Time-varying coefficient modeling (TVCM), which represents regression coefficients as smooth functions of continuous time, provides a flexible framework for uncovering complex patterns of change in levels and associations in intensive longitudinal data. However, conventional TVCM remains limited to investigating directional effects across individuals. By introducing a TVCM formulation of the multivariate normal distribution, the present study extends TVCM to explore change in undirected associations (couplings) and variability, thereby broadening its utility for psychological research. We discuss three versions of this approach: an aggregate-level model and two hierarchical versions capturing interindividual differences in unfolding change, either via person-specific intercepts accounting for onset differences or through fully person-specific coefficient functions smoothed via partial pooling. To illustrate the proposed developments, we apply them to six weeks of intensive longitudinal data from 16 anxiety patients undergoing therapy and examine unfolding changes in the level and volatility of nervousness and threat monitoring, their coupling, as well as between-person heterogeneity in each of these. We further show how inspecting first-order derivatives of the coefficient functions supports identifying periods of stability and change. Finally, we discuss extensions incorporating person-level characteristics to explain heterogeneity in patterns of change and predict outcomes.

Boreal forests play a critical role in the global carbon cycle as they are one of the largest terrestrial carbon sinks globally. In this study, we employ explainable machine learning (ML) techniques to investigate the influence of environmental and vegetation variables on net ecosystem exchange (NEE), focusing specifically on the effects of diffuse radiation. We utilize a sub-hourly resolution data set including satellite and in-situ observations from three boreal or hemiboreal forest research stations across the latitudes 58–68° N to capture latitudinal variability in forest carbon uptake. Using SHAP (Shapley Additive Explanations) values, we identify key drivers influencing NEE and quantify their importance across various ML model architectures. Photosynthetically active radiation (PAR), diffuse radiation, normalized difference vegetation index, and soil temperature were identified as the variables having the largest explanatory power for NEE across the ML models. ML models using only these variables result in $ {R}^2\approx 0.8 $ and RMSE$ \approx 2.3 $ $ \mu \mathrm{mol}\;{\mathrm{m}}^{-2}\hskip0.1em {\mathrm{s}}^{-1} $. Further analysis of SHAP values indicates that higher diffuse radiation (DiffPAR) is associated with more negative NEE (stronger carbon sink). SHAP analysis highlights this effect much more clearly than raw DiffPAR measurements, because it accounts for interactions with other environmental factors. This suggests that the diffuse radiation effect emerges from interactions between DiffPAR and co-varying environmental factors (such as cloudiness and total PAR). Cases identified by SHAP ($ \mathrm{DiffPAR}\ \mathrm{SHAP}<-2 $) have a median NEE $ 1.55\mu \mathrm{mol},{\mathrm{m}}^{-2},{\mathrm{s}}^{-1} $ more negative than cases with raw $ \mathrm{DiffPAR}\ge 400 $. When applied to ML models, SHAP uncovers nonlinear, context-dependent interactions between diffuse radiation and other drivers of NEE without assuming a priori relationships.

Regional One Health antimicrobial stewardship networks require coordinated, adaptive strategies across sectors. This conceptual article combines strategic fit theory with the Ansoff matrix to identify key determinants of sustainable network performance and proposes strategic development pathways to strengthen governance, data integration, cross-sectoral collaboration and long-term network development.

Nitrous oxide is being investigated as a treatment for therapy-resistant depression, yet its environmental implications as a potent greenhouse gas are largely unaddressed. A single 1 h treatment generates ∼150 kg CO2-equivalents, rising to ∼7.8 t per patient-year, highlighting the need to incorporate environmental externalities into evaluation.

This paper reports iterative industrial prototyping of data collection systems for simulating seafood factories. We identify the data necessary to achieve the level of realism factory designers need for effective design exploration, and propose methods to obtain them. Sixteen physical prototypes showed how prototyping shape dynamic requirements in the design process. Findings indicate that models need 3D shape and texture, which can be obtained from smartphone photogrammetry, and bending stiffness and multidirectional friction from cantilever and inclined plane tests.

This study examines how product development activities influence the environmental sustainability of complex mechatronic systems using a 2D-flatbed laser cutting system as a case study. Three levels are identified, the machine, operation and part level, at which design changes can affect environmental sustainability during machine operation. Utilizing operational machine data, nine design changes are derived indicating that ∼36% of the environmental impact in the use phase can be reduced through technical design solutions, enabling EcoDesign principles supported by data-driven approaches.

Natural phonetic variability such as talker differences facilitates second language (L2) word learning. Whether accent variability enhances the talker variability benefit, particularly in the learning of words that differ only in lexical tones, has not yet been examined despite its potential applied value for L2 tone language learning. Two groups of monolingual English speakers completed six training sessions on four minimal-tone quadrads of Mandarin monosyllabic pseudowords, produced either by 12 talkers from Beijing (single accent) or by four talkers each from Beijing, Yantai, and Guangzhou (multiple accents). Bayesian mixed-effects modeling revealed strong learning improvement across sessions in both conditions, but the multiple-accent group improved faster than the single-accent group. In addition, the multiple-accent group demonstrated superior generalization to new talkers with a familiar and an unfamiliar accent, suggesting that natural L2-Mandarin accent variability facilitates English learners’ access to abstract tone-word lexical representations, beyond the benefits of talker variability alone.

We study the existence and the distribution of “long” chains in the Weihrauch degrees, mostly focusing on chains with uncountable cofinality. We characterize when such chains have an upper bound and prove that there are no cofinal chains (of any order type) in the Weihrauch degrees. Furthermore, we show that the existence of coinitial sequences of non-zero degrees is equivalent to $\mathrm {CH}$. Finally, we explore the extendibility of antichains, providing some necessary conditions for maximality.

Healthy seeds are an important component of global food security, and their microbiome was recently identified as crucial for plant growth, resilience and health. Seed vigour is highly affected by storage conditions and aging. To study the impact of seed aging on the Brassica napus seed bacterial community, we conducted accelerated aging tests (45°C, humidity > 95%) with seed lots of four genotypes originating from two field sites in Germany. We found a strong effect of accelerated aging on germination, seedling phenotypes, as well as the seed bacterial community. Control seeds developed mainly into normal seedlings and were characterized by diverse bacterial communities comprising typical core seed microbes. Accelerated aging resulted in abnormal germination and reduced total germination. Furthermore, accelerated aging reduced diversity and evenness of the seed bacterial community and contributed to a shift from Gram-negative to Gram-positive bacteria. This effect, especially the enrichment of Firmicutes, was found irrespective of the genotype and field site; however, the way stress affected bacterial taxa varied, depended on both factors. Tumebacillus and Bacillus showed a significant negative correlation with germination phenotype, whereas alpha diversity correlated positively with a high total germination. At the functional level, the majority of isolated bacteria demonstrated plant-beneficial characteristics, showing a greater beneficial potential in the aged seeds. Our results show that accelerated aging tests affect the seed bacterial community structure and diversity, and correlate with the presence of certain taxa, which might have an effect on germination and seedling phenotype.