Search Results (50,927)

This study employs a wet precipitation–carbonation method to recycle and utilize purification slag from the calcium carbide industry, extracting high-value-added magnesium hydroxide (Mg(OH)₂) and aragonite nanoparticles. Experimental results demonstrate that the reaction temperature significantly influences the yield, morphology, and crystallinity parameters of the products. The optimal preparation temperatures for Mg(OH)₂ and aragonite are 60 °C and 80 °C, respectively. Analysis via X-ray diffraction (XRD) combined with the Williamson–Hall method reveals that within the temperature range of 60–90 °C, the crystallite sizes of Mg(OH)₂ and aragonite are 40.07–59.25 nm and 70.03–109.18 nm, respectively. As the temperature increases, the crystallite size, strain, lattice stress, and energy density of Mg(OH)₂ exhibit a decreasing trend, whereas the corresponding crystallographic parameters of aragonite gradually increase. Full article

Continuous user authentication is critical to mobile device security, addressing vulnerabilities associated with traditional one-time authentication methods. This research proposes a hybrid deep learning framework that combines techniques from computer vision and sequence modeling, namely, ViT-inspired patch extraction, multi-head attention, and BiLSTM networks, to authenticate users continuously from smartphone sensor data. Unlike many existing approaches that directly apply these techniques for specific recognition tasks, our method reshapes raw motion signals into ViT-like patches to capture short-range patterns, employs multi-head attention to emphasize the most discriminative temporal segments, and then processes these enhanced embeddings through a bidirectional LSTM to integrate broader contextual information. This integrated pipeline effectively extracts local and global motion features specific to each user’s unique behavior, improving accuracy over conventional Transformer, Informer, CNN, and LSTM baselines. Experiments on the MotionSense and UCI HAR datasets show accuracies of 97.51% and 89.37%, respectively, indicating strong user-identification performance. Full article

In synthetic aperture radar (SAR) image sea ice classification, the polarization decomposition techniques are used to enhance classification accuracy. However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms, limiting their ability to differentiate between various sea ice types. This paper proposes using the Modified Reflection Symmetry Decomposition (MRSD) method to extract polarization features from Gaofen-3 (GF-3) satellite fully polarimetric SAR data for sea ice classification tests. The study data included three types of sea surface: open water (OW), young ice (YI), and first-year ice (FYI). In this research, backscattering coefficients were combined with FD, H/A/α, and MRSD polarization features to create eight feature combinations for comparative analysis. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM), were also used for the comparative analysis. The results show that MRSD polarization features significantly improve model performance, particularly distinguishing among sea ice categories. Compared to using only the backscatter coefficient, MRSD polarization features increased model classification accuracy by approximately 4% to 13%, outperforming FD and H/A/α polarization features. The XGBoost model trained with MRSD polarization features achieves excellent classification results, with classification accuracies of 0.9630, 0.9126, and 0.9451 for OW, YI, and FYI. Additionally, the model achieved a Kappa coefficient of 0.9105 and an F1-score of 0.9403. Feature importance and SHapley Additive exPlanations (SHAP) analysis further demonstrate the physical significance of the MRSD polarization features and their role in model decision-making, suggesting that the scattered component power plays a crucial role in the model’s classification decision. Compared to traditional decomposition methods, MRSD provides a more detailed characterization of scattering mechanisms, offering a comprehensive understanding of the physical properties of sea ice. This paper systematically demonstrates the superior effectiveness of MRSD polarization features for sea ice classification, presenting a new scheme for more accurate classification. Full article

Background: Ossifying fibromyxoid tumors (OFMTs) are rare mesenchymal neoplasms with behaviors ranging from benign to malignant. Although most occur in the extremities and trunk, 9–13% are found in the head and neck, such as the oral cavity, scalp, and calvarium. Diagnosis is challenging due to their rarity and histological similarity to other neoplasms. This review synthesizes evidence on the clinical presentation, diagnostic features, and treatment outcomes of OFMTs in the head and neck, focusing on surgical management and adjuvant therapies. Methods: A systematic review was conducted according to PRISMA guidelines, with searches in PubMed/MEDLINE, Embase, Scopus, and Web of Science. Studies from 1989 to 2024 reporting OFMTs in the head and neck with clinical, histopathological, and treatment data were included. Extracted data encompassed demographics, tumor features, surgical margins, adjuvant therapy, and outcomes. Results: Forty studies with 99 patients were included. Patient ages ranged from 3 weeks to 88 years (median 47), with a male predominance (63.64%). The most common presentation was a slow-growing, painless mass. Tumors were most often found in the neck, oral cavity, scalp, and calvarium. Histopathology revealed encapsulated tumors with fibromyxoid stroma, spindle-shaped cells, and a peripheral rim of metaplastic bone in 70% of cases. Immunohistochemistry showed positivity for S-100, vimentin, and SOX10. Surgical excision was the main treatment, used in 28.28% of cases, with recurrence in 9.09% and no metastases. Adjuvant therapies, mainly radiotherapy, were employed in 15.15% of cases. Conclusions: OFMTs of the head and neck are rare neoplasms requiring multidisciplinary care. Imaging, histopathology, and immunohistochemistry are key to diagnosis. Surgical excision with clear margins remains the primary treatment, with a low recurrence rate. Adjuvant therapy may be needed for malignant or incompletely excised cases. Further research is needed to optimize follow-up protocols and assess molecular profiling for risk stratification. Full article

In this study, the voltammetric behavior of apocynin on a boron-doped diamond electrode in a phosphate buffer (pH 7.3) has been reported for the first time. The oxidation process is quasi-reversible, diffusion-controlled, and involves one electron and one proton. The product of the electrode reaction is an unstable radical that undergoes successive chemical transformations near the working electrode. The proposed mechanism of this process can be described as E_qC_i and served as the basis for the development of a new voltammetric method for determining apocynin in natural samples. The analytical signal was the anodic peak on DPV curves at a potential of 0.605 V vs. Ag/AgCl. A linear response was observed in the concentration range of 0.213–27.08 mg L⁻¹. The estimated LOD and LOQ values were 0.071 and 0.213 mg L⁻¹, respectively. The effectiveness of the method was demonstrated both in control determinations and in the analysis of the dietary supplement. This procedure is simple, fast, sensitive, selective, and requires no complicated sample preparation, which is limited only to a simple extraction with ethanol. The low consumption of non-toxic reagents makes it environmentally friendly. To the best of our knowledge, this is the first presentation of a voltammetric procedure to determine this analyte studied in a phosphate buffer solution on a boron-doped diamond electrode. It can also be easily adapted to determine other phenolic compounds with antioxidant properties in various matrices. Full article

Bacterial cellulose (BC) is a polysaccharide produced by Gram-positive and Gram-negative bacteria with a strictly aerobic metabolism, having a huge number of significant applications in the biomedical field. This study investigates the development of bacterial cellulose (BC)-based composite systems that incorporate cerium dioxide nanoparticles (CeO₂ NPs) used as antibacterial agents to enhance wound healing, particularly for burn treatments. The innovation of this study resides in the integration of CeO₂ NPs synthesized by using a precipitation method using both chemical and green reducing agents, ammonium hydroxide (NH₄OH) and turmeric extract (TE), in BC membranes composed of ultrathin nanofibers interwoven into a three-dimensional network appearing as a hydrogel mass. Characterization by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FTIR) confirmed the effective deposition of this agent onto the BC matrix. Antibacterial activity tests against E. coli and B. subtilis indicated strong inhibition for the composites synthesized following these routes, particularly for the BC-CeO₂-TE-OH sample, processed by employing both precipitating agents. Cytotoxicity evaluations showed no inhibition of cell activity. Additionally, loading the composites with dexamethasone endowed them with analgesic release over 4 h, as observed through ultraviolet–visible spectroscopy (UV-Vis), while the FTIR spectra revealed a sustained drug presence post-release. These findings highlight BC-based films as promising candidates for advanced wound care and tissue engineering applications. Full article

Remote sensing image change detection plays a vital role in diverse real-world applications such as urban development monitoring, disaster assessment, and land use analysis. As deep learning strives, Convolutional Neural Networks (CNNs) have shown their effects in image processing applications. There are two problems in old-school change detection techniques: First, the techniques do not fully use the effective information of the global and local features, which causes their semantic comprehension to be less accurate. Second, old-school methods usually simply rely on differences and computation at the pixel level without giving enough attention to the information at the semantic level. To address these problems, we propose a multi-scale cross-attention network (MSCANet) based on a CNN in this paper. First, a multi-scale feature extraction strategy is employed to capture and fuse image information across different spatial resolutions. Second, a cross-attention module is introduced to enhance the model’s ability to comprehend semantic-level changes between bitemporal images. Compared to the existing methods, our approach better integrates spatial and semantic features across scales, leading to more accurate and coherent change detection. Experiments on three public datasets (LEVIR-CD, CDD, and SYSU-CD) demonstrate competitive performance. For example, the model achieves an F1-score of 96.19% and an IoU of 92.67% on the CDD dataset. Additionally, robustness tests with Gaussian noise show that the model maintains high accuracy under input degradation, highlighting its potential for real-world applications. These findings suggest that our MSCANet effectively improves semantic awareness and robustness, offering a promising solution for change detection in complex and noisy remote sensing environments. Full article

A low-grade uranium-gold polymetallic ore is associated with many rare elements, such as beryllium (Be), zirconium (Zr), thorium (Th), and cerium (Ce). It has potential development and utilization value. In order to improve the development and utilization rate of a low-grade uranium-gold polymetallic ore, beryllium (Be) in low-grade uranium-gold polymetallic ore was extracted by a combined method of (NH)₂SO₄ and Al₂(SO₄)₃. The effects of different concentrations of (NH₄)₂SO₄ solution on the leaching of beryllium (Be) in low-grade uranium-gold polymetallic ore with different particle sizes after sieving were studied; microstructure and physicochemical analyses were carried out. The leaching mechanism of beryllium (Be) was revealed. The experimental results showed that when the low-grade uranium-gold polymetallic ore in (NH)₂SO₄ solution is 6 g/L and Al₂(SO₄)₃ is 3 g/L, the particle size of the ore sample is 0.01 mm, the concentration of beryllium (Be) in the leaching solution reaches 0.521 mg/L after 3 days of leaching, the concentration of beryllium (Be) in the leaching solution of the sample without Al₂(SO₄)₃ solution is 0.007 mg/L, and the leaching rate of beryllium (Be) reaches 98.6%. SEM and XRD analyses showed that the silicate composition in the sample after leaching was obviously destroyed compared with the control group when the (NH)₂SO₄ solution was 6 g/L, which increased the contact area on the surface of the ore sample and promoted the leaching of beryllium (Be) in the uranium ore sample. The research results lay a theoretical foundation for the development and extraction of beryllium (Be) associated with low-grade uranium-gold polymetallic ore. Full article

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Objectives: Emotion perception in music is shaped by cultural background, yet the extent of cultural biases remains unclear. This study investigated how Western listeners perceive emotion in music across cultures, focusing on the accuracy and intensity of emotion recognition and the musical features that predict emotion perception. Methods: White-European (Western) listeners from the UK, USA, New Zealand, and Australia (N = 100) listened to 48 ten-second excerpts of Western classical and Chinese traditional bowed-string music that were validated by experts to convey happiness, sadness, agitation, and calmness. After each excerpt, participants rated the familiarity, enjoyment, and perceived intensity of the four emotions. Musical features were computationally extracted for regression analyses. Results: Western listeners experienced Western classical music as more familiar and enjoyable than Chinese music. Happiness and sadness were recognised more accurately in Western classical music, whereas agitation was more accurately identified in Chinese music. The perceived intensity of happiness and sadness was greater for Western classical music; conversely, the perceived intensity of agitation was greater for Chinese music. Furthermore, emotion perception was influenced by both culture-shared (e.g., timbre) and culture-specific (e.g., dynamics) musical features. Conclusions: Our findings reveal clear cultural biases in the way individuals perceive and classify music, highlighting how these biases are shaped by the interaction between cultural familiarity and the emotional and structural qualities of the music. We discuss the possibility that purposeful engagement with music from diverse cultural traditions—especially in educational and therapeutic settings—may cultivate intercultural empathy and an appreciation of the values and aesthetics of other cultures. Full article

Background: Vitamin D is vital for musculoskeletal health, with emerging evidence highlighting its role in muscle function. While its preoperative and postoperative benefits for bone health are well documented, the effect of vitamin D supplementation on post-surgical muscle recovery remains underexplored. This systematic review consolidates current evidence on the impact of vitamin D supplementation in enhancing muscle strength following surgery. Methods: This review adhered to PRISMA guidelines and was registered on PROSPERO. A systematic search of PubMed, EMBASE, and Cochrane databases was conducted, covering articles from inception to 15 January 2025. Studies evaluating the effect of vitamin D supplementation on muscle strength in surgical contexts were included. Data extraction focused on study design, population demographics, vitamin D dosage, timing, and measured outcomes. A narrative synthesis was performed due to heterogeneity in study designs and outcomes. Results: From 701 initial records, 10 studies met the inclusion criteria. The findings indicate that vitamin D supplementation, particularly high-dose regimens administered preoperatively or early postoperatively, significantly improves muscle strength and functional outcomes in orthopaedic surgeries, such as hip and knee replacements, and bariatric surgeries. The benefits varied by surgical type, baseline vitamin D levels, and supplementation strategy. However, inconsistent dosing regimens and limited long-term follow-up studies hinder conclusive evidence. Conclusions: Vitamin D supplementation demonstrates potential in enhancing post-surgical muscle recovery and functional outcomes. Tailored supplementation strategies, based on patient-specific needs and surgical context, are essential. Future research should address optimal dosing regimens and evaluate long-term impacts on recovery and quality of life. Full article

Ship type (e.g., Cargo, Tanker and Fishing) classification is crucial for marine management, environmental protection, and maritime safety, as it enhances navigation safety and aids regulatory agencies in combating illegal activities. Traditional ship type classification methods with AIS data are often plagued by problems such as data imbalance, insufficient feature extraction, reliance on single-model approaches, or unscientific model combination methods, which reduce the accuracy of classification. In this paper, we propose an ensemble classification method based on a stacking strategy to overcome these challenges. We apply the SMOTE technique to balance the dataset by generating minority class samples. Then, a more comprehensive ship behavior model is developed by combining static and dynamic features. A stacking strategy is adopted for the classification, integrating multiple tree structure-based classifiers to improve classification performance. The experimental results show that the ensemble classification method based on the stacking strategy outperforms traditional classifiers such as CatBoost, Random Forest, Decision Tree, LightGBM, and the ensemble classification method, especially in terms of improving classification precision, recall, F1 score, ROC curve, and AUC. This method improves the accuracy of ship type recognition, and it is suitable to real-time online classification, which is helpful for applications in marine safety monitoring, law enforcement, and illegal fishing detection. Full article

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote research on high harvest indices and variety breeding; (2) Methods: In this study, we proposed a method to predict the harvest index during the rice growth period based on uncrewed aerial vehicle (UAV) remote sensing technology. UAV obtained visible light and multispectral images of different varieties, and the data such as digital surface elevation, visible light reflectance, and multispectral reflectance were extracted after processing for correlation analysis. Additionally, characteristic variables significantly correlated with the harvest index were screened out; (3) Results: The results showed that TCARI (correlation coefficient −0.82), GRVI (correlation coefficient −0.74), MTCI (correlation coefficient 0.83), and TO (correlation coefficient −0.72) had a strong correlation with the harvest index. Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. The results showed that the Stacking model performed best in predicting the harvest index (R² reached 0.88) and had a high prediction accuracy. (4) Conclusions: Therefore, the harvest index can be accurately predicted during rice growth through UAV remote sensing images and machine learning technology. This study provides a new technical means for screening high harvest index in rice breeding, provides an important reference for crop management and variety improvement in precision agriculture, and has high application potential. Full article

Cross-view vehicle re-identification (ReID) between aerial and ground perspectives is challenging due to limited computational resources on edge devices and significant scale variations. We propose CVNet, a lightweight network with two key modules: the multi-scale localization (MSL) module and the deep–shallow filtrate collaboration (DFC) module. The MSL module employs multi-scale depthwise separable convolutions and a localization attention mechanism to extract multi-scale features and localize salient regions, addressing viewpoint variations. DFC employs a dual-branch design comprising deep and shallow branches, integrating a filtration module optimized via neural architecture search, a collaboration module, and lightweight convolutions. This design effectively captures both unique and shared cross-view features, ensuring efficient and robust feature representation. We also release a new CVPair v1.0 dataset, the first benchmark for cross-view ReID, containing 14,969 images of 894 vehicle identities, offering results of traditional and lightweight methods. CVNet achieves state-of-the-art performance on CVPair v1.0, VehicleID, and VeRi776, advancing cross-view vehicle ReID. The dataset will be released publicly. Full article