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A probabilistic forecasting framework for neighbourhood-level disaggregation of electric vehicle adoption scenarios
Abstract The rapid growth of electric vehicle (EV) adoption presents significant challenges for electricity networks, particularly at the low-voltage level, where clustered neighbourhood demand risks overloading infrastructure. Existing scenario-based planning approaches typically assume uniform EV uptake across neighbourhoods within a region, failing to capture the heterogeneity in historical EV registration data. They provide limited uncertainty quantification, despite the difficulty of predicting future adoption at fine spatial scales. This paper introduces a Gaussian process (GP)-based forecasting framework that combines granular historical EV registration data with top-down regional scenarios to generate probabilistic neighbourhood-level forecasts. The GP captures how local adoption deviates from regional trends, encoded in the GP’s mean function, ensuring consistency with broader scenarios while accounting for local variation and uncertainty. We validate the framework using ten representative local authority districts in England and Wales, covering 1,294 neighbourhoods. The framework demonstrates improved performance compared to baseline methods (scaled scenario, logistic growth, linear extrapolation) in normalised mean absolute error, with statistically significant improvements at horizons of three years and beyond. It also delivers well-calibrated prediction intervals, providing reliable uncertainty estimates. This framework offers a practical tool for network operators, policymakers, and planners to support targeted decision-making and investment.
Trial-by-trial fMRI-neurofeedback dissociates fusiform and occipital contributions to face detection and recognition
Abstract Self-regulation of specific brain regions can be achieved using neurofeedback with real-time functional magnetic resonance imaging (rt-fMRI). We leveraged this technique to dissect the role of two tightly interconnected areas implicated in face perception, by interleaving a visual task with upregulation of either the occipital (OFA) or fusiform face-responsive areas (FFA) in a trial-wise manner. Experimental participants ( N = 22) successfully enhanced their target region when compared to yoked controls ( N = 20). Regulation was face-selective, as evidenced by concomitant increases in other nodes of the face processing network. Critically, face detection was faster with enhanced FFA activity but hindered by enhanced OFA, whereas face identity recognition was optimal with concomitant increases in both FFA and OFA. These results argue against traditional face processing models assuming an information flow from posterior occipital to anterior fusiform cortex and instead support non-hierarchical models where FFA mediates initial face detection and OFA contributes to subsequent identity recognition.
Generation of ultra-broadband frequency comb in strongly bistable nonlinear magnonic resonator
Abstract Magnonic frequency combs (MFCs) offer a promising route to compact, energy-efficient platforms for on-chip coherent microwave signal generation and processing. Conventional on-chip comb generation typically relies on nonlinear resonators supporting equidistant, low-loss resonances driven by a monochromatic signal, resulting in fixed comb spacing. Here we introduce and experimentally demonstrate a distinct mechanism for ultra-broadband MFC generation using a highly nonlinear miniaturized magnonic resonator. The small resonator volume, combined with a slow-wave transducer, drives the system deep into the bistable regime where parametric excitation of propagating spin waves facilitates comb formation. Our approach yields over 350 comb lines spanning a 450 MHz bandwidth, with spacing continuously tunable via a two-tone external drive, representing an order-of-magnitude enhancement over prior reports at relatively low power. The platform is ultra-compact, scalable, and highly tunable, establishing a distinct frequency comb paradigm with transformative opportunities in microwave signal processing, neuromorphic computing, and precision sensing.
Self-assembling proteins compose the chemically resistant shell biomaterial of planktonic tintinnid ciliates
Abstract Biomaterials provide superior properties and sustainable alternatives relevant to medicine, textiles, and high-tech applications. Research has mainly focused on animal-derived proteinaceous biomaterials, which remain challenging to reproduce while retaining their remarkable properties. Here, we show that the shell biomaterial of tintinnid ciliates, a lineage of planktonic unicellular eukaryotes, is composed of self-assembling structural proteins. The shells form in sea- and freshwater, are structurally diverse, and exhibit resistance against high temperatures and the strongest chemicals. Combining single-cell transcriptomics with proteomics of the shells, we identify the amino acid sequences of the shell-forming proteins that represent a new family unique to tintinnid ciliates, which we term Tintinnidorin. The proteins are rich in aromatic residues and possess a coherent architecture with flexible, unfolded segments connecting a folded core structure of beta-sheets. These multivalent capabilities facilitate intracellular storage, extracellular self-assembly, wet adhesion, thermostability, and salt tolerance. Tintinnid ciliates and their Tintinnidorin proteins provide an accessible system to elucidate sequence-structure-material relationships and inspire biomaterial design.
The metal mixture inflammatory index has been associated with an increased prevalence of chronic obstructive pulmonary disease and higher all-cause mortality
Integrative bulk and single-cell transcriptome analyses reveals manganese metabolism-related prognostic genes in clear cell renal cell carcinoma with experimental validation
Hard-object feeding adaptations infer relative predator-prey size relationships in Devonian placoderms
Abstract Devonian placoderms exhibit exceptional diversity in jaw and dental morphology, making them an ideal system for examining early gnathostome feeding mechanisms. Here, we investigate inferognathal (mandible) function in eight eubrachythoracid arthrodire placoderms from Australia’s Late Devonian Gogo Formation. A combination of finite element analysis and quantification of dental surface complexity revealed distinct, size-dependent strategies for processing mechanically resistant materials (durophagy). Taxa with broad, flat dental surfaces experienced low strain during simulated bites, consistent with crushing armoured prey that could be engulfed whole. In contrast, the jaw of the largest taxon combined exceptional structural strength with highly complex, reinforced dentition, enabling force concentration to pierce through armour or shell, and tear apart prey items exceeding its oral capacity. Taxa of intermediate size exhibited higher strain and specialised slicing dentition, consistent with generalist diets or feeding strategies focused on shearing through softer tissues. Consumption of armoured prey in placoderms was therefore governed not by predator size alone, but by the relative size of prey to predator. Hard object feeding in arthrodire placoderms was not a result of a single mechanical solution, but involved several distinct solutions influenced by body size and prey-processing constraints.
Predicting nicotine emissions and plasma nicotine boost in E-cigarette users using machine learning
A patient-specific induced pluripotent stem cell and neural stem cell resource for angelman syndrome with 15q11.2-q13 deletion
Assessing the capability of large language models in answering pediatric critical care board-style questions
Optimized machine learning and artificial neural networks for NIRS-based prediction of mango internal quality
Abstract A robust and updated approach for non-destructive prediction of key quality attributes in intact mangoes was developed by integrating near-infrared reflectance spectroscopy (NIRS) with advanced machine learning and artificial neural network (ANN) algorithms. In this study, NIR spectral data of 136 mango samples were collected and processed using standard normal variate (SNV) correction. Five regression methods namely partial least squares regression (PLSR), support vector machine regression (SVMR), random forest regression (RFR), extreme gradient boosting (XG-Boost), and generalized regression neural network (GRNN), were optimized and evaluated for predicting total acidity (TA) and ascorbic acid (AA). Results indicate that while traditional linear methods like PLSR achieved reasonable predictive power (RPD > 2.5), nonlinear models, especially XGBoost and GRNN, significantly outperformed PLSR, with GRNN models achieving the highest accuracy, with maximum performance reaching R 2 up to 0.98 and RPD > 5.5 across the evaluated parameters (TA and AA). The findings demonstrate that optimized machine learning and ANN models offer robust, accurate, and practical solutions for rapid, non-invasive mango quality assessment. This integrated methodology supports advanced quality control, sorting, and breeding programs, providing substantial benefits for industry and supply chain management through rapid, reliable assessment of fruit nutritional and chemical properties.
Enhancing prediction accuracy for Parkinson’s disease using advanced machine learning models
Effect of multicomponent support intervention on medication adherence and self-efficacy levels in hypertension patients
Exploring hemodynamic measurements from the Tromsø Study for prediction of cardiovascular disease using traditional statistical models and machine learning approaches
Abstract In Norway, NORRISK2 is the government-recommended risk model for predicting an individual’s 10-year probability of getting cardiovascular disease (CVD). This study aims to investigate the potential for improvement of CVD prediction by using hemodynamic measurements from a non-invasive beat-to-beat blood pressure monitor, taken as part of pain sensitivity assessment with the cold-pressor test (CPT) during the Tromsø6 Study (2007–2008). Using 6694 recordings, ultra-short-term pulse rate variability (PRV) and baroreflex sensitivity (BRS) obtained during the CPT were added as additional variables into the existing NORRISK2 survival model (extended model). In addition, the time-series data was used in a machine learning (ML) model without the NORRISK2 background variables. Both models were compared to a recalibration of the original NORRISK2 model. The predictions from the recalibrated NORRISK2 model and the ML model were then combined with logistic regression. The statistical models performed similarly on the test set, with an area under the receiver operating characteristic (AUROC) of 0.8 (95% CI: 0.71–0.86), 0.79 (0.71–0.85) and 0.77 (0.69–0.84) (original, recalibrated and extended NORRISK2, respectively). The ML model using only hemodynamic measurements obtained a test set AUROC of 0.73 (0.67–0.80). Combining the NORRISK2 and ML model did not increase the AUROC. Adding ultra-short-term PRV and BRS derived from Tromsø6 did not improve the prediction of the NORRISK2 model either. Although with lower accuracy, the beat-to-beat time series of hemodynamic variables from a CPT had a significant (p < 0.01) ability to predict future CVD without any other person-specific data.
Patterns of compliance with COVID-19 preventive measures in Armenia: results from a cross-sectional survey
Efficient and interpretable maximal frequent fuzzy pattern mining with multi phase pruning and ternary search
Abstract The exponential growth of quantitative data across various domains has intensified the need for efficient pattern mining techniques that can handle numerical uncertainty while maintaining interpretability. Traditional fuzzy frequent pattern mining algorithms suffer from pattern explosion in dense datasets, generating overwhelming numbers of redundant patterns that hinder practical analysis. This study introduces a novel Maximal Frequent Fuzzy Pattern Mining (MFPM) framework that integrates fuzzy set theory with maximal pattern representation to address these limitations. The proposed methodology employs a multi-phase approach that incorporates aggressive pruning strategies, including maximum cardinality selection and early termination, to reduce the dimensionality of the search space. Evaluation on three datasets (Chess, Connect and Mushroom) demonstrates consistent gains in both effectiveness and efficiency. Time-wise, MFPM accelerates discovery where classical algorithms are slowest: dense regimes and permissive supports. A ternary search algorithm efficiently identifies the longest patterns, while an Anti-Apriori strategy with superset pruning ensures the extraction of only non-redundant maximal patterns. Experimental evaluation on benchmark datasets demonstrates remarkable effectiveness, achieving up to 94.97% pattern reduction compared to traditional FTDA algorithms while maintaining equivalent knowledge representation. Computational efficiency improved by over 65% in challenging low-support scenarios. The framework generates concise, semantically interpretable patterns that capture the most significant relationships in quantitative data, facilitating informed decision-making across diverse application domains, including healthcare analytics, business intelligence, and web usage mining.
Event-preserving feature engineering for intermittent demand forecasting using SHOS
Identification of TBXAS1 as a candidate biomarker and potential microglia-associated inflammatory regulator in Parkinson’s disease
Anchusa azurea enhances cisplatin efficacy in oral and bone cancers through IL-17 and TNF-α pathway modulation: a metabolomic and network pharmacology approach
Abstract Anchusa species have traditionally been used to treat arthritis, gout, rheumatism, and skin wounds. Cisplatin (Cis) is a widely used chemotherapy drug associated with serious adverse effects. The study aimed to evaluate the potential synergistic anticancer effects of Anchusa azurea methanol extract (AAME) in combination with cisplatin against bone, skin, and oral cancer cell lines. This study involved a comprehensive metabolomic profiling of AAME, alongside cytotoxicity assays, cell cycle analysis, autophagy assessment, and evaluation of IL-17 and TNF-α pathway-related protein expression. AAME inhibited the proliferation of MG63 and HNO97 cancer cells while sparing HSF normal cells. AAME and Cis displayed synergistic effects (combination index < 1), especially in HNO97 cells. Treatments led to a synergistic decrease in TNF-α, p/t-JNK, IL-17, pNFκB/tNFκB, TRAF6, pMAPK/tMAPK ratios, and AP1 expression, also increased Casp3 and Casp8 levels, cell cycle arrest, and enhanced autophagy. The TPC and TFC of AAME are 5.46 mgGAE/gE and 0.13 mgRE/gE respectively, reflecting on its radical scavenging activity (EC50 209.67 µ g/mL). HRLC-MS/MS leading to the annotation of 50 metabolites, including phenolics and flavonoid derivatives, notably with a prevalence of rosmarinic acid, quercetin, and kaempferol. In network pharmacology, the 90 genes are common between AAME constituents and oral cancer. A. azurea enhances cisplatin’s anticancer effects by modulating IL-17, TNF-α, and apoptotic pathways, offering a promising adjuvant therapeutic strategy. Further in vivo investigations are warranted to validate the observed in vitro synergistic anticancer effects of A. azurea in combination with Cis.