Search Results (546)

Coastal reclaimed polders with shallow saline groundwater support intensive greenhouse horticulture but require timely diagnosis of root-zone water and salinity conditions. This study developed a compact Internet-of-Things (IoT) monitoring system based on the M5Stamp Pico microcontroller to acquire SDI-12 soil-sensor data, buffer records locally, and transfer them to a low-cost cloud dashboard. Outside-greenhouse validation showed high operational reliability, with a missing observation rate of only 0.9%, and acceptable agreement with a reference TDR100 for both volumetric water content (θ) and bulk electrical conductivity (EC_b). The system was then applied to ridge-position monitoring in a commercial pepper greenhouse on a coastal reclaimed polder. The ridge records captured depth-dependent infiltration and salinity redistribution under drip irrigation, together with contrasting responses between the cultivated layer and shallow groundwater. Potential-based interpretation indicated that the monitored ridge root zone was often not strongly limited by matric potential, whereas osmotic potential derived from pore-water salinity showed reduced water availability even when the soil remained relatively wet. These results demonstrate that continuous real-time monitoring at the ridge position can support diagnosis of root-zone stress and provide useful information for irrigation and fertigation management in salt-affected greenhouse soils. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

Predictive Maintenance (PdM) approach in Combined Sewer Systems (CSS) is gaining momentum due to advances in sensor technology, affordability and availability of data, and the rise of machine learning and data analytics. This study aims to characterize the general behavior of CSS under Dry and Wet weather conditions. To achieve this, 10 min resolution precipitation and water level data were collected from nearby SIAS stations and AMAP radar water level sensors, installed at the outlet chamber of the CSS, respectively. Precipitation data was used to segment continuous time series data into Dry Weather Flow (DWF) and Wet Weather Flow (WWF). DWF analysis exhibited unique flow patterns that strongly correlated with water consumption behaviors of households. For wet weather, a comparison was made between key rainfall parameters (depth, intensity) and peak water level data, and nonlinear relationships were observed that highlight the complex rainfall–runoff process. These findings underscore the need for separate predictive models tailored to DWF and WWF characteristics. Integrating high-resolution sensor data with machine learning models such as Long Short-Term Memory (LSTM) networks and anomaly detection, Autoencoders can enhance PdM, improving CSS management and reducing risks of blockage events and infrastructure failures. Full article

This study aims to quantify the response of sunflower root water uptake to stratified soil water and salinity stress. Based on field observations, the root water uptake function in the existing model was improved by developing a new equation for the root water uptake rate that accounts for spatial differences in root response. Field experiments were conducted in 2021 and 2022 using irrigation water with four salinity levels: CK (0.87 g/L), S1 (1.0 g/L), S2 (1.5 g/L), and S3 (2.0 g/L). Soil moisture and salinity in five soil layers (0–100 cm) were continuously monitored using sensors. The actual crop water requirement (ET_a) was estimated using the soil water balance method, while the actual (T_a) and potential (T_p) plant transpiration rates were calculated based on the canopy-scale water consumption principle. Results indicated that with increasing irrigation water salinity, both soil moisture content and electrical conductivity exhibited an overall increasing trend. Significant differences were observed in the combined soil moisture and salinity conditions across soil depths. In particular, salt accumulation in the surface layer reduced root water uptake in the upper soil profile. Based on the differential root response to soil water and salinity stratification, the root water uptake function was further optimized, and the parameters representing water and salinity conditions in each soil layer were calibrated using the least squares method. Model validation with 2021 and 2022 data demonstrated good agreement between simulated and observed T_a values, with RMSE = 11.41 mm and MRE = 0.32%, R² ranging from 0.66 to 0.98, NSE between 0.52 and 0.96, and regression slope b between 0.90 and 1.10. This enhancement in the root water uptake rate formulation significantly improves model simulation accuracy and provides a robust basis for optimizing irrigation management in saline–alkali environments. Full article

Monitoring coastal and estuarine dynamics is crucial for understanding coupled physical, biogeochemical, and human impacts on coastal waters. Motivated by the availability of high spatial resolution ocean color data from the proof-of-concept SeaHawk-HawkEye ocean color CubeSat, this study assesses the capabilities and limitations of satellite remote sensing in capturing shallow water (<10 m) coastal dynamics by integrating in situ measurements with satellite imagery. A Sea Sciences Acrobat collected detailed transects at the mouth of Masonboro Inlet (Wilmington, NC, USA), with “tow-yo” style profiles from the surface to 10 m. It measured conductivity, temperature, and depth (CTD), chlorophyll a (Chl a), turbidity, and dissolved oxygen. Satellite data from SeaHawk-HawkEye, Aqua-MODIS, and Sentinel 3A/3B-OLCI provided extensive spatial coverage, revealing surface-level physical/biological interactions, but were only available 48 h after in situ sampling due to cloud cover during field sampling. Tow-yo profiles elucidated a three-dimensional phytoplankton plume, the spatial extent of which we further characterize with satellite imagery, demonstrating the value of integrating in situ and satellite data. A spatial matchup comparison between data from each satellite and the in situ sensor package revealed significant discrepancies across all satellite sensors analyzed, attributed to differences in sensor resolution, atmospheric correction approaches, and proximity to land/benthos. This study emphasizes key challenges with study design and data interpretation in dynamic nearshore environments. In particular, results suggest that meaningful comparisons of satellite vs. in situ observations in such systems require near-synchronous sampling, careful consideration of spatial scale, and improved characterization of optical complexity. Full article

Top Submerged Lance (TSL) technology is widely used in non-ferrous smelting, yet in-situ bath dynamics remain challenging to quantify because the process operates in a closed, high-temperature, highly turbulent and optically inaccessible environment. The absence of direct diagnostics limits the ability to relate operating conditions to bubble dynamics, gas penetration and bath agitation and constrains validation of multiphase CFD models under realistic conditions. This study introduces a multimodal sensing framework that combines spectral acoustic analysis with lance-mounted inertial motion sensing to characterize dynamic bath behavior across cold-model, laboratory-scale and pilot-scale systems. Water-glycerin experiments establish repeatable acoustic signatures of individual bubble-collapse events, with dominant emission bands in the 300–900 Hz range and higher-frequency components extending into the kilohertz domain. High-temperature laboratory trials using fayalitic slag reproduce these frequency regions while exhibiting depth-dependent attenuation and clear spectral separation between submerged and non-submerged lance operation. Power Spectral Density (PSD) and cumulative spectral power analyses resolve the influence of gas flow rate and lance submersion depth on acoustic spectral power distribution, while inertial measurements capture corresponding increases in vertical lance acceleration associated with back-pressure fluctuations. Pilot-scale trials at 120 Nm³/h air and 13 L/h diesel confirm that shallow lance submersion substantially increases measured acoustic spectral power below 3 kHz, whereas deeper penetration enhances periodic vertical acceleration response measured by the inertial sensor. The combined acoustic-inertial methodology provides a physically interpretable and cross-scale framework for assessing bubble collapse activity, plume interaction and bath agitation under high-temperature TSL conditions. The approach enables frequency-based diagnostics that can be systematically compared with CFD predictions of plume oscillation and collapse-related dynamics. Once baseline frequency ranges are established for a given slag system, the method can support process monitoring and may provide indirect indicators related to changes in surface agitation or foaming tendency, enabling structured data-driven analysis. The framework thus provides a practical bridge between cold-model experiments, high-temperature measurements, multiphase modeling and industrial TSL operation. Full article

Ocean monitoring is essential for understanding climate change and marine ecosystem dynamics, yet achieving comprehensive global coverage remains a challenge in oceanography. Current technologies face limitations in cost, power, hardware, and depth capacity that restrict widespread monitoring capabilities. Here we show that biohybrid robotic jellyfish (Aurelia aurita) can serve as autonomous vertical ocean profilers by integrating microcontrollers with positively buoyant sensor payloads, achieving controlled vertical-profiling capabilities. Laboratory experiments demonstrated repeatable up–down trajectories, quantified force balance limits, and identified predictable, size-dependent descent swimming speeds. Field deployments in Massachusetts coastal waters and the open ocean off the Florida Keys demonstrated field operation to ocean depths >25 m with successful in situ temperature and depth measurements. To our knowledge, this represents the first biohybrid jellyfish platform to combine autonomous, pressure-triggered vertical profiling with onboard oceanographic sensing in natural marine environments. This approach leverages the global distribution and remarkable swimming efficiency of living jellyfish while eliminating propulsion power requirements by utilizing the animal’s natural swimming capabilities. While further development is required for long-term ocean deployment, this study lays the groundwork for a new class of biohybrid ocean-sensing platforms with advantages in cost, power, and mission flexibility, providing a pathway toward dense sensor networks and increased ocean monitoring observations. Full article

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha⁻¹) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha⁻¹ mm⁻¹) than under CF (6.7 kg ha⁻¹ mm⁻¹). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article

MEMS hydrophones, as critical sensors for maritime security and underwater information acquisition, have sensitive membrane structures that exhibit insufficient ability to withstand hydrostatic pressure, necessitating an overload-protection design. Based on buckling stability theory, a collaborative optimization method for overload-protection column design was proposed, integrating theoretical analysis, finite-element simulation, and process feasibility. An optimized design scheme for hydrophone overload-protection columns was established by comprehensively considering geometric buckling-resistant design, micro-gap anti-adhesion requirements, minimal impact on sensitivity, and micro/nano-fabrication constraints. The results indicate that intermediate slenderness columns with radii between 5.5 μm and 7.5 μm sufficiently meet both fabrication and operational requirements, effectively providing overload protection. Furthermore, at water depths not exceeding 382 m, the MEMS hydrophone can maintain the integrity of its membrane structure without column buckling. Full article

Achieving a high frame rate and high dynamic range (HDR) under complex illumination remains a significant challenge for airborne push-broom visible-near-infrared (VNIR) hyperspectral cameras. Problematic scenarios typically include high-contrast scenes, such as ocean whitecaps alongside deep water or concurrently sunlit and shadowed urban surfaces. To address this, a real-time HDR acquisition system based on a dual-gain complementary metal–oxide–semiconductor (CMOS) image sensor is proposed. Specifically, a four-pixel HDR fusion method is developed, utilizing an optical calibration setup to accurately determine the fusion parameters and configure the spectral region of interest (ROI) for reduced data volume. The complete workflow, encompassing spectral–spatial four-pixel binning and piecewise dual-gain fusion, is implemented on a field-programmable gate array (FPGA) using a dual-port RAM-based buffering strategy and a low-latency five-stage pipeline. Experimental results demonstrate a minimal processing latency of 0.0183 ms and a maximum frame rate of 290 frames/s. By extending the output bit depth from 11 to 15 bits, the system achieves a digital dynamic range of the final output of 2.03 × 10⁴:1, representing a 9.58-fold improvement over the original low-gain data. The fused HDR data maintain high linearity and good spectral fidelity, with spectral angle mapper (SAM) values at the 10⁻³ level. Featuring a compact and low-power design, this system provides a practical engineering solution for efficient airborne VNIR hyperspectral acquisition. Full article

Surface defects in high-temperature continuous cast billets are critical factors affecting the quality of steel products. Owing to high-temperature radiation, heavy dust contamination, varying billet specifications, and background interference from oxide scales and water stains, existing online surface defect detection technologies for high-temperature continuous cast billets still suffer from limitations including high false-positive rates, inefficient identification of pseudo-defects, and the inability to simultaneously detect three-dimensional (3D) depth information alongside two-dimensional (2D) features. To solve these problems, this paper proposes a multi-dimensional online detection technology for surface defects in high-temperature continuous cast billets based on multi-information fusion. A four-channel multispectral image sensor and a corresponding three-light-source imaging system were developed. Furthermore, a defect sample augmentation method, a deep learning-based 2D recognition method, and a photometric stereo-based 3D reconstruction method were designed to mitigate problems of low detection accuracy and poor robustness caused by sample imbalance among different defect types. Finally, industrial applications were conducted on large-section continuous cast billets, beam blanks, and billets during the grinding process. According to the surface defect detection requirements of different continuous cast billets, multispectral multi-information fusion and traditional 2D defect imaging methods were adopted respectively. The results demonstrate high-precision online detection of surface defects in continuous cast billets, with favorable practical application effects. Full article

Tidal inundation dynamics are a principal driver of hydrological and biogeochemical processes in coastal ecosystems, controlling the exchange of carbon, nutrients, and sediments between wetlands and estuaries. In this study, we assessed the utility of L-band radar imagery in deriving tidal wetland inundated volume estimates (pixel-wise water depths), which provide a more robust characterization of wetland–estuary exchange processes than the lateral inundation state estimates. Inundation state products derived using L-band radar were combined with digital elevation models (DEMs) and synthetic tide gauging to estimate the volume of inundation. Synthetic tide gauges, models of water level produced from combined short-term field measurements and long-term monitoring stations were employed to provide calibration and validation for satellite observations for times outside of the water level sensor monitoring period (August–December 2018). Ten synthetic gauges were established across the Charles H. Wheeler Wildlife Management Area (Connecticut, USA) in a regular grid and were used to validate the radar-based inundation state and inundated volume products. To generate volumetric inundation estimates from inundation state products, we employed two bathymetric fill approaches using a DEM to constrain water surface elevations. The first approach assumed a constant water elevation fill for all inundated pixels, while the second introduced a maximum water depth constraint. While both approaches showed strong correlations with synthetic gauges, the depth constraint approach was more accurate, increasing R² from 0.87 to 0.98 and lowering RMSE from 0.79 m to 0.02 m. In this study, PALSAR-1/2 served as a proxy for the recently launched NISAR mission. Future research is planned to leverage the improved temporal sampling of the NISAR data record, combined with in-marsh water level observations (May 2025–present) and synthetic gauge estimates to improve wetland–estuary volumetric exchange characterization, which we demonstrate can be accurately estimated when paired with high-quality DEMs. Full article

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions. Full article

Ground subsidence and shaft lining deformation caused by compressed dewatered bottom aquifers in deep unconsolidated strata mining areas are critical engineering challenges, making the study of the seepage–soil deformation coupling mechanism during groundwater injection remediation vital. This study built a visual cylindrical model (1025 mm × 150 mm); formulated well-graded analogous materials based on the D₂₀ principle to simulate sandy gravel layers; embedded FBG sensors at 200/400/600 mm depths, combined with a dial indicator on the model top; and conducted two water injection–dewatering cycles. Results indicate: water injection generates excess pore water pressure, placing the entire model in a tensile stress state with top rebound; post-injection vertical stress redistributes (tension above the injection point, compression below, and an interlaced transitional band), validating the necessity of full-section injection; during the second injection–dewatering cycle, tensile strain at the upper monitoring point reaches 597.77 με, while compressive strain at lower depths reaches −253.90 με, internal deformation stabilizes within 6.5–10.0 days, injection improves the in situ stress state by reducing effective stress, and the deformation of the field strata remains in a stabilization period, with the stabilization time decreasing as the depth of the strata increases. This study clarifies the temporal evolution and representative spatial variation in internal strain at monitored depths during injection, providing theoretical and design references for optimizing water injection schemes to mitigate coal mine shaft damage. Full article

The green synthesis of nanomaterials has emerged as a sustainable and environmentally friendly approach, gaining significant attention in recent years for its potential in a wide range of multifunctional applications. Among these materials, zinc oxide nanoparticles (ZnO NPs) stand out due to their remarkable versatility and effectiveness in fields such as industry (food, chemistry, and cosmetics), nanomedicine, cancer therapy, drug delivery, optoelectronics, sensors, and environmental remediation. This study focuses on bioinspired strategies for the facile synthesis of ZnO NPs, employing natural polysaccharide gums as mediators. Acting as both reducing and stabilizing agents, natural gums not only facilitate the eco-friendly production of ZnO NPs but also enhance their stability and functionality. Natural gum-mediated green synthesis typically yields stable, spherical ZnO particles, often in the 10–100 nm range. Typical reaction conditions are the use of zinc acetate dihydrate or zinc nitrate (0.01–0.5 M) as precursors, with low gum concentrations of 0.1–1.0% (w/v) in distilled water, alkaline conditions (pH from 8 to 12), often achieved by adding NaOH, which aids in the reduction and capping by the gum, at reaction temperature between 60 °C and 80 °C, under continuous stirring. The dried precipitate is often calcined at 400 °C to 600 °C to remove organic residues and enhance crystallinity. This approach underscores the potential of biopolymer-assisted synthesis in advancing green nanotechnology for sustainable and practical applications. Utilizing environmentally benign materials such as natural gums for the synthesis of ZnO NPs offers significant advantages, including enhanced eco-friendliness and biocompatibility, making them suitable for a wide range of applications without the involvement of toxic reagents. This review provides an in-depth analysis of the synthesis and characterization techniques employed in the eco-friendly production of ZnO NPs using different natural gums from biological sources and its environmental applications (e.g., pollutant removal and increased agriculture sustainability). Full article