The outer solar system is theoretically predicted to harbour an undiscovered planet, often referred to as Planet Nine. Simulations suggest that its gravitational influence could explain the unusual clustering of minor bodies in the Kuiper Belt. However, no observational evidence for Planet Nine has been found so far, as its predicted orbit lies far beyond Neptune, where it reflects only a faint amount of Sunlight. This work aims to find Planet Nine candidates by taking advantage of two far-infrared all-sky surveys, which are IRAS and AKARI. The epochs of these two surveys were separated by 23 years, which is large enough to detect Planet Nine’s $\sim3'$/year orbital motion. We use a dedicated AKARI Far-Infrared point source list for the purpose of our Planet Nine search — AKARI-FIS Monthly Unconfirmed Source List (AKARI-MUSL), which includes sources detected repeatedly only in hours timescale, but not after months. AKARI-MUSL is more advantageous than the AKARI Bright Source Catalogue (AKARI-BSC) for detecting moving and faint objects like Planet Nine with a twice-deeper flux detection limit. We search for objects that moved slowly between IRAS and AKARI detections given in the catalogues. First, we estimated the expected flux and orbital motion of Planet Nine by assuming its mass, distance, and effective temperature to ensure it can be detected by IRAS and AKARI, then applied the positional and flux selection criteria to narrow down the number of sources from the catalogues. Next, we produced all possible candidate pairs including one IRAS source and one AKARI source whose angular separations were limited between 42′ and $69.6'$, corresponding to the heliocentric distance range of 500 – 700 AU and the mass range of 7 – 17M$_{\oplus}$. There are 13 candidate pairs obtained after the selection criteria. After image inspection, we found one good candidate, of which the IRAS source is absent from the same coordinate in the AKARI image after 23 years and vice versa. However, AKARI and IRAS detections are not enough to determine the full orbit of this candidate. This issue leads to the need for follow-up observations, which will determine the Keplerian motion of our Planet Nine candidate.

An unusual orbital element clustering of Kuiper belt objects (KBOs) has been observed. The most promising dynamic solution is the presence of a giant planet in the outer Solar system, Planet Nine. However, due to its extreme distance, intensive searches in optical have not been successful. We aim to find Planet Nine in the far-infrared, where it has the peak of the black body radiation, using the most sensitive all-sky far-infrared survey to date, AKARI. In contrast to optical searches, where the energy of reflected sunlight decreases by $d^{4}$, thermal radiation in the infrared decreases with the square of the heliocentric distance $d^{2}$. We search for moving objects in the AKARI Single Scan Detection List. We select sources from a promising region suggested by an N-body simulation from Millholland and Laughlin 2017: $30^{\circ}\lt$ R.A. $\lt50^{\circ}$ and $-20^{\circ}\lt$ Dec. $\lt20^{\circ}$. Known sources are excluded by cross-matching AKARI sources with 9 optical and infrared catalogues. Furthermore, we select sources with small background strength to avoid sources in the cirrus. Since Planet Nine is stationary in a timescale of hours but moves on a monthly scale, our primary strategy is to select slowly moving objects that are stationary in 24 h but not in six months, using multiple single scans by AKARI. The selected slowly moving AKARI sources are scrutinised for potential contamination from cosmic rays. Our analysis reveals two possible Planet Nine candidates whose positions and flux are within the theoretical prediction ranges. These candidates warrant further investigation through follow-up observations to confirm the existence and properties of Planet Nine.

Brown dwarfs are failed stars with very low mass (13–75 Jupiter mass) and an effective temperature lower than 2 500 K. Their mass range is between Jupiter and red dwarfs. Thus, they play a key role in understanding the gap in the mass function between stars and planets. However, due to their faint nature, previous searches are inevitably limited to the solar neighbourhood (20 pc). To improve our knowledge of the low mass part of the initial stellar mass function and the star formation history of the Milky Way, it is crucial to find more distant brown dwarfs. Using James Webb Space Telescope (JWST) COSMOS-Web data, this study seeks to enhance our comprehension of the physical characteristics of brown dwarfs situated at a distance of kpc scale. The exceptional sensitivity of the JWST enables the detection of brown dwarfs that are up to 100 times more distant than those discovered in the earlier all-sky infrared surveys. The large area coverage of the JWST COSMOS-Web survey allows us to find more distant brown dwarfs than earlier JWST studies with smaller area coverages. To capture prominent water absorption features around 2.7 ${\unicode{x03BC}}$m, we apply two colour criteria, $\text{F115W}-\text{F277W}+1\lt\text{F277W}-\text{F444W}$ and $\text{F277W}-\text{F444W}\gt\,0.9$. We then select point sources by CLASS_STAR, FLUX_RADIUS, and SPREAD_MODEL criteria. Faint sources are visually checked to exclude possibly extended sources. We conduct SED fitting and MCMC simulations to determine their physical properties and associated uncertainties. Our search reveals 25 T-dwarf candidates and 2 Y-dwarf candidates, more than any previous JWST brown dwarf searches. They are located from 0.3 to 4 kpc away from the Earth. The spatial number density of 900–1 050 K dwarf is $(2.0\pm0.9) \times10^{-6}\text{ pc}^{-3}$, 1 050–1 200 K dwarf is $(1.2\pm0.7) \times10^{-6}\text{ pc}^{-3}$, and 1 200–1 350 K dwarf is $(4.4\pm1.3) \times10^{-6}\text{ pc}^{-3}$. The cumulative number count of our brown dwarf candidates is consistent with the prediction from a standard double exponential model. Three of our brown dwarf candidates were detected by HST, with transverse velocities $12\pm5$, $12\pm4$, and $17\pm6$ km s$^{-1}$. Along with earlier studies, the JWST has opened a new window of brown dwarf research in the Milky Way thick disk and halo.

The 2018/2019 trade conflict between the United States and China impacted a broad array of agricultural products, including soybeans. Previous trade studies using gravity models fail to account for trends and complex seasonal patterns observed in the data. This study uses a machine learning (ML) approach to estimate losses in soybean export value and volume from the trade war. We find that models using ML techniques outperform traditional models and estimate losses in the value of soybean exports of $10.16 billion/year. The ML models fit the complex export trade data series well, highlighting the importance of utilizing improved modeling approaches.

Insulin-like growth factor 1 (IGF-1) is an important growth factor in childhood. We aimed to investigate the impact of food supplements for the treatment of moderate acute malnutrition (MAM) on serum IGF-1 (sIGF-1). Secondary analysis of a randomised 2 × 2 × 3 factorial nutrition trial was performed. Children aged 6–23 months with MAM received 2093 kJ/d as lipid-based nutrient supplement (LNS) or corn soy blend (CSB), containing either dehulled soya or soya isolate and different quantities of dried skimmed milk (0 %, 20 % or 50 % of total protein) for 12 weeks. The trial was double-blind with regard to soya and milk but not to matrix (LNS v. CSB). sIGF-1 was measured at inclusion and after 12 weeks of supplementation. Of 1609 children enrolled, 1455 (90 %) had sIGF-1 measured at both time points. During supplementation, sIGF-1 increased 6·7 (95 % CI 6·1, 7·3) ng/ml compared with an expected age-dependent decrease of 0·3 (95 % CI 0·2, 0·4) ng/ml. Children who received LNS v. CSB had a lower increase in sIGF-1 (–8 %, 95 % CI − 12, −3). The effect of LNS was partly attenuated when sIGF-1 was corrected for inflammation. Children who received soya isolate compared with dehulled soya had a higher increase in sIGF-1 (6 %, 95 % CI 1, 12). Milk content did not affect sIGF-1. Overall, sIGF-1 increased during supplementation. The lower increase with LNS v. CSB was only partly explained by increased inflammation with LNS and needs further investigation. Isolate v. dehulled soya led to a higher increase which may be due to antinutrients in dehulled soya.

This pilot 2-week, randomized controlled trial examined integrating digital storytelling (DST) with heart rate variability biofeedback (HRVB) to enhance psycho-emotional well-being of hematopoietic cell transplantation (HCT) patients. HCT patients (N = 25; Mage = 51.9 years) were randomly assigned: (1) DST + HRVB intervention, or (2) DST-only control. Both groups viewed four emotionally-rich digital stories. The DST + HRVB group practiced HRVB at home for ten minutes/day. DST + HRVB participants decreased anger, depression, fatigue (d = 0.53) and increased emotional processing (d = 0.20), and HRV-assessed autonomic nervous system balance (3.5 vs. 0.9). This study supports feasibility of integrating DST with HRVB, and effect sizes indicate superiority of combining DST with HRVB.

Statistical methodology for handling omitted variables is presented in a multilevel modeling framework. In many nonexperimental studies, the analyst may not have access to all requisite variables, and this omission may lead to biased estimates of model parameters. By exploiting the hierarchical nature of multilevel data, a battery of statistical tools are developed to test various forms of model misspecification as well as to obtain estimators that are robust to the presence of omitted variables. The methodology allows for tests of omitted effects at single and multiple levels. The paper also introduces intermediate-level tests; these are tests for omitted effects at a single level, regardless of the presence of omitted effects at a higher level. A simulation study shows, not surprisingly, that the omission of variables yields bias in both regression coefficients and variance components; it also suggests that omitted effects at lower levels may cause more severe bias than at higher levels. Important factors resulting in bias were found to be the level of an omitted variable, its effect size, and sample size. A real data study illustrates that an omitted variable at one level may yield biased estimators at any level and, in this study, one cannot obtain reliable estimates for school-level variables when omitted child effects exist. However, robust estimators may provide unbiased estimates for effects of interest even when the efficient estimators fail, and the one-degree-of-freedom test helps one to understand where the problem is located. It is argued that multilevel data typically contain rich information to deal with omitted variables, offering yet another appealing reason for the use of multilevel models in the social sciences.

Five different ability estimators—maximum likelihood [MLE (θ)], weighted likelihood [WLE (θ)], Bayesian modal [BME (θ)], expected a posteriori [EAP (θ)] and the standardized number-right score [Z (θ)]—were used as scores for conventional, multiple-choice tests. The bias, standard error and reliability of the five ability estimators were evaluated using Monte Carlo estimates of the unknown conditional means and variances of the estimators. The results indicated that ability estimates based on BME (θ), EAP (θ) or WLE (θ) were reasonably unbiased for the range of abilities corresponding to the difficulty of a test, and that their standard errors were relatively small. Also, they were as reliable as the old standby—the number-right score.

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior.

For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors.

Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness.

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators.

In this work we consider the problem of optimizing a stellarator subject to hard constraints on the design variables and physics properties of the equilibrium. We survey current numerical methods for handling these constraints, and summarize a number of methods from the wider optimization community that have not been used extensively for stellarator optimization thus far. We demonstrate the utility of new methods of constrained optimization by optimizing a quasi-axisymmetric stellarator for favourable physics properties while preventing strong shaping of the plasma boundary, which can be difficult to create with external current sources.

The COVID-19 pandemic has presented youth and families with a broad spectrum of unique stressors. Given that adolescents are at increased risk for mental health and emotional difficulties, it is critical to explore family processes that confer resilience for youth in the face of stress. The current study investigated caregiver emotion regulation (ER) as a familial factor contributing to youth ER and risk for psychopathology following stressful life events. In a longitudinal sample of 224 youth (Mage = 12.65 years) and their caregivers, we examined whether caregiver and youth engagement in ER strategies early in the pandemic mediated the associations of pandemic-related stress with youth internalizing and externalizing symptoms six months later. Leveraging serial mediation analysis, we demonstrated that caregiver and youth rumination, but not expressive suppression or cognitive reappraisal, mediated the prospective associations of pandemic-related stress with youth internalizing and externalizing symptoms. Greater exposure to pandemic-related stressors was associated with greater caregiver rumination, which, in turn, related to greater rumination in youth, and higher levels of youth internalizing and externalizing symptoms thereafter. Family interventions that target caregiver ER, specifically rumination, may buffer against the consequences of stress on youth engagement in maladaptive ER strategies and risk for psychopathology.

GX is a code designed to solve the nonlinear gyrokinetic system for low-frequency turbulence in magnetized plasmas, particularly tokamaks and stellarators. In GX, our primary motivation and target is a fast gyrokinetic solver that can be used for fusion reactor design and optimization along with wide-ranging physics exploration. This has led to several code and algorithm design decisions, specifically chosen to prioritize time to solution. First, we have used a discretization algorithm that is pseudospectral in the entire phase space, including a Laguerre–Hermite pseudospectral formulation of velocity space, which allows for smooth interpolation between coarse gyrofluid-like resolutions and finer conventional gyrokinetic resolutions and efficient evaluation of a model collision operator. Additionally, we have built GX to natively target graphics processors (GPUs), which are among the fastest computational platforms available today. Finally, we have taken advantage of the reactor-relevant limit of small $\rho _*$ by using the radially local flux-tube approach. In this paper we present details about the gyrokinetic system and the numerical algorithms used in GX to solve the system. We then present several numerical benchmarks against established gyrokinetic codes in both tokamak and stellarator magnetic geometries to verify that GX correctly simulates gyrokinetic turbulence in the small $\rho _*$ limit. Moreover, we show that the convergence properties of the Laguerre–Hermite spectral velocity formulation are quite favourable for nonlinear problems of interest. Coupled with GPU acceleration, which we also investigate with scaling studies, this enables GX to be able to produce useful turbulence simulations in minutes on one (or a few) GPUs and higher fidelity results in a few hours using several GPUs. GX is open-source software that is ready for fusion reactor design studies.