Search Results (4,839)

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

The discharge of oily wastewater threatens the ecosystem and human health, and the efficient treatment of oily wastewater is confronted with problems of high mass transfer resistance at the oil-water-solid multiphase interface, significant light shielding effect, and easy deactivation of photocatalysts. Although traditional physical separation methods avoid secondary pollution by chemicals and can effectively separate floating oil and dispersed oil, they are ineffective in removing emulsified oil with small particle sizes. To address these complex challenges, photocatalytic technology and photocatalysis-based improved technologies have emerged, offering significant application prospects in degrading organic pollutants in oily wastewater as an environmentally friendly oxidation technology. In this paper, the degradation mechanism, kinetic mechanism, and limitations of conventional photocatalysis technology are briefly discussed. Subsequently, the surface interface modulation functions of metal doping and heterojunction energy band engineering, along with their applications in enhancing the light absorption range and carrier separation efficiency, are reviewed. Focus on typical studies on the separation and degradation of aqueous and oily phases using photocatalytic membrane technology, and illustrate the advantages and mechanisms of photocatalysts loaded on the membranes. Finally, other new approaches and converging technologies in the field are outlined, and the challenges and prospects for the future treatment of oily wastewater are presented. Full article

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

Firstly, this paper reviews the fundamental theories of solid surface wettability and contact angle hysteresis. Subsequently, it further introduces four typical wettability-engineered surfaces with low hysteresis (superhydrophobic, superamphiphobic, super-slippery, and liquid-like smooth surfaces). Finally, it focuses on the latest research progress in the field of droplet manipulation on open planar surfaces with engineered wettability. To achieve droplet manipulation, the core driving forces primarily stem from natural forces guided by bioinspired gradient surfaces or the regulatory effects of external fields. In terms of bioinspired self-propelled droplet movement, this paper summarizes research inspired by natural organisms such as desert beetles, cacti, self-aligning floating seeds of emergent plants, or water-walking insects, which construct bioinspired special gradient surfaces to induce Laplace pressure differences or wettability gradients on both sides of droplets for droplet manipulation. Moreover, this paper further analyzes the mechanisms, advantages, and limitations of these self-propelled approaches, while summarizing the corresponding driving force sources and their theoretical formulas. For droplet manipulation under external fields, this paper elaborates on various external stimuli including electric fields, thermal fields, optical fields, acoustic fields, and magnetic fields. Among them, electric fields involve actuation mechanisms such as directly applied electrostatic forces and indirectly applied electrocapillary forces; thermal fields influence droplet motion through thermoresponsive wettability gradients and thermocapillary effects; optical fields cover multiple wavelengths including near-infrared, ultraviolet, and visible light; acoustic fields utilize horizontal and vertical acoustic radiation pressure or acoustic wave-induced acoustic streaming for droplet manipulation; the magnetic force acting on droplets may originate from their interior, surface, or external substrates. Based on these different transport principles, this paper comparatively analyzes the unique characteristics of droplet manipulation under the five external fields. Finally, this paper summarizes the current challenges and issues in the research of droplet manipulation on the open planar surfaces and provides an outlook on future development directions in this field. Full article

Multi-modal object detection that fuses RGB (Red-Green-Blue) and infrared (IR) data has emerged as an effective approach for addressing challenging visual conditions such as low illumination, occlusion, and adverse weather. However, most existing multi-modal detectors prioritize accuracy while neglecting computational efficiency, making them unsuitable for deployment on resource-constrained edge devices. To address this limitation, we propose PONet, a lightweight and efficient multi-modal vehicle detection network tailored for real-time edge inference. PONet incorporates Polarized Self-Attention to improve feature adaptability and representation with minimal computational overhead. In addition, a novel fusion module is introduced to effectively integrate RGB and IR modalities while preserving efficiency. Experimental results on the VEDAI dataset demonstrate that PONet achieves a competitive detection accuracy of 82.2% mAP@0.5 while sustaining a throughput of 34 FPS on the OrangePi AIpro 20T device. With only 3.76 M parameters and 10.2 GFLOPs (Giga Floating Point Operations), PONet offers a practical solution for edge-oriented remote sensing applications requiring a balance between detection precision and computational cost. Full article

This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, enabling both training and inference directly on the device using 32-bit floating-point arithmetic. The system is trained on a reduced MNIST dataset (1000 resized samples) and evaluated using EMNIST and MNIST-C for anomaly detection. Seven fully connected autoencoder architectures are first evaluated on a PC to explore the impact of model size and batch size on training time and anomaly detection performance. Selected models are then re-implemented in the C programming language and deployed on a single ESP32 device, achieving training times as short as 12 min, inference latency as low as 9 ms, and F1 scores of up to 0.87. Autoencoders are further tested on ten devices in a real-world federated learning experiment using Wi-Fi. We explore non-IID and IID data distribution scenarios: (1) digit-specialized devices and (2) partitioned datasets with varying content and anomaly types. The results show that small unmodified autoencoder models can be effectively trained and evaluated directly on low-power hardware. The best models achieve F1 scores of up to 0.87 in the standard IID setting and 0.86 in the extreme non-IID setting. Despite some clients being trained on corrupted datasets, federated aggregation proves resilient, maintaining high overall performance. The resource analysis shows that more than half of the models and all the training-related allocations fit entirely in internal RAM. These findings confirm the feasibility of local float32 training and collaborative anomaly detection on low-cost hardware, supporting scalable and privacy-preserving edge intelligence. Full article

The feasibility of utilizing biochar as a static floating material for agricultural applications was researched to prevent evaporation from open water static storage systems or as a floating barrier in slurry pits, for instance. Five types of biochar were created from chips, bark, and pellets of pine and residues from two acacia species using a pyrolysis time between 60 and 120 min and mean temperatures between 380 and 690 °C in a simple double-chamber reactor. Biomass and biochar were characterized for their main properties: bulk density, moisture content, volatile matter, ash content, fixed carbon, and pH. Biochar was also evaluated through a basic floatability test over 27 days (648 h) in distilled water. The highest fixed carbon content was observed in pine bark biochar (69.5%), followed by the pine pellets (67.4%) and pine chips (63.4%). Despite their high carbon content, the pellets exhibited a low floatability level, whereas pine bark biochar showed superior static floatage times, together with chip and ground chip biochar. These results suggest that biochar produced from bark and wood chips may be suitable for application as floatability material in water or slurry management systems. These results warrant further research into the static floating of biochar. Full article

The optical floating zone technique was utilized to purify chromium and a single-phase chromium–silicon alloy in this work. The impurity content (carbon, nitrogen, and oxygen) can be reduced by decreasing the withdrawal speed of samples during the zone refining process, and the coarsening of grains was also observed. The effect of the impurities on mechanical properties was determined by hardness measurements at room temperature, and the hardness of both chromium and the chromium–silicon alloy decreased with lower concentrations of nitrogen and oxygen. In contrast, brittle material behavior is observed in samples prepared by arc melting process with higher concentrations of impurities. To use chromium–silicon alloys for future high-temperature applications, their brittle behavior must be improved, which can be achieved by reducing their carbon, nitrogen, and oxygen concentrations. Full article

We demonstrate the potential of using the convolutional equivalent layer to jointly process large gravity-gradient datasets. Based on the equivalent-layer principle, we assume a single fictitious physical property distribution on a planar layer can approximate all components of the gravity-gradient tensor. Estimating this distribution using the classical technique ensures physical consistency among components. However, the classical approach becomes computationally prohibitive for large datasets due to the need to solve a large-scale inversion with a massive sensitivity matrix. To overcome this limitation, we exploit the block-Toeplitz Toeplitz-block structure of the sensitivity matrix for data on a regular horizontal grid. This structure significantly reduces computational cost—by orders of magnitude—compared to the classical method. Applications to synthetic and real datasets show that our method offers a computationally efficient alternative for processing large gravity-gradient data from various acquisition systems (AGG and FTG), even when data are irregularly spaced or flight lines are misaligned. On a standard laptop configuration, our method processed over 290,000 AGG data points in a few tens of seconds. It also handled between 726,000 FTG and 1,250,000 AGG data points within seconds to a couple of minutes, demonstrating practical computational efficiency for large-scale datasets. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

As the development of offshore wind energy transforms from coastal to deep-sea regions, designing a cost effective mooring system while ensuring the safety of floating offshore wind turbine (FOWT) remains a critical challenge, especially considering extreme wave environments. This study employs a model coupling computational fluid dynamics (CFD) and finite element method (FEM) to investigate the responses of a parked FOWT platform with its mooring system under rogue wave conditions. Specifically, the mooring dynamics are solved using a local discontinuous Galerkin (LDG) method, which is believed to provide high accuracy. Firstly, rogue wave generation and the coupled CFD-FEM are validated through comparisons with existing experimental and numerical data. Secondly, FOWT platform motions and mooring tensions caused by a rogue wave are obtained through simulations, which are compared with the ones caused by a similar peak-clipped rogue wave. Lastly, analysis of four different mooring design schemes is conducted to evaluate their performance on reducing the mooring tensions. The results indicate that the rogue wave leads to significantly enlarged FOWT platform motions and mooring tensions, while doubling the number of mooring lines with specific line angles provides the most balanced performance considering cost-effectiveness and structural safety under identical rogue wave conditions. Full article

Plastic (P) and microplastic (MP) pollution in marine and freshwater environments is an increasingly urgent issue that needs to be addressed at many levels. The Seabin (an easily operated and cost-effective floating debris collection device) can help clean up buoyant plastic debris in calm waters while monitoring water pollution. A Seabin was used to conduct a comparative analysis of plastic and microplastic concentrations in northern Lake Garda (Italy) during peak and low tourist seasons. The composition of the litter was further investigated using Fourier-Transform Infrared (FTIR) spectroscopy. The analysis showed a decreased mean amount of plastic from summer (32.5 mg/m³) to autumn (17.6 mg/m³), with an average number of collected microplastics per day of 45 ± 15 and 15 ± 3, respectively. Packaging and foam accounted for 92.2% of the recognized plastic waste products. The material composition of the plastic mass (442 pieces, 103.0 g) was mainly identified as polypropylene (PP, 47.1%) and polyethylene (PE, 21.8%). Moreover, 313 microplastics (approximately 2.0 g) were counted with average weight in the range of 1–16 mg. A case study of selected plastic debris was also conducted. Spectroscopic, microscopic, and thermal analysis of specimens provided insights into how aging affects plastics in this specific environment. The purpose of this study was to establish a baseline for further research on the topic, to provide guidelines for similar analyses from a multidisciplinary perspective, to monitor plastic pollution in Lake Garda, and to inform policy makers, scientists, and the public. Full article

Taking a composite goaf in goaf-side entry retaining as our research focus, a kilogram-level spontaneous combustion experiment was carried out, and limit parameters for coal spontaneous combustion characteristics were assessed. Combined with the key parameters of the site, a numerical model of a multi-area composite goaf was constructed, and the distribution features of the dangerous area for coal spontaneous combustion in the lower layer of in goaf-side entry retaining were determined by means of the upper and lower layer composite superposition division method. The results show that at a floating coal thickness in the goaf of 1.9 m, the lower limit of oxygen concentration C_min, upper limit of air leakage intensity, and corresponding seepage velocity are 6%, 0.282 cm⁻³·s⁻¹·cm⁻², and 11.28 × 10⁻³ m/s respectively. The dangerous area regarding residual coal on the intake side is 23~38 m away from the working face, while that on the return air side is concentrated amid the goaf at 23~75 m, and that on the flexible formwork wall is concentrated at 0~121 m. The research results are of crucial practical importance for the prevention and control of coal spontaneous combustion within a composite goaf. Full article

The subject of ship structural dynamics has faced new technological obstacles due to scientific and technological advancements, and one of the main concerns in related sectors is how to effectively reduce the vibration levels of different ships. This article focuses on the application scenarios of ship floating raft isolation systems, establishing a wave propagation model for acoustic black hole (ABH) structures based on the idea of the ABH effect. Then, a transfer matrix model for serially connected ABH structures is derived, which serves as a basis for subsequent structural designs. Second, the finite element method is used to study the energy distribution and vibration characteristics of a symmetrically distributed periodic non-uniform multi-level ABH structure. Meanwhile, it examines its bandgap distribution under a one-dimensional periodic arrangement and then investigates the vibration properties of non-uniform multi-level ABH thin-plate constructions with different periods from the perspective of engineering applications. Moreover, parameter optimization studies of non-uniform multi-level ABH structures with finite periods are carried out with an emphasis on engineering applications. The first step is to use the design space to determine the range of values for the parameters that need to be optimized. The hyper Latin cubic sampling method is then employed to select samples, and the EI criterion and PSO optimization algorithm are applied to add new samples to improve the Kriging surrogate model’s accuracy. When the optimal structural parameters have been determined, they are applied to the raft rib plate to verify the isolation effect of the non-uniform multi-level ABH structure by analyzing the vibration level difference at specific raft positions before and after embedding it. Full article