Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Aim Since there is no short screening instrument that measures the most important factors for assessing an organization’s health situation, this study aims to develop and validate an instrument for assessing the state of health of organizations. The instrument should enable a quantitative screening of an organization’s health situation on individual mental health, teamwork, supportive leadership, and healthy culture and may be beneficial to professionals in health promotion settings. Subject and methods The study used a cross-sectional design and yielded 790 complete responses from employees from seven organizations across different sectors. The questionnaire was designed to measure four key dimensions of organizational health: mental health, good teamwork, supportive leadership, and healthy culture. Exploratory factor analysis was used to develop reliable scales. To better understand the nature of the interrelationships between these dimensions, a mediation model was tested using regression analysis. Results Factor analysis showed that after removing some items from the questionnaire, supportive leadership, good teamwork, healthy culture, and mental health formed independent, distinguishable factors. The scales measuring these constructs were reliable. Supportive leadership, good teamwork and healthy culture correlated positively with mental health. The relationship between supportive leadership and mental health was mediated by good teamwork and healthy culture. Conclusion The study successfully developed a reliable organizational health questionnaire that provides a practical tool for professionals. It enables a quick assessment of organizational health and identifies problem areas for further analyses. Future studies should use a multilevel design to not only collect data at the individual level.

Humans differ in their levels of aversive (“dark”) personality traits such as egoism or psychopathy. Building upon theories suggesting that socioecological factors coshape the development of personality traits, it can be predicted that prior aversive societal conditions (ASC) (herein assessed via corruption, inequality, poverty, and violence) explain individuals’ levels of aversive personality (assessed via the Dark Factor of Personality, the common core underlying all aversive traits). Results considering individuals from 183 countries ( N = 1,791,542) and 50 US states ( N = 144,576) support the idea that ASC coshape individuals’ levels of aversive personality.

Grape pomace (GP), a by-product of winemaking, is rich in organic carbon and nutrients, offering potential as an alternative to synthetic soil amendments. However, its broader use in agriculture remains limited due to uncertainties about long-term environmental and agronomic impacts. This review assesses the potential of GP as a soil amendment, highlighting its ability to enhance soil organic matter, nutrient availability, and soil physicochemical properties. At the same time, concerns remain regarding its acidic nature, wide carbon-to‑nitrogen (C/N) ratio, and bioactive compounds, such as mycotoxins and (poly)phenols, which could negatively impact soil microbial communities and nutrient cycling. Furthermore, residual contaminants such as pesticides and heavy metals in GP may pose ecotoxicological risks, potentially disrupting soil ecosystem functions and contaminating surrounding environments. Besides these challenges, research on the efficiency, fate and mobility of GP in soil, particularly in relation to soil type, climate, and agricultural practices, is limited. Furthermore, the effects of various (pre)treatments (e.g., composting, fermentation) on GP properties and soil interactions require more systematic investigation. Future research should focus on long-term field trials, advanced analytical methods, and effective monitoring frameworks. It is essential to refine regulatory guidance based on comprehensive risk assessments to ensure safe application and maximize GP's agronomic and environmental benefits. Overcoming these challenges could transform GP into a valuable resource for sustainable agriculture, contributing to soil health, climate resilience, and a circular economy.

Video-recordings are considered crucial for effective supervision, but empirical evidence is limited. We thus conducted a naturalistic study to assess supervisees’ evaluations of a structured video-supervision technique (‘Give Me 3’ [GM3]) compared with video-supervision as usual (VSAU) and supervision based on self-report (SAU). Twenty-four participants in postgraduate training in cognitive behavioural therapy conducted each supervision method and provided quantitative ratings of supervision satisfaction. A subsample of nine trainees participated in semi-structured qualitative interviews. Repeated-measure ANOVAs revealed no difference in post-session ratings of supervisees’ satisfaction with supervision. Retrospective comparisons of the three supervision methods resulted in significant differences in perceived effort and comfort but not in usefulness. The qualitative interviews suggested that the supervisees generally appreciated the benefits of video-supported supervision, while evaluations of GM3 and VSAU were mixed. VSAU and GM3 both appeared to have distinct advantages which might enrich supervision if aligned with the therapeutic context and supervisees’ goals. Key learning aims (1) How does trainees’ supervision satisfaction differ between supervision with or without the review of video-recorded therapy sessions? (2) Is structured video-supervision perceived by trainee supervisees as being more satisfactory than unstructured video-supervision? (3) How do supervisees perceive the differences between structured versus unstructured (video)supervision? When is each method deemed most useful?

Key message We link key aspects of land plant reproductive evolution and detail how successive molecular changes leading to novel tissues and organs require co-evolution of communication systems between tissues. Abstract The transition of water-dependent reproduction of algae to mechanisms with very limited water dependence in many land plant lineages allowed plants to colonize diverse terrestrial environments, leading to the vast variety of extant plant species. The emergence of modified cell types, novel tissues, and organs enabled this transition; their origin is associated with the co-evolution of novel or adapted molecular communication systems and gene regulatory networks. In the light of an increasing number of genome sequences in combination with the establishment of novel genetic model organisms from diverse green plant lineages, our knowledge and understanding about the origin and evolution of individual traits that arose in a concerted way increases steadily. For example, novel members of gene families in signaling pathways emerged for communication between gametes and gametophytes with additional tissues surrounding the gametes. Here, we provide a comprehensive overview on the origin and evolution of reproductive novelties such as pollen grains, immobile sperms, ovules and seeds, carpels, gamete/gametophytic communication systems, double fertilization, and the molecular mechanisms that have arisen anew or have been co-opted during evolution, including but not limited to the incorporation of phytohormones, reactive oxygen species and redox signaling as well as small RNAs in regulatory modules that contributed to the evolution of land plant sexual reproduction.

A design optimization task in the setting of fluid–structure interaction (FSI) with a periodic filter medium is considered. On the microscale, the thin filter has a small in-plane period and thickness $\varepsilon$ and consist of flexural yarns in contact. Its topology, as well as its linear material properties, are dependent on a discrete design variable. A desired flow-induced displacement profile of the filter in steady-state is to be obtained by optimal choice of this variable. The governing state system is a one-way coupled, homogenized and dimension reduced FSI model, attained by the scale limit $\varepsilon \rightarrow 0$. The design variable enters in the arising macroscopic model parameters, namely the filter’s homogenized stiffness tensors and its permeability tensor. The latter are attained by the solution of cell-problems on the smallest periodic unit of the filter. The existence of optimal solutions is verified by proving the continuous dependence of these macroscopic model parameters, as well as the design-to-state operator, on changes of the design. A numerical optimization example is provided.

Patients with heart failure are usually affected by cognitive impairment, which is associated with lower adherence to treatments and higher morbidity and mortality. Previous studies in healthy samples showed that cognitive functions were improved after cognitive training up to a very old age. We therefore investigated the effect of a three-week computerized cognitive training on cognitive functions in patients with heart failure. 107 patients with stable heart failure (male = 88%, median age = 69 years, range = 31 to 84 years) participated in the study and were followed up at three and six months. Participants were either randomized to a cognitive training group that trained shifting and working memory, an active control group that trained general knowledge or a waiting list control group. Significant performance gains occurred on the trained tasks for shifting and working memory as well as transfer of these gains to overall cognitive performance, particularly working memory, short-term memory, episodic memory and processing speed. Improvements in overall cognitive performance were stable at three-month follow-up, but not after six months. Cognitive training led to broad and sustained cognitive gains in patients with stable heart failure that may support adherence and reduce morbidity and mortality. These effects need to be further investigated in controlled randomized clinical trials. ClinicalTrials.gov registration Identifier. NCT02415517.

Semantic knowledge is a defining property of human cognition, profoundly influenced by cultural experiences. In this study, we investigated whether literacy enhances lexical-semantic processing independently of schooling. Three groups of neurotypical adults - unschooled illiterates, unschooled ex-illiterates, and schooled literates from the same residential and socioeconomic background in Portugal were tested on serial rapid automatized naming (RAN) and on discrete naming of everyday objects (concrete concepts) and basic color patches (abstract concepts). The performance of readers, whether schooled literate or unschooled ex-illiterate, was not affected by stimulus category, whereas illiterates were much slower on color than object naming, irrespective of task. This naming advantage promoted by literacy was not significantly mediated by vocabulary size. We conclude that literacy per se, regardless of schooling, contributes to faster naming of depicted concepts, particularly those of more abstract categories. Our findings provide further evidence that literacy influences cognition beyond the mere accumulation of knowledge: Literacy enhances the quality and efficiency of lexical-semantic representations and processing.

Traditional language models have been extensively evaluated for software engineering domain, however the potential of ChatGPT and Gemini have not been fully explored. To fulfill this gap, the paper in hand presents a comprehensive case study to investigate the potential of both language models for development of diverse types of requirement engineering applications. It deeply explores impact of varying levels of expert knowledge prompts on the prediction accuracies of both language models. Across 4 different public benchmark datasets of requirement engineering tasks, it compares performance of both language models with existing task specific machine/deep learning predictors and traditional language models. Specifically, the paper utilizes 4 benchmark datasets; Pure (7445 samples, requirements extraction), PROMISE (622 samples, requirements classification), REQuestA (300 question answer (QA) pairs) and Aerospace datasets (6347 words, requirements NER tagging). Our experiments reveal that, in comparison to ChatGPT, Gemini requires more careful prompt engineering to provide accurate predictions. Moreover, across requirement extraction benchmark dataset the state-of-the-art F1-score is 0.86 while ChatGPT and Gemini achieved 0.76 and 0.77, respectively. The State-of-the-art F1-score on requirements classification dataset is 0.96 and both language models 0.78. In name entity recognition (NER) task the state-of-the-art F1-score is 0.92 and ChatGPT managed to produce 0.36, and Gemini 0.25. Similarly, across question answering dataset the state-of-the-art F1-score is 0.90 and ChatGPT and Gemini managed to produce 0.91 and 0.88 respectively. Our experiments show that Gemini requires more precise prompt engineering than ChatGPT. Except for question-answering, both models under-perform compared to current state-of-the-art predictors across other tasks.

This article provides a scaling limit for a family of skew interacting Brownian motions in the context of mesoscopic interface models. Let $M,d\in \mathbb {N}$, $y_1,\dots ,y_M\in \mathbb {R}$, $f\in C_b(\mathbb {R})$. For $N\in \mathbb {N}$ we consider a $k_N$-dimensional, skew reflecting distorted Brownian motion $(X^{N,i}_t)_{i=1,\dots ,k_N}$, $t\ge 0$, and investigate its scaling limit for $N\rightarrow \infty$. The drift includes skew reflections at height levels $\tilde{y}_j:=N^{1-\frac{d}{2}}y_j$ with intensities $\beta _j/N^d$ for $j=1,\dots ,M$. The corresponding SDE is given by $\begin{aligned}&\text {d} X^{N,i}_t=-\big (A_N X^{N}_t\big )_i\,\text {d} t-\frac{1}{2}N^{-\tfrac{d}{2}-1}\,f\big (N^{\frac{d}{2}-1}X^{N,i}_t\big )\,\text {d} t \\&\qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \quad +\sum _{j=1}^M\tfrac{1-e^{-\beta _j/N^d}}{1+e^{-\beta _j/N^d}}\,\text {d} l_t^{N,i, \tilde{y}_j} +\text {d} B_t^{N,i}, \end{aligned}$where ${(B_t^{N,i})}_{t\ge 0}$, $i=1,\dots , k_N$, are independent Brownian motions, $A_N\in \mathbb {R}^{k_N\times k_N}$ is symmetric positive definite and $l_t^{N,i, \tilde{y}_j}$ denotes the local time of ${(X^{N,i}_t)}_{t\ge 0}$ at $\tilde{y}_j$. We prove the weak convergence of the equilibrium laws of $\begin{aligned} u_{t}^{N}=\Lambda _{N}\circ X^{N}_{N^2t},\quad t\ge 0, \end{aligned}$for $N\rightarrow \infty$, choosing suitable injective, linear maps $\Lambda _{N}:{\mathbb {R}}^{k_N}\rightarrow \{h\,|\,h:{\mathbb {R}}^d\supset D\rightarrow {\mathbb {R}}\}$, where D is an open domain. The scaling limit is a distorted Ornstein–Uhlenbeck process whose state space is the Hilbert space $H=L^2(D,\text {d} z)$. We characterize a class of height maps, such that the scaling limit of the dynamic is not influenced by the particular choice of ${(\Lambda _{N})}_{N\in {\mathbb {N}}}$ within that class.

Previous studies have applied a variable-centered approach to conduct extensive investigations of preservice early childhood teachers’ (PECTs’) epistemic beliefs in the domain of mathematics (application-related beliefs, process-related beliefs, static orientation), enjoyment of mathematics, mathematics anxiety, mathematical content knowledge, and mathematics pedagogical content knowledge. However, person-centered approaches, which have been fruitfully applied to other constructs and domains concerning pre- and inservice teachers, have not yet been applied to the aforementioned constructs. We addressed this research gap by investigating relationships between mathematics-related beliefs, emotions, and knowledge in terms of the well-established control-value theory in combination with a mixture distribution path analysis. About 1,851 PECTs took part in the study. Participants worked on tests and questionnaires during regular class time in teacher education. The results yielded two latent classes with structural differences in the coefficients of the path model, which we termed the application and static learning classes . In Class 1, higher levels of application-related beliefs were in line with lower levels of anxiety and higher levels of knowledge. In Class 2, higher levels of static orientation were in line with lower levels of enjoyment and higher levels of anxiety and knowledge. These novel results indicate two pathways for learning, with implications for research and practice. For research, the results are interesting with regard to static orientation and show the need for further research. For practice, they indicate the need to respect individual differences even during teacher education.

Selective aggregation of gold and silver nanoparticles in water, leading to distinctly coloured states can be achieved using particles with suitable ligands and bis(cyclodextrins) as the linking units.

Fouling in heat exchangers, particularly in the dairy industry, presents significant operational challenges, increasing energy consumption and maintenance costs. Polymeric heat exchangers, with their favorable fouling mitigation behavior, offer a potential solution to reduce these impacts. A mechanistic and an empirical fouling model were developed to predict the unique detachment mechanism of whey protein concentrate (WPC) fouling layers on polyetheretherketone (PEEK) heat exchanger surfaces caused by boiling beneath the fouling deposits. Model parameters were estimated using experimental data of the total fouling mass. Fouling experiments were carried out for different process conditions. To identify the dependency of the model parameters on the process condition, symbolic regression was applied. Previously unseen experimental data was used to validate the prediction capabilities of the models, which aim to predict fouling mass and, in case of the mechanistic model, thermal resistance. The results demonstrate that the empirical model predicts the fouling mass with an accuracy of ±20% for untrained operating conditions within the boundaries of the training set. Larger deviations (< 70%) were observed for the mechanistic model. When predicting fouling mass outside the training data set, the empirical model fails to do so when extrapolating. While the mechanistic model provides better results compared to the empirical model when extrapolating, an error of < 130% remains. The calculated thermal resistance shows discrepancies, particularly for high WPC concentrations and high heat flux. The findings suggest that PEEK heat exchangers may significantly reduce fouling‐related downtime and energy costs in dairy processing.

Abstrakt Die Normung von Prüfverfahren für Beton ist ein wesentlicher Bestandteil der Qualitätssicherung in der Bauindustrie. Diese Normen legen fest, wie die Eigenschaften von Beton zu prüfen sind, um sicherzustellen, dass er den erforderlichen Standards und Spezifikationen entspricht. Prüfverfahren für Beton werden in den Normenreihen DIN EN 12350 für Frischbeton, in DIN EN 12390 für Festbeton und in DIN EN 12504 für die Prüfung an Bauwerken geregelt. Diese Prüfnormen werden auf europäischer Ebene erarbeitet und in den nationalen Ausschüssen der Mitgliedsländer gespiegelt. Im vorliegenden Beitrag werden die Vorgehensweise zur Erarbeitung und Veröffentlichung der Normen sowie die Verknüpfungen der jeweiligen Gremien erläutert. Ferner wird ein aktueller Überblick über die vorliegenden genormten Prüfverfahren für Beton gegeben. Darüber hinaus wird auch auf rein nationale standardisierte Prüfverfahren eingegangen, wie sie z. B. durch den Deutschen Ausschuss für Stahlbeton (DAfStb) herausgegeben werden.

In this paper, we present a novel heuristic algorithm for the stable but NP-complete deformation-based edit distance on merge trees. Our key contribution is the introduction of a user-controlled look-ahead parameter that allows to trade off accuracy and computational cost. We achieve a fixed parameter tractable running time that is polynomial in the size of the input but exponential in the look-ahead value. This extension unlocks the potential of the deformation-based edit distance in handling saddle swaps, while maintaining feasible computation times. Experimental results demonstrate the computational efficiency and effectiveness of this approach in handling specific perturbations.

Political microtargeting has been widely debated as a potential threat to democratic societies. However, after more than a decade of research on its implementation in political campaigns and effects on the electorate, questions about its actual impact remain unanswered. This article examines the current state of research on political microtargeting, with a focus on content personalisation as a defining feature. First, the term microtargeting is defined based on previous studies to also consider adequate operationalisations. Further, empirical findings on its use by political actors and findings of the potential impact on voters are summarised, highlighting content personalisation as a crucial element of microtargeting. A theoretical framework is proposed to explain the mechanisms of content personalisation in political social media campaigns. This framework considers the role of political and personal predispositions in shaping the effectiveness of personalised political communication. Distinguishing relevant factors of personalisation and their respective influence contributes to future research and the ongoing discussion on the impact of content personalisation on democratic societies.