Search Results (177)

Marine boundary layer (MBL) clouds strongly influence radiation and precipitation over the Southern Ocean (SO), yet their vertical structures and microphysical properties remain poorly constrained across observational platforms. This study compares macrophysical and microphysical properties of single-layer, liquid-dominant MBL clouds below 3 km using aircraft observations from the SO Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), ship-based observations from Measurements of Aerosols, Radiation, and Clouds over the SO (MARCUS), and satellite observations from CloudSat. An empirical reflectivity–microphysics retrieval framework developed from in situ droplet size distributions (DSDs) measured during SOCRATES was applied to MARCUS M-WACR and CloudSat CPR reflectivity observations to retrieve vertical profiles of number concentration (N), effective radius (r_e), and liquid water content (LWC) for cloud and drizzle particles. Cloud boundary heights and retrieved microphysical properties show broad agreement across the three platforms within the limitations imposed by instrumental sensitivity, sampling differences, and retrieval uncertainties. However, CloudSat CPR observations exhibit larger deviations because of their coarser vertical resolution and lower reflectivity sensitivity, including limited detection of low clouds below ~500 m. The observed vertical structures are consistent with condensational growth, entrainment, and collision–coalescence processes. Overall, the results demonstrate broad consistency in cloud and drizzle properties across the three platforms, while highlighting the impacts of instrumental sensitivity, vertical resolution, and sampling differences on cloud boundary detection and microphysical retrievals. Full article

Accurate retrieval of cloud liquid water content (LWC) and ice water content (IWC) vertical profiles remains limited by strong vertical variability and nonlinear dependencies among observed variables. Ground-based cloud radar reflectivity and microwave radiometer-derived thermodynamic profiles provide complementary constraints, but their joint use requires consistent time–height matching and bias-controlled predictors. This study develops a vertically structured machine-learning framework that explicitly represents profile-level dependencies by constructing vertical-structure-enhanced features to encode local gradients and contextual information, integrating multiple tree-based learners with heterogeneous configurations through a profile-aware stacking strategy, and introducing a profile-level refinement step to suppress layer-to-layer inconsistencies. The framework is evaluated using year-round Cloudnet observations from the Lindenberg site, where IWC RMSE decreases from 0.0152 g m⁻³ to 0.0092 g m⁻³ with R² increasing from 0.412 to 0.784, and LWC RMSE decreases from 0.0786 g m⁻³ to 0.0591 g m⁻³ with R² increasing from 0.303 to 0.606. Additional boundary-region evaluation shows that the improvement is particularly evident near radar-derived cloud boundaries, where cloud structure and hydrometeor content may vary rapidly with height. These results indicate that treating cloud retrieval as a vertically structured learning problem reduces inconsistencies inherent in pointwise models and establishes a data-driven baseline for incorporating vertical constraints into atmospheric profile retrieval. Full article

Livestock intensification drives biodiversity loss, making impact quantification essential. Life Cycle Assessment (LCA) can evaluate whether regenerative practices, such as silvopastoral systems, mitigate this loss, but it requires specific characterization factors (CFs). In this pilot study, we applied the countryside Species-Area Relationship (SAR) model to derive the first invertebrate-specific CFs using dung beetles (Scarabaeinae). From field surveys, we calculated intensity-specific CFs for potential species loss (PSL/m²) in pastureland and cropland. We assessed biodiversity impacts per 1 kg calf live weight (LWC) across three livestock regimes: native silvopastoral (NSP, minimal land use), intensive silvopastoral (ISP, light land use), and monoculture (MC, intense land use). Results show high dung beetle affinity for NSP. The CFs distinguished impact intensity levels: MC had the highest PSL per area (6.76 × 10⁻¹⁰ PSL/m²), followed by ISP (5.93 × 10⁻¹⁰ PSL/m²) and NSP (4.99 × 10⁻¹⁰ PSL/m²). However, normalizing by yield reversed this trend: MC showed the lowest impact per 1 kg LWC (7.64 × 10⁻⁸ PSL/kg LWC), ISP was intermediate (1.06 × 10⁻⁷ PSL/kg LWC), and NSP had the highest impact (1.31 × 10⁻⁷ PSL/kg LWC). Incorporating upstream feed production significantly increased the overall biodiversity footprint, underscoring the need for comprehensive system boundaries. Integrating broader biodiversity components and landscape context remains essential to fully capture livestock management effects. Full article

Deep learning has significantly improved the quality of infrared and visible image fusion. However, existing mainstream deep fusion networks often come with complex architectures and a large number of parameters. While general lightweight techniques can effectively reduce model complexity, they often weaken feature interactions during the lightweighting process, resulting in the loss of complementary texture and thermal information in fused images and making it difficult to balance fusion performance and model efficiency. To address these issues, this paper constructs an infrared and visible image fusion network based on a self-compensating lightweight convolution mechanism, named LWC-DenseFuse. The core of the network lies in a self-compensating lightweight convolution module, which goes beyond conventional convolution replacement and explicitly addresses feature degradation introduced by lightweight design. The module decouples spatial and channel correlations of standard convolution through depthwise convolution and pointwise convolution, while incorporating a channel attention mechanism to adaptively enhance salient features. Additionally, channel shuffle technology is employed to promote information exchange between groups, thereby enhancing feature interaction and compensating for the loss of critical information caused by lightweight design. To further improve the representation capability of the lightweight network during optimization, a staged training strategy with progressive loss weighting is introduced. Experimental evaluations demonstrate that the proposed fusion network significantly reduces the number of model parameters while ensuring real-time inference performance. Meanwhile, it effectively alleviates the performance degradation typically associated with lightweight architectures, as evidenced by improvements in information entropy and visual fidelity. Full article

Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water content (LWC) were measured for Populus pruinosa in the Tarim River headwater region and Awati County, Xinjiang, from July to October 2023. The aim was to estimate CHD using hyperspectral data combined with machine learning and to evaluate the effect of LWC on model accuracy. Raw spectra were preprocessed using Savitzky–Golay (SG) smoothing and continuous wavelet transform (CWT). A two-step feature selection strategy comprising Random Frog and iterative retaining informative variables (IRIV) was applied to extract characteristic bands. Three machine learning models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—were developed for CHD estimation with and without LWC as an additional input. Incorporating LWC consistently improved the predictive performance of all models. Without LWC, the RF model achieved the best accuracy (training R² = 0.842, test R² = 0.830), whereas after LWC integration, XGBoost reached the optimal performance (training R² = 0.871, test R² = 0.865). SHAP analysis identified the 687 nm wavelength and its interaction with LWC as the most important predictors. These results indicate that combining spectral information with LWC effectively improves the accuracy and stability of CHD estimation for Populus pruinosa, providing a reliable non-destructive approach for assessing forest ecosystem physiological status—a key contribution to the sustainable management of arid riparian forests. Full article

Grey cast iron brake discs remain standard in automotive braking systems due to their favourable thermal conductivity and mechanical strength. However, increasingly stringent environmental regulations, including Euro 7, necessitate enhanced surface durability to reduce particulate emissions and mitigate corrosion-related degradation. In this context, laser metal deposition (LMD) offers a promising route to engineer wear-resistant coating systems with tailored microstructures. This study investigates phase formation and microstructural evolution in a 316L/430L-WC multilayer coating deposited on grey cast iron (GJL) brake discs and subjected to brake-shock testing to replicate thermomechanical load cycles representative of real braking conditions. X-ray diffraction (XRD) performed on the interlayer region between the 316L and 430L-WC layers revealed clear evidence of σ-phase formation, indicating intermetallic transformations facilitated by thermal cycling. Microstructural characterization using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) identified localized enrichment of Cr- and Fe-rich regions that support the XRD-based interpretation of σ-phase development. These results provide insights into phase transformations and elemental diffusion in LMD-fabricated brake-disc coatings. The findings advance the understanding of thermally induced transformations in multilayer steel systems and support the optimization of LMD coatings for high-temperature and wear-intensive applications through advanced analytical evaluation. Full article

Quantifying the distribution and spatiotemporal variation of water vapor and liquid water is of great significance for understanding the atmospheric thermodynamic processes during extreme meteorological events. The water vapor and liquid water data obtained from ground-based measurements by three MP-3000A microwave radiometers (MWRs) over the middle reaches of the Yangtze River Basin were analyzed. Firstly, a comparison between MWRs and radiosonde was conducted, and the co-located observation results indicated that MWRs used in this study feature high detection accuracy and favorable consistency. The integrated water vapor (IWV) measured by one of MWRs (Serial No. 3115) was with the best performance for IWV observation, and the bias and RMSE were 0.22 cm and 0.18 cm. In addition, the detection biases of integrated liquid water (ILW) between three MWRs in pre-precipitation were smaller than those in non-precipitation. All three instruments captured the diurnal variation characteristics of vapor density (VD) and liquid water content (LWC) profiles. The variation in ILW and IWV in different stations showed that ILW maintained low values before precipitation and increased sharply during the pre-precipitation stage, indicating strong indicative significance for rainfall occurrence. The ILW increment was more remarkable in Wuhan station, where mostly covered with urban and water body underlying surfaces. However, the magnitude of IWV variation before precipitation was smaller than that of ILW, especially in Jingzhou station. Under non-precipitation condition, VD and LWC vertical profiles at the three stations were relatively stable. Before precipitation, they exhibited substantial increases with obvious spatial discrepancies: sharp growth in Wuhan, moderate enhancement in Xianning, and slight increment in Jingzhou. Overall, atmospheric water vapor and liquid water increase significantly before precipitation, and their distribution spatiotemporal differences are closely related to local underlying surfaces and precipitation characteristics, which can provide meaningful references for short-term precipitation forecasting. Full article

Leaf water content (LWC) is a key physiological indicator for assessing crop water status. However, its spectral response may vary under different irrigation practices, which limits the general applicability of existing models. This study aims to develop irrigation-specific LWC estimation models for spring wheat based on UAV multispectral imagery. Field experiments were conducted during two growing seasons (2023–2024) under three irrigation methods, with five water treatments and three replicates, resulting in a total of 45 experimental plots. Multispectral data and in situ measurements were collected at key growth stages. Irrigation-dependent sensitive vegetation indices were identified through correlation analysis, and machine learning models, including Random Forest (RF), Multiple Linear Regression (MLR), and Backpropagation Neural Network (BPNN), were constructed and evaluated using a five-fold cross-validation framework. The results showed that spectral sensitivity to LWC varied significantly across irrigation methods, with different dominant indicators under FD, ND, and MD. Model performance also exhibited irrigation-dependent differences. Among the three models, RF showed the most stable performance, achieving mean R² values of 0.70, 0.74, and 0.62 and corresponding RMSE values of 0.04, 0.06, and 0.08 under FD, ND, and MD, respectively. In contrast, MLR showed lower predictive accuracy, while BPNN exhibited limited robustness under the current dataset, particularly under ND. These findings highlight the importance of irrigation-specific modeling strategies for improving LWC estimation reliability. Full article

This study presents a comprehensive numerical investigation into the combined bending–shear behaviour of hollow-flanged cold-formed steel (HFCFS) beams filled with lightweight concrete (LWC). Although previous research has independently examined the pure bending and pure shear responses of these composite members, their structural performance under simultaneous bending and shear remains unexplored. In this work, advanced three-dimensional finite element (FE) models were developed in ABAQUS to simulate the nonlinear behaviour of LWC-filled HFCFS beams subjected to various shear-span ratios. The modelling approach was validated using published experimental data and extended through a systematic parametric study that considered three beam geometries, two steel yield strengths (350 MPa and 450 MPa), two lightweight-concrete strengths (30 MPa and 50 MPa), and aspect ratios ranging from 1.5 to 3.5. The results demonstrated a clear progression of governing failure modes, from web shear buckling at low aspect ratios to combined shear–flexure interaction at intermediate spans and flexural-dominated failure at larger spans. Normalised shear and bending demand–capacity ratios (V/Vu and M/Mu) were used to identify the dominant limit state, revealing a predictable transition from shear-controlled to flexure-controlled behaviour. The findings enhance the understanding of composite thin-walled steel–concrete systems under combined actions and highlight the need for dedicated design rules for CF-HFCFS beams operating within the bending–shear interaction domain. Full article

This study presents the development and experimental evaluation of HygroCatch, a portable hybrid fog water harvesting prototype that integrates active and passive collection mechanisms. The device operates by combining fog droplet ionization in a high-voltage direct-current (HV DC) electrostatic field, thermoelectric cooling based on the Peltier effect, and mechanical deposition of droplets on vertical rods of symmetrical triads of electrodes. This hybrid approach enables adaptive operation across a wide range of fog liquid water content (LWC) conditions. The work establishes operating parameters for stable electrostatic ionization and evaluates the contribution of thermoelectric cooling to additional water harvesting. The results indicate that an operating voltage of 13–14 kV provides a stable ionization over a broad LWC range. The average fog water harvesting rate reached 3.15 kg/m²/h, with a maximum observed value of 4.44 kg/m²/h. On average, 56% of the collected water was obtained through HV DC ionization, 25% through Peltier-based thermoelectric cooling, and 19% through mechanical deposition on electrode grids under high LWC conditions. The total electrical power consumption of the device did not exceed 38.3 Wh/kg. The results demonstrate that a hybrid fog water harvesting strategy enables stable and efficient water collection under environmental conditions in which individual passive or active methods become ineffective. Full article

The cloud spatial uniformity in the test section is crucial for icing wind tunnels in aircraft icing research and airworthiness certification. To achieve uniform supercooled large droplet (SLD) icing conditions, both the spatial variation in droplet size distribution and the concentration should be considered. In this study, the spatial distribution of droplets under three SLD conditions is explored in the Aviation Industry Corporation of China Aerodynamics Research Institute (AVICARI)’s FL-61 icing wind tunnel. Measurements are conducted at 12 test points in vertical and horizontal directions using the holographic airborne cloud particle imager (HACPI) in conjunction with a two-axis traversing system. The droplet images obtained at specific test points below the test section centerline show deformation phenomena for droplets larger than 400 $\mathsf{\mu }$m. Additionally, the aspect ratio of deformed droplets increases with droplet size. The spatial evolution of the median volume diameter (MVD) and liquid water content (LWC) is examined. For two spray arrangements where the activated nozzles are positioned close, the test point where the LWC peak in the vertical direction occurs is higher than that of the MVD peak. Further analysis focuses on the size distribution of droplets in the vertical direction. The results show that the settling effect of the droplets larger than 50 $\mathsf{\mu }$m is evident under a flow velocity of 78 m/s. Meanwhile, the position where large droplets tend to appear lowers as the droplet size increases. Finally, the spatial uniformity of droplet size distributions at the same radial distance is discussed. Full article

In order to better understand the mechanical properties of lightweight cement-based composite concrete (LWC), expanded polystyrene (EPS) beads are used as lightweight aggregate (LWA) in this paper. 50%, 70%, and 90% of EPS foam beads by volume are used to partially replace normal fine aggregate in different EPS concrete compositions. In addition, Ordinary Portland cement (OPC) was substituted with silica fume (SF) in EPS concrete at varying weight percentages of 15%. Nine mixes are made in order to examine the properties of EPS concrete. In the testing program, fresh density, slump, compressive strength, splitting tensile strength, flexural strength, thermal conductivity, and absorption are all determined. Although workability is improved, the mechanical properties of concrete are generally decreased when EPS beads are used. The addition of silica fume (SF) successfully counteracted the mixture’s overall decline in mechanical properties across all the mixtures that have been used. More solid material can be found per square inch of surface area in materials with a higher density, which results in more continuous heat-conduction pathways. In comparison to the control mix, the compressive strength of the polystyrene modified mixes showed a noticeable decline, falling by roughly 62% for P-50%, 69% for P-70%, and 71% for P-90%. In contrast, mixes P-90%-1.2, P-90%-1.4, and P-90%-1.6 reduced absolute strength compared to P-90%; their performance is nonetheless noteworthy because of their extraordinarily high EPS content. Despite having lesser absolute strengths than P-90%, mixes of P-90%-1.2, P-90%-1.4, and P-90%-1.6 nevertheless performed admirably considering their remarkably high EPS content. Full article

Offshore wind turbine blades operating in cold climates are frequently affected by surface icing, which compromises aerodynamic performance and reduces power output. To address this challenge, the present study conducted controlled icing wind tunnel experiments to investigate how salinity and liquid water content (LWC) influence ice formation on the S809 airfoil surface. Results indicate that increased salinity substantially inhibits ice accretion: as salinity rises from 0‰ to 35‰, the total icing area rate drops by approximately 20.5% within 6 min, and the maximum ice thickness declines from 17.21 mm to 6.03 mm. Conversely, LWC emerges as a dominant factor intensifying icing severity: raising LWC from 0.5 g/m³ to 1.5 g/m³ leads to a 135% increase in icing area and an increase in maximum ice thickness from 7.69 mm to 18.17 mm. A notable synergistic interaction is observed—higher LWC enhances the inhibitory effect of salinity on ice formation. These findings offer valuable insights into the icing dynamics under marine atmospheric conditions and provide a theoretical foundation for the development of anti-icing strategies for offshore wind turbine blades. Full article

Background: Age-related hormonal changes in older women accelerate bone and muscle loss, leading to postural dysfunction and chronic musculoskeletal pain. This study aimed to evaluate the short-term effects of a physical therapy program combining elastic band exercises and vigorous walking on pain, thoracic mobility, and functional capacity in older adult women. Methods: A multicenter randomized controlled trial was conducted older adult women (60–80 years) with chronic non-specific musculoskeletal pain, allocated to an elastic band plus vigorous walking group (EBWG), a vigorous walking group (VWG), or a control group (CG). A total of 91 participants completed all of the assessments. Outcomes included pressure pain threshold (PPT), self-reported pain (VAS), thoracic mobility (UPC, LWC), functional capacity (5XSTS), and perceived improvement (PGIC), evaluated at baseline, after a 4-week intervention, and at 4-week follow-up. Results: The EBWG demonstrated greater improvements in PPT (+0.66 kg/cm² at T2), upper chest expansion (+1.00 cm), and 5XSTS performance (−1.7 s) compared to the control group. The VWG showed significant reductions in overall pain (−0.9 points) and lumbar pain (−1.7 points). Improvements in PPT and thoracic mobility in the EBWG exceeded MDC/MCID thresholds, indicating clinically meaningful changes. Vigorous walking alone improved self-reported pain but was less effective than the multicomponent program. Conclusions: A 4-week combined program of elastic band exercises and vigorous walking produced clinically relevant improvements in pain threshold, thoracic mobility, functional capacity, and perceived change compared to walking alone or usual activity. These findings support the clinical utility of short, feasible, multicomponent interventions for managing chronic musculoskeletal pain in older women. Full article

This study focuses on maize to explore spectral estimation methods for agricultural traits in maize leaves under water-saving and fertilizer-reduction strategies. A factorial experiment was conducted with different nitrogen application rates (N0–N4) and irrigation levels (W1–W4). Hyperspectral data were collected at V12, R1, and R3 stages, alongside measurements of agricultural traits ((relative chlorophyll content) SPAD values, leaf water content (LWC), and leaf nitrogen content (LNC)). Results indicated that reducing nitrogen by 10% (N3) had no significant effect on physiological indicators, whereas reducing irrigation by 10% (W3) led to significant differences. First- and second-derivative transformations of spectral data enhanced the correlation with agricultural traits. Support vector regression (SVR) and random forest (RF) models were developed for estimation. RF outperformed SVR in predicting agricultural traits (SPAD, LWC, and LNC), with estimation accuracy R² values reaching 0.92, 0.94, and 0.95, respectively. The RF model demonstrated higher accuracy, providing technical support for growth monitoring and precise water and nutrient management in maize. Full article