Search Results (223)

This study investigates the early-age performance of large-area C30 concrete slabs under different curing regimes using a multi-scale approach combining laboratory experiments, field monitoring, and numerical simulation. The experimental results indicated that standard curing (SC7) maximized the mechanical properties. In contrast, the thermal insulation and moisture retention curing (TC) regime significantly reduced temperature gradients and stress mutation amplitudes by 42% compared to wet curing (WC) by leveraging the synergistic effect of aluminum foil and insulating cotton. This makes TC a preferred solution in situations where engineering constraints apply. Field monitoring demonstrated that WC is suitable for humidity-sensitive scenarios with low-temperature control requirements, while TC is more suitable for large-area concrete or low-temperature environments, balancing early strength development and long-term durability. This multi-field coupled model exhibits significant deviations during the early stage (0–7 days) due to complex boundary interactions, but achieves high quantitative accuracy in the long-term steady state (after 14 days), with a maximum error below 8%. The analysis revealed that the key driving factors for stress evolution are early hydration heat–humidity coupling and mid-term boundary transient switching. The study provides a novel, multi-scale validated curing optimization path for crack control in large-area concrete slabs. Full article

We present a new method for mapping global gas flaring using a multiyear spatio-temporal database of VIIRS Nightfire (VNF) nighttime infrared detections from the Suomi NPP, NOAA-20, and NOAA-21 satellites. The method is designed to resolve closely spaced industrial combustion sources and to produce a stable, physically meaningful flare catalog suitable for long-term monitoring and emissions analysis. The method combines adaptive spatial aggregation of high-temperature detections with a hierarchical clustering that super-resolves individual flare stacks within oil and gas fields. Post-processing yields physically consistent flare footprints and attraction regions, allowing separation of closely spaced sources. Flare clusters are assigned to operational categories (e.g., upstream, midstream, LNG) using prior catalogs combined with AI-assisted expert interpretation. In this step, a multimodal large language model (LLM) provides contextual classification suggestions based on geospatial information, high-resolution daytime imagery, and detection time-series summaries, while final attribution is performed and validated by domain experts. Compared with annual flare catalogs commonly used for national flaring estimates, the new catalog demonstrates substantially improved performance. It is more selective in the presence of intense atmospheric glow from large flares, identifies approximately twice as many active flares, and localizes individual stacks with ~50 m precision, resolving emitters separated by ~400–700 m. For the well-defined class of downstream flares at LNG export facilities, the catalog achieves complete detectability. These improvements support more accurate flare inventories, facility-level attribution, and policy-relevant assessments of gas flaring activity. Full article

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability. Full article

Fluidized bed combustion of biomass requires maintaining stable properties of the inert bed material, which plays a key role in heat transfer, temperature stabilization and uniform fuel distribution in circulating fluidized bed (CFB) boilers. During long-term operation, quartz sand, i.e., the most commonly used inert material, undergoes physical and chemical degradation processes such as attrition, sintering and coating with alkali-rich ash, leading to changes in particle size distribution (PSD), deterioration of fluidization quality, temperature non-uniformities and an increased risk of bed agglomeration. This study analyzes quality management strategies for inert bed materials in biomass-fired CFB systems, with particular emphasis on the influence of PSD on boiler hydrodynamics and thermal behavior. Based on industrial operating data, sieve analyses and CFD simulations performed under representative operating conditions, a recommended mean particle diameter range of approximately 150–200 μm is identified as critical for maintaining stable circulation and uniform temperature fields. Numerical results demonstrate that deviations toward coarser bed materials significantly reduce solids circulation, promote segregation in the lower furnace region and lead to local temperature increases, thereby increasing agglomeration risk. The study further discusses practical approaches to bed material monitoring, regeneration and make-up management in relation to biomass type and ash characteristics. The results confirm that systematic control of inert bed material quality is an essential prerequisite for reliable, efficient and low-emission operation of biomass-fired CFB boilers. Full article

Air temperature is commonly used to represent plant thermal conditions, although temperatures within woody tissues and the soil–root zone can differ substantially under field conditions. This study characterized the thermal dynamics of xylem tissue and the soil–root interface in almond and olive orchards under Mediterranean field conditions in Southern Spain. Using long-term in-field measurements, temperatures were monitored in branch and trunk xylem tissues and at the soil–root interface, and regression models were developed to provide empirical correction relationships between air and internal temperatures across seasons and sensor position. Branch xylem temperatures closely matched air temperature for both minima and maxima. In contrast, trunk xylem and the soil–root interface showed pronounced thermal buffering. Trunk xylem maximum temperature was significantly (3.4 to 5.4 °C) lower than air temperature during summer. Shaded soil–root interface temperatures were 5.2 to 9.0 °C lower than air temperature in spring and summer but 5.9 to 11.7 °C higher than air temperature in autumn and winter. These patterns indicate a strong capacity of woody tissues and the soil–root system to moderate external thermal conditions. By quantifying air-to-tissue and air-to-soil relationships under field conditions, this study provides microclimatic data that can improve agronomic models and temperature-driven disease risk frameworks for vascular pathogens infecting woody crops. Full article

Mating disruption (MD) is an environmentally friendly pest management approach that uses synthetic pheromones to interfere with insect mate location and reproduction. This review summarizes current progress in the application of MD for stored-product pests, with emphasis on Lepidoptera (Plodia interpunctella Hübner and Ephestia kuehniella Zeller (Pyralidae)) and Coleoptera (Sitophilus spp. (Curculionidae)). For moth pests, numerous studies have demonstrated substantial suppression of mating and population growth under both laboratory and field conditions, particularly when MD is integrated with sanitation, monitoring and other IPM measures. Conversely, MD applications against beetles have been less successful due to their aggregation-based communication and lower volatility of their pheromones. Advances in pheromone formulation technology, including polymer dispensers, microencapsulated sprays and aerosol emitters, have improved pheromone stability and controlled release, although achieving uniform coverage in large and aerated storage environments remains challenging. The integration of MD with biological control, temperature management and reduced fumigant use offers promising directions for sustainable pest suppression. Continued development of smart-release devices, long-term field validation and integration with automated monitoring systems will further enhance the feasibility and cost-effectiveness of MD. Overall, MD represents a key behavioral component in reducing pesticide reliance and promoting sustainable management of stored-product pests. Full article

This study addressed the core challenge of intelligent pest and disease monitoring and early warning in smart horticultural production by proposing a multimodal deep learning framework based on multi-parameter environmental sensor arrays. The framework integrates visual information with electrical signals to overcome the inherent limitations of conventional single-modality approaches in terms of real-time capability, stability, and early detection performance. A long-term field experiment was conducted over 18 months in the Hetao Irrigation District of Bayannur, Inner Mongolia, using three representative horticultural crops—grape (Vitis vinifera), tomato (Solanum lycopersicum), and sweet pepper (Capsicum annuum)—to construct a multimodal dataset comprising illumination intensity, temperature, humidity, gas concentration, and high-resolution imagery, with a total of more than $2.6\times {10}^6$ recorded samples. The proposed framework consists of a lightweight convolution–Transformer hybrid encoder for electrical signal representation, a cross-modal feature alignment module, and an early-warning decision module, enabling dynamic spatiotemporal modeling and complementary feature fusion under complex field conditions. Experimental results demonstrated that the proposed model significantly outperformed both unimodal and traditional fusion methods, achieving an accuracy of $0.921$, a precision of $0.935$, a recall of $0.912$, an F1-score of $0.923$, and an area under curve (AUC) of $0.957$, confirming its superior recognition stability and early-warning capability. Ablation experiments further revealed that the electrical feature encoder, cross-modal alignment module, and early-warning module each played a critical role in enhancing performance. This research provides a low-cost, scalable, and energy-efficient solution for precise pest and disease management in intelligent horticulture, supporting efficient monitoring and predictive decision-making in greenhouses, orchards, and facility-based production systems. It offers a novel technological pathway and theoretical foundation for artificial-intelligence-driven sustainable horticultural production. Full article

This study presents an optimization-driven design of a wireless communications network to continuously transmit environmental variables—temperature, humidity, weight, and water usage—in poultry farms. The reference site is a four-shed facility in Quito, Ecuador (each shed $120\mathrm{m}\times 12\mathrm{m}$) with a data center located $200\mathrm{m}$ from the sheds. Starting from a calibrated log-distance path-loss model, coverage is declared when the received power exceeds the receiver sensitivity of the selected technology. Gateway placement is cast as a mixed-integer optimization that minimizes deployment cost while meeting target coverage and per-gateway capacity; a capacity-aware greedy heuristic provides a robust fallback when exact solvers stall or instances become too large for interactive use. Sensing instruments are Tekon devices using the Tinymesh protocol (IEEE 802.15.4g), selected for low-power operation and suitability for elongated farm layouts. Model parameters and technology presets inform a pre-optimization sizing step—based on range and coverage probability—that seeds candidate gateway locations. The pipeline integrates MATLAB R2024b and LpSolve 5.5.2.0 for the optimization core, Radio Mobile for network-coverage simulations, and Wireshark for on-air packet analysis and verification. On the four-shed case, the algorithm identifies the number and positions of gateways that maximize coverage probability within capacity limits, reducing infrastructure while enabling continuous monitoring. The final layout derived from simulation was implemented onsite, and end-to-end tests confirmed correct operation and data delivery to the farm’s data center. By combining technology-aware modeling, optimization, and field validation, the work provides a practical blueprint to right-size wireless infrastructure for agricultural monitoring. Quantitatively, the optimization couples coverage with capacity and scales with the number of endpoints M and candidate sites N (binaries $M+N+MN$). On the four-shed case, the planner serves 72 environmental endpoints and 41 physical-variable endpoints while keeping the gateway count fixed and reducing the required link ports from 16 to 4 and from 16 to 6, respectively, corresponding to optimization gains of up to 82% and 70% versus dense baseline plans. Definitions and a measurement plan for packet delivery ratio (PDR), one-way latency, throughput, and energy per delivered sample are included; detailed long-term numerical results for these metrics are left for future work, since the present implementation was validated through short-term acceptance tests. Full article

This study was carried out to investigate stand structure, growth dynamics, and carbon fluxes in Pinus rigida plantations of varying ages in South Korea. Field measurements across four mountain sites quantified diameter-class distributions, net primary productivity (NPP), soil respiration, and net ecosystem production (NEP). P. rigida exhibited normally distributed diameter structures in larger classes, whereas Quercus spp. showed reverse J-shaped patterns, indicating active regeneration and ongoing succession toward mixed broadleaved stands. Individual NPP was highest in P. densiflora (4.77 kg yr⁻¹) and P. rigida (4.31 kg yr⁻¹), while Quercus spp. displayed lower growth due to light limitation. Stand-level NPP peaked in 20–40-year-old stands (4.27–4.88 ton C ha⁻¹ yr⁻¹) and declined with age (2.30 ton C ha⁻¹ yr⁻¹). Soil respiration averaged 1.0 ton C ha⁻¹ yr⁻¹ and was strongly temperature dependent (R² = 0.56; Q₁₀ = 2.70). NEP on Mt. Galmi reached 4.38 ton C ha⁻¹ yr⁻¹, demonstrating substantial carbon sink capacity. These findings indicate that aging P. rigida plantations maintain ecosystem-level carbon uptake through successional compensation. Policy efforts should prioritize adaptive thinning, assisted natural regeneration, and long-term monitoring frameworks to accelerate the transition toward climate-resilient mixed forests and to strengthen national forest carbon neutrality strategies. Future research should integrate long-term carbon flux observations, species interaction modeling, and assessments of climate-driven disturbance regimes to refine management pathways for resilient mixed-forest landscapes. Full article

To overcome the limitations of lengthy laboratory testing cycles and insufficient on-site responsiveness, this study developed an online rapid monitoring device for volatile organic compounds (VOCs) in soil–water–air systems based on photoionization detection (PID) technology. The device integrates modular sensor units, incorporates an electromagnetic valve-controlled multi-medium adaptive switching system, and employs an internal heating module to enhance the volatilization efficiency of VOCs in water and soil samples. An integrated system was developed featuring “front-end intelligent data acquisition–network collaborative transmission–cloud-based warning and analysis”. The effects of different temperatures on the monitoring performance were investigated to verify the reliability of the designed system. A polynomial fitting model between concentration and voltage was established, showing a strong correlation (R² > 0.97), demonstrating its applicability for VOC detection in environmental samples. Field application results indicate that the equipment has operated stably for nearly three years in a mining area of Shandong Province and an industrial park in Anhui Province, accumulating over 600,000 valid data points. These results demonstrate excellent measurement consistency, long-term operational stability, and reliable data acquisition under complex outdoor conditions. The research provides a distributed, low-power, real-time monitoring solution for VOC pollution control in mining and industrial environments. It also offers significant demonstration value for standardizing on-site emergency monitoring technologies in multi-media environments and promoting the development of green mining practices. Full article

In controlled environment agriculture (CEA), CO₂ enrichment can promote photosynthesis while simultaneously reducing evapotranspiration, but the optimal settings vary depending on crop type, growth stage, and microclimate. This study presents a near-field remote sensing framework that fuses RGB image features with environmental variables to predict the CO₂ uptake/respiration dynamics of five leafy vegetables grown in a hydroponic culture system and evaluate their impact on resource efficiency under CO₂ control. A hybrid deep model incorporating You Only Look Once version 11 (YOLOv11) and a Residual Network with 50 layers (ResNet50) extracts growth-related visual cues and integrates them with tabular features (CO₂, temperature, and light conditions) to predict chamber CO₂ dynamics. Performance was evaluated by Mean Absolute Error (MAE)/Mean Squared Error (MSE) on withheld data, and the system-level impacts on water use (ET), pumping energy, and relative yield were analyzed using a conventional greenhouse model. The model exhibited high accuracy (MAE = 0.95; MSE = 1.62). Scenario analysis results showed that increasing ambient CO₂ concentration from 400 to 1200 ppm reduced modeled water demand by approximately 11%, increased modeled yield by approximately 9%, and resulted in a corresponding reduction in pumping energy per unit area. Unlike conventional single-crop, table-based approaches, this study demonstrates multi-crop generalization and image-environment fusion for CO₂ dynamic prediction, establishing proximity sensing as a viable decision-making layer for CEA. While yield/ET results were simulated rather than measured in long-term trials, and leaf area normalization was not available, the proposed framework provides a viable path for data-driven CO₂ control in indoor farms by linking image-based monitoring with operational optimization. Full article

The distribution of seepage field in embankment dams is an important aspect of the safe operation of in-service embankment dams. The distributed optical fiber temperature monitoring technology has some advantages of high sensitivity, strong real-time performance, and rich data. This is a problem worthy of study for the monitoring of seepage field in embankment dams. This paper takes a certain embankment dam as an example. It sets up some optical fiber temperature measurement sections near the traditional seepage monitoring section. It elaborately introduces the optical fiber layout, on-site construction, long-term monitoring, and simulation. The result shows that the position of the infiltration line can be measured by using heated distributed optical fibers; the error is within the range of 0.1 to 0.2 m. The monitoring results are basically consistent with the traditional seepage monitoring results, indicating that it is feasible to use distributed optical fiber temperature measurement technology for dam seepage monitoring. Long-term monitoring and numerical simulation have obtained the infiltration lines, velocity vectors, and streamlines at different water levels, verifying the reliability of the distributed optical fiber temperature monitoring technology. As summarized, the distributed optical fiber temperature measurement technology can accurately obtain the seepage information inside the dam body, providing a new idea for the analysis and safety assessment of the seepage field of embankment dams. Full article

The long-term operational stability of a fiber optic current transformer (FOCT) is critically dependent on the integrity of its internal fiber optic loop. Conventional testing methods often fall short in providing high-precision, spatially resolved diagnosis of FOCT internal fiber links. To overcome this limitation, this paper proposes a distributed sensing and testing scheme based on Optical Frequency Domain Reflectometry (OFDR). The implemented OFDR system offers a measurement range of up to several hundred meters, with a spatial resolution of 10 μm and a localization accuracy of 1 mm. Capitalizing on these capabilities, the proposed approach enables a comprehensive inspection of the FOCT sensing coil and lead fibers. At the same time, the OFDR response of various devices in the FOCT system is analyzed, while providing precise measurements of both optical loss and reflectance. In addition, the temperature stress variation of the sensing coil is measured by using the sensing characteristics of OFDR. This work provides a powerful and indispensable tool for FOCT factory testing, field fault diagnosis, and condition monitoring, contributing significantly to the safety and stability of smart grid systems. Full article

Soil moisture exhibits high spatio-temporal variability that necessitates dense monitoring networks, yet the cost of commercial sensors often limits widespread deployment. Despite the mass production of low-cost capacitive soil moisture sensors driven by IoT applications, significant gaps remain in their robust characterisation and in the availability of open-source, reproducible monitoring systems. This study pursues two primary objectives: (1) to develop an open-source, low-cost, off-grid soil moisture monitoring station (picoSMMS) and (2) to conduct a sensor-unit-specific calibration of a popular low-cost capacitive soil moisture sensor (LCSMS; DFRobot SEN0193) by relating its raw output to bulk static relative dielectric permittivity (${ϵ}_s$), with the additional aim of transferring technological gains from consumer electronics to hydrological monitoring while fostering community-driven improvements. The picoSMMS was built using readily available consumer electronics and programmed in MicroPython. Laboratory calibration followed standardised protocols using reference media spanning permittivities from 1.0 (air) to approximately 80.0 (water) under non-conducting, non-relaxing conditions at 25 ± 1 °C with temperature-dependency characterisation. Models were developed relating the sensor’s output and temperature to ${ϵ}_s$. Within the target permittivity range (2.5–35.5), the LCSMS achieved a mean absolute error of 1.29 ± 1.07, corresponding to an absolute error of 0.02 ± 0.01 in volumetric water content (VWC). Benchmarking revealed that the LCSMS is competitive with the ML2 ThetaProbe, and outperforms the PR2/6 ProfileProbe, but is less accurate than the SMT100. Notably, applying the air–water normalisation procedure to benchmark sensors significantly improved their performance, particularly for the ML2 ThetaProbe and PR2/6 ProfileProbe. A brief field deployment demonstrated the picoSMMS’s ability to closely track co-located HydraProbe sensors. Important limitations include the following: inter-sensor variability assessment was limited by the small sensor ensemble (only two units), and with a larger sample size, the LCSMS may exhibit greater variability, potentially resulting in larger prediction errors; the characterisation was conducted under non-saline conditions and may not apply to peat or high-clay soils; the calibration is best suited for the target permittivity range (2.5–35.5) typical of mineral soils; and the brief field deployment was insufficient for long-term validation. Future work should assess inter-sensor variability across larger sensor populations, characterise the LCSMS under varying salinity, and conduct long-term field validation. Full article

The precise prediction of high-speed railway bridge pier settlement plays a crucial role in construction, maintenance, and long-term operation; however, current mainstream prediction methods mostly rely on independent analyses based on traditional or hybrid models, neglecting the impact of geological and environmental factors on subsidence. To address this issue, this paper proposes a multi-factor settlement prediction model for high-speed railway bridge piers named the Reversible Instance Normalization Multi-Scale Adaptive Resolution Stream CMamba, abbreviated as RMCMamba. During the data preprocessing process, the Enhanced PS-InSAR technology is adopted to obtain the time series data of land settlement in the study region. Utilizing the cubic improved Hermite interpolation method to fill the missing values of monitoring and considering the environmental parameters such as groundwater level, temperature, precipitation, etc., a multi-factor high-speed railway bridge pier settlement dataset is constructed. RMCMamba fuses the reversible instance normalization (RevIN) and the multiresolution forecasting head (MARSHead), enhancing the model’s long-range dependence capture capability and solving the time series data distribution drift problem. Experimental results demonstrate that in the multi-factor prediction scenario, RMCMamba achieves an MAE of 0.049 mm and an RMSE of 0.077 mm; in the single-factor prediction scenario, the proposed method reduces errors compared to traditional prediction approaches and other deep learning-based methods, with MAE values improving by 4.8% and 4.4% over the suboptimal method in multi-factor and single-factor scenarios, respectively. Ablation experiments further verify the collaborative advantages of combining reversible instance normalization and the multi-resolution forecasting head, as RMCMamba’s MAE values improve by 5.8% and 4.4% compared to the original model in multi-factor and single-factor scenarios. Hence, the proposed method effectively enhances the prediction accuracy of high-speed railway bridge pier settlement, and the constructed multi-source data fusion framework, along with the model improvement strategy, provides technological and experiential references for relevant fields. Full article