Search Results (2,954)

Lychee (Lychee chinensis Sonn.) is a tropical fruit highly appreciated for its vivid red color, sweet flavor, and nutritional properties. However, it is highly perishable, with postharvest losses often due to oxidative browning and dehydration. This study evaluated the organic olive oil coating (OC), a natural lipidic system with the potential to act as a precursor for organogel development, combined with polyamide/polyethylene (PA/PE) packaging under passive modified atmosphere. Fruits were harvested at commercial maturity and divided into two groups: OC-treated and untreated control (CTR). Both groups were stored at 5 ± 1 °C and 90 ± 5% relative humidity and analyzed on days 0, 3, 6, and 9. The OC-treated fruits showed significantly better retention of physical, chemical, microbiological, and sensory qualities. The coating reduced oxidative stress and enzymatic browning, preserving color and firmness. The PA/PE packaging regulated gas exchange, lowering oxygen levels and delaying respiration and ripening. As a result, OC fruits had lower weight loss, a slower increase in browning index and maturity index, and better visual and sensory scores than the CTR group. This dual strategy proved effective in extending shelf life while maintaining the fruit’s appearance, flavor, and nutritional value. It represents a sustainable and natural approach to enhancing the postharvest stability of lychee. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

The operation of heavy-haul railway trains with large loads results in significant cracking issues in reinforced concrete beams. Atmospheric carbon dioxide, oxygen, and moisture from the atmosphere penetrate into the beam interior through these cracks, accelerating the carbonation of the concrete and the corrosion of the steel bars. The rust-induced expansion of steel bars further exacerbates the cracking of the beam. The interaction between environmental factors and beam cracks leads to a rapid decline in the durability of the beam. To address this issue, a multi-physics field coupling durability assessment method was proposed, considering concrete beam cracking, concrete carbonation, and steel bar corrosion. The interaction among these three factors is achieved through sequential coupling, using crack width, carbonation passivation time, and steel bar corrosion rate as interaction parameters. Using this method, the deterioration morphology and stiffness degradation laws of 8 m reinforced concrete beams under different load conditions, including those of heavy and light trains in heavy-haul railways, are compared and assessed. The analysis reveals that within a 100-year service cycle, the maximum relative stiffness reduction for beams on the heavy train line is 20.0%, whereas for the light train line, it is only 7.4%. The degree of structural stiffness degradation is closely related to operational load levels, and beam cracking plays a critical role in this difference. Full article

As a key tropospheric photochemical pollutant, ground-level ozone (O₃) poses significant threats to ecosystems through its strong oxidative capacity. With China’s rapid industrialization and urbanization, worsening O₃ pollution has emerged as a critical environmental concern. This study examines O₃’s impacts on forest ecosystems in Southwestern China (Yunnan, Guizhou, Sichuan, and Chongqing), which harbors crucial forest resources. We analyzed high-resolution monitoring data from over 200 stations (2019–2023), employing spatial interpolation to derive the regional maximum daily 8 h average O₃ (MDA8-O₃, ppb) and accumulated O₃ exposure over 40 ppb (AOT40) metrics. Through AOT40-based exposure–response modeling, we quantified the forest relative yield losses (RYL), economic losses (ECL) and ECL/GDP (GDP: gross domestic product) ratios in this region. Our findings reveal alarming O₃ increases across the region, with a mean annual MDA8-O₃ anomaly trend of 2.4% year⁻¹ (p < 0.05). Provincial MDA8-O₃ anomaly trends varied from 1.4% year⁻¹ (Yunnan, p = 0.059) to 4.3% year⁻¹ (Guizhou, p < 0.001). Strong correlations (r > 0.85) between annual RYL and annual MDA8-O₃ anomalies demonstrate the detrimental effects of O₃ on forest biomass. The RYL trajectory showed an initial decline during 2019–2020 and accelerated losses during 2020–2023, peaking at 13.8 ± 6.4% in 2023. Provincial variations showed a 5-year averaged RYL ranging from 7.10% (Chongqing) to 15.85% (Yunnan). O₃ exposure caused annual ECL/GDP averaging 4.44% for Southwestern China, with Yunnan suffering the most severe consequences (ECL/GDP averaging 8.20%, ECL averaging CNY 29.8 billion). These results suggest that O₃-driven forest degradation may intensify, potentially undermining the regional carbon sequestration capacity, highlighting the urgent need for policy interventions. We recommend enhanced monitoring networks and stricter control methods to address these challenges. Full article

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries. Full article

The high spatial resolution of radar data enables the detailed resolution of typhoon vortices and their embedded structures; the assimilation of radar data in the initialization of numerical weather prediction exerts an important influence on the forecasting of typhoon track, intensity, and structures up to at least 12 h. For the case of typhoon Morakot (2009), Taiwan radar data was assimilated to adjust the dynamic and thermodynamic structures of the vortex in the model initialization by the three-dimensional variation data assimilation system in the Advanced Region Prediction System (ARPS). The radial wind was directly assimilated to tune the original unbalanced velocity fields through a 3-dimensional variation method, and complex cloud analysis was conducted by using the reflectivity data. The influence of radar data assimilation on typhoon prediction was examined at the stages of Morakot landing on Taiwan Island and subsequently going inland. The results showed that the assimilation made some improvement in the prediction of vortex intensity, track, and structures in the initialization and subsequent forecast. For example, besides deepening the central sea level pressure and enhancing the maximum surface wind speed, the radar data assimilation corrected the typhoon center movement to the best track and adjusted the size and inner-core structure of the vortex to be close to the observations. It was also shown that the specific humidity adjustment in the cloud analysis procedure during the assimilation time window played an important role, producing more hydrometeors and tuning the unbalanced moisture and temperature fields. The neighborhood-based ETS revealed that the assimilation with the specific humidity adjustment was propitious in improving forecast skill, specifically for smaller-scale reflectivity at the stage of Morakot landing, and for larger-scale reflectivity at the stage of Morakot going inland. The calculation of the intensity-scale skill score of the hourly precipitation forecast showed the assimilation with the specific humidity adjustment performed skillful forecasting for the spatial forecast-error scales of 30–160 km. Full article

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these challenges by implementing the Local Analysis and Prediction System (LAPS) enhanced with a forward advection nowcasting module, integrating multiple remote sensing rainfall datasets. Specifically, we combine weather radar data with three different satellite-derived rainfall products (H-SAF, GPM, and TRMM) to assess their impact on nowcasting performance for a rainfall event in Attica, Greece (29–30 September 2018). The results demonstrate that combined high-resolution radar data with the broader coverage and high temporal frequency of satellite retrievals, particularly H-SAF, leads to more accurate predictions with lower uncertainty. The assimilation of H-SAF with radar rainfall retrievals (HX experiment) substantially improved forecast skill, reducing the unbiased Root Mean Square Error by almost 60% compared to the control experiment for the 60 min rainfall nowcast and 55% for the 90 min rainfall nowcast. This work validates the effectiveness of the specific LAPS/advection configuration and underscores the importance of multi-source data assimilation for weather prediction. Full article

The charge structure and lightning activities during the development stage of a thunderstorm with a hail-falling process in Datong County of Qinghai Province on 16 August 2014 were studied by using a multi-station observation network composed of a very-high-frequency, three-dimensional, lightning-radiation-source location system and broadband electric field. The research results show that two discharge regions appeared during the development stage of the thunderstorm. The charge structure was all a negative dipolar polarity in two discharge regions; however, the heights of the charge regions were different. The positive-charge region at a height of 2–3.5 km corresponds to −1–−10 °C and the negative-charge region at a height of 3.5–5 km corresponds to −11–−21 °C in one discharge region; the positive-charge region at a height of 4–5 km corresponds to −15–−21 °C and the negative-charge region at a height of 5–6 km corresponds to −21–−29 °C in another region. The charge regions with the same polarity at different heights in the two discharge regions gradually connected with the occurrence of the hail-falling process during the development stage of the thunderstorm, and the overall height of the charge regions decreased. All the intracloud lightning flashes that occurred in the thunderstorm were of inverted polarity discharge, and the horizontal transmission distance of the discharge channel was short, all within 10 km. The negative intracloud lightning flash, negative cloud-to-ground lightning flash, and positive cloud-to-ground lightning flash generated during the thunderstorm process accounted for 83%, 16%, and 1% of the total number of lightning flashes, respectively. Negative cloud-to-ground lightning flashes mainly occurred more frequently in the early phase of the thunderstorm development stage. As the thunderstorm developed, the frequency of intracloud lightning flashes became greater than that of negative cloud-to-ground lightning flashes, and finally far exceeded it. The frequency of lightning flashes decreases sharply and the intensity of thunderstorms decreases during the hail-falling period. Full article

The stretch film production is highly energy intensive. The components of the technological line are powered by electrical energy, and the heat is used to change the physical state of the raw material (granules). The raw material is poured into FCR (the first calender roller). To solidify the liquid raw material, the calendar must be cooled. The low-temperature heat, treated as waste heat, has dissipated in the atmosphere. Technological innovations were proposed: (a) the raw material comprises raw material (primary) and up to 80% recyclate (waste originating mainly from agriculture), (b) the use of low-temperature waste heat (the cooling of FCR in the process of foil stretch production). A heat recovery line based on two compressor heat pumps (HP, hydraulically coupled) was designed. The waste heat (by low-temperature HP) was transformed into high-temperature heat (by high-temperature HP) and used to prepare the raw material. The proposed technological line enables the management of difficult-to-manage post-production waste (i.e., agriculture and other economic sectors). It reduces energy consumption and raw materials from non-renewable sources (CO₂ and other greenhouse gas emissions are reducing). It implements a closed-loop economy based on renewable energy sources (according to the European Green Deal). Full article

Quantifying long-term variations in the cloud liquid water path (LWP) is crucial to obtain a better understanding of the processes relevant to cloud–climate feedback. The 12-year (2013–2024) time series of LWP values obtained from ground-based measurements by the RPG-HATPRO radiometer near the Gulf of Finland is analysed, and the linear trends of the LWP for different sampling subsets of data are assessed. These subsets include all-hour, daytime, and night-time measurements. Two different approaches have been used for trend assessment, which produced similar results. Statistically significant linear trends have been detected for most data subsets. The most pronounced general trend over the period 2013–2024 has been detected for the daytime LWP, and it constitutes −0.0011 ± 0.00015 kg m⁻² yr⁻¹. This trend is driven mainly by the daytime LWP trend for the warm season (May–July, −0.0014 ± 0.00015 kg m⁻² yr⁻¹), which is considerably larger than the trend for the cold season (November–January, −0.00064 ± 0.00026 kg m⁻² yr⁻¹). Additionally, the analysis shows that the absolute number of clear-sky measurements decreased approximately by a factor of 4 if the years 2013 and 2024 are compared. Full article

Increasing soil carbon storage is an important strategy for achieving sustainable development. Enhancing soil carbon sequestration capacity can effectively reduce the concentration of atmospheric carbon dioxide, which not only contributes to the carbon neutrality goal but also helps maintain ecosystem stability. Based on 146 soil samples collected at plot locations selected across Beijing, we examined relationships between soil organic carbon (SOC) and key characteristics of urban forests, including their spatial structure and species complexity. The results showed that SOC in the topsoil with a depth of 20 cm was highest over forested plots (6.384 g/kg–20.349 g/kg) and lowest in soils without any vegetation cover (5.586 g/kg–6.783 g/kg). The plots with herbaceous/shrub vegetation but no tree cover had SOC values in between (5.586 g/kg–15.162 g/kg). The plot data revealed that SOC was better correlated with the physical structure than the species diversity of Beijing’s urban trees. The correlation coefficients (r) between SOC and five physical structure indicators, including average diameter at breast height (DBH), average tree height, basal area density, and the diversity of DBH and tree height, ranged from 0.32 to 0.52, whereas the r values for four species diversity indicators ranged from 0.10 to 0.25, two of which were not statistically different from 0. Stepwise linear regression analyses revealed that the species diversity indicators were not very sensitive to SOC variations among a large portion of the plots and were about half as effective as the physical structure indicators for explaining the total variance of SOC. These results suggest that urban planning and greenspace management policies could be tailored to maximize the carbon co-benefits of urban land. Specifically, trees should be planted in urban areas wherever possible, preferably as densely as what can be allowed given other urban planning considerations. Protection of large, old trees should be encouraged, as these trees will continue to sequester and store large quantities of carbon in above- and belowground biomass as well as in soil. Such policies will enhance the contribution of urban land, especially urban forests and other greenspaces, to nature-based solutions (NBS) to climate change. Full article

The Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate predictions. This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. The model successfully predicts monthly/annual average PDO index of up to 15 months/5 years in advance, achieving a correlation coefficient of 0.56/0.55. By utilizing the WOA to effectively optimize hyperparameters, the model enhances the PDO prediction skill compared to existing deep learning PDO prediction models, improving the correlation coefficient from 0.47 to 0.68 at a 6-month lead time. The combination of MMD and WOA further minimizes prediction errors and extends the forecasting effective time to 15 months by capturing essential modes. The BWM model can be employed for future PDO prediction and the predicted PDO will remain in its cool phase in the next year both using the PDO index from NECI and derived from near-time satellite data. This proposed model offers an effective way to advance the prediction skill of climate variability on multiple time scales by utilizing all kinds of data available including satellite data, and provides a large-scale background to monitor marine heatwaves. Full article

This study evaluated the wind speed simulation performance of the Weather Research and Forecasting (WRF) model at three resolutions in Hebei Province based on wind speed data from 2022. The results show that the simulation effectiveness of the WRF model for wind speeds at different heights varies significantly under different seasons and topographic conditions. In general, the model simulates the wind speed at the high level most accurately, followed by the mid level, and the simulation of low level wind speed shows the largest bias. Increasing the model resolution significantly improves the simulation of low-level wind speed, and the 5 km resolution performs best at most stations; while for the mid-level and high-level wind speeds, increasing the resolution does not significantly improve the simulation effect, and the high-resolution simulation has a greater bias at some stations. In terms of topographic features, wind speeds are generally better simulated in mountainous areas than in the plains during spring, summer, and autumn, while the opposite is true in winter. These findings provide scientific reference for WRF model optimal resolution selection and wind resource assessment. Full article

This study presents a comprehensive analysis of the microphysical structures of Meiyu heavy rainfall (near-surface rainfall intensity > 8 mm/h) across different life stages in the Yangtze-Huaihe River Valley (YHRV). We classified the heavy rainfall events into three life stages of developing, mature, and dissipating using ERA5 reanalysis and IMERG precipitation estimates, and examined vertical microphysical structures using Dual-frequency Precipitation Radar (DPR) data from the Global Precipitation Measurement (GPM) satellite during the Meiyu period from 2014 to 2023. The results showed that convective heavy rainfall during the mature stage exhibits peak radar reflectivity and surface rainfall rates, with the largest near-surface mass weighted diameter (D_m ≈ 1.8 mm) and the smallest droplet concentration (dBN_w ≈ 38). Downdrafts in the dissipating stage preferentially remove large ice particles, whereas sustained moisture influx stabilizes droplet concentrations. Stratiform heavy rainfall, characterized by weak updrafts, displays narrower particle size distributions. During dissipation, particle breakups dominate, reducing D_m while increasing dBN_w. The analysis of the relationship between microphysical parameters and rainfall rate revealed that convective heavy rainfall shows synchronized growth of D_m and dBN_w during the developing stage, with D_m peaking at about 2.1 mm near 70 mm/h before stabilizing in the mature stage, followed by small-particle dominance in the dissipating stage. In contrast, stratiform rainfall exhibits a “small size, high concentration” regime, where the rainfall rate correlates primarily with increasing dBN_w. Additionally, convective heavy rainfall demonstrates about 22% higher precipitation efficiency than stratiform systems, while stratiform rainfall shows a 25% efficiency surge during the dissipation stage compared to other stages. Full article