Search Results (1,192)

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article

Portal venous or late (equilibrium) phase washout is one of the well-known major imaging features of hepatocellular carcinoma (HCC). However, these two washouts stand for distinct intratumoral pathophysiological states and should be considered separately. Positive portal venous phase (PVP) washout has been shown to be related to high grade HCC, poor post operative survival rate, and positive PD-L1 or VETC. In contrast, there is indirect evidence that negative washout at equilibrium or late phase (EqP) may be related to biliary/stem cell subtype, which is biologically aggressive, and associated with an immune hot tumor microenvironment. Thus, although these two washouts represent different intratumoral pathophysiological conditions, both are closely related to biological aggressiveness or tumor microenvironment, which may be associated with the response to systemic therapies or post-surgical survival. In contemporary practice, gadoxetate-enhanced MRI restricts washout assessment in the PVP, whereas extracellular agent CT permits assessment in both the PVP and EqP; accordingly, this review addresses PVP washout on CT or extracellular agent MRI, PVP washout on gadoxetate-enhanced MRI, and EqP washout on CT. When washout information is integrated with other clinico-radiological features, more precise prediction of patient survival or response to systemic therapies would become possible in the future. Full article

To improve the adaptability of 4WID electric vehicles under various operating conditions, this study introduces a model predictive control approach utilizing a neural network for adaptive weight parameter prediction, which integrates four-wheel steering and longitudinal driving force control. To address the difficulty in adjusting the MPC weight parameters, the neural network undergoes offline training, and the Snake Optimization method is used to iteratively optimize the controller parameters under diverse driving conditions. To further enhance vehicle stability, the real-time stability state of the vehicle is assessed using the $\beta -\dot{\beta }$ phase plane method. The influence of vehicle speed and road adhesion on the instability boundary of the phase plane is comprehensively considered to design a stability controller based on different instability degree zones. This includes an integral sliding mode controller that accounts for both vehicle tracking capability and stability, as well as a PID controller, which calculates the additional yaw moment based on the degree of instability. Finally, an optimal distribution control algorithm coordinates the longitudinal driving torque and direct yaw moment while also considering the vehicle’s understeering characteristics in determining the torque distribution for each wheel. The simulation results show that under various operating conditions, the proposed control strategy achieves smaller tracking errors and more concentrated phase trajectories compared to traditional controllers, thereby improving path tracking precision, vehicle stability, and adaptability to varying conditions. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

A fundamental challenge in lithium-ion battery charging is the inherent trade–off between charging speed and battery health. Fast charging tends to accelerate battery degradation, while slow charging extends downtime and intensifies range anxiety, heightening concerns over inadequate driving range during operation. This contradiction has become a key bottleneck restricting the advancement of electric vehicles. In response to the limitations of conventional charging strategies and optimization methods, which typically intensify this trade–off, this study proposes a novel two–stage fast charging optimization strategy for lithium–ion batteries. The proposed method first introduces a hybrid clustering algorithm that combines the canopy algorithm with bisecting K–means to achieve adaptive SOC staging. This staging is guided by the nonlinear characteristics of the internal resistance with respect to the state of charge (SOC), allowing for a data–driven division of charging phases. Following staging, a closed–loop optimization framework is developed. A wavelet neural network (WNN) is employed to precisely capture and approximate the nonlinear characteristics of the charging process for performance prediction, upon which a multi–strategy enhanced multi–objective particle swarm optimization (MOPSO) algorithm is applied to efficiently search for Pareto–optimal solutions that balance charging time and ohmic loss. In addition, an active learning mechanism is incorporated to refine the WNN using selectively sampled data iteratively, thereby improving prediction accuracy and the robustness of the optimization process. Experimental results demonstrate that when the SOC reaches 70%, the proposed method shortens the charging time by 12.5% and reduces ohmic loss by 31% compared with the conventional constant current–constant voltage (CC–CV) strategy, effectively achieving a balance between charging efficiency and battery health. Full article

Accurate estimation of State of Health (SOH) is pivotal for managing the lifecycle of lithium-ion batteries (LIBs) and ensuring safe and reliable operation in electric vehicles (EVs) and energy storage systems. While feature fusion methods show promise for battery health assessment, they often suffer from suboptimal integration strategies and limited utilization of complementary health indicators (HIs). In this study, we propose a Feature Accretion Method (FAM) that systematically integrates four carefully selected health indicators–voltage profiles, incremental capacity (IC), and polynomial coefficients derived from IC–voltage and capacity–voltage curves—via a progressive three-phase pipeline. Unlike single-indicator baselines or naïve feature concatenation methods, FAM couples’ progressive accretion with tuned ensemble learners to maximize predictive fidelity. Comprehensive validation using Gaussian Process Regression (GPR) and Random Forest (RF) on the CALCE and Oxford datasets yields state-of-the-art accuracy: on CALCE, RMSE = 0.09%, MAE = 0.07%, and R² = 0.9999; on Oxford, RMSE = 0.33%, MAE = 0.24%, and R² = 0.9962. These results represent significant improvements over existing feature fusion approaches, with up to 87% reduction in RMSE compared to state-of-the-art methods. These results indicate a practical pathway to deployable SOH estimation in battery management systems (BMS) for EV and energy storage applications. Full article

In this study, a novel comparison between single phase 7-Level Packed U—Cell (PUC) inverter and single phase 9-Level Cross Switches Cell (CSC) inverter with Model Predictive Controller (MPC) for solar grid-tied applications is presented. Our innovation introduces a unique approach by integrating PV solar panels in PUC and CSC inverters in their two DC links rather than just one which increases power density of the system. Another key benefit for the proposed models lies in their simplified design, offering improved power quality and reduced complexity relative to traditional configurations. Moreover, both models feature streamlined control architectures that eliminate the need for additional controllers such as PI controllers for grid reference current extraction. Furthermore, the implementation of Maximum Power Point Tracking (MPPT) technology directly optimizes power output from the PV panels, negating the necessity for a DC-DC booster converter during integration. To validate the proposed concept’s performance for both inverters, extensive simulations were conducted using MATLAB/Simulink, assessing both inverters under steady-state conditions as well as various disturbances to evaluate its robustness and dynamic response. Both inverters exhibit robustness against variations in grid voltage, phase shift, and irradiation. By comparing both inverters, results demonstrate that the CSC inverter exhibits superior performance due to its booster feature which relies on generating voltage level greater than the DC input source. This primary advantage makes CSC a booster inverter. Full article

To address the building settlement issues induced by an urban geological hazard in a northern city, this study utilizes settlement monitoring data from 16 high-rise buildings. The non-uniform temporal data were processed using the Akima interpolation method to construct a settlement prediction model based on a backpropagation (BP) neural network. The model’s predictive performance was validated against traditional approaches, including the hyperbolic and exponential curve methods, and was further employed to estimate the stabilization time of building settlements. Additionally, spatiotemporal characteristics of settlement behavior under the influence of geological hazards were investigated through a comparative analysis of deformation data across the building group. The results demonstrate that the BP neural network model achieves a 58.3% improvement in predictive accuracy compared to traditional empirical methods, effectively capturing the settlement evolution of buildings. The model also provides reliable predictions for the time required for buildings to reach a stable state. The temporal evolution of building settlement exhibits a distinct three-stage pattern: (1) an initial abrupt phase dominated by rapid water and soil loss; (2) a rapid settlement phase primarily driven by the consolidation of sandy and clayey soils; and (3) a slow consolidation phase governed by the prolonged consolidation of cohesive soils. Spatially, building deformations show significant regional heterogeneity, and the existence of potential finger-like preferential pathways for water and soil loss appears to exert a substantial influence on differential settlements. Full article

In many real-world control tasks, agents operate under partial observability, where access to complete state information is limited or corrupted by noise. This poses significant challenges for reinforcement learning algorithms, as methods relying on full states or long observation histories can be computationally expensive and less robust. Four-Phase Rapid Motor Adaptation (FRMA) is a reinforcement learning framework designed to address these challenges in high-frequency control tasks under partial observability. FRMA proceeds through four sequential stages: (i) full-state pretraining to establish a strong initial policy, (ii) auxiliary hidden-state prediction for LSTM memory initialization, (iii) aligned latent representation learning to bridge partial observations with full-state dynamics, and (iv) latent-state policy fine-tuning for robust deployment. Notably, FRMA leverages full-state information ($s_t$) only during training to supervise latent representation learning, while at deployment it requires only short sequences of recent observations and actions. This allows agents to infer compact and informative latent states, achieving performance comparable to policies with full-state access. Extensive experiments on continuous control benchmarks show that FRMA attains near-optimal performance even with minimal observation–action histories, reducing reliance on long-term memory and computational resources. Moreover, FRMA demonstrates strong robustness to observation noise, maintaining high control accuracy under substantial sensory corruption. These results indicate that FRMA provides an effective and generalizable solution for partially observable control tasks, enabling efficient and reliable agent operation when full state information is unavailable or noisy. Full article

Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug–polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques including hot-melt extrusion, spray drying, and KinetiSol^® dispersing are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug–polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data-driven approach. Full article

Efficient prediction of aerodynamic loads on wind turbine blades under yawed inflow remains challenging due to the complexity of three-dimensional unsteady flow phenomena. In this work, a modified blade element momentum (BEM) method, incorporating multiple correction models, is systematically compared with high-fidelity computational fluid dynamics (CFD) simulations for the NREL Phase VI wind turbine across a range of inflow velocities (7–15 m/s) and yaw angles ($0^{°}$–${60}^{°}$). A normalized absolute error metric, referenced to experimental measurements, is employed to quantify prediction discrepancies at different yaw conditions, wind speeds, and spanwise blade locations. Results indicate that the corrected BEM method maintains good agreement with measurements under non-yawed attached flow, with errors within 2%, but its accuracy declines substantially in separated and yawed flow regimes, where errors can exceed 20% at high yaw angles (e.g., ${60}^{°}$) and low tip-speed ratios. CFD flow-field visualizations, including vorticity and Q-criterion iso-surfaces, reveal that yawed inflow strengthens vortex interactions on the leeward side and generates Coriolis-driven spanwise vortex structures, promoting stall progression from tip to root. These unsteady phenomena induce load fluctuations that are not captured by steady-state BEM formulations. Based on these insights, future studies could incorporate vortex structure and spanwise flow features extracted from CFD into unsteady correction models for BEM, enhancing prediction robustness under complex operating conditions. Full article

Estimated population growth and increased demand for food production bring with them the evident need for more efficient and sustainable production systems. Because of this, computer vision plays a fundamental role in the development and application of solutions that help producers with the issues that limit livestock production in Brazil and the world. In addition to being stressful for the producer and the animal, the conventional pig weighing system causes productive losses and can compromise meat quality, being considered a practice that does not value animal welfare. The objective was to develop a computational procedure to predict the live weight of pigs in the growth and finishing phases, through the volume of the animals extracted through the processing of 3D images, as well as to analyze the real and estimated biometric measurements to define the relationships of these with live weight and volume obtained. The study was conducted at Roçadinho farm, in the municipality of Capoeiras, located in the Agreste region of the state of Pernambuco, Brazil. The variables weight and 3D images were obtained using a Kinect^®—V2 camera and biometric measurements of 20 animals in the growth phase and 24 animals in the finishing phase, males and females, from the crossing of Pietrain and Large White, totaling 44 animals. To analyze the images, a program developed in Python (PyCharm Community Edition 2020.1.4) was used, to relate the variables, principal component analyses and regression analyzes were performed. The coefficient of linear determination between weight and volume was 73.3, 74.1, and 97.3% for pigs in the growing, finishing, and global phases, showing that this relationship is positive and satisfactorily expressed the weight of the animals. The relationship between the real and estimated biometric variables had a more expressive coefficient of determination in the global phase, having presented values between 77 and 94%. Full article

Software development projects are highly susceptible to risks during the design phase, which plays a crucial role in shaping the architecture, functionality, and quality of the final product. Decisions made during the design stage significantly affect the outcomes of the subsequent phases, including coding, testing, deployment, and maintenance. However, the complexities and uncertainties inherent in the design phase are often inadequately addressed by traditional risk management tools as they rely on deterministic models that oversimplify interdependent risks. This research introduces a fuzzy logic-based risk assessment model tailored specifically for the design phase of software development projects. The proposed fuzzy model, unlike the existing state-of-the-art models, regards the iterative nature of the design phase, the interaction between diverse stakeholders, and the potential inconsistencies that may arise between the initial and final version of the software design. More specifically, it develops a customized fuzzy model that incorporates design-specific risk factors such as evolving architectural requirements, technical feasibility concerns, and stakeholder misalignment. Finally, it integrates expert-driven rule definitions to enhance model accuracy and real-world applicability, ensuring that risk assessments reflect actual challenges faced by software design teams. Simulations conducted across diverse real-world scenarios demonstrate the model’s robustness in predicting risk levels and supporting mitigation strategies. The simulation results confirm that the proposed fuzzy logic model outperforms conventional approaches by offering greater flexibility and adaptability in managing design-phase risks, assisting project managers in prioritizing mitigation efforts more effectively to improve project outcomes. Full article

First-principles density-functional theory (PBE, Quantum ESPRESSO) was employed to quantify how Fe substitution modulates the structural, elastic, electronic, and optical behaviour of cubic fluorite Fe_xZr_1−xO₂ (x = 0.00–1.00). The fluorite FeO₂ end member was treated as a hypothetical ambient-pressure limit to trace trends across the solid solution (experimental FeO₂ being stabilized in the high-pressure pyrite phase). Mechanical stability was verified via the cubic Born criteria, and composition-dependent stiffness and anisotropy were assessed through Voigt–Reuss–Hill moduli, Pugh ratio, and elastic indices. A strong band-gap narrowing was found—from 3.41 eV (x = 0) to ≈0.02 eV (x = 0.50)—which was accompanied by a visible–NIR red-shift, large absorption (α ≈ 10⁵ cm⁻¹ at higher x), and enhanced refractive index and permittivity; metallic-like response was indicated at high Fe content. Spin-polarized calculations converged to zero total and absolute magnetization, indicating a non-magnetic ground state at 0 K within PBE. The effect of oxygen vacancies (V₀)—expected under Fe³⁺ charge compensation—was explicitly considered: V₀ is anticipated to influence lattice metrics, elastic moduli (B, G, G/B), and sub-gap optical activity, potentially modifying stability and optical figures of merit. Stoichiometric (formal Fe⁴⁺) predictions were distinguished from V₀-rich scenarios. Absolute band gaps may be underestimated at the PBE level. Full article

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article