Previous studies demonstrated that ultra-processed foods (UPF) affect overall diet quality. However, none have yet examined this relation across different age groups in Brazil. This study assessed the relationship between diet quality and the consumption of UPF in a Brazilian population according to age groups. This was a cross-sectional study that analysed food consumption data from 46 164 Brazilians aged ≥10 years who participated in the 2017–2018 National Dietary Survey. Food and beverages consumed were recorded by two 24-h recalls. All food items were classified as UPF or non-UPF according to the Nova system. Diet quality was evaluated using nutritional density and the prevalence of inadequate nutrient consumption, according to the quintiles of energy contribution of UPF. The association between diet quality and UPF consumption was evaluated by linear and Poisson regressions, with adjustment for sociodemographic variables, stratified by age groups (adolescents, adults and older adults). The consumption of UPF increased the densities of carbohydrates, free sugar, saturated fat and Na and decreased the densities of proteins, fibres and potassium in three age groups. Higher prevalence ratios (PR) of inadequate consumption of free sugar and fibre among the lower and higher quintiles of energy contribution of UPF among adolescents (PR = 2·02, 95 % CI = 1·82, 2·25; PR = 1·88, 95 % CI = 1·68, 2·10), adults (PR = 1·86, 95 % CI = 1·75, 1·98; PR = 1·70, 95 % CI = 1·60, 1·80) and older adults (PR = 1·48, 95 % CI = 1·30, 1·69; PR = 1·24, 95 % CI = 1·09, 1·40). UPF consumption was negatively associated with diet quality across different age groups. Thus, interventions targeting UPF consumption should be implemented across life stages to improve overall diet quality.

This study investigates the incorporation of advanced heating, ventilation, and air conditioning (HVAC) systems with reinforcement learning (RL) control to enhance energy efficiency in low-energy buildings amid the extreme seasonal temperatures of Tehran. We conducted comprehensive simulation assessments using the EnergyPlus and HoneybeeGym platforms to evaluate two distinct reinforcement learning models: traditional Q-learning (Model A) and deep reinforcement learning (DRL) with neural networks (Model B). Model B consisted of a deep convolutional network architecture with 256 neurons in each hidden layer, employing rectified linear units as activation functions and the Adam optimizer at a learning rate of 0.001. The results demonstrated that the RL-managed systems resulted in a statistically significant reduction in energy-use intensity of 25 percent (p < 0.001), decreasing from 250 to 200 kWh/m² annually in comparison to the baseline scenario. The thermal comfort showed notable improvements, with the expected mean vote adjusting to 0.25, which falls within the ASHRAE Standard 55 comfort range, and the percentage of anticipated dissatisfaction reduced to 10%. Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. This indicates enhanced adaptability to consistent daily trends and irregular periodicities, such as weather patterns. The proposed reinforcement learning method achieved energy savings of 10–15 percent compared to both rule-based and model predictive control and approximately 10 percent improvement over rule-based control, while employing fewer building features than existing state-of-the-art control systems.

We consider a finite-dimensional vector space $W\subset K^E$ over a field K and a set E. We show that the set $\mathcal {C}(W)\subset 2^E$ of minimal supports of W are the circuits of a matroid on E. When the cardinality of K is large (compared to that of E), then the family of supports of W is a matroid. Afterwards we apply these results to tropical differential algebraic geometry (tdag), studying the set of supports of spaces of formal power series solutions $\text {Sol}(\Sigma )$ of systems of linear differential equations (ldes) $\Sigma$ in variables $x_1,\ldots ,x_n$ having coefficients in . If $\Sigma $ is of differential type zero, then the set $\mathcal {C}(Sol(\Sigma ))\subset (2^{\mathbb {N}^{m}})^n$ of minimal supports defines a matroid on $E=[n]\times \mathbb {N}^{m}$, and if the cardinality of K is large enough, then the set of supports is also a matroid on E. By applying the fundamental theorem of tdag (fttdag), we give a necessary condition under which the set of solutions $Sol(U)$ of a system U of tropical ldes is a matroid. We give a counterexample to the fttdag for systems $\Sigma $ of ldes over countable fields for which is not a matroid.

Glacial lakes in the Himalayas have expanded significantly in recent decades, increasing the potential risk of outburst floods. However, limited field surveys and systematic assessments leave downstream communities vulnerable. Accurate volume estimation of glacial lakes is essential for modelling flood dynamics, yet in-situ bathymetric data remain scarce. In this study, we surveyed four glacial lakes—Kya Tso Lake, Panchi Nala Lake, Gepang Gath Lake and Samudra Tapu Lake—in the Chandrabhaga basin, western Himalayas. Depth measurements were conducted using a portable inflatable kayak in August 2022 and an echo sounder mounted on an uncrewed surface vehicle in August 2024. Bathymetric modelling revealed maximum depths of 16 m, 10 m, 46 m, and 59 m, with corresponding storage capacities of 0.89, 0.44, 24.12, and 24.69 × 10⁶ m3, respectively. Volume estimates derived from empirical equations showed substantial discrepancies of ± 36–1736% compared to in-situ measurements. Despite several operational challenges, this study provides valuable in-situ bathymetric data for future modelling and hazard assessment of rapidly expanding glacial lakes in the region. The findings emphasise the need for robust field-based bathymetric datasets to refine empirical volume estimation models for Himalayan glacial lakes.

The article looks at instances of specialisation for specific linguistic contexts in ‘command’ and ‘inference’ uses of will and must. It tests the feasibility of different motivations for this specialisation, such as statistical and construal pre-emption. It also proposes a new motivation for specialisation, polysemous pre-emption, i.e. whether a strongly entrenched polyseme of a given expression might pre-empt the use of an expression with a less strongly entrenched polyseme. The investigation uses corpus analysis and distinctive collexeme analysis to test the three motivations (statistical, construal, and polysemous pre-emption). The results show that all instances of specialisation with will and must could be explained through construal pre-emption and/or polysemous pre-emption, thus making recourse to statistical pre-emption unnecessary.

The rise of U.S. inflation in 2021 and 2022 and its partial subsiding have sparked debates about the relative role of supply and demand factors. The initial surge surprised many macroeconomists despite the unprecedented jump in money growth in 2020–21. We find that the relationship between consumption and the theoretically based Divisia M3 measure of money (velocity) can be well modeled both in the short- and long-runs. We use the estimated long-run relationship to calculate the deviation of actual velocity from its long-run equilibrium and incorporate it into a P-Star framework. Our model of velocity significantly improves the performance of the P-Star model relative to using a one-sided HP filter to calculate trend velocity as used by other researchers. We also include a global supply pressures index in the model and find that recent movements in U.S. inflation largely owed to aggregate demand driven macroeconomic factors that are tracked by Divisia money with a smaller role played by supply factors.

Greenland’s peripheral glaciers and ice caps contribute disproportionately to sea-level rise relative to their small area. Winter snow accumulation directly influences glacier mass balance and downstream hydrology, but spatially extensive observations of this important mass balance component remain sparse. In this study, we present a unique multi-year (2008–2024) dataset of winter snow accumulation over A.P. Olsen Ice Cap, Northeast Greenland, from ground-penetrating radar surveys covering an average of 47 km per survey year. Our results reveal strong spatial heterogeneity that is likely influenced by wind redistribution and local topography, especially in the ablation zone. We compare our findings with automatic weather station data from three sites and outputs from the Copernicus Arctic Regional Reanalysis (CARRA). Governed by the high spatial variability, the automatic weather station point-based observations significantly underestimate regional accumulation by 40–45%. Despite the high spatial variability, the CARRA accumulated precipitation variable provides a reasonable overall mean winter snow accumulation (RMSE of 0.07 m w.e.); however, it fails to reproduce the complex non-linear relationship between snow depth and elevation observed in the radar data. Our findings emphasize the need for high-resolution, spatially extensive measurements to better understand snow accumulation on ice caps and glaciers and improve reanalysis assessments.

Al-Hoorie, Hiver, and In’nami (2024) offer compelling arguments for why L2 motivational self-system research is currently in a state of validation crisis. Seeking a constructive resolution to the crisis, in this response we argue that two fundamental conditions are needed for the field to emerge stronger: psychological readiness and methodological maturity. For psychological readiness, we call for a reframing of the “crisis” narrative. We highlight the need to value controversy, to normalize failure and (self-)correction, and to resist the allure of novelty. For methodological maturity, we suggest that an argument-based approach to validation can provide a constructive solution to current controversies. We present an integrated framework that can guide systematic validation efforts, and we demonstrate its application using a recent validation study as an example.

Small-leaf spiderwort (Tradescantia fluminensis Vell.) is a low-growing perennial ground cover that has become increasingly problematic in Florida due to its ability to quickly spread vegetatively over large areas and outcompete native vegetation. Prior research has identified several herbicides that can be used to manage T. fluminensis, but the effect of application timing on herbicidal efficacy is unknown. Therefore, the objective of this study was to evaluate the efficacy of selected postemergence herbicide applications and to understand differences in the efficacy of these timings (i.e., spring and fall), including on the rate of regrowth of target plants. Specific herbicides evaluated in this study included 2,4-D, 2,4-D + triclopyr amine, aminopyralid, glyphosate, and triclopyr (as acid and amine). An additional aspect of this study was to reapply treatments when coverage ratings exceeded 25% to assess the impact of sequential applications when using less efficacious herbicides that might provide greater selectivity to non-target plants. Overall, the data showed that initial treatment timing had little to no impact on efficacy for most of the herbicides evaluated. Triclopyr (acid or amine) tended to provide the highest level of control and required no retreatment over a 12-mo evaluation period. Other effective options included glyphosate and 2,4-D + triclopyr amine, which provided results similar to triclopyr on most evaluation dates. The 2,4-D and aminopyralid treatments were in general the least efficacious options, requiring retreatment at either 3, 6, or 9 mo following the initial application to achieve less than 25% T. fluminensis coverage. Results indicate that practitioners would likely achieve similar levels of T. fluminensis control regardless of application timing. Data also suggest triclopyr would be the most effective option, while a low level of control would be expected with 2,4-D, even following multiple applications.

Theories, models, and frameworks (TMFs) are essential tools in dissemination and implementation (D&I) research, yet selecting and applying the most appropriate TMF is routinely a challenge, particularly for those new to the field. To address this need, we developed the Dissemination and Implementation Models in Health webtool (www.dissemination-implementation.org) a free, interactive, and evolving online resource designed to support the thoughtful use of D&I TMFs across all phases of research and practice – from planning through assessment. Created through a multi-institutional collaboration and refined using human-centered design, the webtool includes features such as logic model development, D&I TMF selection and comparison, guidance on combining and adapting models, strategies for application, and linkages to measurement tools. Since its initial release in 2014, the webtool has expanded to include over 110 D&I TMFs and new thematic content areas, including a section dedicated to health equity. It can be used in D&I trainings, proposal development, consultations, and academic coursework. Usage analytics and community feedback reflect ongoing relevance, utility, and evolving needs. The webtool continues to address a significant gap in D&I infrastructure by guiding users in selecting and operationalizing D&I TMFs, ultimately supporting more rigorous, context-sensitive translational research and practice.

Kenomicrolite (IMA 2024-097), ideally □2Ta2[O4(OH)2]□, (where □ = vacancy) is a newly approved mineral species from the Volta Grande pegmatite, Nazareno, Minas Gerais, Brazil. It is the first member of the pyrochlore supergroup to exhibit dominant vacancies at both A and Y sites and a Ta-dominated B site, making it a tantalum oxyhydroxide. It occurs as an accessory mineral within a microlite group assemblage, probably of secondary origin via weathering-induced leaching and hydration of fluorcalciomicrolite. Kenomicrolite appears as pale orange, transparent, isotropic octahedral crystals up to 200 μm in size, with a calculated density of 5.599 g·cm⁻3 and calculated refractive index of 1.880. Its empirical formula is A(□1.61Ba0.29Ce0.03U0.03Pb0.02Mn0.01Sr0.01)Σ2.00B(Ta1.74Nb0.11Sn0.08Si0.05Al0.01Ti0.01)Σ2.00 X[O4.65(OH)1.35]Σ6.00Y[□0.57(H2O)0.35F0.07K0.01]Σ1.00. The structure has been determined by single-crystal X-ray diffraction in the $Fd\bar 3m$ space group type, with a = 10.5911(6) Å. Spectroscopic analyses confirmed the presence of structural water primarily at the Y site. Ion-exchange experiments in Tl+-rich solutions showed minimal incorporation (∼0.19 Tl+ pfu), highlighting limited ion-exchange capacity due to vacancy-dominated tunnel sites. The structural stability and low hydration make kenomicrolite an important end-member for understanding vacancy-rich pyrochlore systems and their constraints on ion incorporation mechanisms.

This article offers a view of the prospects of nuclear fusion as a sustainable energy source, with a focus on magnetic confinement fusion and tokamaks. It highlights the key theme of integration and presents the EUROfusion programme as a model for coordinated fusion R&D in Europe while stressing the need for public–private partnerships to bridge the gap between laboratory innovation and industrial implementation. A crucial element is human capital development, i.e. the training of a new, diverse generation of scientists, engineers, and technicians. A broader educational effort is called for, with industry–academia collaboration, hands-on training, and mechanisms to retain and transfer knowledge from legacy projects such as JET.