Search Results (1,292)

The high-efficiency polishing of large-sized polycrystalline diamond (PCD) wafers continues to pose significant challenges in its practical applications. Conventional mechanical polishing suffers from a low material removal rate (MRR) and surface damage. To improve the process efficiency, this study investigates the effect of chemomechanical abrasive polishing (CMAP) with a slurry containing high-concentration H₂O₂ and varying mass percentages of SiO₂ powder and diamond particles on surface morphology, surface roughness, material removal rate (MRR), and microstrain of PCD disks. The contributions of mechanical action, chemical action, and bubble cavitation to the CMAP process are analyzed. Scanning electron microscopy (SEM) observations indicate that large grains present in PCD are effectively eliminated after CMAP, leading to a notable reduction in surface roughness. The optimal results are obtained with 60 wt% SiO₂ powder and 40 wt% diamond particles, achieving a maximum MRR of 1039.78 μm/(MPa·h) (15.5% improvement compared to the mechanical method) and a minimum surface roughness (Sa) of 3.59 μm. Additionally, the microstrain on the PCD disk shows a slight reduction following the CMAP process. The material removal mechanism is primarily attributed to mechanical action (70.8%), with bubble cavitation and chemical action (27.5%) and action of SiO₂ (1.7%) playing secondary roles. The incorporation of SiO₂ leads to the formation of a lubricating layer, significantly reducing surface damage and decreasing the surface roughness Sa to 1.39 µm. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

Background/Objectives: To evaluate the importance of image processing in a previously validated model for detecting pancreatic neuroendocrine tumors (PanNETs) and to introduce Image2Radiomics, a new framework that ensures reproducibility of the image processing pipeline and facilitates the deployment of radiomics models. Methods: A previously validated model for identifying PanNETs from CT images served as the reference. Radiomics features were re-extracted using Image2Radiomics and compared to those from the original model using performance metrics. The impact of nine alterations to the image processing pipeline was evaluated. Prediction discrepancies were quantified using the mean ± SD of absolute differences in PanNET probability and the percentage of classification disagreement. Results: The reference model was successfully replicated with Image2Radiomics, achieving a Cohen’s kappa coefficient of 1. Alterations to the image processing pipeline led to reductions in model performance, with AUC dropping from 0.87 to 0.71 when image windowing was removed. Prediction disagreements were observed in up to 45% of patients. Even minor changes, such as switching the library used for spatial resampling, resulted in up to 21% disagreement. Conclusions: Reproducing image processing pipelines remains challenging and limits the clinical deployment of radiomics models. While this study is limited to one model and imaging modality, the findings underscore a common risk in radiomics reproducibility. The Image2Radiomics framework addresses this issue by allowing researchers to define and share complete processing pipelines in a standardized way, improving reproducibility and facilitating model deployment in clinical and multicenter settings. Full article

Sorption and advanced oxidative processes (AOPs) are potential strategies for the removal of organic compounds, such as caffeine, from aqueous media. Such strategies tend to be more promising when combined with biopolymeric membranes as sorbents and photocatalyst supports. Therefore, the aim of the present study was to investigate sorption and AOP parameters in the performance of chitosan membranes and chitosan/TiO₂ composite membranes in individual and hybrid systems involving the photolysis, photocatalysis, and sorption of caffeine. Caffeine degradation by photolysis was 19.51 ± 1.14, 28.61 ± 0.05, and 30.64 ± 6.32%, whereas caffeine degradation by photocatalysis with catalytic membrane was 18.33 ± 2.20, 20.83 ± 1.49, and 31.41 ± 3.08% at pH 6, 7, and 8, respectively. In contrast, photocatalysis with the dispersed catalyst achieved degradation of 93.56 ± 2.12, 36.42 ± 2.59, and 31.41 ± 1.07% at pH 6, 7, and 8, respectively. These results indicate that ions present in the buffer solutions affect the net electrical charge on the surface of the composite biomaterial with the change in pH variation, occupying active sorption sites in the structure of the biomaterial, which was characterized by Fourier transform infrared spectrometry, thermogravimetric analysis, differential scanning thermogravimetry, and X-ray diffraction. Thus, it is verified that in a combined process of caffeine removal under UV irradiation and use of chitosan/TiO₂ composite membranes in phosphate-buffered medium, the photolysis mechanism is predominant, with little or no contribution from sorption, and that the TiO₂ catalyst promotes a significant reduction in the percentage of pollutant in the medium only when used dispersed and at low pH. Full article

Enhancing farmers’ digital literacy is both an inevitable requirement for adapting to the digital age and an important measure for promoting the sustainable development of livestock and poultry manure resource utilization. This study surveyed and obtained data from 1047 farm households in Ningxia and Gansu, two provinces in China that have long implemented livestock manure resource utilization policies, from December 2023 to January 2024, and employed the binary probit model to analyze how digital literacy influences farmers’ willingness to adopt two livestock manure resource utilization modes, as well as to analyze the moderating role of three policy regulations. This paper also explores the heterogeneous results in different village forms and income groups. The results are as follows: (1) Digital literacy significantly and positively impacts farmers’ willingness to adopt both the “household collection” mode and the “livestock community” mode. For every one-unit increase in a farmer’s digital literacy, the probability of farmers’ willingness to adopt the “household collection” mode rises by 22 percentage points, and the probability of farmers’ willingness to adopt the “livestock community” mode rises by 19.8 percentage points. After endogeneity tests and robustness checks, the conclusion still holds. (2) Mechanism analysis results indicate that guiding policy and incentive policy have a positive moderation effect on the link between digital literacy and the willingness to adopt the “household collection” mode. Meanwhile, incentive policy also positively moderates the relationship between digital literacy and the willingness to adopt the “livestock community” mode. (3) Heterogeneity analysis results show that the positive effect of digital literacy on farmers’ willingness to adopt two livestock manure resource utilization modes is stronger in “tight-knit society” rural areas and in low-income households. (4) In further discussion, we find that digital literacy removes the information barriers for farmers, facilitating the conversion of willingness into behavior. The value of this study is as follows: this paper provides new insights for the promotion of livestock and poultry manure resource utilization policies in countries and regions similar to the development process of northwest China. Therefore, enhancing farmers’ digital literacy in a targeted way, strengthening the promotion of grassroots policies on livestock manure resource utilization, formulating diversified ecological compensation schemes, and establishing limited supervision and penalty rules can boost farmers’ willingness to adopt manure resource utilization models. Full article

Injurious birds pose a significant threat to food production and the agricultural economy. To address the challenges posed by their small size, irregular shape, and frequent occlusion in complex farmland environments, this paper proposes YOLO-AWK, an improved bird detection model based on YOLOv11n. Firstly, to improve the ability of the enhanced model to recognize bird targets in complex backgrounds, we introduce the in-scale feature interaction (AIFI) module to replace the original SPPF module. Secondly, to more accurately localize and identify bird targets of different shapes and sizes, we use WIoUv3 as a new loss function. Thirdly, to remove the noise interference and improve the extraction of bird residual features, we introduce the Kolmogorov–Arnold network (KAN) module. Finally, to improve the model’s detection accuracy for small bird targets, we add a small target detection head. The experimental results show that the detection performance of YOLO-AWK on the farmland bird dataset is significantly improved, and the final precision, recall, mAP@0.5, and mAP@0.5:0.95 reach 93.9%, 91.2%, 95.8%, and 75.3%, respectively, which outperforms the original model by 2.7, 2.3, 1.6, and 3.0 percentage points, respectively. These results demonstrate that the proposed method offers a reliable and efficient technical solution for farmland injurious bird monitoring. Full article

To remove nickel (II) from an aqueous solution, cellulose nanocrystals (CNCs) were modified as an adsorbent. The FTIR and SEM were used to characterise the properties of CNCs. In addition to how well they predicted reaction (adsorption capacity), the central composite design was used. The response surface model method performs well, according to statistical data. Four operational variables were studied: The initial concentration of the nickel (II) solution in mg/L, the pH, the contact period in minutes, and the adsorbent dose in g/100 mL. The removal percentage (%) was the result. The percentage removal was 98% after 178 min of contact, a starting concentration of 110 mg/L, an adsorbent dosage of 9.3 g, and an initial pH of 3.5. The R² was 0.996, the adjusted R² was 0.921, and the predicted R² was 0.945. The quadratic equation was determined using central composite design. The FTIR examination revealed that the functional groups, hydroxyl groups (OH), peaked around 3300–3500 cm^−1, and carboxyl groups (COOH) peaked around 1700 cm⁻¹. Full article

This study evaluated the efficacy of various cleaning protocols on two nickel–titanium (NiTi) file systems—RaCe EVO(RE) and EdgeFile X7(EE)—using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Eighty-four NiTi files (42RE, 42EE) were divided into seven groups (n = 12), including a group with unused, sterilized files and a group of used files without cleaning. The remaining files were subjected to simulated clinical use, followed by different cleaning methods, such as soaking in sodium hypochlorite (NaOCl), ethanol wiping (with or without magnification), enzymatic spray, and enzymatic solution. SEM images were imported into ImageJ to quantify surface changes, while EDX assessed elemental composition. The p-value was set to ≤0.05 for significance. Apart from the unused files, calcium and phosphorus—indicators of dentin debris—were present in all groups, especially those cleaned with enzymatic spray (p ≤ 0.0001). Their percentage in RE files soaked in NaOCl or wiped with ethanol was statistically lower than the positive control (p ≤ 0.0001). Post-use, all files showed significantly higher surface asymmetry in Groups 2 and 6 (p = 0.001). Cleaning efficacy depends on the type of NiTi file. RE files responded well to both wiping and soaking, while EE required soaking for effective debris removal. Enzymatic spray was ineffective. Full article

Ozone pollution decreases rice yield and quality in general, but how ozone stress changes grain-filling capacity is unclear. A chamber experiment was conducted to compare the effects of ozone exposure during the rice growth season on the grain-filling capacity and quality of spikelets located on the upper primary rachis (superior spikelets, SS) and the lower secondary rachis (inferior spikelets, IS). Ozone stress significantly decreased filled grain percentage by 41.4% and grain mass by 10.2% in IS, but had little effect on grain-filling capacity in SS. Consistent with the reduction in grain mass, ozone stress decreased grain volume, mainly due to reduced grain thickness, and IS was reduced more than SS. After removing the hull, brown rice obtained from ozone treatment exhibited higher proportions of immature and abnormal kernels, resulting in a substantially lower proportion of perfect kernels. Under ozone stress, the proportion of perfect kernels was only one-third in IS, compared with two-thirds in SS. Ozone stress affected the pasting properties of brown rice for both SS and IS, as shown by the decreased amylose content, and the increased maximum viscosity, minimum viscosity, final viscosity, setback, and peak time of the rapid visco analyzer profile. Out of fourteen traits related to nutritional quality of brown rice, only five showed significant increases under ozone stress, and they were the concentrations of albumin, prolamin, sulfur, copper, and manganese. The differential ozone responses between SS and IS were rather small for rice pasting properties and chemical compositions as shown by very few significant interactions between ozone and grain position. It is concluded that ozone stress during plant growth imposed more adverse effects on IS than SS in terms of grain-filling capacity and appearance quality, suggesting an enlarged asynchronous grain-filling pattern in rice panicles under ozone pollution. Strategies to improve the grain-filling capacity of IS are needed to mitigate ozone-induced damage to rice production. Full article

The integration of computational intelligence techniques into pharmaceutical wastewater treatment offers promising opportunities to improve process efficiency and minimize operational costs. This study compares the predictive capabilities of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in forecasting the rejection efficiencies of caffeine and paracetamol using AFC 40 and AFC 80 nanofiltration (NF) membranes. Experiments were conducted under varying operating conditions, including transmembrane pressure, feed concentration, and flow rate. The predictive performance of both models was evaluated using statistical metrics such as the Coefficient of Determination (R²), Root Mean Square Error (RMSE), Marquardt’s Percentage Squared Error Deviation (MPSED), Hybrid fractional error function (HYBRID), and Average Absolute Deviation (AAD). Both models demonstrated strong predictive accuracy, with R² values of 0.9867 and 0.9832 for RSM and ANN, respectively, in AFC 40 membranes, and 0.9769 and 0.9922 in AFC 80 membranes. While both approaches closely matched the experimental results, the ANN model consistently yielded lower error values and higher R² values, indicating superior predictive performance. These findings support the application of ANNs as a robust modelling tool in optimizing NF membrane processes for pharmaceutical removal. Full article

Background/Objectives: Burned tissue has traditionally been removed surgically, but the effectiveness of enzymatic debridement with NexoBrid has been reported in burn patients and has gained attention in recent years. This agent was approved for use in Japan in 2023. However, even in Japan, there have been few studies examining its effectiveness in patients with extensive burns. The purpose of this study was to analyze the association of combined NexoBrid and surgical excision with clinical outcomes in extensive burn patients. Methods: Between January 2020 and December 2024, seventeen flame burn patients requiring surgical excision were divided into two groups based on whether NexoBrid was used. Clinical outcomes between the two groups were compared using the propensity score overlap weighting method to adjust for baseline differences. Results: Seven of the patients received combined NexoBrid and surgical excision. After weighting, NexoBrid was significantly associated with a shorter time to complete debridement of burned tissue (difference −4 days, 95% CI −5 to −2) and lower percentage of bacteremia (odds ratio 0.06, 95% CI 0.00 to 0.96). However, no significant differences were observed in the length of ICU stay, the amount of blood transfusion required for complete tissue removal, hospitalization costs, and in-hospital mortality. Conclusions: Combining conventional surgical excision with enzymatic debridement may reduce the time required to complete debridement of burned tissue and decrease the rate of bacteremia. Further studies are needed to evaluate the effectiveness of NexoBrid combined with surgical excision in patients with extensive burns. Full article

Poly(acrylic acid) (PAA) and hydroxypropylcellulose (HPC) hydrogels were synthesized in the absence of a crosslinker. Chemical crosslinking between PAA and HPC was demonstrated through free radical polymerization by a precipitation reaction in acetone as the solvent. These hydrogels exhibited smaller swelling ratios (1 to 5 g H₂O/g) than homo PAA hydrogels synthesized in water as the solvent. They were swollen in a 0.1 M NaOH solution and subsequently used to remove Ni²⁺ ions from aqueous solutions with concentrations ranging from 1000 to 4000 ppm. The absorption capacity of these hydrogels ranged from 91 to 340 mg of Ni²⁺/g in a rapid 1 h process, and from 122 to 435 mg of Ni²⁺/g in a 24 h process, demonstrating an improvement in Ni²⁺ absorption compared to previously reported hydrogels. The colored 1000 and 2000 ppm Ni²⁺ solutions became clear after treatment, while the PAA-HPC hydrogels turned green due to the uptake of Ni²⁺ ions, which were partially chelated by carboxylate groups as nickel polyacrylate and partially precipitated as Ni(OH)₂, resulting in an average absorption efficiency of 80%. The hydrogel was able to release the absorbed Ni²⁺ upon immersion in an HCl solution, with an average release percentage of 76.4%, indicating its potential for reuse. These findings support the use of PAA-HPC hydrogels for cleaning Ni²⁺-polluted water. The cost of producing 1 g of these hydrogels in laboratory conditions is approximately 0.2 USD. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

Dietary bitter compounds such as caffeine have the potential to reduce backfat in pigs. However, the use of caffeine as a feed additive has restrictions in many countries. It was hypothesised that grape seed and gentian plant extracts (GG) could replace caffeine in feed due to their bitterness and antiadipogenic effects. The effect of caffeine (0.5 g/kg), GG (2 g/kg) alone or in combination with caffeine (BM) at increasing concentrations (0.5, 1, 1.5, or 2 g/kg) on feed efficiency, carcass, and meat quality was assessed in finishing pigs (Large White × Landrace). Growth performance and carcass traits were evaluated at a pen level (n = 14). Loins (longissimus thoracis) were removed from eight pig/treatment at the abattoir to assess drip loss, lightness (L*), redness (a*), yellowness (b*), chroma (C*), hue angle (h°), pH, cook loss, and shear force. A linear increase (p < 0.05) in loin a*, b*, and C* values and a linear decrease (p < 0.05) in ADFI, ADG, backfat, dressing percentage, and HSCW were observed with increasing BM levels. At 1.5 g/kg, BM increased the loins a* (p < 0.05), b* (p < 0.05) and C* values (p < 0.05) compared to the control. Twenty-two proteins related to energy metabolism and myofibril assembly were identified to be upregulated (FDR < 0.05) in BM vs. control loins. In conclusion, GG could be used in combination with low doses of caffeine to modulate appetite and carcass leanness and improve pork colour. Full article

In this study, polysulfone-based nanocomposites with carbon black (CB) nanoparticles were fabricated to evaluate their urea-removal properties. The nanocomposites were obtained using two different methods: solution mixing and melt extrusion. These materials were evaluated using Fourier transform infrared spectroscopy (FTIR), which allowed for the identification of the corresponding functional groups within the polysulfone polymer matrix. X-ray diffraction (XRD) analysis was performed, confirming the amorphous structure of the polysulfone. The addition of modified carbon black shifted the most intense peak of the polysulfone. Thermogravimetric analysis (TGA) showed an increase in thermal stability with the addition of different concentrations of modified carbon black for solution-mixing method. Scanning electron microscopy (SEM) revealed that the melt-extrusion method presented a better dispersion of the nanoparticles, since large agglomerates were not observed. Additionally, a urea adsorption study was conducted, obtaining removal percentages of 76% and 72% for the extrusion and solution-mixing methods, respectively. It was demonstrated that the nanocomposite can be used for up to five cycles without losing urea-removal efficiency, whereas the efficiency of pure polysulfone decreases as the number of cycles increases. Finally, the hemolysis test was performed, and the nanocomposites showed less than 1% hemolysis, indicating that the material is non-hemolytic. Full article