Introduction: The postpartum period goes along with an increased risk of unhealthy weight gain and numerous physical and psychological challenges, which are associated with mental well-being and resilience. Given the individual circumstances and the accompanying time constraints, evidence-based mHealth interventions may be useful for flexible, spot-on complementary care. Thus, the mHealth app I-PREGNO aims to enhance mental well-being and resilience by offering cognitive-behavioural and behaviour change skills training to prevent unhealthy weight development in this vulnerable life episode. In a randomised controlled trial, we will examine its effectiveness and acceptance. Protocol: Parents of infants between 0 and 12 months will be randomised to the intervention or control group. Both groups will take part in a baseline survey (t0) and further assessments after 12 weeks (intervention duration, t1), and 6 months (t2). The intervention group will use the self-guided mHealth app after t0 and both groups will get unlimited access after t2. The primary outcomes will be i) weight (difference of weight in kg after the intervention and pre-pregnancy); ii) mental well-being assessed through the Edinburgh-Postnatal-Depression-Scale and Parenting Stress Index; iii) resilience assessed through the General Self-Efficacy Scale and Difficulties in Emotion Regulation Scale. Secondary outcomes will be sociodemographic variables, eating behaviour, physical activity, relationship experiences, childhood trauma, postpartum bonding, regulatory disorders, and app usability. Gender differences between mothers and fathers will be considered. Conclusion: Positive effects on weight development in the postpartum period, mental well-being, and resilience due to the I-PREGNO intervention will support parental health in the critical postpartum phase. The study results will contribute to the growing field of evidence-based, highly scalable, low-cost, geographically independent, just-in-time mobile support for a target group that is restricted in time and resources. Trial registration: The trial has been registered at the German Clinical Trials Register (DRKS00031067) in January 2023 prospectively.

In dem kurzen Artikel geht es um das glagolitische kryptographische Ornament von Pamvo Berynda, das er seinem "Triodion" (Kiev 1631) beifügte.

We present a categorical formulation of the cognitive frameworks of Predictive Processing and Active Inference, expressed in terms of string diagrams interpreted in a monoidal category with copying and discarding. This includes diagrammatic accounts of generative models, Bayesian updating, perception, planning, active inference, and free energy. In particular we present a diagrammatic derivation of the formula for active inference via free energy minimisation, and establish a compositionality property for free energy, allowing free energy to be applied at all levels of an agent's generative model. Aside from aiming to provide a helpful graphical language for those familiar with active inference, we conversely hope that this article may provide a concise formulation and introduction to the framework.

In times of all-encompassing digitization, where learners have constant access to authentic language material through their presence on the Internet, the question arises as to how to move this material from the periphery of the learning process to the center. Such a shift raises a variety of problems for teachers in two ways: technically and content-wise. This paper deals with the search for and adaptation of authentic texts, videos, and audio and visual materials for teaching Russian at universities. It offers search assistance in a Russian media landscape that has changed after February 24, 2022, and shows possible solutions to the difficulties associated with converting authentic sources into learning materials that can be used in heterogeneous groups of learners consisting of foreign language learners and heritage language speakers.

In the automotive industry, platform configuration and software integration are mostly manual tasks performed during the development phase, requiring consideration of various safety and non-safety requirements. This manual process often leads to prolonged development cycles and provides limited flexibility. This paper introduces a novel approach to automate platform configuration and software integration for software-defined vehicles (SDVs), shifting these activities from the development phase to runtime. Our approach features an integration manager that combines model-based methods and virtualization technologies to generate and execute deployment plans. By leveraging model-based systems engineering (MBSE), our method automatically generates platform configuration and software integration plans, which are then converted into deployment-ready formats using code generation techniques. Utilizing virtualization and container orchestration technologies, the proposed system enables dynamic and flexible resource allocation while ensuring compliance with safety requirements. Communication between the development and runtime platforms is facilitated via a REST API. A proof of concept was implemented on a simulated SDV platform with the Intel Whiskey Lake Board. This demonstration showcases the integration manager on an SDV with a central computer, highlighting the potential to shorten development cycles and adapt to diverse vehicle configurations.

A shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic. It is demonstrated (i) how meta-theoretical studies can be automated this way, and (ii) how non-trivial reasoning in the target logic (public announcement logic), required e.g. to obtain a convincing encoding and automation of the wise men puzzle, can be realized. Key to the presented semantical embedding is that evaluation domains are mod- eled explicitly and treated as an additional parameter in the encodings of the con- stituents of the embedded target logic; in previous related works, e.g. on the em- bedding of normal modal logics, evaluation domains were implicitly shared between meta-logic and target logic. The work presented in this article constitutes an important addition to the pluralist LogiKEy knowledge engineering methodology, which enables experimen- tation with logics and their combinations, with general and domain knowledge, and with concrete use cases — all at the same time.

This paper presents meta-logical investigations based on category theory using the proof assistant Isabelle/HOL. We demonstrate the potential of a free logic based shallow semantic embedding of category theory by providing a formalization of the notion of elementary topoi. Additionally, we formalize symmetrical monoidal closed categories expressing the denotational semantic model of intuitionistic multiplicative linear logic. Next to these meta-logical-investigations, we contribute to building an Isabelle category theory library, with a focus on ease of use in the formalization beyond category theory itself. This work paves the way for future formalizations based on category theory and demonstrates the power of automated reasoning in investigating meta-logical questions.