Search Results (6,899)

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs provide a powerful tool for climate science. This review examines the role of machine learning (ML) in advancing DTOs applications, addressing the limitations of traditional methodologies under current conditions of increasing data availability from satellites, in situ sensors, and high-resolution numerical models. We highlight how ML serves as a versatile tool for enhancing DTOs capabilities, including real-time forecasting, correcting model biases, and filling data gaps where conventional approaches fall short. Furthermore, we review surrogate models that aim to complement or replace traditional physical models, offering increasing accuracy and the appeal of much faster inference for forecasts, and the insertion of hybrid models, which couple physics-based simulations with ML algorithms and are proving to be continuously improving in accuracy for complex oceanographic tasks as bigger datasets become available and methodologies evolve. This paper provides a comprehensive review of ML applications within DTOs, focusing on key areas such as water quality and marine biodiversity, ports, marine pollution, fisheries, and renewable energy. The review concludes with a discussion of future research directions and the potential of ML to foster more robust and practical DTOs, ultimately supporting informed decision-making for sustainable ocean management. Full article

The visibility of vertical road signs is a crucial factor for driving safety, especially in low-light conditions. The retroreflectivity of signs is imperative to ensure that drivers are able to perceive the information in a timely manner. However, the effectiveness of signs can be compromised by factors such as material degradation, wear and tear, and dirt on the surface. The objective of this study is to analyze how different surface conditions and different levels of retroreflectivity of vertical signs affect users’ perception and driving behavior in a real controlled environment. A total of twenty-five volunteers undertook the same road test twice. During the initial trial, the subjects encountered signs with a Class II retro-reflective film (EN 12899-1:2007), and during the second trial, they encountered the same signs in the same positions as the first trial but with varied characteristics and additional factors such as dirt, water, and degradation. Through a Mobile Eye Tracker and a Racelogic Video Vbox, it was possible to investigate the alterations in the visual and kinematic behavior of participants across the two tests. The statistical analysis was conducted using the Wilcoxon test, Spearman’s correlation and regression analysis. The analysis revealed that the signal with a dirty surface had the most significant impact on participants’ perception, showing a substantial reduction in the distance of the first fixation (−15%), a decrease in the number of fixations (−37%), and an increase in the time required for it to be perceived (+40%). This study demonstrates that the maintenance of road sign surfaces is a critical factor in their effectiveness and is as influential as the level of retroreflectivity of the material. Full article

The durability of concrete in immersed tunnels is critically influenced by the coupled effects of stress, seepage, and chemical erosion, particularly in inland water environments. However, the spatio-temporal evolution of mechanical property degradation under such multi-field coupling remains insufficiently quantified. Unlike previous studies focused on “load-ion” or “hydraulic pressure-ion” dual coupling, this work introduces a complete stress–seepage–chemical tri-coupling that incorporates the critical seepage effect, representing a fundamental expansion of the experimental scope to better simulate real-world conditions. This study investigates the degradation mechanisms of concrete in the Shunde Lungui Road inland immersed tunnel subjected to such coupled erosion. A novel aspect of our approach is the application of the micro-indentation technique to quantitatively characterize the spatio-temporal evolution of the local elastic modulus at an unprecedented spatial resolution (0.5 mm intervals), a dimension of analysis not achievable by conventional macro-scale testing. Key findings reveal that the mechanical properties of concrete exhibit an initial enhancement followed by deterioration. This behavior is attributed to the filling of pores by reaction products (gypsum, ettringite, and Friedel’s salt) in the short term, which subsequently induces microcracking as the volume of products exceeds the pore capacity. Furthermore, increasing hydro-mechanical loading significantly accelerates the erosion process. When the load increases from 1.596 kN to 3.718 kN, the influence range of elastic modulus variation expands by 9.2% (from 5.186 mm to 5.661 mm). To quantitatively describe this acceleration effect, a novel load-acceleration erosion coefficient is proposed. The erosion rate increases from 0.0688 mm/d to 0.0778 mm/d, yielding acceleration coefficients between 1.100 and 1.165, quantifying a 10–16.5% acceleration effect beyond what is typically captured in dual-coupling models. These quantitative results provide critical parameters for employing laboratory accelerated tests to evaluate the ionic erosion durability of concrete structures under various loading conditions, thereby contributing to more accurate service life predictions for engineering structures. Full article

Rubber components filled with carbon black are widely used in vehicles for sealing, preventing water ingress, and reducing vibration and aerodynamic noise. However, carbon particles increase the electrical conductivity of rubber. When a carbon-filled rubber part comes into contact with the metal car body, it may act as a cathode, accelerating metal corrosion via galvanic coupling. This study combined volume resistivity and zero-resistance ammeter (ZRA) measurements, resistometric corrosion monitoring, and accelerated corrosion testing to assess the effect of rubber conductivity on the corrosion degradation of painted car body panels in defects. More conductive rubber induced a higher galvanic current and accelerated paint delamination from defects. Real-time monitoring confirmed an earlier onset of corrosion and higher corrosion rates for steel coupled with conductive rubber. These findings emphasize the importance of using low-conductive rubber with resistivity from 10⁴ Ω·m to minimize the risk of galvanic corrosion of the car body. Full article

Accurate environmental monitoring in outdoor and indoor settings is critical for exposure assessment in environmental and public health research. Conventional methods, predominantly relying on high-end instruments or laboratory analyses, face limitations in real-world applications due to their high cost and inflexibility. Recent advances in low-cost sensor technologies have enabled more adaptable monitoring. This study systematically reviews research utilizing low-cost sensors for environmental monitoring in real-world settings across China. A literature search was performed using the Web of Science database, resulting in the inclusion of 43 eligible studies out of 31,003 initially identified records. These studies primarily investigated air pollution (17 studies), noise (14), light (7), and water pollution (5). Results reveal that air and noise pollution were the most extensively examined factors. Nevertheless, the reviewed studies exhibited notable shortcomings, including limited geographical/thematic coverage, inadequate reliability validation, small sample sizes (typically under 100 participants), and short durations (often under one month). This review discusses these challenges and suggests future research directions. By synthesizing current practices and identifying gaps, this work offers valuable insights to guide the design of future sensor-based environmental monitoring projects and inform the selection of suitable sensors. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

Accurate monitoring of chlorophyll-a (Chl-a) is essential for managing coastal aquaculture, as Chl-a indicates phytoplankton biomass and water quality. This study developed a hybrid deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (Attention) to classify Chl-a using hourly, water quality datasets collected from the GOT001 station in Si Racha Bay, Eastern Gulf of Thailand (2020–2024). A random forest (RF) identified sea surface temperature (SEATEMP), dew point temperature (DEWPOINT), and turbidity (TURB) as the most influential variables, accounting for over 90% of the accuracy. Chl-a concentrations were categorized into ecological groups (low, medium, and high) using quantile-based binning and K-means clustering to support operational classification. Model performance comparison showed that the CNN–BiLSTM model achieved the highest classification accuracy (81.3%), outperforming the CNN–LSTM model (59.7%). However, the addition of the Attention did not enhance predictive performance, likely due to the limited number of key predictive variables and their already high explanatory power. This study highlights the potential of CNN–BiLSTM as a near-real-time classification tool for Chl-a levels in highly variable coastal ecosystems, supporting aquaculture management, early warning of algal blooms or red tides, and water quality risk assessment in the Gulf of Thailand and comparable coastal regions. Full article

To enhance the purification of exhaust gas, a g-C₃N₄/CeO₂/Bi₂O₃ dual type-II heterojunction photocatalysis was designed and prepared to suppress the recombination of electron–hole pairs and improve light energy utilization. The dual type-II heterojunction structure effectively reduced the bandgap (Eg) from 2.5 eV to 2.04 eV, thereby extending the light absorption of photocatalysis into the visible region. Following the design of the heterojunction, a self-cleaning process was developed and applied to asphalt pavement rut plates to evaluate its efficiency in degrading vehicle exhaust under real-road conditions. The coating was systematically characterized in terms of exhaust degradation efficiency, hardness, adhesion, water resistance, freeze–thaw durability, and skid resistance. Under 60 min of natural light irradiation, the purification efficiencies for HC, CO, CO₂, and NO_x reached 22.60%, 19.27%, 14.83%, and 50.01%, respectively. After three-repetition tests, the efficiencies remained high at 21.75%, 19.04%, 14.66%, and 49.83%, demonstrating excellent photocatalytic stability. All other road-performance indicators met the relevant China national standards. The application of this self-cleaning coating in road infrastructure presents a viable strategy for environmental remediation in transportation systems. Full article

Rapid evaluation of water toxicity requires biological methods capable of detecting sub-lethal physiological changes without depending on chemical identification. Conventional microscopy-based bioassays are limited by low throughput and difficulties in observing small, transparent and fast-moving microorganisms. This study applies a laser-biospeckle, non-imaging microbioassay to assess the motility responses of Paramecium caudatum and Euglena gracilis exposed to two organic pollutants, trichloroacetic acid (TCAA) and acephate. Dynamic speckle patterns were recorded using a 638 nm laser diode (Thorlabs Inc., Tokyo, Japan) and a CCD camera (Gazo Co., Ltd., Tokyo, Japan) at 60 fps for 120 s. Correlation time, derived from temporal cross-correlation analysis, served as a quantitative indicator of motility. Exposure to TCAA (0.1–50 mg/L) produced strong concentration-dependent inhibition, with correlation time increasing up to 16-fold at 500× PL in P. caudatum (p < 0.01), whereas E. gracilis showed a delayed response, with significant inhibition only above 250× PL. In contrast, acephate exposure (0.036–3.6 mg/L) induced motility enhancement in both species, reflected by decreases in correlation time of up to 57% in P. caudatum and 40% in E. gracilis at 100× PL. Acute trends diminished after 24–48 h, indicating time-dependent physiological adaptation. These results demonstrate that biospeckled-derived correlation time sensitively captures both inhibitory and stimulatory behavioral responses, enabling real-time, high-throughput water toxicity screening without microscopic imaging. The method shows strong potential for integration into automated water-quality monitoring systems. Full article

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks. Full article

This work presents the design of a novel electrochemical sensor for highly sensitive determination of LEV, utilizing a sensing platform based on a newly synthesized, high-purity manganese (III) porphyrin complex [5,10,15,20-tetrayltetrakis(2-methoxybenzene-4,1-diyl) tetraisonicotinateporphyrinato] manganese (III) porphyrin (MnTMIPP). The successful synthesis of the MnTMIPP complex was verified using ultraviolet–visible (UV–Vis) and infrared spectroscopy (IR). The sensing electrode was fabricated by depositing the synthesized material onto an indium tin oxide (ITO) electrode via a drop-coating method. Under optimized experimental conditions, the proposed sensor demonstrated a wide dynamic range, from 10⁻⁹ M to 10⁻³ M, with a low calculated detection limit of 4.82 × 10⁻¹⁰ M. Furthermore, the MnTMIPP/ITO electrode displayed interesting metrological performance: high selectivity, reproducibility, and stability. Successful application in spiked river water and saliva samples with satisfactory recovery rates confirms the sensor’s potential as a reliable and cost-effective platform for monitoring LEV in real-world environments. Full article

Inefficient management of dissolved oxygen (DO) in intensive aquaculture systems limits fish welfare and productivity by creating oxygen-deficient zones and promoting hydrodynamic conditions that hinder their dispersion. Because water movement directly influences how oxygen is transported and mixed within the culture unit, inadequate flow management can allow localized hypoxia to persist even when total oxygen input appears sufficient. To address this issue, this study proposes an integrated methodology that combines in situ respirometry measurements with Computational Fluid Dynamics (CFD) simulations to evaluate the spatial distribution of DO and diagnose the operational performance of aquaculture systems. The methodology quantifies oxygen consumption using intermittent-flow respirometry, applies a three-dimensional two-phase CFD model (water–oxygen) incorporating experimental oxygen consumption rates as boundary conditions, and validates the model under real operating conditions, focusing on active metabolism as the most demanding physiological state. The model generates a spatial distribution of DO patterns that are significantly modified by pond geometry, water flow characteristics, the metabolism of the fish and fish positioning. The differences between experimental and simulated values ranged from 7.8% to 10.7%, confirming the accuracy of the proposed method. The integration of in situ metabolic measurements with CFD modeling provides a realistic representation of DO dynamics, enabling system optimization and promoting more efficient and sustainable aquaculture. Full article

The enormous applications of various nanoparticles (NPs) have raised the possibility that humans may be simultaneously exposed to mixtures of them in real life. Realistically, this situation may apply to plastic NPs, mainly derived from the breakdown of larger plastics, and to silver NPs, both of which are among the most frequently detected NPs in the envirnment due to their applications in healthcare, consumer products, and water purification. Although numerous studies have examined the toxicity of plastic and silver NPs individually, knowledge of their combined toxicity remains limited. Hence, the main objective of our study was to investigate the toxicity of high-density polyethene nanoparticles (HDPE NPs), thermally isolated from food-cooking bags, in combination with citrate-stabilised silver nanoparticles (AgNPcit) to Caco-2 and HT29MTX cells growing in 2D monoculture or in 3D triple-culture with Raji cells. Cellular uptake of NPs was quantified from the side-scatter (SSC) signal in flow cytometry; toxicity was evaluated by the neutral red assay; apoptosis was evaluated by the Annexin V method; and induction of oxidative stress was evaluated by a fluorescent method using DCFDA and DHR probes. Both cell lines took up both types of NPs; however, HT29MTX cells were more effective in the NPs’ uptake. Interestingly, HDPE NPs and AgNPcit mutually inhibited each other’s uptake, which suggests a similar mechanism of entry. Both types of NPs were toxic to both cell lines growing in monoculture; Caco-2 cells were more susceptible than HT29MTX. The toxicity was attributed to the induction of oxidative stress and associated apoptosis. In line with the mutual inhibition of the NPs’ uptake, the toxic effect of both NPs in the mixture was less than that expected as the sum of individual treatments. The toxic effects of both NPs or their mixture were less pronounced in the triple-culture Caco-2/HT29MTX/Raji, than in Caco-2 and HT29MTX growing in monoculture. Full article

Hydrological models play a critical role in advancing environmental modeling. They are particularly significant in contexts requiring short-term decision-making, where real-time simulation capabilities support timely and informed actions. The advancement of Internet of Things (IoT) technology has provided new opportunities for enhancing real-time hydrological modeling. However, most widely used hydrological models were originally designed as desktop applications with process-oriented execution workflows, which hinder fine-grained state access and standardized integration with IoT systems, thereby limiting their suitability for real-time, observation-driven modeling scenarios. This paper proposes a method for describing hydrological model components and data using a standard IoT conceptual model. By establishing a generic object-oriented framework, we integrate hydrological models with IoT systems, systematically representing model elements and data while mapping them to the Open Geospatial Consortium (OGC) SensorThings API conceptual model. This approach enables real-time, observation-driven hydrological modeling and facilitates fine-grained state acquisition. Finally, we developed a prototype system based on the Storm Water Management Model (SWMM) and validated the feasibility of our methodology through case studies. Full article

Aquaculture monitoring increasingly relies on computer vision to evaluate fish behavior and welfare under farming conditions. This dataset was collected in a commercial recirculating aquaculture system (RAS) integrated with hydroponics in Queretaro, Mexico, to support the development of robust visual models for Nile tilapia (Oreochromis niloticus). More than ten hours of underwater recordings were curated into 31 clips of 30 s each, a duration selected to balance representativeness of fish activity with a manageable size for annotation and training. Videos were captured using commercial action cameras at multiple resolutions (1920 × 1080 to 5312 × 4648 px), frame rates (24–60 fps), depths, and lighting configurations, reproducing real-world challenges such as turbidity, suspended solids, and variable illumination. For each recording, physicochemical parameters were measured, including temperature, pH, dissolved oxygen and turbidity, and are provided in a structured CSV file. In addition to the raw videos, the dataset includes 3520 extracted frames annotated using a polygon-based JSON format, enabling direct use for training object detection and behavior recognition models. This dual resource of unprocessed clips and annotated images enhances reproducibility, benchmarking, and comparative studies. By combining synchronized environmental data with annotated underwater imagery, the dataset contributes a non-invasive and versatile resource for advancing aquaculture monitoring through computer vision. Full article