A healthy diet includes a variety of nutritious foods, and dietary diversity, defined as the consumption of a sufficient variety of nutritious food groups, is an important indicator of nutritional adequacy and is associated with reduced risk of noncommunicable diseases(1). However, definitions and measurement of diversity vary, with most approaches using simple counts(2). This study proposes capturing both the number of food groups consumed (coverage) and the distribution of intake across these groups (evenness) by examining diversity at eating occasions (EOs), where contextual factors may influence dietary choices(3). Furthermore, this study aims to use machine learning (ML) models to predict dietary diversity at EOs. Data from the Measuring Eating in Everyday Life Study, a cross-sectional study of young adults (n = 675, 18–30 years), were analysed. Dietary intake was recorded over 3–4 non-consecutive days via a mobile app. Foods were classified into the five Australian Dietary Guidelines (ADG) food groups. An EO was defined as all foods and drinks starting within 15 minutes and totalling at least 210 kJ. Between-food group diversity score for each EO was calculated using the Shannon index. Vegetable variety (within-group diversity) was also assessed. K-means clustering partitioned EOs into diverse or less diverse groups. Person- and EO-level contextual factors were compared using Welch’s t-tests and chi-squared tests. Gradient boosting (GBM) and random forest (RF) ML models predicted EO diversity, with model performance and variable importance assessed using Local Interpretable Model-agnostic Explanations (LIME). Results showed that participants meeting physical activity guidelines and reporting greater social support from friends were more likely to have higher dietary diversity at EO (p = 0.0017 and p = 0.0038, respectively). Eating alone was more common during less diverse EOs, whereas EOs with family, friends, or others were more diverse (p < 0.0001). Diverse EOs also occurred more often at cafés/restaurants and while visiting family or friends (p < 0.0001), whereas less diverse EOs were more common at work, university, or in transit. RF outperformed GBM in predicting both between-food group diversity (accuracy: 0.83 vs. 0.64) and vegetable variety (accuracy: 0.81 vs. 0.68). Age and self-efficacy were the strongest predictors across models, with RF further highlight meal preparation and food proximity as key factors influencing dietary diversity at EOs. LIME showed that person-level factors such as income, meal preparation, physical activity, and self-efficacy had consistent but mild influence on dietary diversity across EOs. In contrast, EO-level contextual factors showed more varied and pronounced effects, with some strongly increasing dietary diversity. These findings highlight the dynamic role of contextual factors at EO in shaping dietary diversity, suggesting that interventions using ML could target EO-level factors to effectively promote diverse and nutritionally adequate diets among young adults.

Yeast species have several adaptations that enable them to survive in harsh environments. These adaptations include biofilm formation, where the secretion of extracellular polymeric substances can protect the cells from a hostile environment, or, under nutrient-limited conditions, pseudohyphal or hyphal growth, where the colony can send out long tendrils to explore the environment and seek nutrients. Recently, we observed a spiral colony morphology emerge in an isolate of the hyphae-forming yeast Magnusiomyces magnusii (M. magnusii) grown under laboratory conditions. We use an off-lattice agent-based model (ABM) that simulates colony development to investigate the hypothesis that bias in the angle between successive hyphal segments causes the spiral morphology. The model involves biologically motivated rules of hyphal extension, with key model parameters including the colony size at the onset of hyphal filaments, and the angle between the penultimate and the apical segments. Using one example of an experimentally grown colony, we use a sequential neural likelihood method to perform likelihood-free Bayesian inference to infer the model parameters. Our results indicate a mean angle between hyphal segments of ${2.3}^{\circ } [{1.1}^{\circ }, {3.6}^{\circ }]$ (95% credible interval). To confirm the model’s applicability to colony growth, we use biologically feasible parameter values to yield morphologies observed in M. magnusii experiments.

Area coverage optimisation is a hotspot in cooperative interception research for highly manoeuverable targets. In this paper, a geometric coverage-based fast approach is proposed to rapidly calculate cooperative interception regions for interceptors, achieving full coverage of the target predicted manoeuvering escape area. The method analytically derives both the required number of interceptors and the centre coordinates of each interceptable region. Simulation results verify the correctness and effectiveness of the proposed method. It demonstrates that for highly manoeuverable targets, this cooperative interception region calculation method can stably achieve full coverage of predicted manoeuvering escape areas while significantly reducing computation time, exhibiting excellent real-time performance.

Case control analysis of breast tissue expander (TE) infections after clinic-based expansion procedures from 2019 to 2022 in a large county hospital found no significant modifiable risk factors, including implant type. Suboptimal sterile access may be an independent contributor to TE infections following clinic procedures. Ongoing protocol adherence and monitoring are needed.

For efficient altitude transition and cruise maintenance, the wing-in-ground craft (WIG) quickly adjusts to and then stabilises at the target flight altitude, with minimal deviation during the fastest transition. Conventional methods typically rely on multi-objective functions to minimise settling time (ST) and integrated absolute error (IAE). These methods achieve the target trajectory through iterative trial-and-error adjustment on their weights that balance the two objectives, leading to time-consuming tuning. A reward function is proposed to prioritise minimising ST first, followed by minimising IAE. When trajectory points deviate from the target altitude, rewards are calculated based on absolute error (AE) values. Once the target altitude is reached, the reward takes the value of an exponential based on the current step number. The proposed reward function ensures that positive rewards for reducing ST outweigh the reward losses from adjusting previous trajectory points, and it is mathematically validated. Example of a WIG’s altitude change compares the proposed reward function and conventional multi-objective methods via deep reinforcement learning. Results show that the proposed reward function directly plans the trajectory that prioritises fast-settling requirements first and then minimising deviation, without needing to introduce or tune any parameter.

Slotted blade technology is a passive flow control strategy that can effectively suppress the boundary layer separation within compressors. To reduce the iteration time of the traditional Design-Experiment-Design method, this study innovatively proposes a fast and universal three-dimensional design method for the slotted blade technology, enabling slot modeling completion within 1 s. Furthermore, combined with machine learning (ML), the mapping relationships between eight design parameters and two key aerodynamic performances – compressor design point efficiency ($\eta$DE) and stator total pressure recovery coefficient at the near-stall point ($\sigma ^{*}_{NS}$) – were pioneeringly established. In this study, the prediction performances of six models were compared: one-dimensional convolutional neural network (1D-CNN), random forest (RF), support vector regression (SVR), Gaussian process regression (GPR), multi-layer perceptron (MLP) and long short-term memory network (LSTM). The results indicate that 1D-CNN achieves the highest prediction accuracy: for the $\eta$DE, the mean absolute error (MAE) and coefficient of determination (R2) are 0.041 and 0.987, respectively; for the $\sigma ^{*}_{NS}$, the MAE and R² are 0.479 × 10−3 and 0.955, respectively. Notably, the computational time of the 1D-CNN model is 99.11% less than that of the computational fluid dynamics (CFD). The Shapley Additive exPlanations (SHAP) method was employed to reveal the effects of design parameters on the compressor aerodynamic performance. Notably, the slot outlet axial position (Zout) exerts the most significant influence on the $\eta$DE, while the slot outlet radial position close to the casing (R1_out) has the strongest impact on the $\sigma ^{*}_{NS}$. This study provides theoretical support and valuable references for the intelligent design of slotted blade technology.

The mechanical feedback from the central active galactic nuclei (AGNs) can be crucial for balancing the radiative cooling of the intracluster medium (ICM) at the cluster centre. We aim to understand the relationship between the power of AGN feedback and the cooling of gas in the centres of galaxy clusters by correlating the radio properties of the brightest cluster galaxies (BCGs) with the X-ray properties of their host clusters. We used the catalogues from the first SRG/eROSITA All-Sky Survey (eRASS1) along with radio observations from the Australian SKA Pathfinder (ASKAP). In total, we identified 134 radio sources associated with BCGs of the 151 eRASS1 clusters located in the PS1, PS2, and SWAG-X ASKAP fields. Non-detections were treated as upper limits. We correlated the radio properties of the BCGs (radio luminosity, largest linear size/LLS, and BCG offset from the cluster centre) with the integrated X-ray luminosity of the host clusters. We utilised the concentration parameter, $c_{R_{500}}$, to categorise the clusters into cool cores (CCs) and non-cool cores (NCCs). By combining $c_{R_{500}}$ with the BCG offset, we assessed the dynamical states of the clusters in our sample. Furthermore, we analysed the correlation between radio mechanical power and X-ray luminosity within the CC subsample. We observe a potential positive trend between LLS and BCG offset, which may hint at an environmental influence on the morphology of central radio sources. We find a weak trend suggesting that more luminous central radio galaxies are found in clusters with higher X-ray luminosity. Additionally, there is a positive but highly scattered relationship between the mechanical luminosity of AGN jets and the X-ray cooling luminosity within the CC subsample. This finding is supported by bootstrap resampling and flux-flux analyses. The correlation observed in our CC subsample indicates that AGN feedback is ineffective in high-luminosity (high-mass) clusters. At a cooling luminosity of $L_{\mathrm{X},\,r} \lt \mathrm{R}_{\mathrm{cool}}\approx 5.50\times10^{43}\,\mathrm{erg\,s^{-1}}$, on average, AGN feedback appears to contribute only about $13\%-22\%$ of the energy needed to offset the radiative losses in the ICM.