Search Results (19,674)

High-resolution imagery is essential for monitoring heterogeneous grassland ecosystems, yet the performance of generative adversarial network (GAN) super-resolution under varying landscape heterogeneity and operational application scenarios remains unclear. This study presents a landscape-aware evaluation of super-resolution methods in semi-arid savanna grasslands of the Edwards Plateau (Texas, USA) using paired multispectral imagery from PlanetScope (3 m) and unmanned aerial vehicle (UAV) platforms (0.03 m). Two GAN models, SRGAN and ESRGAN, were compared with a bicubic interpolation baseline. Image tiles were systematically stratified along ecologically relevant gradients of vegetation condition (NDVI quartiles), spatial structure (woody patch-based clusters), and textural complexity (GLCM entropy quartiles). Model performance was evaluated across three operational frameworks: intra-sensor downscaling, cross-sensor downscaling, and intra-to-cross generalization. Reconstruction fidelity was quantified using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), complemented by variability analysis to assess performance stability. Landscape heterogeneity strongly influenced downscaling outcomes. SRGAN performance declined in areas with dense vegetation, aggregated woody structure, and high-entropy textures, with large variability under cross-sensor and generalization scenarios. In contrast, ESRGAN demonstrated consistently robust performance across landscape gradients, whereas bicubic interpolation performed well only under intra-sensor conditions and drastically degraded under sensor transfer. These results demonstrate that vegetation condition, structural heterogeneity, and sensor-transfer scenarios jointly constrain super-resolution performance. Rather than serving as a model comparison exercise, this study emphasizes a landscape-aware framework for understanding how ecological heterogeneity and operational domain shifts jointly shape super-resolution behavior in grassland ecosystems, providing guidance for more reliable applications of deep learning-based remote sensing methods. Full article

To address the challenges of insufficient feature fusion and incomplete multi-scale information capture in complex traffic scenarios, we propose a vehicle type recognition network based on feature comparison and the Mixture of Experts (MoE) model. Specifically, the MobileNetV4 backbone is introduced to enhance deep feature extraction for vehicle targets. Meanwhile, we design a Multi-scale Interleaving Fusion Module (MSIFM), which progressively transmits feature channels via an interleaving structure to capture multi-scale features while enhancing vehicle feature representation. Moreover, we devise a Feature Compare Enhancement Module (FCEM) to efficiently fuse feature maps with different semantic information. By performing feature comparison, it strengthens strongly correlated features while suppressing weakly correlated ones. Finally, we design a Mixture of Experts Feature Enhancement Module (MOEFEM) to aggregate multi-scale feature maps and adaptively capture detailed vehicle features through multiple expert units. Experimental results demonstrate that our method achieves mAP improvements of 2.2% and 2.4% over YOLOv11 on UA-DETRAC and BDD100K, respectively. The proposed method not only improves detection accuracy significantly but also maintains real-time efficiency, providing a practical solution for high-precision vehicle type recognition. It offers valuable technical support for intelligent transportation systems, smart city management, and autonomous driving safety. Full article

This paper presents the design and field evaluation of a low-cost automatic steering system for agricultural tractors. The system employs a PID controller whose gains are tuned using a metaheuristic optimization method. Core hardware includes an ESP32 microcontroller, an MPU9250 inertial measurement unit, a GPS module, and a servo motor for closed-loop yaw angle control, with a complementary filter fusing gyroscope and magnetometer data for robust heading estimation. Nine optimization algorithms were systematically compared: Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Salp Swarm Algorithm (SSA). A cost function combining overshoot and settling time was used. Step response analysis showed that WOA achieved the best performance, with an integral absolute error of 6.31°·s, a settling time of 2.15 s, and a minimal overshoot of 0.08°. In field tests on asphalt and farmland, the WOA-tuned system reduced lateral deviation by 69% (from 12.4 cm to 3.8 cm) and 67% (from 18.7 cm to 6.2 cm), respectively, compared to manual steering. Repeated-measures ANOVA and paired t-tests confirmed statistically significant improvements (p < 0.001) with large effect sizes (Cohen’s d > 2.7). The core components cost under $150 USD. The study offers a reproducible pipeline for comparative metaheuristic evaluation in agricultural vehicle guidance. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

Background: Facial skin aging is a multifactorial process characterized by wrinkles, pigmentary alterations, reduced elasticity, and dermal structural changes, in which oxidative stress and low-grade inflammation play key roles. Polyphenols have gained interest in cosmetic science due to their antioxidant and skin-protective properties. Objective: We evaluated the antioxidant activity, clinical–instrumental performance, and tolerability of a topical cream containing 4% w/w Aliophen^®, a polyphenol-rich malt–hop extract, after 56 days of twice-daily application. Methods: Antioxidant activity was assessed in HaCaT keratinocytes exposed to tert-butyl hydroperoxide (tBHP, 500 μM), with intracellular reactive oxygen species (ROS) measured by DCFH-DA assay after Aliophen^® treatment (4–16 mg/mL). A prospective, single-center, open-label study included 20 women aged 45–65 years with facial aging signs. Instrumental assessments included wrinkle depth (PrimosCR SF), pigmentation (ITA°), skin biomechanics (Cutometer^® R0, R2), and dermal echogenicity (50 MHz ultrasound) at baseline, Day 28, and Day 56. A small subgroup with mild-to-moderate atopic skin (N = 5) was descriptively monitored using SCORAD. Results: Aliophen^® significantly reduced ROS in a dose-dependent manner. Wrinkle depth decreased at Day 28 (−8.1%; p = 0.003) and Day 56 (−15.9%; p < 0.001). ITA° increased (+11.5% and +18.2%; p ≤ 0.003). Skin biomechanics improved (R0 −5.3%; R2 +5.5%; p ≤ 0.004). Dermal echogenicity increased at Day 56 (+1.38; p = 0.002). SCORAD showed descriptive improvement. No serious adverse events occurred. Conclusions: A topical cream containing 4% Aliophen^® improved instrumental markers of facial aging with good tolerability, supporting further randomized, vehicle-controlled studies. Full article

Electric vehicles are an important carrier for achieving energy savings and emission reductions in the transportation sector. As the decision-making core of the powertrain, the energy management strategy is responsible for power allocation and energy scheduling and directly determines vehicle economy, power-source lifetime, and overall performance. Model predictive control can handle multiple constraints and objectives within a prediction horizon and realize online closed-loop decision-making via receding-horizon optimization and has become an important research direction for energy management of electric vehicles. This paper presents the basic principles and typical modeling framework of model predictive control and reviews its research progress in hybrid electric vehicle energy management. The related studies are categorized and comparatively analyzed from three perspectives—prediction methods, solution strategies, and optimization objectives—and the characteristics of different approaches are summarized. The review shows that model predictive control has advantages in multi-objective trade-offs and adaptation to time-varying operating conditions. However, practical implementation still faces significant barriers, including prediction uncertainty and computational complexity. Finally, the challenges and future directions of model-predictive-control-based energy management strategies are discussed. Full article

The transportation sector, a major source of urban air pollution and CO₂ emissions, is the focus of extensive research aimed at developing cleaner and more efficient technologies. In this context, hydrogen–methane blends (HCNG) represent a promising alternative fuel, combining the zero-carbon combustion potential of hydrogen with the availability and cleaner profile of methane. This solution can be implemented in existing internal combustion engines, enabling a technically and economically feasible transition toward more sustainable mobility. This work investigates the use of an HCNG blend in a bus originally powered by natural gas, focusing on pollutant emissions under real driving conditions representative of typical urban operation. Measurements were performed using a Portable Emission Measurement System installed on-board. Two test campaigns were carried out: the first using methane, and the second using an HCNG blend (15% H₂, 85% CH₄ by volume), over identical urban and extra-urban routes with varying drivers and traffic conditions. Results show a reduction in CO₂ emissions with HCNG, along with a more significant decrease in CO, HC, and PN emissions, while NOx exhibited a slight increase due to unchanged engine calibration. The analysis also includes the RPA index, which is related to fuel energy release characteristics, indicating improved vehicle responsiveness and torque delivery with HCNG. Full article

Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. Full article

The second-life application of vehicle batteries is getting attention as millions of battery systems, modules, or cells are going to enter the market in the coming decade. The performance uncertainty with or without historical knowledge of the batteries’ vehicle usage is a concern. Moreover, detailed studies on second-life battery cell behavior is sparse and an improved understanding is required for reuse/repurpose. In this work, two second-life battery packs are dismantled, and the extracted prismatic and pouch Nickel–Manganese–Cobalt (NMC) cells with 141 Ah and 65 Ah, respectively, are extensively investigated to understand the second-life degradation behavior. The one-and-a-half-year-long test campaign has followed dedicated suitable stationary test matrices, generating a valuable dataset. The aging dataset is then filtered with the most correlated features via Pearson correlation analysis (PCA) and used to train different machine learning algorithms, resulting in a root-mean-square-error (RMSE) of 0.065 and 0.109 for prismatic and pouch cells, respectively, with the best-performing ElasticNet model validated against real-life stationary profiles. The developed framework is suitable for edge computation where the SoH could be evaluated online, facilitating state-based performance and lifetime extension. Full article

Indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GNSS-denied environments due to the difficulty of position calibration of multiple ultra-wideband (UWB) anchors and the asynchronous fusion of heterogeneous sensors. This paper proposes a multi-sensor fusion localization framework that integrates multi-anchor one-shot calibration with factor graph optimization (FGO). First, Landmark Multidimensional Scaling (LMDS) is used to reconstruct the relative geometry of the anchors and the onboard tag from ranging measurements. Then, rigid Procrustes alignment is performed using a small number of anchors with known coordinates in the East–North–Up (ENU) frame to recover the transformation to the ENU frame, thereby enabling efficient position calibration of multiple UWB anchors and UAV pose initialization. Subsequently, a tightly coupled factor graph is constructed by incorporating inertial measurement unit (IMU) pre-integration, UWB ranging, laser rangefinder height measurements, and visual–inertial odometry (VIO) pose constraints. The resulting nonlinear optimization problem is solved using incremental smoothing, which improves robustness against non-line-of-sight (NLOS) errors and long-term drift. Experimental results on anchor calibration, public datasets, and real-world indoor UAV flights demonstrate that the proposed method improves the accuracy and robustness of indoor UAV localization. In particular, on the real-world rectangle trajectory, FGO-TC reduces the RMSE by approximately 38.8% compared with FGO-LC. Full article

The continuous advancement of autonomy and intelligence in marine shipping has made the safe and efficient navigation of unmanned surface vehicles in complex waters a major research focus. As a key link of the autonomous decision-making system for unmanned surface vehicles (USVs), local path planning needs to achieve real-time collision avoidance and motion optimization under dynamic obstacles, multiple rule constraints, and strong environmental uncertainty. In recent years, reinforcement learning has gradually become an important technical route for local path planning of USVs by virtue of its autonomous decision-making ability in high-dimensional continuous state space and adaptability to complex nonlinear problems. Combined with the evolution of the algorithm paradigm and its functional positioning in different water scenarios, this paper systematically reviews the relevant literature by examining the evolution of algorithmic paradigms; focuses on summarizing deep Q-network (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), along with the collaborative architectures integrated with traditional planning methods such as A* and Rapidly-exploring Random Tree (RRT); and summarizes the performance characteristics, advantages, and limitations of various methods in typical scenarios. The review shows that the main bottlenecks of current research include insufficient reward mechanism design, low sample utilization efficiency, difficulty in transferring from simulation to real ships, and insufficient safety and trustworthiness verification. This paper looks forward to the future development trends from the two directions of data fusion and security enhancement in order to provide reference for related research. Full article

This paper offers a critical review of cutting-edge research on multi-energy microgrids (MEMs), with a novel exploration of their potential role in supporting urban air mobility (UAM), specifically electric vertical takeoff and landing (eVTOL) aircraft. While extensive research has focused on improving the economic performance and emission reductions of MEMs, particularly in the context of electric vehicle (EV) charging, there remains a significant gap in understanding how microgrids can support the decarbonization of UAM. The paper examines the opportunities and challenges of integrating microgrids with UAM operations, highlighting the need for more research to optimize energy management systems that balance renewable energy use with the growing demand for aerial transport. Thermal energy storage systems are emphasized as a critical component for addressing transportation energy needs, offering a promising solution to reduce carbon emissions while enhancing system efficiency. This review aims to provide new insights into how the coupling of microgrids and UAM can contribute to the development of economically and environmentally sustainable smart cities. Full article

In the context of evasive maneuvering for exo-atmospheric vehicles, reinforcement learning and other data-driven decision-making techniques have been explored extensively. However, most existing studies focus on scenarios where interceptors use a single guidance strategy, leading to significant performance degradation when the vehicle faces interceptors with different strategies. To address this, we introduce a novel Deep Policy Inference Twin Delayed Deep Deterministic Policy Gradient (DPI-TD3) algorithm that enhances evasive capabilities against interceptors employing a variety of guidance laws. We present a interception simulation framework that includes multiple types of interceptors. The deep policy inference model identifies the guidance law of the interceptor using the relative motion vector between the interceptor and the vehicle. Depending on the identified interceptor type, the algorithm either reuses an existing experience buffer or creates new ones through deep Bayesian inference and an experience mixing network. The updated TD3 algorithm then uses the selected buffer to train against the current interceptor, generating acceleration commands for the vehicle. Experimental results show that, compared to baseline methods, the proposed algorithm converges faster and produces more effective evasive maneuvers in response to various guidance laws. Under baseline conditions, DPI-TD3 achieves a penetration success rate of 96.4% and a miss distance of 15.58 m, outperforming TD3, Deep Deterministic Policy Gradient (DDPG), and the differential game method. In more complex scenarios with sensor noise and reduced interceptor maneuverability, DPI-TD3 still maintains success rates of 92.5% and 92.3%, showing less performance degradation than baseline methods. Full article

Hydroxyapatite–calcium sulfate (HACaS) bone cements have been clinically established. Combining HACaS with an antiresorptive (zoledronic acid, ZA) and osteoanabolic agent (bone morphogenic protein 2; BMP-2) may enhance the performance of HACaS bone cements in challenging indications, but it must be ensured that this does not impair their setting and mechanical properties. This study established a Vicat/Gillmore-inspired indentation protocol to quantify force-based endpoints and the setting of HACaS with biological adjuvants. HACaS was mixed with or without ZA and/or BMP-2 at 0 min and after a 2 min pre-setting phase with reduced NaCl content (lower liquid-to-powder ratio). For each time point (3–90 min), three cylindrical pellets (Ø 4 mm, height 6 mm) underwent single indentation. Setting was defined as the maximum force at needle penetration, and endpoint hardness was defined as peak force at failure. For 24 h endpoints, specimens were incubated in blood at 37 °C. One-way ANOVA with Tukey’s H post hoc test was performed per time point (n = 3; 24 h endpoints n = 5). All 2 min protocols showed accelerated setting, consistent with the initial lower liquid-to-powder ratio. ZA significantly delayed setting and remained lowest at 90 min and after 24 h in blood. Mixing sequence and vehicle composition critically influenced early mechanical properties and should be considered in the further preclinical evaluation of HACaS with osteoanabolic or antiresorptive agents. Full article

This study presents a comparative computational fluid dynamics (CFD) investigation of the aerodynamic performance of four simplified crossover/sports utility vehicle (SUV)-type vehicle body configurations. The models were developed with systematic geometric variations, including front face inclination, roof spoiler length, roof spoiler slotting, and rear underbody diffuser integration. Steady-state Reynolds-averaged Navier–Stokes (RANS) simulations using the k–ω SST turbulence model were conducted in ANSYS Fluent to evaluate key aerodynamic parameters, including the drag coefficient, drag force, pressure distribution, velocity field, and modeled turbulence kinetic energy. The results indicate that the baseline configuration exhibits the highest drag due to early flow separation and poor rear pressure recovery. Progressive geometric modifications led to improved aerodynamic performance, with the configuration incorporating a slotted roof spoiler and rear diffuser achieving the lowest drag coefficient, corresponding to an approximate 13% reduction compared to the baseline model. The findings demonstrate that coordinated front- and rear-end design modifications play a critical role in reducing wake intensity and enhancing aerodynamic efficiency. This study provides insight into effective drag reduction strategies for crossover-type vehicles and highlights the importance of integrated aerodynamic design approaches. Full article