This study presents high-power mode-selective operation in a large-mode-area (LMA) fiber laser. A spatial mode-adaptive control system incorporating a 5×1 photonic lantern was employed to facilitate mode conversion between the LP01 and LP11 modes. The coherence length between the five single-mode arms and the stimulated Brillouin scattering threshold in the amplifier were well balanced by tuning the seed linewidth. In addition, the specific design of the fiber amplifier’s bending radius enabled stable mode-selective output with high mode purity. Consequently, a near-fundamental mode control was achieved in a 42-μm LMA fiber laser, yielding a beam quality M2 factor of 1.97 at an output power of 1 kW. Subsequently, a stable LP11 mode laser output with an output power of 219 W and an optical conversion efficiency of 75% was obtained. This research provides a significant technical foundation for the mode-selective operation of high-power LMA fiber lasers.

Expanded access (EA) is a United States regulatory approach for the use of investigational drugs/devices that do not yet have conventional approval, in clinical care contexts. We conducted a retrospective study to analyze the neighborhood characteristics of patients who have received EA treatments at our academic medical center between 2018 and 2023. EA patient neighborhoods showed lower median family income, lower proportion of bachelor’s degree graduates, and a higher proportion of people identifying as non-Hispanic White ethnicity compared to the surrounding (Washtenaw) county. These differences may underly differential interest in EA treatments or may point to disparities in access to evidence-based care.

This study incorporates a random forest (RF) approach to probe complex interactions and nonlinearity among predictors into an item response model with the goal of using a hybrid approach to outperform either an RF or explanatory item response model (EIRM) only in explaining item responses. In the specified model, called EIRM-RF, predicted values using RF are added as a predictor in EIRM to model the nonlinear and interaction effects of person- and item-level predictors in person-by-item response data, while accounting for random effects over persons and items. The results of the EIRM-RF are probed with interpretable machine learning (ML) methods, including feature importance measures, partial dependence plots, accumulated local effect plots, and the H-statistic. The EIRM-RF and the interpretable methods are illustrated using an empirical data set to explain differences in reading comprehension in digital versus paper mediums, and the results of EIRM-RF are compared with those of EIRM and RF to show empirical differences in modeling the effects of predictors and random effects among EIRM, RF, and EIRM-RF. In addition, simulation studies are conducted to compare model accuracy among the three models and to evaluate the performance of interpretable ML methods.

Researchers and policymakers propose a job guarantee as a means of overcoming long-term unemployment and the associated risk of social exclusion. Such a programme implies that all long-term-unemployed individuals within a certain territory are offered subsidised employment in not-for-profit enterprises or the public sector. This results in heterogeneous participation because the group of long-term-unemployed people is more diverse than often assumed. Against the background of the literature on subsidised employment, this contribution presents findings from an evaluation study of a job guarantee that was implemented in a small town in Lower Austria between 2020 and 2024. The aim of this paper is to explore the changes brought about by re-employment within a job creation scheme and in particular how the scheme coped with the diversity among the participants, which is a consequence of offering employment to all long-term-unemployed individuals. Based on data from a longitudinal mixed-methods study, the contribution shows how the project was implemented, to what extent the participants benefited from it and how the form of implementation met the different needs of participants. A typology based on qualitative data captures the diversity among the participants and shows how the scheme fits different groups. Finally, we discuss the pros and cons of the inclusion of diverse participants within one project.

In the design and construction of ultra-high-peak-power laser systems, it is necessary to control the accumulated B-integral of the laser pulse, but currently there are no reasonable B-integral control standards for picosecond and femtosecond lasers. We systematically evaluate the influence of the B-integral on the output capability of picosecond and femtosecond laser systems for the first time, to our knowledge, taking Nd:glass lasers and Ti:sapphire lasers as examples. For picosecond lasers, the temporal domain compressibility and the small-scale self-focusing effect restrict the B-integral to 1.7 and 1.9, respectively. For femtosecond lasers, the B-integral is mainly restricted by the small-scale self-focusing effect and the far-field focusability, which limit the B-integral to 1.5 and 1.7, respectively. The restriction made by far-field focusability can be largely relaxed by inserting a deformable mirror. The study of the factors restricting the B-integral will provide guidance for the design of ultra-high-peak-power laser systems.

Accurate redshift measurements are essential for studying the evolution of quasi-stellar objects (QSOs) and their role in cosmic structure formation. While spectroscopic redshifts provide high precision, they are impractical for the vast number of sources detected in large-scale surveys. Photometric redshifts, derived from broadband fluxes, offer an efficient alternative, particularly when combined with machine learning techniques. In this work, we develop and evaluate a neural network model for predicting the redshifts of QSOs in the Dark Energy Spectroscopic Instrument (DESI) Early Data Release spectroscopic catalogue, using photometry from DESI, the Widefield Infrared Survey Explorer (WISE), and the Galactic Evolution Explorer (GALEX). We compare the performance of the neural network model against a k-Nearest Neighbours approach, these being the most accurate and least resource-intensive of the methods trialled herein, optimising model parameters and assessing accuracy with standard statistical metrics. Our results show that incorporating ultraviolet photometry from GALEX improves photometric redshift estimates, reducing scatter and catastrophic outliers compared to models trained only on near infrared and optical bands. The neural network achieves a correlation coefficient with spectroscopic redshift of $0.9187$ with normalised median absolute deviation of $0.197$, representing a significant improvement over other methods. Our work combines DESI, WISE, and GALEX measurements, providing robust predictions which address the difficulties in predicting photometric redshift of QSOs over a large redshift range.