Search Results (7,404)

In recent years, biomass utilization has significantly increased, presenting challenges in its incorporation into various systems. Effective handling requires reliable data on biomass flow properties for designing warehouses and processing equipment. This study investigates the physical properties of ground barley grains, ground oak leaves, ground straw, and cut jute. Barley grains, oak leaves, and straw bales were milled, and jute was cut into 2–3 mm lengths and oven-dried. Particle size distribution, bulk density, Hausner ratio, Carr’s index, moisture content, static angle of repose, and flowability tests and SEM analysis were conducted. The study found that ground barley, having the smallest particle size and highest bulk density, showed superior flow properties due to its rounded particles and clusters, as reflected by a low Hausner ratio. In contrast, jute fibers had a low bulk density and poor flowability, while ground straw exhibited hindered flow due to its larger, more irregular particles. Additionally, the biomass sliding behavior varied with particle size and surface irregularities, with ground barley adhering well to plywood and ground oak leaves adhering well to aluminum. These findings underscore the pivotal roles of particle shape and interparticle forces in determining the biomass flow properties, pointing towards a future where precise environmental control and advanced analytical methods drive innovations in biomass utilization. Full article

Perovskites can absorb solar energy and are extensively used in various catalytic and photocatalytic reactions. However, noble metal particles may enhance the catalytic, photocatalytic, and antibacterial activities. This study demonstrates the cost-effective green synthesis of the photocatalyst perovskite LaMnO₃ and its modification with noble metal Ag nanoparticles. The green synthesis of nanocomposite was achieved through a hydrothermal method employing aqueous extract derived from Citrus limon (L.) Burm peels. The properties of fabricated perovskites LaMnO₃ and LaMnO₃-Ag nanocomposites were evaluated and characterized by Ultraviolet-Visible spectroscopy (UV-Vis), Diffuse Reflectance Spectroscopy (DRS), X-ray diffraction (XRD), Fourier-Transform Infrared Spectroscopy (FT-IR), High-Resolution Transmission Electron Microscopy (HRTEM), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX) and Brunauer–Emmett–Teller (BET) surface area techniques. The particle size distribution % of LaMnO₃ and LaMnO₃-Ag was observed to be 20 to 60 nm after using TEM images. The maximum percentage size distribution was 37 nm for LaMnO₃ and 43 nm for LaMnO₃-Ag. In addition, LaMnO₃-Ag nanocomposite was utilized as a photocatalyst for the degradation of Rose Bengal (RB) dye and its antibacterial activities against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The surface area and band gap for perovskite LaMnO₃ nanoparticles were calculated as 12.642 m²/g and 3.44 eV, respectively. The presence of noble metal and hydrothermal-bio reduction significantly impacted the crystallinity. The BET surface area was found to be 16.209 m²/g, and band gap energy was calculated at 2.94 eV. The LaMnO₃ nanocomposite with noble metal shows enhanced photocatalytic effectiveness against RB dye (20 PPM) degradation (92%, R² = 0.995) with pseudo-first-order chemical kinetics (rate constant, k = 0.05057 min⁻¹) within 50 min due to the ultimate combination of the hydrothermal and bio-reduction technique. The photocatalytic activity of the LaMnO₃-Ag nanocomposite was optimized at different reaction times, photocatalyst doses (0.2, 0.4, 0.6, and 0.8 g/L), and various RB dye concentrations (20, 30, 40, and 50 ppm). The antibacterial activities of green synthesized LaMnO₃ and LaMnO₃-Ag nanoparticles were explored based on colony-forming unit (cfu) reduction and TEM images of bacterial and nanoparticle interactions for S. aureus and E. coli. An amount of 50 µg/mL LaMnO₃-Ag nanocomposite was sufficient to work as the highest antibacterial activity for both bacteria. The perovskite LaMnO₃-Ag nanocomposite synthesis process is economically and environmentally friendly. Additionally, it has a wide range of effective and exclusive applications for remediating pollutants. Full article

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%. Full article

Adhesive bonds, including car putty coatings, are used in the construction of modern motor vehicles. Therefore, it is important to improve methods that allow nondestructive evaluation of the properties of these joints. The main objective of this study was to evaluate selected properties of putty coatings such as the width of the applied coating and adhesion to the substrate based on changes in ultrasonic wave parameters. The research was carried out in two stages. In the first, the values of the surface wave amplitude were determined as a function of the width of the coating to the substrate. It was found that as the width of the coating increases, the amplitude of the surface wave pulse decreases. The second stage involved correlation studies to relate the reflection coefficient |r| to the adhesion of the coating to the substrate. Based on the results, it was found that as the value of the reflection coefficient decreases, the value of the coating’s adhesion to the substrate increases. The determined values of this parameter range from 0.30 to 0.86, which correspond to the adhesion of the range 1.51 to 18.97 MPa. The obtained test results have practical significance and can be used in evaluating the condition of coatings in vehicle body repair shops. Full article

The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R² ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately. Full article

This study aimed to use waste figs as an alternative substrate for bacterial cellulose (BC) production by Komagataeibacter xylinus and optimize the identified process parameters to maximize the concentration of BC. Among the nutrients screened by Plackett–Burman (PB) design, yeast extract was found to be significant in BC production. Response surface methodology was used to investigate the effect of fermentation parameters on BC production. A maximum BC concentration of (8.45 g/L), which is among the highest BC concentrations reported so far, was achieved at the optimum levels of fermentation variables (initial pH 6.05, initial sugar concentration 62.75 g/L, temperature 30 °C). The utilization of response surface methodology (RSM) proved valuable in both optimizing and finding the interactions between process variables during BC production. Scanning electron microscope (SEM) analysis showed a dense structure of BC, characterized by ribbon-like nanofibrils with diameters ranging from 23 to 90 nm while the attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectrum of BC confirmed that the material obtained was cellulose. The X-ray diffraction (XRD) analysis showed that the crystallinity of the BC samples was 70% for BC produced on waste fig medium and 61% for BC produced on Hestrin–Schramm (HS) medium. This is the first detailed study on the production of BC from waste figs, and the findings of this study demonstrated that waste figs can be used as an effective substrate for the production of BC. Full article

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods. Full article

Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper proposes a deep learning-based joint beamforming (DLBF) design, aiming to maximize the system data rate for multi-hop RIS-aided communication systems. The proposed DLBF design consists of the reflection matrices design of all RISs and the transmit beamforming design at the base station, which has a reduced computational complexity. Numerical results show that the proposed DLBF can achieve $1.8$ bit/s/Hz sum rate gain compared to the conventional beamforming method for the two-user scenario, which can be enhanced by large-scale users. The sum rate performance can be improved by increasing the number of RISs due to the reflection gain, and corresponding results provide a guidance of the multi-hop number selection for further investigation. Full article

Previous studies were mostly conducted based on sparse time series and different phenological images, which often ignored the dramatic changes in salinization evolution throughout the year. Based on Landsat and moderate-resolution-imaging spectroradiometer (MODIS) images from 2000 to 2020, this study applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm to obtain similar phenological images for the month of April for the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized, and the feature space index models were constructed. Combined with the measured ground data, the optimal monitoring index model of salinization was determined, and then the spatiotemporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed. The main conclusions were as follows: (1) The derived long-time-series and similar phenological-fusion images enable us to reveal the patterns of change in the dramatic salinization in the year that we examined using the ESTARFM algorithm. (2) The NDSI-TGDVI feature space salinization monitoring index model based on point-to-point mode had the highest accuracy of 0.92. (3) From 2000 to 2020, the soil salinization in the Yellow River Delta showed an aggravating trend. The average value of salinization during the past 20 years was 0.65, which is categorized as severe salinization. The degree of salinization gradually decreased from the northeastern coastal area to the southwestern inland area. (4) The dominant factors affecting soil salinization in different historical periods varied. The research results could provide support for decision-making regarding the precise prevention and control of salinization in the Yellow River Delta. Full article

Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value. Full article

This research evaluated the effects of energy supplementation on sheep’s feeding behavior, feed preference, and thermoregulatory responses using technical cashew nutshell liquid (CNSL) and different vegetable oils with different unsaturated fatty acid (UFA) compositions. The experiment was completely randomized with five treatments: a mixture of CNSL (0.5%) + vegetable oils [canola (high in monounsaturated fatty acids—MUFA), and corn, soybean, sunflower, or cottonseed oil (high in polyunsaturated fatty acids-PUFA) at 1.5%] based on total diet dry matter, with eight replications. Forty uncastrated male sheep, with an average initial BW of 24.44 ± 1.5 kg, were evaluated for 70 days. The CNSL + vegetable oil blend did not affect DM and neutral detergent fiber (aNDF) intake (p > 0.05). However, diets with canola oil resulted in higher SFA intake (p < 0.05) than other oils. The canola oil + CNSL blend led to a higher intake of UFA and MUFA and lower PUFA intake than other oil blends (p < 0.05). Sheep fed canola oil ruminated fewer boli per day than those fed soybean and sunflower oils. Using three sieves (pef_1.18) reflected in higher sheep aNDF intake. Respiratory frequency and surface temperature of sheep were lower before feeding than 3 h after, without effects of the type of oil. Higher serum creatinine and cholesterol levels were observed in sheep fed CNSL with corn and canola oils compared to other oils. Serum calcium was lower in sheep fed CNSL with soybean and canola compared to sunflower and corn. Including CNSL with vegetable oils with different FA compositions did not affect physiological and thermographic variables. However, sheep showed better diet selectivity and lower bolus rumination with higher MUFA (canola oil) content. Including CNSL with canola oil in sheep diets is recommended, as it increases dietary energy content, enhances diet selectivity, reduces PUFA intake, and does not impact animal health. Full article

The surface symmetry of the substrate plays an important role in the epitaxial high-quality growth of 2D materials; however, in-depth and in situ studies on these materials during growth are still limited due to the lack of effective in situ monitoring approaches. In this work, taking the growth of MoSe₂ as an example, the distinct growth processes on Al₂O₃ ($11\overline{2}0$) and Al₂O₃ (0001) are revealed by parallel monitoring using in situ reflectance anisotropy spectroscopy (RAS) and differential reflectance spectroscopy (DRS), respectively, highlighting the dominant role of the surface symmetry. In our previous study, we found that the RAS signal of MoSe₂ grown on Al₂O₃ ($11\overline{2}0$) initially increased and decreased ultimately to the magnitude of bare Al₂O₃ ($11\overline{2}0$) when the first layer of MoSe₂ was fully merged, which is herein verified by the complementary DRS measurement that is directly related to the film coverage. Consequently, the changing rate of reflectance anisotropy (RA) intensity at 2.5 eV is well matched with the dynamic changes in differential reflectance (DR) intensity. Moreover, the surface-dominated uniform orientation of MoSe₂ islands at various stages determined by RAS was further investigated by low-energy electron diffraction (LEED) and atomic force microscopy (AFM). By contrast, the RAS signal of MoSe₂ grown on Al₂O₃ (0001) remains at zero during the whole growth, implying that the discontinuous MoSe₂ islands have no preferential orientations. This work demonstrates that the combination of in situ RAS and DRS can provide valuable insights into the growth of unidirectional aligned islands and help optimize the fabrication process for single-crystal transition metal dichalcogenide (TMDC) monolayers. Full article

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. Full article

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 $m_V$, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services. Full article

In this work, nanocellulose aqueous dispersions were studied as a bio-inspired consolidating agent for the recovery and conservation of ancient wood and compared with two of the most used traditional consolidants: the synthetic resins Paraloid B-72 and Regalrez 1126. The morphology of crystalline nanocellulose (CNC), determined using Scanning Electron Microscopy (SEM), presents with a rod-like shape, with a size ranging between 15 and 30 nm in width. Chemical characterization performed using the Fourier-Transform Infrared Spectroscopy (FT-IR) technique provides information on surface modifications, in this case, demonstrating the presence of only the characteristic peaks of nanocellulose. Moreover, conductometric, pH, and dry matter measurements were carried out, showing also in this case values perfectly conforming to what is found in the literature. The treated wood samples were observed under an optical microscope in reflected light and under a scanning electron microscope to determine, respectively, the damage caused by xylophages and the morphology of the treated surfaces. The images acquired show the greater similarity of the surfaces treated with nanocellulose to untreated wood, compared with other consolidating agents. Finally, a colorimetric analysis of these samples was also carried out before and after a first consolidation treatment, and after a second treatment carried out on the same samples three years later. The samples treated with CNC appeared very homogeneous and uniform, without alterations in their final color appearance, compared to other traditional synthetic products. Full article