptic plane despite its initial inclination being around 45$^\circ$ when erupted. This suggests that the CME may have deformed and/or rotated, allowing it to be observed near its legs with spacecraft at a separation angle greater than 100$^\circ$. The CME structure interacted with high-speed streams generated by the surrounding CHs. The piled-up plasma in the sheath region exhibited an unexpected correlation in magnetic field strength despite the large separation in longitude. In situ observations reveal that at both locations there was a flank encounter, where the spacecraft crossed the first part of the CME, then encountered ambient solar wind, and finally passed near the legs of the structure., Comment: Accepted in A&A. Previous to final editorial changes

We assessed the benefit of combining stress cardiac CT perfusion (CCTP) myocardial blood flow (MBF) with coronary CT angiography (CCTA) using our innovative CCTP software. By combining CCTA and CCTP, one can uniquely identify a flow limiting stenosis (obstructive-lesion + low-MBF) versus MVD (no-obstructive-lesion + low-MBF. We retrospectively evaluated 104 patients with suspected CAD, including 18 with diabetes, who underwent CCTA+CCTP. Whole heart and territorial MBF was assessed using our automated pipeline for CCTP analysis that included beam hardening correction; temporal scan registration; automated segmentation; fast, accurate, robust MBF estimation; and visualization. Stenosis severity was scored using the CCTA coronary-artery-disease-reporting-and-data-system (CAD-RADS), with obstructive stenosis deemed as CAD-RADS>=3. We established a threshold MBF (MBF=199-mL/min-100g) for normal perfusion. In patients with CAD-RADS>=3, 28/37(76%) patients showed ischemia in the corresponding territory. Two patients with obstructive disease had normal perfusion, suggesting collaterals and/or a hemodynamically insignificant stenosis. Among diabetics, 10 of 18 (56%) demonstrated diffuse ischemia consistent with MVD. Among non-diabetics, only 6% had MVD. Sex-specific prevalence of MVD was 21%/24% (M/F). On a per-vessel basis (n=256), MBF showed a significant difference between territories with and without obstructive stenosis (165 +/- 61 mL/min-100g vs. 274 +/- 62 mL/min-100g, p <0.05). A significant and negative rank correlation (rho=-0.53, p<0.05) between territory MBF and CAD-RADS was seen. CCTA in conjunction with a new automated quantitative CCTP approach can augment the interpretation of CAD, enabling the distinction of ischemia due to obstructive lesions and MVD.

Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE). Objectives: Create novel, hand-crafted EAT features, 'fat-omics', to capture the pathophysiology of EAT and improve MACE prediction. Methods: We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE. Results: Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/ BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001). Conclusion: Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction., Comment: 7 pages, 1 central illustration, 6 figures, 5 tables

We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary CTA (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images. To identify coronaries in CTCS images that have subtle visual evidence of vessels, we registered CTCS with paired CCTA images having coronary labels. We developed a novel axial-disk method giving regions for analyzing PCAT features in three main coronary arteries. We analyzed novel hand-crafted and radiomic features using univariate and multivariate logistic regression prediction of MACE and compared results against those from CCTA. Registration accuracy was sufficient to enable the identification of PCAT regions in CTCS images. Motion or beam hardening artifacts were often present in high-contrast CCTA but not CTCS. Mean HU and volume were increased in both CTCS and CCTA for MACE group. There were significant positive correlations between some CTCS and CCTA features, suggesting that similar characteristics were obtained. Using hand-crafted/radiomics from CTCS and CCTA, AUCs were 0.82/0.79 and 0.83/0.77 respectively, while Agatston gave AUC=0.73. Preliminarily, PCAT features can be assessed from three main coronary arteries in non-contrast CTCS images with performance characteristics that are at the very least comparable to CCTA., Comment: 24 pages,10 figures

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries. Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,theta) images. Data were augmented in a natural way by changing theta in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837+/-0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0+/-0.3%, Dice: 0.846+/-0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95+/-20.73 um), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9+/-128.0 deg / 202.0+/-121.1 deg). Our method will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC., Comment: 24 pages, 9 figures, 2 tables, 2 supplementary figures, 3 supplementary tables

The Beta-decay Paul Trap is an open-geometry, linear trap used to measure the decays of $^8$Li and $^8$B to search for a tensor contribution to the weak interaction. In the latest $^8$Li measurement of Burkey et al. (2022), $\beta$ scattering was the dominant experimental systematic uncertainty. The Beta-decay Paul Trap Mk IV reduces the prevalence of $\beta$ scattering by a factor of 4 through a redesigned electrode geometry and the use of glassy carbon and graphite as electrode materials. The trap has been constructed and successfully commissioned with $^8$Li in a new data campaign that collected 2.6 million triple coincidence events, an increase in statistics by 30% with 4 times less $\beta$ scattering compared to the previous $^8$Li data set., Comment: 17 pages, 7 figures

Analysis of 8,804,545 electron velocity distribution functions (VDFs), observed by the $\mathit{Wind}$ spacecraft near 1 AU between January 1, 2005 and January 1, 2022, was performed to determine the spacecraft floating potential, $\phi{\scriptstyle_{sc}}$. $\mathit{Wind}$ was designed to be electrostatically clean, which helps keep the magnitude of $\phi{\scriptstyle_{sc}}$ small (i.e., $\sim$5--9 eV for nearly all intervals) and the potential distribution more uniform. We observed spectral enhancements of $\phi{\scriptstyle_{sc}}$ at frequencies corresponding to the inverse synodic Carrington rotation period with at least three harmonics. The 2D histogram of $\phi{\scriptstyle_{sc}}$ versus time also shows at least two strong peaks with a potential third, much weaker peak. These peaks vary in time with the intensity correlated with solar maximum. Thus, the spectral peaks and histogram peaks are likely due to macroscopic phenomena like coronal mass ejections (solar cycle dependence) and stream interaction regions (Carrington rotation dependence). The values of $\phi{\scriptstyle_{sc}}$ are summarized herein and the resulting dataset is discussed., Comment: 14 pages, 6 figures, 3 tables, submitted to ApJS

Models of invasive species spread often assume that landscapes are spatially homogeneous; thus simplifying analysis but potentially reducing accuracy. We extend a recently developed partial differential equation model for invasive conifer spread to account for spatial heterogeneity in parameter values and introduce a method to obtain key outputs (e.g. spread rates) from computational simulations. Simulations produce patterns of spatial spread remarkably similar to observed patterns in grassland ecosystems invaded by exotic conifers, validating our spatially explicit strategy. We find that incorporating spatial variation in different parameters does not significantly affect the evolution of invasions (which are characterised by a long quiescent period followed by rapid evolution towards to a constant rate of invasion) but that distributional assumptions can have a significant impact on the spread rate of invasions. Our work demonstrates that spatial variation in site-suitability or other parameters can have a significant impact on invasions, Comment: 13 pages, 18 figures

Resource adequacy studies typically use standard metrics such as Loss of Load Expectation and Expected Energy Unserved to quantify the risk of supply shortfalls. This paper critiques present approaches to adequacy assessment and capacity procurement in terms of their relevance to decision maker interests, before demonstrating alternatives including risk-averse metrics and visualisations of wider risk profile. This is illustrated with results for a Great Britain example, in which the risk profile varies substantially with the installed capacity of wind generation. This paper goes beyond previous literature through its critical discussion of how current practices reflect decision maker interests; and how decision making can be improved using a broader range of outputs available from standard models., Comment: 23 pages, 4 figures

Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction. Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTri-als.gov Identifier: NCT04075162). We employed sampling techniques to enhance model training. We also investigated Cox models with selected features to identify explainable high-risk characteristics. Results. Our proposed calcium-omics model with modified synthetic down sampling and up sampling gave C-index (80.5%/71.6%) and two-year AUC (82.4%/74.8%) for (80:20, training/testing), respectively (sampling was applied to the training set only). Results compared favorably to Agatston which gave C-index (71.3%/70.3%) and AUC (71.8%/68.8%), respectively. Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were important determinants of increased risk, with dense calcification (>1000HU) associated with lower risk. The calcium-omics model reclassified 63% of MACE patients to the high risk group in a held-out test. The categorical net-reclassification index was NRI=0.153. Conclusions. AI analysis of coronary calcification can lead to improved results as compared to Agatston scoring. Our findings suggest the utility of calcium-omics in improved prediction of risk., Comment: 12 pages, 8 figures, 2 tables, 4 pages supplemental, journal paper format (under review)

Invasive pine trees pose a threat to biodiversity in a variety of Southern Hemisphere countries, but understanding of the dynamics of invasions and the factors that retard or accelerate spread is limited. Here, we consider the past models of wilding pine spread and develop a new model of pine invasion. We show that many prior models feature parameter estimates which are not biologically supported and rely on a conjecture to obtain an asymptotic spread speed of invasive pine populations, the main output of these models. In contrast to prior approaches, we use partial differential equations to model an invasion. We show that invasions are almost static for a significant period of time before rapidly accelerating to spread at a constant rate, matching observed behaviour in at least some field sites. Our work suggests that prior methods for estimating invasion speeds may not accurately predict spread and are sensitive to assumptions about the distribution of parameters. However, we present alternative estimation methods and suggest directions for further research., Comment: 36 pages, 9 figures

Features of pericoronary adipose tissue (PCAT) assessed from coronary computed tomography angiography (CCTA) are associated with inflammation and cardiovascular risk. As PCAT is vascularly connected with coronary vasculature, the presence of iodine is a potential confounding factor on PCAT HU and textures that has not been adequately investigated. Use dynamic cardiac CT perfusion (CCTP) to inform contrast determinants of PCAT assessment. From CCTP, we analyzed HU dynamics of territory-specific PCAT, myocardium, and other adipose depots in patients with coronary artery disease. HU, blood flow, and radiomics were assessed over time. Changes from peak aorta time, Pa, chosen to model the time of CCTA, were obtained. HU in PCAT increased more than in other adipose depots. The estimated blood flow in PCAT was ~23% of that in the contiguous myocardium. Comparing PCAT distal and proximal to a significant stenosis, we found less enhancement and longer time-to-peak distally. Two-second offsets [before, after] Pa resulted in [ 4-HU, 3-HU] differences in PCAT. Due to changes in HU, the apparent PCAT volume reduced ~15% from the first scan (P1) to Pa using a conventional fat window. Comparing radiomic features over time, 78% of features changed >10% relative to P1. CCTP elucidates blood flow in PCAT and enables analysis of PCAT features over time. PCAT assessments (HU, apparent volume, and radiomics) are sensitive to acquisition timing and the presence of obstructive stenosis, which may confound the interpretation of PCAT in CCTA images. Data normalization may be in order., Comment: 13 pages, 8 figures

Information on the $^{38}$S level scheme was expanded through experimental work utilizing a fusion-evaporation reaction and in-beam $\gamma$-ray spectroscopy. Prompt $\gamma$-ray transitions were detected by the Gamma-Ray Energy Tracking Array (GRETINA) and recoiling $^{38}$S residues were selected by the Fragment Mass Analayzer (FMA). Tools based on machine-learning techniques were developed and deployed for the first time in order to enhance the unique selection of $^{38}$S residues and identify any associated $\gamma$-ray transitions. The new level information, including the extension of the even-spin yrast sequence through $J^{\pi} = 8^{(+)}$, was interpreted in terms of a basic single-particle picture as well shell-model calculations which incorporated the empirically derived FSU interaction. A comparison between the properties of the yrast states in the even-$Z$ $N=22$ isotones from $Z=14$ to $20$, and for $^{36}$Si-$^{38}$S in particular, was also presented with an emphasis on the role and influence of the neutron $1p_{3/2}$ orbital on the structure in the region.