Search Results (36)

The advancement of mobile robot technology has made path planning a necessary condition for autonomous navigation, but traditional algorithms have issues with efficiency and reliability in dynamic and unstructured environments. This study proposes a Dynamic Hybrid A* (DHA*)–Adaptive Dynamic Window Approach (ADA-DWA) fusion algorithm for efficient and reliable path planning in dynamic unstructured environments. This paper improves the A* algorithm by introducing a dynamic hybrid heuristic function, optimizing the selection of key nodes, and enhancing the neighborhood search strategy, and collaboratively optimizes the search efficiency and path smoothness through curvature optimization. On this basis, the local planning layer introduces a self-adjusting weight-adaptive system in the DWA framework to dynamically optimize the speed, sampling distribution, and trajectory evaluation metrics, achieving a balance between obstacle avoidance and environmental adaptability. The proposed fusion algorithm’s comprehensive advantages over traditional methods in key operational indicators, including path optimality, computational efficiency, and obstacle avoidance capability, have been widely verified through numerical simulations and physical platforms. This method successfully resolves the inherent trade-off between efficiency and reliability in complex robot navigation scenarios, providing enhanced operational robustness for practical applications ranging from industrial logistics to field robots. Full article

This study addresses the persistent challenge of balancing interpretability and robustness in black-box deep learning models for automatic modulation recognition (AMR), a critical task in wireless communication systems. To bridge this gap, we propose a novel explainable AI (XAI) framework that integrates symbolic feature interaction concepts into communication signal analysis for the first time. The framework combines a modulation primitive decomposition architecture, which unifies Shapley interaction entropy with signal physics principles, and a dual-branch XAI mechanism (feature extraction + interaction analysis) validated on ResNet-based models. This approach explicitly maps signal periodicity to modulation order in high-dimensional feature spaces while mitigating feature coupling artifacts. Quantitative responsibility attribution metrics are introduced to evaluate component contributions through modular adversarial verification, establishing a certified benchmark for AMR systems. The experimental validation of the RML 2016.10a dataset has demonstrated the effectiveness of the framework. Under the dynamic signal-to-noise ratio condition of the benchmark ResNet with an accuracy of 94.88%, its occlusion sensitivity increased by 30% and stability decreased by 22% compared to the SHAP baseline. The work advances AMR research by systematically resolving the transparency–reliability trade-off, offering both theoretical and practical tools for deploying trustworthy AI in real-world wireless scenarios. Full article

Marine diesel engines face persistent challenges in balancing transient black smoke emissions and maneuverability under low-speed conditions due to inherent limitations of single turbocharger systems, such as high inertia and delayed intake response, compounded by control strategies prioritizing steady-state efficiency. To address this gap, this study proposes a dual -turbocharger dynamic matching framework integrated with a speed–pitch synergistic control strategy—the first mechanical-control co-design solution for transient emission suppression. By establishing a λ-opacity correlation model and a multi-physics ship–engine–propeller simulation platform, we demonstrate that the Type-C dual turbocharger reduces rotational inertia by 80%, shortens intake pressure buildup time to 25.8 s (54.7% faster than single turbochargers), and eliminates high-risk black smoke regions (maintaining λ > 1.5). The optimized system reduces the fuel consumption rate by 12.9 g·(kW·h)⁻¹ under extreme loading conditions and decreases the duration of high-risk zones by 74.4–100%. This study provides theoretical and practical support for resolving the trade-off between transient emissions and maneuverability in marine power systems through synergistic innovations in mechanical design and control strategies. Full article

The Baluchistan melon fly (Myiopardalis pardalina) is a highly invasive tephritid pest. It poses a critical threat to global cucurbit production, with crop losses exceeding 90% during outbreaks. This review synthesises current research on the pest’s biology, ecology, and management, focusing on its severe economic repercussions for key crops—including melon, watermelon, and cucumber—across Africa, Asia, and Europe. M. pardalina has a four-stage life cycle (egg, larva, pupa, and adult) and distinct morphological adaptations. The species’ geographic range continues to expand, driven by global trade networks and its adaptability to shifting climatic conditions. Infestations by this pest severely reduce fruit yields, undermining food security and destabilising rural economies reliant on cucurbit cultivation. We evaluate diverse control strategies, including monitoring and quarantine methods, cultural practices, physical controls, chemical management, biological agents, and emerging genetic tools. This review emphasises the urgency of adopting integrated pest management (IPM) to strategically balance efficacy, ecological sustainability, and operational scalability. By consolidating fragmented knowledge and identifying critical research gaps, this work provides a framework for mitigating M. pardalina’s impacts, offering actionable insights to safeguard agricultural productivity and enhance resilience in vulnerable regions. Full article

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems. Full article

Proton Exchange Membrane Fuel Cells (PEMFCs), recognised for their high efficiency and zero emissions, represent a promising solution for automotive applications. Despite their potential, durability challenges under real-world automotive operating conditions—arising from chemical, mechanical, catalytic, and thermal degradation processes intensified by contaminants—limit their broader adoption. This review aims to systematically assess recent advancements in understanding and modelling PEMFC degradation mechanisms. The article critically evaluates experimental approaches integrated with advanced physicochemical modelling techniques, such as impedance spectroscopy, microstructural analysis, and hybrid modelling approaches, highlighting their strengths and specific limitations. Experimental studies conducted under dynamic, realistic conditions provide precise data for validating these models. The review explicitly compares physics-based, data-driven, and hybrid modelling strategies, discussing trade-offs between accuracy, computational demand, and generalizability. Key findings emphasise that hybrid models effectively balance precision with computational efficiency. Finally, the article identifies apparent research gaps. It suggests future directions, including developing degradation-resistant materials, improved simulation methodologies, and intelligent control systems to optimise PEMFC performance and enhance operational lifespan. Full article

Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging. Full article

Educational buildings are recognized as one of the largest consumers of electrical energy. Inadequate lighting can also have negative physical and psychological effects on these environments. Therefore, optimal lighting design that meets electrical energy needs while providing visual comfort is essential. Reducing glare, primarily caused by artificial lighting in educational environments, is particularly important. Glare can lead to discomfort and eye fatigue, adversely affecting learning performance. To measure and assess this phenomenon, the “Unified Glare Rating (UGR)” metric is employed, which helps designers accurately evaluate the level of glare caused by lighting. This paper examines the parameters of height and surface reflectance as variable factors to achieve an optimal design that reduces lamp demand and minimizes glare, using a three-phase methodology: (1) using Dialux software, two primary scenarios—varying heights (2.5 and 3 m) and reflectance coefficients (ceiling, walls, floor)—were examined, (2) across 100 simulations followed by correlation and regression analyses to assess the effect of each reflectance coefficient (ceiling, walls, floor) on illuminance and the UGR, and (3) energy performance evaluation. Results demonstrate trade-offs: reducing lamps from 15 to 9 lowered energy use by 40% but increased UGR from 13 to 18 (approaching the discomfort threshold of 19), while 12 lamps achieved a balance—20% energy savings, a UGR of 14, and uniformity of 0.67. Surface reflectance emerged as critical, with high-reflectance ceilings (≥85%) and walls (≥80%) contributing 50.9% and 32% to illuminance variance, respectively. This study concludes that multi-parameter optimization—integrating height, lamp quantity, and reflectance—is essential for energy-efficient classroom lighting with acceptable glare levels, providing actionable guidelines for urban educational environments constrained by artificial lighting dependency. Full article

Advancements in thermophotovoltaic (TPV) technologies enable a new alternative for the electrification of nuclear power. These solid-state heat engines are more robust and likely cheaper to manufacture than the turbomachinery used in traditional microreactor concepts. The Radiantly Integrated TPV-microreactor system (RITMS) described in this work takes a novel approach to utilizing direct electric conversion of thermal power radiated from the active core. Without intermediary energy transfer, this direct coupling allows for system efficiencies well above 30%. While providing an introduction to the concept, the early RITMS work lacked an integrated computational sequence and economics-by-design approach, resulting in a failure to fully capture the physics of the system or to properly evaluate design parameter importance. The primary purpose of this paper is to describe and demonstrate a computational sequence that fully couples the conductive-radiative heat transfer with a neutronic solution and to provide design-specific cost estimation. This new computational framework is deployed in re-examining the multi-physics behavior of the RITMS design and to perform consistent trade-off studies. A favorable RITMS design was selected based on performance and fuel cycle costs, which was deemed feasible when considering cost uncertainty. Able to operate on 7% enriched fuel, this RITMS case was selected to balance fuel utilization with total power output. Full article

Coastal zones, at the interface between land and sea, face increasing challenges from erosion, sea-level rise, and anthropogenic interventions, necessitating innovative tools for effective management and protection. This study introduces COAST-PRO_SIM, a novel numerical model specifically designed to predict shoreline evolution and assess the impacts of coastal defence structures on coastal morphology. Unlike existing models that often face a trade-off between computational efficiency and physical accuracy, COAST-PRO_SIM balances these demands by integrating two-dimensional wave propagation routines with advanced shoreline evolution equations. The model evaluates the effects of interventions such as breakwaters and groynes, enabling simulations of shoreline dynamics with reduced computational effort. By using high-resolution input data, COAST-PRO_SIM captures the interplay between hydrodynamics, sediment transport, and structural impacts. Tested on real-world case studies along the coasts of San Leone, Porto Empedocle, and Villafranca Tirrena, the model demonstrates its adaptability to diverse coastal environments. The results highlight its potential as a reliable tool for sustainable coastal management, allowing stakeholders to anticipate long-term changes in coastal morphology and design targeted mitigation strategies. Full article

This study examines the effects of the thermal pre-treatment of hemp hurds on the physical, mechanical, and thermal properties of hempcrete, evaluating its potential as a sustainable building material. Hemp hurds were pre-treated at various temperatures (120–280 °C) and characterized by proximate analysis, CHNS elemental analysis, and thermogravimetric analysis (TGA). The resulting hempcrete samples were analyzed for density, water absorption, compressive strength, and thermal conductivity. Three different hempcrete formulations, with varying lime:hemp proportions, were analyzed. The findings indicate that higher pre-treatment temperatures lead to reduced density and water absorption across all formulations. Formulations containing a higher hemp hurd content had lower densities but higher water absorption values. Compressive strength increased consistently with the pre-treatment temperature, suggesting that higher temperatures enhance matrix bonding and structural rigidity, and with the lime content. However, thermal conductivity also rose with pre-treatment, with only the composition containing the highest hemp hurd content maintaining the optimal insulation threshold (0.1 W/mK). This suggests a trade-off between compressive strength and insulation performance, influenced by the balance of hemp hurd and lime content. These findings underscore the potential of thermal pre-treatment to tailor hempcrete properties, promoting its application as a durable, moisture-resistant material for sustainable building, though the optimization of hurd–lime ratios remains essential. Full article

In modern cyber-physical systems, the integration of AI into vision pipelines is now a standard practice for applications ranging from autonomous vehicles to mobile devices. Traditional AI integration often relies on cloud-based processing, which faces challenges such as data access bottlenecks, increased latency, and high power consumption. This article reviews embedded AI vision systems, examining the diverse landscape of near-sensor and in-sensor processing architectures that incorporate convolutional neural networks. We begin with a comprehensive analysis of the critical characteristics and metrics that define the performance of AI-integrated vision systems. These include sensor resolution, frame rate, data bandwidth, computational throughput, latency, power efficiency, and overall system scalability. Understanding these metrics provides a foundation for evaluating how different embedded processing architectures impact the entire vision pipeline, from image capture to AI inference. Our analysis delves into near-sensor systems that leverage dedicated hardware accelerators and commercially available components to efficiently process data close to their source, minimizing data transfer overhead and latency. These systems offer a balance between flexibility and performance, allowing for real-time processing in constrained environments. In addition, we explore in-sensor processing solutions that integrate computational capabilities directly into the sensor. This approach addresses the rigorous demand constraints of embedded applications by significantly reducing data movement and power consumption while also enabling in-sensor feature extraction, pre-processing, and CNN inference. By comparing these approaches, we identify trade-offs related to flexibility, power consumption, and computational performance. Ultimately, this article provides insights into the evolving landscape of embedded AI vision systems and suggests new research directions for the development of next-generation machine vision systems. Full article

The lack of data on flood events poses challenges in flood management. In this paper, we propose a novel approach to enhance flood-forecasting models by utilizing the capabilities of Generative Adversarial Networks (GANs) to generate synthetic flood events. We modified a time-series GAN by incorporating constraints related to mass conservation, energy balance, and hydraulic principles into the GAN model through appropriate regularization terms in the loss function and by using mass conservative LSTM in the generator and discriminator models. In this way, we can improve the realism and physical consistency of the generated extreme flood-event data. These constraints ensure that the synthetic flood-event data generated by the GAN adhere to fundamental hydrological principles and characteristics, enhancing the accuracy and reliability of flood-forecasting and risk-assessment applications. PCA and t-SNE are applied to provide valuable insights into the structure and distribution of the synthetic flood data, highlighting patterns, clusters, and relationships within the data. We aimed to use the generated synthetic data to supplement the original data and train probabilistic neural runoff model for forecasting multi-step ahead flood events. t-statistic was performed to compare the means of synthetic data generated by TimeGAN with the original data, and the results showed that the means of the two datasets were statistically significant at 95% level. The integration of time-series GAN-generated synthetic flood events with real data improved the robustness and accuracy of the autoencoder model, enabling more reliable predictions of extreme flood events. In the pilot study, the model trained on the augmented dataset with synthetic data from time-series GAN shows higher NSE and KGE scores of NSE = 0.838 and KGE = 0.908, compared to the NSE = 0.829 and KGE = 0.90 of the sixth hour ahead, indicating improved accuracy of 9.8% NSE in multistep-ahead predictions of extreme flood events compared to the model trained on the original data alone. The integration of synthetic training datasets in the probabilistic forecasting improves the model’s ability to achieve a reduced Prediction Interval Normalized Average Width (PINAW) for interval forecasting, yet this enhancement comes with a trade-off in the Prediction Interval Coverage Probability (PICP). Full article

The digital divide (DD) reflects the inequality of the digital economy, while existing research lacks a comprehensive framework for investigating the measurement of DD and its determinants. This study constructs a new framework with a five-dimensional comprehensive index system. City-level data are used to measure China’s DD index from 2010 to 2020 at the national, regional, and provincial levels. Furthermore, this study investigates the decomposition of DD at both regional and provincial levels and the determinants of DD from the perspectives of physical, human, and social capital. The key results are: (1) China’s DD has generally exhibited a fluctuating downward trend. While it remains high in the eastern and western regions, it has shown a decline year by year. However, the DD within most provinces is on the rise. (2) The intra-regional and inter-provincial are the primary drivers of changes in national DD, with both intra-regional and intra-provincial contribution rates on the rise. (3) Economic growth, infrastructure, foreign trade, education, and online interaction significantly impact DD, and these determinants may change at different periods. This study intends to provide empirical support for bridging the DD, fostering the balanced development of the digital economy, and reducing social inequality. Full article

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes. Full article