Search Results (1,604)

This review examines the current state of development and application of artificial intelligence (AI) tools for monitoring nutrition and physical activity in individuals with obesity, with a focus on the physiological complexity of energy balance and the role of chrono-nutrition. Energy intake and expenditure are dynamically coupled and circadian-regulated: meal timing and movement patterns influence insulin sensitivity, thermogenesis, and Non-Exercise Activity Thermogenesis within the same day. Traditional monitoring methods suffer from recall bias and low granularity, while isolated sensors operate in data silos, limiting accuracy. Effective solutions require multimodal, continuous, and temporally aligned data streams. Current AI models exhibit critical limitations in obesity-specific contexts: inaccurate gait and energy expenditure estimates due to biomechanical differences, dietary models underestimating glycemic variability, poor performance on mixed dishes, sauces, and culturally diverse foods, and a lack of validation against gold standards such as doubly labelled water (DLW) and weighed food records. This review proposes a paradigm shift toward obesity-specific AI design, including enriched datasets and multimodal integration. Physical activity monitoring faces similar challenges: systematic measurement bias in wearables, sensor placement issues, and algorithms trained on normal-weight cohorts. In the GLP-1/GIP era, if transparency, ethical safeguards, and equitable access are ensured, AI will act as a catalyst for personalized care, remote monitoring, trial optimization, and next-generation drug discovery. In conclusion, the integration of AI with rigorous validation procedures and inclusive sampling strategies is essential to achieve reliable, fair, and clinically relevant monitoring approaches for obesity management. Full article

For projects such as tailings ponds, slopes, and foundations, loose materials such as rock, slag, and sand, which are composed of particles, often have low cohesion and rely mainly on friction to maintain stability. The shear strength parameters, namely, the internal friction angle and cohesion, are the core parameters that describe the mechanical properties of materials and are directly related to the engineering stability of the above projects. The shear strength properties of loose media are related to the geometric morphological characteristics of particles. Particles with high irregularity will increase the bite and friction of the contact interface between particles, thereby affecting the overall peak shear strength of the material. This study takes sand as the research object. Based on the Mask R-CNN algorithm in deep learning, a sand particle image dataset consisting of single, contact, and sand surface particles is established. An image segmentation model that can identify particles on the surface of the sand layer and obtain the corresponding particle mask is trained; a Python 3.11.4 program is written to automatically calculate seven characteristic parameters of particle morphological characteristics parameters, including the Feret major diameter, the particle Feret minor diameter, the particle aspect ratio, the particle roundness, the comprehensive shape coefficient, the roughness, and the convexity through the particle mask. This method can obtain the overall morphological characteristics of sand particles in real time and is a particle processing method that is a prerequisite for the subsequent rapid prediction of the strength properties of granular materials. Full article

This work investigated the influence of thickness-direction boundary conditions on the flow characteristics of granular material in a quasi-two-dimensional silo using the discrete element method (DEM). Two types of boundary conditions were considered in the thickness direction: wall conditions and periodic boundary conditions. The simulation results indicate that under wall conditions, velocity waves propagate upward, manifested by the formation of bubble-like sub-flow zones in the velocity field, and the particle motion in the upper bed region exhibits a clear stick–slip feature. In contrast, under periodic boundary conditions, particle motion displays a resonant mode. Further statistical analysis reveals that, despite the distinct macroscopic motion mode under the two boundary conditions, the probability distributions of particle vertical fluctuating velocities share similar characteristics: both exhibit fat-tailed and asymmetric features and deviate from Gaussian distribution. Additionally, under wall conditions, the horizontal distributions of particle vertical velocity conform to the kinematic model throughout the bed, whereas under periodic boundary conditions, the horizontal distributions in the upper bed region display plug flow characteristics. In summary, the results of this work demonstrate that thickness-direction boundary conditions play a crucial role in determining the flow characteristics of granular assembly in silos. Full article

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

Pedestrian safety at urban crosswalks remains a major public concern, as both vehicle speeds and roadway characteristics strongly influence drivers’ behaviour when approaching these locations. This study investigates driver behaviour patterns when approaching pedestrian crossings by integrating operating speed with key road-layout features derived from a naturalistic driving experiment conducted in Florence. A dataset of 401 observations was analysed using an unsupervised clustering framework specifically designed to handle mixed numerical and categorical variables. After preprocessing, the optimal number of clusters was identified using an elbow-based model selection applied to the K-Prototypes algorithm. The analysis produced four distinct clusters, primarily differentiated by operating speed and secondarily by contextual variables such as lane number, lane width, and acceleration behaviour. Lower-speed clusters were associated with single narrow-lane configurations, whereas higher-speed clusters were characterised by wider or multilane segments and more frequent acceleration near crossings. Information Gain analysis confirmed the dominant role of lane-related attributes, while the presence of crosswalks alone did not systematically reduce speeds. Complementary clustering excluding speed resulted in fewer clusters, indicating that speed adds essential granularity to behavioural segmentation. These findings highlight the interplay between road design and driver behaviour and provide evidence-based insights to support crosswalk configurations that mitigate high-speed conflicts in urban settings. Full article

Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing deep learning-based architectures predominantly concentrate on spatial feature modeling and context capturing while inherently neglecting the exploration and utilization of critical frequency-domain features, which is crucial for addressing issues of semantic confusion and blurred boundaries in complex remote sensing scenes. To address the challenges of feature fusion and the lack of frequency-domain information, we propose a novel dual-path feature extraction network (DFENet) in this paper. Specifically, a dual-path module (DPM) is developed in DFENet to extract global and local features, respectively. In the global path, after applying the channel splitting strategy, four feature extraction strategies are innovatively integrated to extract global features from different granularities. According to the strategy of supplementing frequency-domain information, a frequency-domain feature extraction block (FFEB) dominated by discrete Wavelet transform (DWT) is designed to effectively captures both high- and low-frequency components. Experimental results show that our method outperforms existing state-of-the-art methods in terms of segmentation performance, achieving a mean intersection over union (mIoU) of 83.09% on the ISPRS Vaihingen dataset and 86.05% on the ISPRS Potsdam dataset. Full article

High-velocity fluid flow in porous media frequently exhibits non-Darcy behavior, where inertial losses lead to nonlinear pressure gradient velocity behavior. Predicting the Forchheimer coefficient β remains challenging because β varies sensitively with pore geometry and is often not constrained by porosity and permeability alone. This study develops a structure-based method to estimate β using intrinsic descriptors obtained from the Darcy regime flow characterization and image-based geometry analysis. A set of two-dimensional granular porous media was generated with controlled variations in porosity, particle size distribution, and grain size variability. Single phase simulations are simulated with a body-force multiple-relaxation-time lattice Boltzmann method. The transition from Darcy flow to non-Darcy flow is identified from the velocity and pressure gradient response, and β is determined by fitting the inertial flow regime. Two tortuosity responses were observed. In uniform media, hydraulic tortuosity remained nearly constant in the Darcy regime and then gradually decreased. In disordered media, hydraulic tortuosity first increased with the onset of recirculation and then decreased as dominant flow paths became stable. Based on these results, a dimensionless inertial factor was correlated with porosity, intrinsic hydraulic tortuosity, and a pore structure index derived from specific surface area and hydraulic pore size. The resulting model predicts β from permeability and structural descriptors. The resulting correlation provides β estimates from Darcy permeability and geometry descriptors. Validation with quasi-two-dimensional microfluidic pillar array data showed that the model captured both the magnitude and relative ordering of β for the tested geometries. The proposed framework should be regarded as a proof of concept for idealized granular porous media and quasi-two-dimensional structured systems. Full article

Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. As these models are increasingly deployed in real-world and regulated settings, the consequences of such memorization become practical and high-stakes, reinforced by data-protection frameworks that grant individuals a Right to be Forgotten (e.g., the GDPR). Simply removing a record from the training dataset does not guarantee the elimination of its influence from the model, while retrain-from-scratch procedures are often prohibitive for modern architectures, including Transformers and Large Language Models (LLMs). In this work, we provide a perspective on Machine Unlearning (MU) in supervised learning settings, with a particular focus on Natural Language Processing (NLP) scenarios, grounded in a PRISMA-driven systematic review. We propose a multi-level taxonomy that organizes MU techniques along practical and conceptual dimensions, including exactness (exact versus approximate), unlearning granularity, guarantees, and application constraints. To complement this perspective, we run an illustrative benchmark evaluation using a standardized unlearning protocol on DistilBERT trained on a public corpus of news headlines for topic classification, contrasting the retraining gold standard with representative design-for-unlearning and approximate post hoc techniques. For completeness, we also report two oracle-assisted upper-bound baselines (distillation and scrubbing) that rely on a clean retrained reference model, and we account for their incremental cost separately. Our analysis jointly considers model utility, probabilistic quality, forgetting and privacy indicators, as well as computational efficiency. The results highlight systematic trade-offs between accuracy, computational cost, and removal effectiveness, providing practical guidance for selecting machine unlearning techniques in realistic deployment scenarios. Full article

Traffic flow prediction plays an important role in intelligent transportation systems. However, accurately modeling traffic dynamics remains challenging due to complex temporal correlations and spatial interactions across road networks. In this work, we propose a Mamba-based macro–micro spatio-temporal model for traffic flow prediction. Unlike graph-based approaches that rely on predefined adjacency matrices to model spatial relationships, our method treats sensor nodes as sequence elements and applies Mamba blocks along the spatial dimension. Through the global receptive field of the structured state space model, spatial dependencies are implicitly learned without requiring explicit graph structures. The proposed architecture consists of stacked spatio-temporal blocks, each composed of two Macro Feature Blocks and one Micro Feature Block. The Macro Feature Blocks are designed to capture global temporal dependencies and spatial interactions across all nodes, while the Micro Feature Block focuses on modeling localized spatio-temporal patterns at a finer granularity. By applying structured state space modeling along both temporal and spatial dimensions, the model is able to capture long-range temporal dependencies and global spatial correlations without relying on explicit graph structures. Experiments conducted on four real-world datasets demonstrate that the proposed model achieves competitive or improved performance compared with existing baseline methods under standard evaluation metrics. Full article

Adsorption is a promising solution to the presence of contaminants in water resources that involves the use of adsorbent materials, such as granular activated carbon (GAC) and nanoparticles like silver (Ag) and copper (Cu). However, the practical challenge of using pure GAC lies in its susceptibility to biofouling. This study aimed to develop a multifunctional GAC/AgCu nanocomposite to address the dual challenge of pharmaceutical contamination and bacterial activity of Escherichia coli. Characterization by SEM, XRF, XRD and FTIR confirmed the successful impregnation of nanoparticles. Kinetic studies showed that the pseudo-first-order model was more suitable for both caffeine and paracetamol contaminants. The Langmuir model provided the best fit for isotherms, achieving maximum adsorption capacities of 138.35 $\mathrm{m}\mathrm{g} {\mathrm{g}}^{-1}$ for caffeine and 92.21 $\mathrm{m}\mathrm{g} {\mathrm{g}}^{-1}$ for paracetamol. In antibacterial tests, GAC/AgCu achieved a bacterial reduction of over 97%, whereas pure GAC showed no inhibitory effect, confirming that the antimicrobial properties are derived from the Ag and Cu nanoparticles. These results highlight GAC/AgCu as a promising multifunctional material for the simultaneous removal of emerging pharmaceutical pollutants and biological contaminants, offering a solution to mitigate biofouling and enhance water treatment efficiency. Full article

This study situates Environmental Product Declarations (EPDs) within the broader challenge of decarbonising the built environment, arguing that efficiency-oriented approaches remain insufficient unless complemented by a sufficiency paradigm that already questions “how much is necessary” in the meta-design phase. Building on an interdisciplinary reading of standards and the scientific literature, the paper analyses the regulatory architecture of Type III environmental declarations and discusses the operational implications of the two main reference frameworks for construction EPDs—ISO 21930 (global) and EN 15804 (European)—with attention paid to methodological rigidity, system boundaries, and the granularity of climate-related indicators. The paper highlights that the declared aim of comparability is frequently undermined in practice by heterogeneous Product Category Rules, background databases, modelling assumptions, and verification practices, producing an “illusion of comparability” and limiting the reliability of product-to-product comparisons. Emphasis is placed on the epistemic role of the functional unit and reference service life, showing how narrowly product-based units can conceal system-level effects and bias decision-making. The paper concludes that EPDs are most effective when interpreted as boundary objects linking policy, industry, and design, and when embedded in a sufficiency-oriented “critical ecology of materials” that integrates embodied and operational carbon within contextualised project decisions. Full article

Unmanned Aerial Vehicle (UAV) fertilization is important for precision agriculture. However, multi-rotor UAVs show a lot of inconsistencies in homogeneity and unclear deposition patterns when they spread granular fertilizer in different operational situations. This study utilized the DJI T40 UAV to measure discharge rates and create a correlation model. An orthogonal design combined DEM simulation with field experiments to look at how flight height and disc speed affected spreading uniformity and effective swath for single and overlapping flight paths. The discharge rate has a strong linear relationship with control parameters (R² > 0.94), which means that it is very easy to predict for all particle sizes. Single-pass deposition shows an “M-shaped” bimodal profile with particles of different sizes arranged in a radial pattern. The best values for H and n were found to be 7 m and 1200 rpm, respectively, and gave a 10 m effective swath width and a coefficient of variation (CV) of 13.79%. Deposition patterns change nonlinearly with flight height and disc speed. Particle size consistency is critical for distribution stability, with flight height being the key quality determinant and particle size variation the primary source of instability. Full article

Calculation of heat transfer in granular materials is an important task for many applications, from thermal management in electronics to exploring celestial soils. Usually, an effective thermal-conductivity model is employed to predict heat flux in unstructured granular media, such as a packed bed. However, a more advanced approach, the discrete element method (DEM), can capture the complex effects of mechanical loading and material mixtures on thermal transport coefficients, which traditional models struggle with. Pivotal for this approach is knowing the heat transfer coefficient between two adjacent particles. Currently, in most DEM-capable software, only particles in direct surface contact are considered to have non-zero heat conduction. We propose considering particles that are close to each other but don’t have a contact area with a non-zero surface area. We perform numerical modeling of the conductive heat transfer coefficient between equal spherical particles separated by media, assuming the fluid’s thermal conductivity is at least an order of magnitude lower. We use numerical solutions of differential equations to account for both thermal resistance within particles and through the gap between them. We found a simple generalized correlation for the heat transfer coefficient between particles and a general formula for the angular distribution of heat flux density across the particle surface. By employing a non-dimensional approach, the obtained formulas are constructed using non-dimensional parameters: the ratio of the particle’s thermal conductivity to that of the medium, and the ratio of the gap width between particles to their radius. The resulting formula is simple and convenient for DEM heat transfer calculations in packed and fluidized beds. Full article

Accurate flood detection is critical for disaster response, yet the scarcity of diverse annotated datasets hinders robust model development. Existing resources typically suffer from limited geographic scope and insufficient annotation granularity, restricting the generalization capabilities of computer vision methods. To bridge this gap, we introduce AIFloodSense, a comprehensive evaluation benchmark designed to advance domain-generalized Artificial Intelligence for climate resilience. The dataset comprises 470 high-resolution aerial images capturing 230 distinct flood events across 64 countries and six continents. Unlike prior benchmarks, AIFloodSense ensures exceptional global diversity and temporal relevance (2022–2024), supporting three complementary tasks: (i) Image Classification, featuring novel sub-tasks for environment type, camera angle, and continent recognition; (ii) Semantic Segmentation, providing precise pixel-level masks for flood, sky, buildings, and background; and (iii) Visual Question Answering (VQA), enabling natural language reasoning for disaster assessment. We provide baseline benchmarks for all tasks using state-of-the-art architectures, demonstrating the dataset’s complexity and its utility in fostering robust AI tools for environmental monitoring. Crucially, we show that despite its compact size, AIFloodSense enables better generalization on external test sets than much larger alternatives, validating the premise that rigorous diversity is more effective than scale for training robust flood detection models, and is made publicly available to accelerate further research in the field. Full article

The physical structure formed during the packing of granular grain constitutes a fundamental ecological factor within the grain bulk ecosystem. Accurate simulations of grain-packing behavior help to deepen our understanding of this ecosystem. In this study, a white hard wheat was selected as the test material, and a high-fidelity multi-sphere discrete element model of wheat kernels was constructed using three-dimensional laser scanning. Physical experiments were conducted to determine the basic physical properties of the kernels, including true density and bulk density. Using the angle of repose as the calibration parameter, the wheat-packing process was investigated with the discrete element method (DEM). The results indicated that the coefficients of static and rolling friction between particles were highly significant factors governing the angle of repose. The optimal parameter combination consisted of a particle–particle coefficient of restitution of 0.500, a coefficient of static friction of 0.388, and a coefficient of rolling friction of 0.054. The mean angle of repose obtained from the DEM packing simulation was 28.46°, corresponding to a relative error of 3.16% compared with the physical experiment. This calibrated parameter set is therefore considered accurate and reliable, and it provides baseline data for DEM simulations of wheat grain bulks. Full article