Search Results (3,869)

Leveraging ubiquitous wireless signals for environmental sensing provides a highly promising pathway toward constructing low-cost and high-density flood monitoring systems. However, in real-world flood scenarios, the wireless channel is simultaneously affected by rainfall-induced signal attenuation and complex multipath effects caused by surface runoff (water accumulation). These two physical phenomena become intertwined in the received signals, resulting in severe feature ambiguity. This not only greatly limits the accuracy of environmental sensing but also hinders communication systems from performing effective channel compensation. How to disentangle these combined effects from a single wireless link represents a fundamental scientific challenge for achieving high-precision wireless environmental sensing and ensuring communication reliability under harsh conditions. To address this challenge, we propose a novel signal processing framework that aims to effectively decouple the effects of rainfall and surface runoff from Channel State Information (CSI) collected using commercial Wi-Fi devices. The core idea of our method lies in first constructing a two-dimensional CSI spatiotemporal spectrogram from continuously captured multicarrier CSI data. This spectrogram enables high-resolution visualization of the unique “fingerprints” of different physical effects—rainfall manifests as smooth background attenuation, whereas surface runoff appears as sparse high-frequency textures. Building upon this representation, we design and implement a Dual-Decoder Convolutional Autoencoder deep learning model. The model employs a shared encoder to learn the mixed CSI features, while two distinct decoder branches are responsible for reconstructing the global background component attributed to rainfall and the local texture component associated with surface runoff, respectively. Based on the decoupled signal components, we achieve simultaneous and highly accurate estimation of rainfall intensity (mean absolute error below 1.5 mm/h) and surface water accumulation (detection accuracy of 98%). Furthermore, when the decoupled and refined channel estimates are applied to a communication receiver for channel equalization, the Bit Error Rate (BER) is reduced by more than one order of magnitude compared to conventional equalization methods. Full article

Lead (Pb(II)) contamination in water poses severe environmental and health risks due to its toxicity and persistence. This study compares almond shell-derived biochars produced by slow pyrolysis (SP) and microwave pyrolysis (MW), with and without KOH activation, focusing on structural properties and Pb(II) adsorption performance. Biochars were characterized by proximate and elemental analysis, BET surface area, FTIR spectroscopy, and adsorption experiments including pH dependence, kinetics, and equilibrium isotherms. Non-activated SP exhibited the highest surface area (S_BET = 693 m²·g⁻¹), pronounced mesoporosity (≈73% of total pore volume), and the largest observed equilibrium capacities. KOH activation increased surface hydroxyl content but degraded textural properties; in MW samples, it induced severe pore collapse. Given the very fast uptake, kinetic modeling was treated cautiously: for non-activated biochars, Elovich adequately captured the time-course trend, whereas activated samples returned non-physical kinetic constants (e.g., negative k₂) likely due to high post-adsorption pH (>11) and probable Pb(OH)₂ precipitation. Equilibrium data (fitted over 50–500 mg·L⁻¹) were better captured by the Freundlich and Redlich–Peterson models, indicating a mixed adsorption behaviour with contributions from heterogeneous site distribution and site-specific interactions. Optimal Pb(II) removal occurred at pH 4, with no measurable leaching from the biochar matrix. Overall, non-activated SP biochar is the most effective, sustainable and low-cost option among the tested materials for Pb(II) removal from water, avoiding aggressive chemical activation while maximizing adsorption performance. Full article

Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A machine vision inspection method based on a two-stage fusion network is proposed in this paper, aiming to achieve accurate cutting of salmon parts and efficient recognition of defects. The fish body image is collected by building a visual inspection system, and the dataset is constructed by preprocessing and data enhancement. For the part cutting, the improved U-Net model that introduces the CBAM attention mechanism is used to strengthen the extraction ability of the fish body texture features. For defect detection, the two-stage fusion architecture is designed to quickly locate the defective region by adding the YOLOv5 of the P2 small target detection layer first, and then the cropped region is fed into the improved U-Net for accurate cutting. The experimental results demonstrate that the improved U-Net achieves a mean average precision (mAP) of 96.87% and a mean intersection over union (mIoU) of 94.33% in part cutting, representing improvements of 2.44% and 1.06%, respectively, over the base model. In defect detection, the fusion model attains an mAP of 94.28% with a processing speed of 7.30 fps, outperforming the single U-Net by 28.02% in accuracy and 236.4% in efficiency. This method provides a high-precision, high-efficiency solution for intelligent salmon processing, offering significant value for advancing automation in the aquatic product processing industry. Full article

Popcorn maize (Zea mays everta) exhibits complex morphologies that challenge structural analysis. This study assessed the fidelity of the three-dimensional (3D) reconstruction and printing of four popcorn morphologies, unilateral, bilateral, multilateral, and mushroom, by integrating structured-light 3D scanning and (DIA), which can support the construction of food replicas. Morphometric parameters (projected area, perimeter, Feret diameter, circularity, and roundness) and fractal descriptors (fractal dimension, lacunarity, and entropy) were quantified as the relative ratios of printed/real parameters (P/R) to compare real flakes with their 3D-printed counterparts. Results revealed the lowest mean errors for Feret diameter (6%) and projected area (10%), while deviations in circularity and roundness were more pronounced in mushroom flakes. With respect to the actual mean values of the morphological parameters, real flakes showed slightly larger perimeter values (86 mm for real and 82 mm for printed objects) and a higher fractal dimension (1.36 for real and 1.33 for printed), indicating greater texture irregularity, whereas the projected area remained highly comparable (225 mm² in real/229 mm² in printed). These parameters reinforced that the overall morphological fidelity remained high (P/R = 0.9–1.0), despite localized deviations in circularity and fractal descriptors. Less complex morphologies (unilateral and bilateral) demonstrated higher structural fidelity (P/R = 0.95), whereas multilateral and mushroom types showed greater variability due to surface irregularity. Fractal dimension and lacunarity effectively described textural complexity, highlighting the role of flake geometry and moisture in determining expansion patterns and printing accuracy. Principal Component Analysis confirmed that circularity and fractal indicators are critical descriptors for distinguishing morphological fidelity. Overall, the findings demonstrated that 3D scanning and printing provided reliable physical replicas of irregular food structures as popcorn flakes supporting their application in food engineering. Full article

Friction pairs in heavy-duty power-shift tractor wet clutches operate under complex conditions, making them vulnerable to damage and reducing reliability. Optimizing their tribological performance requires a trade-off between a high coefficient of friction (COF) for torque transmission and a low temperature rise ($∆T$) to prevent thermal damage. Surface texturing is an effective method for improving the tribological performance of friction pairs. This study simulated the friction of wet clutch pairs via pin-on-disk tests and designed micro-textures on the pin surface to enhance tribological performance. Based on the experimental data, a Gaussian Process Regression (GPR) surrogate model was developed to accurately predict COF and $∆T$ as a function of the clutch’s operating and micro-texture’s geometric parameters. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was then employed to obtain the optimal set of solutions. The obtained pareto front clearly revealed the COF–temperature rise trade-off. From the optimal solution set, optimal micro-texture parameters for two typical operating conditions of different clutches were extracted. Compared with the untextured surface, the optimal solutions increased COF by 2.6%/1.2% and reduced $∆T$ by 39.2%/12.1%. Relative to neighboring experimental points, COF further increased by 11.3%/2.7% and $∆T$ decreased by 16.6%/1.7%. This work establishes a method for balancing the frictional and thermal performance of friction pairs. Full article

Understanding the effectiveness of engineering measures in mitigating surface erosion is crucial for sustainable land management. However, studies explicitly quantifying the combined effects of large-scale engineering measures and environmental factors remain limited. In this study, multisource remote sensing data were integrated with interpretable machine learning to quantify and analyze the regional influence of erosion control measures. We constructed a comprehensive indicator system encompassing spectral, textural, and topographic variables derived from high-resolution satellite imagery and DEM data. To address model transparency and enhance the interpretability of the results, we employed an interpretable machine learning framework capable of both accurate prediction and explicit attribution of feature importance. The results indicate that the implementation of engineering measures substantially reduces erosion intensity across the study area. Spatial heterogeneity in erosion mitigation effectiveness was closely associated with the distribution patterns of engineering measures and site-specific environmental conditions. Basins with a high proportion of check dams showed average elevation gains of up to 2.5 m compared with those without check dams, and terraces contributed to elevation increases of ~1.9 m in typical loess hilly regions. The interpretable machine learning model achieved R² = 0.62 at Basin 1 (average area ~100 km²) and R² = 0.73 at Basin 2 (~600 km²), demonstrating reliable predictive capability. The findings not only validate the role of engineering interventions in erosion mitigation but also provide a transparent analytical framework that connects remote sensing analytics with process-based geomorphological understanding. Full article

This study employs a multi-technique approach to elucidate how graphene incorporation affects phase formation, microstructure, and thermal behavior in PVA-assisted sol–gel synthesized Ca_2.95Eu_0.05Co₄O_x nanomaterials. XRD confirms the preservation of the primary phases (hexagonal CaCO₃ and cubic CoO) alongside a distinct graphene (002) reflection; a systematic low-angle shift of the calcite (104) peak evidences partial relaxation of residual lattice strain with increasing graphene content, while Scherrer analysis indicates tunable crystallite size. Raman spectroscopy corroborates graphene incorporation through pronounced D (~1300 cm⁻¹) and G (~1580 cm⁻¹) bands and supports the XRD-identified phase coexistence via cobalt-oxide and calcite vibrations in the 200–700 cm⁻¹ region, also indicating increased defect/disorder with graphene loading. SEM shows grain refinement, denser/bridged lamellar textures, and reduced porosity at low–moderate graphene contents (1–3 wt.%), contrasted by agglomeration-driven heterogeneity at higher loadings (5–7 wt.%). EDX reveals increasing carbon with Ca/Co redistribution at accessible surfaces, and TG–DSC corroborates the removal of oxygen-containing groups and oxidative combustion of graphene at mid temperatures. Collectively, Raman–XRD-consistent evidence demonstrates that graphene provides a tunable handle over lattice strain, crystallite size, and grain-boundary architecture, establishing a processing–composition basis for optimizing functional (e.g., electrical/thermoelectric) performance. Full article

Furfural and 5-hydroxymethylfurfural are considered as essential platform molecules for the chemical industry, acting as precursors and intermediates of numerous products. They are produced from pentoses and hexoses, respectively, in an acid medium. In this work, biomass from a green macroalgae, Ulva rigida, was treated under acidic conditions provided by heterogeneous catalysts in order to promote the dehydration of its monosaccharides into furfural and 5-hydroxymethylfurfural. Particularly, two functionalized mesoporous silicas, HMS and SBA-supported metal oxides (Nb₂O₅ and ZrO₂), were used as catalysts. Their textural, structural, and acid properties were deeply studied, providing excellent BET surface areas (ranging 424 to 1204 m²/g) and a high concentration of acid sites (220–460 µmol/g), which then translated into great catalytic performances (77.8% and 64.1% of furfural and HMF molar yields, respectively, using HMS-Nb) after a 4 h of reaction time at 180 and 160 °C, respectively. The catalyst showed excellent stability and recyclability as it could be reused for up to five reaction runs with only a slight decrease in performance. Full article

Structured CBN (cubic boron nitride) grinding wheels usually have a specially designed texture on their surface to reduce the grinding heat and grinding force. However, most structured grinding wheels are fabricated by electroplating, brazing, sintering, and mechanical or laser removal on the surface of conventional grinding wheels, which may have problems such as complicated processes, low processing efficiency, and unstable effects. In this paper, additive manufacturing was used to fabricate a radial straight groove-structured grinding wheel. Meanwhile, a corresponding mathematical model of the grinding wheel was also established considering the shape and position of the abrasive grains. Subsequently, the ground surface morphologies of the fabricated wheel and simulated wheel under different machining parameter conditions were compared to further prove the rationality of the simulated grinding wheel. The results showed that the ground surfaces of the fabricated wheel and simulated wheel had similar morphological characteristics. The trend in the surface roughness under the different machining parameter conditions was also analyzed and showed the same variation for fabricated and simulated wheels; the error rate was confined within 8%. This paper elucidates the grinding mechanism and surface morphology formation process of a radial straight groove-structured grinding wheel fabricated by additive manufacturing. Full article

Amending reservoir sediments with organic matter provides a sustainable alternative to conventional rice (Oryza sativa L.) seedling substrates, simultaneously reducing dependence on agricultural soils and promoting the recycling of dredged sediments and agricultural by-products. Preliminary tests showed that adding rice husk (RH) improved the porosity and water retention of the sediments while preventing surface cracking. This study further examined the effects of RH and rice husk biochar (RHB) on sediment fertility and rice seedling growth. Seedlings were grown for 15 days in a fine- or coarse-texture sediment amended with 0, 5, 10, or 20% (w/w) RH or RHB. A 10% amendment was identified as the optimal ratio for promoting seedling growth (increasing ca. 20% biomass). Nitrogen (N) availability was the primary factor influencing seedling performance, outweighing the effects of salinity and phosphorus availability. Compared with RH, RHB amendment resulted in lower substrate available N, likely due to greater losses through denitrification and ammonia volatilization, leading to reduced growth. In contrast, RH amendment maintained higher levels of available N, resulting in greater shoot biomass and higher leaf chlorophyll concentrations. Overall, amending reservoir sediments with 10% RH provides the most effective substrate formulation, offering a practical and sustainable strategy for rice seedling production. Full article

To address the issues of high stalk breakage rate and the mismatch between extraction force and operational speed in current horizontal shaft counter-rolling cotton stalk pullers, this study presents a novel clamping mechanism. The mechanism enables precise adjustment of the rollers’ rotational speed, inter-roller gap, and surface topography. The objective is to systematically investigate the effects of these key parameters on the peak extraction force and its timing during the stalk pulling process. Initially, pre-compressed cotton stalks were employed as test specimens. Their tensile properties post-compression were investigated by simulating the extraction forces using a universal testing machine. Subsequently, the structural design of the critical components for the test rig was created based on these experimental findings. Theoretical analysis identified the surface texture of the clamping rollers, their rotational speed, and the clamping gap as the primary experimental factors. The effects of these factors on the peak extraction force and its timing were analyzed using Response Surface Methodology (RSM). The results indicated that the optimal combination—striped surface texture for both rollers, a speed of 220 rpm, and a zero gap—yielded a time to peak force of 0.05 s and a peak force of 710.77 N, which is significantly below the measured tensile strength limit of 994.60 N for compressed stalks. This indicates that the designed clamping device for the horizontal shaft counter-rolling cotton stalk extraction machine achieves faster extraction speed while ensuring stalk integrity, and the research results can provide theoretical foundation and design guidance for the development of horizontal shaft counter-rolling cotton stalk extraction machinery. Full article

Accurate detection of strip steel surface defects is a critical step to ensure product quality and prevent potential safety hazards. In practical inspection scenarios, defects on strip steel surfaces typically exhibit sparse distributions, diverse morphologies, and irregular shapes, while background regions dominate the images, exhibiting highly similar texture characteristics. These characteristics pose challenges for detection algorithms to efficiently and accurately localize and extract defect features. To address these challenges, this study proposes a Supervised Focused Feature Network for steel strip surface defect detection. Firstly, the network constructs a supervised range based on annotation information and introduces supervised convolution operations in the backbone network, limiting feature extraction within the supervised range to improve feature learning effectiveness. Secondly, a supervised deformable convolution layer is designed to achieve adaptive feature extraction within the supervised range, enhancing the detection capability for irregularly shaped defects. Finally, a supervised region proposal strategy is proposed to optimize the sample allocation process using the supervised range, improving the quality of candidate regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 81.2% on the NEU-DET dataset and 72.5% mAP on the GC10-DET dataset. Ablation studies confirm the contribution of each proposed module to feature extraction efficiency and detection accuracy. Results indicate that the proposed network effectively enhances the efficiency of sparse defect feature extraction and improves detection accuracy. Full article

This paper presents the development and evaluation of a method for rendering realistic haptic textures in virtual environments, with the goal of enhancing immersion and surface recognizability. By using Blender for the creation of geometric models, Unity for real-time interaction, and integration with the Touch haptic device from 3D Systems, virtual surfaces were developed with parameterizable characteristics of friction, stiffness, and relief, simulating different physical textures. The methodology was assessed through two experimental phases involving a total of 47 participants, examining both tactile recognition accuracy and the perceived realism of the textures. Results demonstrated improved overall performance and reduced variability between textures, suggesting that the approach can provide convincing haptic experiences. The proposed method has potential applications across a wide range of domains, including education, medical simulation, cartography, e-commerce, entertainment, and artistic creation. The main contribution of this research lies in the introduction of a simple yet effective methodology for haptic texture rendering, which is based on the flexible adjustment of key parameters and iterative optimization through human feedback. Full article

Increasing attention is being paid to the use of cover crops as a means of improving soil quality, particularly in relation to soil organic matter (SOM) accumulation and aggregate stability. This study evaluated the effects of soil texture, soil depth, and cover crop type on soil organic carbon (Corg), labile carbon (C_L), and soil structure under field conditions in western Slovakia. A field experiment compared two texturally distinct Phaeozem soils—silty clay loam and sandy loam —and two cover cropping strategies: pea (Pisum sativum L.) monoculture and a four-species mixture of flax (Linum usitatissimum L.), camelina (Camelina sativa L.), white mustard (Sinapis alba L.), and Italian millet (Setaria italica L.). Fine-textured soil accumulated up to 50% more Corg and 1.5 times more C_L than sandy soil, while aggregate stability was up to 90% higher. The surface layer (0–10 cm) contained more SOM, but the deeper layer (10–20 cm) showed greater aggregate stability. Pea cultivation increased total organic carbon, whereas the diverse mixture enhanced labile carbon content and promoted the formation of smaller yet more stable aggregates. Strong correlations between C_L and aggregate stability confirmed the key role of labile organic matter fractions in soil structural stabilisation. Overall, the results demonstrate that the interaction between soil texture and cover crop diversity critically shapes SOM dynamics and soil structure. Combining diverse cover crops with fine-textured soils provides an effective strategy to enhance soil quality, carbon sequestration, and long-term agricultural sustainability. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article