Search Results (2,922)

Accurate three-dimensional representations of tree structure are essential for fire modeling, radiative transfer simulation, synthetic data generation, and digital twins of forests, yet detailed 3D structure is rarely available at required scales. Current approaches approximate crowns with smooth geometric primitives, discarding the clumping, gaps, and irregular branching present in real trees. We present TreeFlow, a conditional flow matching model that generates realistic 3D tree point clouds from species, acquisition platform, and height. The model uses a transformer trained on real laser scanning data from the FOR-species20K benchmark to learn a velocity field transporting samples from a Gaussian distribution to the source data distribution. We evaluate generation quality by comparing conditioning and distributional fidelity metrics to scans of real trees. Generated trees match or approach the intra-class baseline on five of six metrics, with a Chamfer distance of 0.581 m versus 0.559 m for real trees of the same genus and height class. Performance is strongest below 25 m and degrades with increasing height. TreeFlow generates individual-tree point clouds conditioned on scalar inventory attributes using a model trained entirely on real laser scanning data. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

The proliferation of large-scale interaction datasets, from scientific collaboration networks and legislative records to online communication platforms, has made the analysis of group-based, time-varying systems one of the central challenges of modern data analytics. Hypergraphs provide a natural formalism for such systems, where interactions involve arbitrary groups of agents rather than isolated pairs, and temporal hypergraphs extend this to sequential data by capturing how group interactions evolve over time. Yet quantifying how complex, predictable, or volatile this evolution is remains an open problem: existing entropy-based measures either operate on pairwise projections and thus discard multi-way dependencies or are not naturally defined for varying hyperedge sizes. In this paper, we propose an information–theoretic framework for characterizing how the diversity of interaction orders in a temporal hypergraph evolves over time. We introduce the hyperedge-size distribution entropy of a snapshot and, building on the theory of entropy rates for stochastic processes, we define the temporal hypergraph entropy rate as a principled, dataset-agnostic measure of the average diversity of interaction orders exhibited by the snapshot sequence over time. We further equip the framework with a bias-corrected sliding-window estimator and a lightweight change-point detector, assembling a complete pipeline that runs in time linear in the total number of hyperedges and requires no node alignment across datasets or snapshots. We prove that the measure collapses to zero under clique expansion, demonstrating that it captures interaction-order information that is discarded by the standard size-blind pairwise projection. Experiments on six small and large publicly available benchmark datasets show that the entropy rate spans 1.60 bits across domains, detects unsupervised structural change points, and discriminates between structurally distinct interaction cultures even within the same domain. Our framework is computationally lightweight and applicable to any dataset that can be represented as a temporal sequence of hypergraphs, paving the way for practical, scalable, interaction-order-aware analysis of large-scale higher-order temporal data. Full article

Gaussian process-based Bayesian optimization (BO) is a sample-efficient sequential strategy for optimizing expensive black-box functions. The Gaussian process provides a probabilistic approximation of the unknown function, while an acquisition function balances exploration and exploitation to select the next evaluation point. Despite significant research efforts, no master acquisition function has been identified. This paper proposes a novel adaptive acquisition function that dynamically adjusts the exploration–exploitation trade-off based on the evolution of the optimization process, rather than using fixed or random scheduling. While implemented here within a GP-based BO framework, the core switching mechanism is surrogate-agnostic: the exploitative component requires only a surrogate point prediction, and the explorative component is entirely model-free. Unlike traditional approaches, where mechanisms like UCB/LCB lean toward exploration over iterations, or fixed strategies that switch from exploratory (EI) to exploitative (PI) behavior at predetermined points, the proposed method makes purely exploitative decisions using only the GP’s prediction. However, it discards these decisions when they have low potential for significant improvement, instead focusing on uncertainty reduction. Notably, this approach uses inverse distance weighting for uncertainty quantification rather than the GP’s predictive uncertainty, avoiding bias from the GP’s predictions. Testing on benchmark functions demonstrates that the proposed acquisition function is almost always Pareto optimal, offering the most balanced trade-off between convergence to the global optimum and exploration capability compared to state-of-the-art alternatives. Full article

Pancreatic β-cells maintain glucose homeostasis through tightly regulated insulin biosynthesis, storage, and secretion. To prevent pathological accumulation of excess or aging secretory granules (SGs), β-cells use crinophagy, a selective lysosomal degradation pathway in which mature insulin-containing granules fuse directly with lysosomes to form hybrid organelles termed crinosomes. Crinophagy was historically considered a simple mechanism for discarding obsolete, aged SGs. The acidic, protease-rich environment of crinosomes is proposed to generate unconventional insulin-derived epitopes through cathepsin-mediated proteolysis and transpeptidation reactions. These cryptic epitopes, which include hybrid insulin peptides (HIPs) resulting from the covalent fusion of insulin fragments with peptides from co-resident granule proteins, are largely absent from the thymic epitope repertoire. This creates a “peripheral–thymic mismatch” that allows autoreactive CD4+ T cells to escape central tolerance, ultimately driving β-cell destruction in type 1 diabetes (T1D). Recent studies demonstrate that pharmacological or genetic inhibition of crinophagy reduces crinosome abundance, narrows the pathogenic epitope repertoire, and delays the onset of diabetes in preclinical models. In type 2 diabetes (T2D), a related pathway termed stress-induced nascent granule degradation (SINGD) diverts newly synthesized insulin granules to lysosomes under glucolipotoxic conditions, contributing to insulin depletion and progressive β-cell failure. This review summarizes the current understanding of the molecular mechanisms behind crinophagy. It discusses its two main functions: maintaining physiological quality control and generating pathological antigens. Additionally, the review explores how crinophagy interacts with other cellular stress pathways and highlights new therapeutic strategies aimed at targeting this process to protect pancreatic β-cell function and potentially prevent or delay diabetes. Full article

Urban waste-governance programs rely on household source segregation, yet often assume that discards can be classified through stable technical categories. In culturally governed settings, post-use materials may also be classified through ritual status, propriety, edibility, and social obligation. This focused ethnography examined why source-based management of post-ritual offering materials, locally referred to as sisa upakara, remains difficult to sustain in urban Denpasar, Bali. Data were collected between January and March 2026 through 18 semi-structured interviews, four focus group discussions with 30 participants, six directed observation episodes totalling approximately 21 h, and document review across four anonymized urban sites. A hybrid deductive–inductive thematic analysis produced 2183 selectively coded segments. Five interdependent mechanisms explained practice formation and breakdown: post-ritual classification and legitimacy, domestic routinization, material-infrastructure fit, local-to-downstream verification, and system absorptive capacity. Management weakened when households could not distinguish edible remnants, ritually sensitive materials, and ordinary discards; when ceremonial peaks overloaded domestic routines; when fibrous, wet, bulky, or contaminated materials exceeded available infrastructure; and when downstream systems failed to preserve separated materials. The findings show that sisa upakara constitutes a hidden ritual-urban material sub-stream embedded within household waste. Culturally responsive waste governance requires alignment between classification guidance, household routines, material design, collection reliability, downstream verification, and decentralized processing capacity. Full article

Residual stress in brittle films on compliant substrates is routinely inferred from fragmentation experiments by combining an elastic stress-transfer model with a fracture strength criterion. This inversion is inherently coupled because the observed crack spacing depends jointly on the residual stress and the film fracture strength. Conventional closed-form estimators typically rely on a single feature, such as the cracking onset strain, and prescribe the fracture strength a priori, often at its bulk value. This practice discards most of the information encoded in the full crack-spacing evolution. It also obscures two sources of uncertainty: the intrinsic variability of thin-film fracture strength and the limited sensitivity of any single observable to individual parameters. Here, we recast the inversion as a Bayesian physics-informed neural network (B-PINN) in which the entire measured curve of the mean crack spacing versus applied strain is likely to occur. Stochastic gradient Langevin dynamics then sample the joint posterior of residual stress and fracture strength. A central finding is that crack-spacing data alone constrain only the difference between fracture strength and residual stress, confining the posterior to a one-dimensional manifold in parameter space and leaving each quantity individually unresolved. A single substrate curvature measurement, which, through the Stoney relation, depends on the residual stress but not on the fracture strength, provides the missing orthogonal constraint and collapses the posterior to a tight, well-resolved region. We further derive an identifiability condition under which buckle-wavelength observations serve as a third independent channel for recovering interface adhesion energy, and provide a synthetic proof-of-concept of this three-channel extension on DLC/Si and Mo/Si datasets; an experimental validation of the adhesion channel is identified as the natural next step but lies beyond the present scope. Requiring only standard fragmentation measurements and a single non-destructive curvature scan, the framework converts a point-estimate procedure into a posterior-quantified inverse method that makes explicit what can, and cannot, be learned from thin-film mechanics experiments. Full article

By the end of 2024, global photovoltaic (PV) capacity exceeded 2.2 TW, shifting planning from feasibility demonstration toward site–technology co-selection under energy, technical, economic, environmental, territorial, and socio-regulatory constraints. The existing multicriteria literature treats site and technology selection as independent problems under an implicit infinite-grid assumption, which is untenable in markets such as Chile and Peru. This study develops and validates an integrated Delphi–AHP framework with six criteria and eighteen subcriteria calibrated by twenty-eight experts from six Latin American countries. The framework underwent Delphi binary validation, AHP consistency control ($C{R}_{\mathrm{agg}}$ between 0.0013 and 0.0247; discard rate 2.6%), geometric-mean aggregation, deterministic sensitivity analysis, Monte Carlo simulation (10,000 iterations), rank-reversal testing, and nonparametric subgroup analysis. The dominant pair $\{I_2,E{c}_2\}$, consisting of grid hosting capacity and LCOE, appears as Top-2 in 84.77% of Monte Carlo iterations and is preserved across 15 of 16 leave-one-out scenarios. Grid hosting capacity surpasses useful solar resource by a factor of 3.41. A demonstrative application to 18 site–technology alternatives confirms the ranking, with an objective-weighting benchmark (entropy, CRITIC) yielding concordant results (Spearman $\rho \ge 0.89$). The findings formalize a shift in the PV planning bottleneck from solar resource to grid capacity. Full article

This study proposes a time series-aware Large Language Model (LLM)-driven feature engineering framework for tabular prediction tasks. Existing automated feature engineering methods, including LLM-based approaches such as CAAFE and OCT-Tree and established libraries such as tsfresh and Featuretools, can generate useful features for general tabular data but do not explicitly address temporal availability constraints in time series settings. This can lead to data leakage when variables that are only available after the prediction event are used directly during model training. To address this limitation, the proposed framework classifies variables into antecedent features, consequent features, and historical aggregated features. The key innovation is that consequent variables are not discarded to prevent leakage but are instead routed into a leakage-safe historical aggregation pipeline, recovering predictive signal from post-event variables through temporally valid past values. The framework guides an LLM to generate structured feature engineering configurations, applies temporally valid transformations, performs feature selection, and evaluates the selected features using predictive models. A formal leakage control mechanism ensures that all aggregations use strictly past observations, applied within entity groups and before the temporal train–validation–test split. The framework is evaluated on two time series tabular tasks: Tesla stock prediction and English Premier League match outcome prediction. The results show that the proposed approach improves predictive performance compared with raw-feature baselines and selected existing automated feature engineering methods. On the Tesla dataset, the framework reduced MAE compared with both the baseline and the reported OCT-Tree result. On the EPL dataset, it improved accuracy compared with the odds-only baseline and the reported Azure ML preprocessing result. These findings suggest that combining LLM reasoning with explicit temporal constraints is a practical direction for automated feature engineering in time series tabular machine learning. Full article

Rice straw, a byproduct of global rice production (~530 million tons annually), is generated at 80–100 million tons per year, yet a significant portion is incinerated or discarded, causing environmental problems. This study investigated the sound absorption properties of straw from IR8, a high-yielding long-grain rice variety. The normal incidence sound absorption coefficient was measured at three bulk densities (0.140, 0.150, and 0.160 g/cm³) for bundled rice straw structures. Cross-sectional images obtained using a micro-computed tomography (CT) scanner were then used to theoretically estimate the sound absorption coefficient. Each CT cross-section, oriented perpendicular to the incident sound wave direction, was modeled as a clearance between two parallel planes. The characteristic impedance and propagation constant were calculated from this model, and the normal incidence sound absorption coefficient was determined using the transfer matrix method with measured tortuosity incorporated. The experimental and theoretical absorption peaks showed similar trends across bulk densities. A parameter study was also conducted by scaling cross-sectional images according to the diameter ratios of Koshihikari short-grain rice straw and Yumekaori wheat straw relative to IR8. Additionally, reducing the number of CT images to as few as ten adequately approximated the full dataset for a 20 mm thick sample. Full article

Palm fruits are produced extensively in tropical and subtropical regions and consumed worldwide. However, over 20% of the total yield is discarded due to inferior quality, resulting in significant agricultural waste and economic loss. To mitigate this challenge and enable the safe valorization of discarded dates (DD) in animal feeding systems, this study employed solid-state fermentation (SSF) to upgrade the nutritional quality of DD and evaluated its potential as a functional feed ingredient for goats. Twenty-four male goats (6 months old; initial body weight 25.86 ± 0.25 kg) were randomly assigned to one of three dietary treatments: a basal diet (control), a diet containing 10% raw DD (D1), and a diet containing 10% solid-state fermented DD (D2). Inclusion of DD in the diet significantly increased average daily gain (ADG), final body weight (BW), and feed efficiency, with the highest values recorded for D2 (p < 0.05). Feeding of DD altered (p < 0.001) all measured rumen fermentation parameters, except pH, with higher levels (p < 0.05) of total volatile fatty acids, propionate, microbial crude protein, and ammonia nitrogen recorded for D1 and D2, as compared to control. Similarly, blood biochemistry revealed elevated total protein, albumin, and globulin in both supplemented groups (p < 0.05), whereas higher glucose and cholesterol levels were recorded for the D1 group (p < 0.05). Notably, systemic antioxidant status improved with the inclusion of SSF fermented DD, as evidenced by increased superoxide dismutase, glutathione peroxidase, and catalase activities, alongside reduced malondialdehyde levels (p < 0.05). The inclusion of DD in the diet decreased cooking and drip losses, and decreased shear force (indicating enhanced tenderness) and water-holding capacity (p < 0.05), with better values recorded for D2. Carcass protein and fat contents increased with the inclusion of DD in the diet, with higher values recorded for D2 (p < 0.05). Fatty acid analysis revealed higher (p < 0.05) contents of rumenic acid and octadecenoic acid in D2, as compared to D1 and control. The concentrations of lysine, methionine, threonine, leucine, and valine in meat were also higher in D2-fed goats (p < 0.05). In conclusion, incorporating solid-state-fermented discarded dates into goat diets represents a promising and sustainable strategy to valorize agricultural waste while concurrently improving growth performance, antioxidant status, meat quality, and selected nutrient profiles of goat meat. These preliminary findings warrant validation in larger-scale production. Full article

Ensuring safe coexistence between human operators and industrial robot manipulators is a critical challenge in collaborative manufacturing environments. Existing approaches rely either on dedicated safety-rated hardware, which is expensive and difficult to retrofit, or on purely vision-based classifiers that discard the precise kinematic state available from the robot controller, leading to unresolved visual ambiguities when different joint configurations produce similar appearances from fixed camera viewpoints. Kinematics-only approaches, while precise, lack the spatial context needed to disambiguate configurations near workspace boundaries. We propose RGBJointsNet, a late-fusion multimodal deep learning classifier that combines RGB visual features extracted by a frozen EfficientNet-B2 convolutional backbone with a compact kinematic stream processing the 12-dimensional joint angle vector of a dual-UR5 robotic cell. The model maps each observation to one of five mutually exclusive workspace zones: rest ($C_0$), nominal ($C_1$), extended ($C_2$), shared/collision-risk ($C_3$), and joint-limit/singularity ($C_4$). A dedicated simulation environment built on ROS 2 Humble Hawksbill and Gazebo Classic 11 was used to generate a labelled dataset of 54,309 frames and 162,927 RGB images from three calibrated overhead cameras, with analytic ground-truth labels derived from closed-form forward kinematics. Training on a CPU with a feature-caching strategy brings the per-epoch wall-clock time to seconds, making the approach tractable without GPU hardware. On the held-out test set, the model achieves 87.1% overall accuracy and a macro-averaged F1 score of 90.0%, with near-perfect recall of 99.3% for the safety-critical shared zone $C_3$. The trained classifier is integrated as an ROS 2 inference node capable of running at 10$\mathrm{Hz}$ on a standard workstation. Our results demonstrate that joint angle information is a decisive complement to RGB imagery for fine-grained, safety-oriented workspace classification in simulation-derived settings. Full article

Sentinel-1 Synthetic Aperture Radar (SAR) is a multi-purpose monitoring satellite suite that, among many applications, provides sea surface wind speeds at high spatial resolution. The overall aim of the study is to quantify the accuracy of the SAR wind products from Copernicus Ocean Wind, called OCN OWI, and from the Technical University of Denmark (DTU) Department of Wind and Energy Systems’ product called DTU SAR. Both products serve as a basis for offshore wind resource mapping for offshore wind energy planning. With the growth in offshore wind farms, offshore wind resource information is highly relevant. However, a comparison between the two products is lacking. This study fills this gap by presenting a comprehensive validation of the two Sentinel-1 wind speed products using wind speed measurements from 18 weather buoys and 10 floating wind lidars in the European Seas. It is the first time a comprehensive wind lidar dataset has been used for SAR wind validation. Key findings: OCN OWI vs. lidar (buoy) shows R² = 0.93 (0.84), root mean square error (RMSE) = 1.18 m/s (1.61 m/s), mean absolute error (MAE) = 0.86 m/s (1.24 m/s), and bias = −0.5 m/s (−0.6 m/s). DTU SAR vs. lidar (buoy) shows R² = 0.88 (0.84), RMSE = 1.3 m/s (1.6 m/s), MAE = 0.92 m/s (1.22 m/s), and bias = 0.02 m/s (−0.04 m/s). OCN OWI provides a filtered dataset and validation vs. lidar shows R² = 0.95 and RMSE = 0.88 m/s; however, this is achieved at the expense of discarding more than 50% of all data. The lidar vs. SAR wind speed statistics outperformed the buoy comparison statistics for all metrics studied. Lidar wind speed data are more accurate than buoy data and give a more trustworthy validation of SAR wind speeds than buoy data. Lidar data are recommended for validation studies on Geophysical Model Functions on SAR winds. Full article

Large volumes of orange residues generated by domestic and industrial consumption in Ecuador are commonly discarded, contributing to environmental burdens despite their high content of bioactive antioxidant compounds. This study evaluated the impact of osmotic dehydration (OD) on the physicochemical properties and functional attributes of Ecuadorian Valencia orange (Citrus sinensis) peels. A 2³ factorial design was applied, evaluating blanching time (BT: 5–10 min), sucrose concentration (SC: 50–70 °Brix), and immersion time (IT: 12–24 h). Results revealed highly significant (p < 0.01) non-linear effects of processing variables on mass transfer kinetics. Notably, milder intermediate conditions (50 °Brix, 12 h, 5 min BT) yielded significantly lower water activity (0.70 ± 0.005) and moisture content (11.83% ± 0.12%) compared to severe processing (70 °Brix, 24 h, 5 min BT), which trapped internal water (aw = 0.81 ± 0.009, moisture = 13.77% ± 0.20%), which suggested the occurrence of solute-induced surface case hardening, minimizing subsequent moisture diffusion. Processing induced an extraordinary reduction in total phenolic content (TPC) by 86% to 93% (p < 0.01) from the fresh baseline down to a range of 1.40–2.70 mg GAE/g dw, alongside a critical drop in antioxidant capacity, with post-dehydration ABTS retained at <65% and FRAP at <30% of fresh values due to cellular membrane disruption and subsequent hydrophilic leaching. Conversely, lipophilic total carotenoid content was maximized under severe configurations (10 min BT, 70 °Brix SC, 24 h IT) at 52.92 ± 2.19 µg/g dw (p < 0.01) due to protective sugar matrix encapsulation. Ultimately, these findings demonstrate that while osmotic processing involves an inherent trade-off in soluble antioxidant depletion, it establishes a precise technological process window to stabilize highly perishable citrus by-products into microstructurally stable, value-added dietary fiber matrices, providing a predictable and scalable upcycling strategy for functional ingredient development within the regional circular bioeconomy. Full article

To tackle the dynamic assignment problem of unmanned aerial vehicle (UAV) swarms, a merged coordination-optimized pigeon-inspired optimization (MCOPIO) algorithm based on the pigeon-inspired optimization (PIO) algorithm is proposed in this paper. The algorithm disrupts the original pigeon distribution via random grouping and performs mutual learning and optimization within the new groups. After dynamic optimization, the underperforming pigeons are discarded, and the flock is reorganized. Subsequently, the two stages of the basic PIO are integrated through a dynamic factor. These improvements overcome the limitations of the basic PIO algorithm, such as insufficient global search capability, poor stability, and disconnection between the two algorithm stages. Comparative experiments are conducted with the state-of-the-art intelligent computing algorithms, such as the basic PIO, particle swarm optimization (PSO), genetic algorithm (GA), and improved consensus-based bundle algorithm (ICBBA), the comparative results verify the feasibility and effectiveness of our improved PIO for UAV swarm dynamic task allocation. Full article