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3D printed gyroid scaffolds enabling strong and thermally insulating mycelium-bound composites for greener infrastructures
BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs
Graph neural networks (GNNs) have shown great promise for representation learning on complex graph-structured data, but existing models often fall short when applied to directed heterogeneous graphs. In this study, we proposed a novel embedding method, a bidirectional heterogeneous graph neural network with random teleport (BHGNN-RT) that leverages the bidirectional message-passing process and network heterogeneity, for directed heterogeneous graphs. Our method captures both incoming and outgoing message flows, integrates heterogeneous edge types through relation-specific transformations, and introduces a teleportation mechanism to mitigate the oversmoothing effect in deep GNNs. Extensive experiments were conducted on various datasets to verify the efficacy and efficiency of BHGNN-RT. BHGNN-RT consistently outperforms state-of-the-art baselines, achieving up to 11.5% improvement in classification accuracy and 19.3% in entity clustering. Additional analyses confirm that optimizing message components, model layer and teleportation proportion further enhances the model performance. These results demonstrate the effectiveness and robustness of BHGNN-RT in capturing structural, directional information in directed heterogeneous graphs.
Different effects of verbal and visual working memory loads on Language prediction
Abstract Mounting studies suggest that working memory (WM) plays a crucial role in language prediction, but how varying types of WM loads influence language prediction remains unclear. This study investigated whether verbal and visual WM loads differentially impact language predictions during speech comprehension. Using a dual-task paradigm combined with eye-tracking in a visual world setting, we asked 48 participants to complete a sentence comprehension task under concurrent WM load conditions. Participants were divided into two groups, one of which performed a visual dots memory task and the other completed a visual words memory task, with memory load being applied in half of the trials. Results revealed anticipatory gaze towards target objects, suggesting the prediction of upcoming linguistic information. Notably, early fixations during the tonal cue window indicated tonal prediction in spoken sentence processing. Furthermore, WM load significantly disrupted participants’ language prediction effects, highlighting the involvement of working memory resources in this process. Importantly, the verbal memory task imposed a more severe disruption to language prediction than the visual memory task, suggesting differential roles of WM subtypes in linguistic prediction. This offers novel insights into how verbal WM and visual-spatial WM differentially influence predictive language processing.
Specific energy reduction in a semi-autogenous grinding mill circuit by an automatic control system
Abstract Grinding operations, especially those involving semi-autogenous mills, account for a significant portion of energy use in mineral processing. In this work, we describe the application of an advanced regulatory control strategy in a copper plant aimed at improving energy efficiency through automation. The system combines cascade and feedforward control structures to attenuate variations in the mill load, a key factor influencing energy consumption and process stability. The control scheme was integrated into the plant’s existing automation infrastructure and evaluated through a three-month industrial trial. By shifting from manual to automatic regulation of the feed rate, the plant reduced the influence of process disturbances and maintained more consistent operation. The automated system achieved a 5.84% reduction in specific energy consumption and a 1.90% increase in productivity. These results demonstrate the potential of enhanced regulatory control to deliver measurable performance gains with minimal changes to existing operations.
Deep quanvolutional neural networks with enhanced trainability and gradient propagation
The influence of attachments on the adaptability and quality of life of patients using Invisalign
Circulating amino acids and cardiometabolic risk profile in offspring of women with type 1 diabetes: cross-sectional case-control study
Abstract Branched-chain amino acids (BCAAs) are known to be associated with cardiovascular disease risk in adults. The aim of this study is to investigate whether an increased cardiometabolic risk profile can be observed in the amino acid profile of young adult offspring of women with type 1 diabetes. This cross-sectional case-control study included 73 offspring born to women with type 1 diabetes (cases) and 82 control participants (controls). At the age of 18–23 years, they participated in a clinical assessment including laboratory tests and questionnaires. Amino acid levels were analyzed from venous serum samples after 10 h of fasting using nuclear magnetic resonance (NMR) spectroscopy. No differences in cardiovascular disease or cardiometabolic risk factors were observed between the cases and the controls. Circulating amino acid levels were similar in both groups. The glucogenic score (combined alanine, glycine) was higher in overweight case men (case versus controls adjusted p = 0.015 (mean ratio 1.25 [95% CI 1.11 to 1.49]). The present findings do not support our hypothesis that serum amino acid profiles, determined in early adulthood, are associated with a more adverse cardiometabolic risk profile in offspring of women with type 1 diabetes. Further studies are warranted to clarify the potential role of amino acids in the development of cardiovascular disease in offspring of women with type 1 diabetes.
Moral judgments influence emotional responses and comment lengths through the moderating role of linguistic style matching
Experimental investigation on addition of furfuryl alcohol to diesel plastic fuel blends and optimization using Kissing Numbers
An ensemble-based enhanced short and medium term load forecasting using optimized missing value imputation
Abstract Electricity load forecasting is integral to planning, energy management, and the energy market. Utility companies serve a massive number of customers by supplying electricity. These utility companies require a precise forecast of electricity usage. This paper presents a forecasting model for energy load based on the ensemble voting regressor method. In addition, to enhance the accuracy of forecasting, develop an imputation method for handling missing values in the user’s energy consumption data. A real-time data set is used for performance comparison with multiple imputation techniques to validate the imputation approach by generating random missing data for different missing rates of 10–30%. The proposed forecasting model is compared with other state-of-the-art methods to show its effectiveness in terms of MAPE, MAE, and RMSE. The experimental results demonstrate that the proposed methodology significantly improves the accuracy of the predicted load for a day and week ahead of energy consumption.
Keeping the immuno-oncology flame burning
Cryo-EM structures reveal the PP2A–B55α and Eya3 interaction that can be disrupted by a peptide inhibitor
Transfer printing micro-assembly of silicon photonic crystal cavity arrays: beating the fabrication tolerance limit
Abstract Photonic crystal cavities (PhCCs) can confine optical fields in ultra-small volumes, enabling efficient light-matter interactions for quantum and non-linear optics, sensing and all-optical signal processing. The inherent nanometric tolerances of micro-fabrication platforms can induce cavity resonant wavelength shifts two-orders of magnitude larger than cavity linewidths, prohibiting fabrication of arrays of nominally identical devices. We address this device variability by fabricating PhCCs as releasable pixels that can be transferred from their native substrate to a receiver where ordered micro-assembly can overcome the inherent fabrication variance. We demonstrate the measurement, binning and transfer of 119 PhCCs in a single session, producing spatially ordered arrays of PhCCs, sorted by resonant wavelength. Furthermore, the rapid in-situ measurement of the devices enables measurements of the PhCCs dynamic response to the print process for the first time, showing plastic and elastic effects in the seconds to hours range.
Orchestrating intratumoral DC-T cell immunity for enhanced tumor control via radiotherapy-activated TLR7/8 prodrugs in mice
Structural impact of synonymous mutations in six SARS-CoV-2 Variants of Concern
SARS-CoV-2 continues to spread and infect people worldwide. While most effort into characterizing variants of this virus have focused on non-synonymous changes, accumulation of synonymous mutations in different viral variants has also occurred. Here we characterize six Variants of Concern in terms of their mutational content, and make predictions regarding the impact of those mutations on potential genomic RNA secondary structure and stability. Our hypothesis is that if non-protein changing, yet RNA structure-changing mutations impact viral fitness by imposing deleterious change to predicted RNA structure, we would expect to those mutations to be less abundant, while if those synonymous mutations do not impact viral fitness through influence of RNA structure, we would see them more frequently than non-synonymous mutations. We find that synonymous mutations typically have no or modest impact to RNA secondary structure. As synonymous mutations are free from the selective pressure imposed on protein-altering mutations, the impact of synonymous mutations is largely limited to RNA secondary structure considerations. The absence of major, structure-altering synonymous mutations emphasize the importance of RNA structure, including within coding regions, to viral fitness. Synonymous mutations should be included in the characterization of emerging RNA viruses as these mutations may confer effects to viral fitness via RNA secondary structural modifications.