Search Results (36,102)

In chemical absorption for carbon capture, the regeneration heat is a key factor determining solvent regeneration energy consumption, and the sterically hindered amine 2-amino-2-methyl-1-propanol (AMP) has great potential for application. In this paper, a CO₂ reaction heat measurement system designed and constructed by our team was used to perform a comparative study on AMP and monoethanolamine (MEA). Moreover, five additives—MEA, diglycolamine (DGA), diethanolamine (DEA), methyldiethanolamine (MDEA), and piperazine (PZ)—were introduced into AMP-based solutions to investigate the promotion performance of these blended solvents. The results revealed that although AMP exhibited a slower absorption rate compared to MEA, it demonstrated a higher CO₂ loading capacity and cyclic capacity, as well as a lower reaction heat, making it advantageous in terms of regeneration energy consumption. At the same total concentration, the absorption capacity of blended solutions (excluding AMP-MEA solutions) was generally lower than that of single-component AMP solutions. Among these additives, MEA and PZ could enhance the absorption rate clearly yet increase the reaction heat at the same time; DGA and DEA could decrease the overall absorption performance. Generally, AMP-MDEA solutions showed the best desorption performance, with the 15 wt% AMP + 5 wt% MDEA mixture demonstrating the lowest regeneration heat and good cyclic capacity. Full article

High chromium cast iron (HCCI) is widely used in the manufacturing of equipment parts in the fields of mining, cement, electric power, metallurgy, the chemical industry, and paper-making because of its excellent wear and corrosion resistance. Although the microstructure and properties of HCCI can be modified by controlling the casting and heat treatment process, alloying is still the most basic and important method to improve the properties of HCCI. There are about 14 common alloying elements in HCCI, among which nickel, copper, and manganese are typical austenite stabilizing elements, which can increase austenite content and matrix electrode potential. The addition of elements such as silicon, nitrogen, boron, and rare earth (RE) is often small, but it has a significant effect on tailoring the microstructure, thereby improving wear resistance and impact toughness. It was thought that after years of development, the research on the role of the above elements in HCCI was relatively complete, but in the past 5 to 10 years, there has been a lot of new research progress. Moreover, the current development situation of HCCI is still relatively extensive, and there are still many problems regarding the alloying of HCCI to be further studied and solved. In this paper, the research results of austenitic stabilizing elements and modifying elements in HCCI are reviewed. The existing forms, distribution law of these elements in HCCI, and their effects on the microstructure, hardness, wear resistance, and corrosion resistance of HCCI are summarized. Combined with the current research situation, the future research and development direction of HCCI alloying is prospected. Full article

Entropy-based thresholding is a widely used technique for medical image segmentation. Its principle is to determine the optimal threshold by maximizing or minimizing the image’s entropy, dividing the image into different regions or categories. The intensity distributions of objects and backgrounds often overlap and contain many outliers, making segmentation extremely difficult. In this paper, we introduce a novel thresholding method that incorporates the Cauchy distribution into the Tsallis entropy framework based on Bayesian estimation. By introducing Bayesian prior probability estimation to address the overlap in intensity distributions between the two classes, we enhance the estimation of the probability that a pixel belongs to either class. Additionally, we utilize the Cauchy distribution, known for its heavy-tailed characteristics, to fit grayscale pixel distributions with outliers, enhancing tolerance to extreme values. The optimal threshold is derived through the optimization of an information measure formulated using updated Tsallis entropy. Experimental results demonstrate that the proposed method, called Cauchy-TB, achieves significant superiority to existing approaches on two public medical brain image datasets. Full article

The advancement of 3D printing technology has changed material design and fabrication across various industries. Among its many applications, the development of high-wear-resistance polymer composites, particularly using Fused Deposition Modelling (FDM), has received increasing interest from both academic and industrial sectors. This paper provides an overview of recent advances in this field, focusing on the selection of key printing parameters (such as layer thickness, print speed, infill density, and printing temperature) and material compatibility optimisation to enhance print quality and tribological performance. The effects of various tribo-fillers, such as fibres and nanoparticles, on the tribological properties of the printed polymer composites were studied. Generally, in the case of nano-sized particles, the wear rate can be reduced by approximately 3 to 5 times when the nanoparticle content is below 5 vol.%. However, when the nanoparticle concentration exceeds 10 vol.%, wear resistance may deteriorate due to the formation of agglomerates, which disrupts the uniform dispersion of reinforcements and weakens the composite structure. Similarly, in short fibre-reinforced polymer composites, a fibre content of 10–30 vol.% has been observed to result in a 3 to 10 times reduction in wear rate. Special attention is given to the synergistic effects of combining micro- and nano-sized fillers. These advancements introduce novel strategies for designing wear-resistant polymer composites without requiring filament fabrication, making 3D printing more accessible for tribological applications. In the last part of the review, the impact of emerging AI technologies on the field is also reviewed and discussed. By identifying key research gaps and future directions, this review aims to drive further innovation in the development of durable, high-performance materials for wide industry applications in aerospace, biomedical, and industrial engineering. Full article

Sheeppox, goatpox, and lumpy skin disease continue to negatively impact the sheep, goat, and cattle industries in countries where these diseases are present and threaten to spread into new regions. Effective vaccines are available for disease control and eradication. However, commercial vaccines are based on live attenuated virus isolates and therefore it is not currently possible to differentiate between infected and vaccinated animals (DIVA), which severely limits the use of these vaccines in countries that are free from disease and at risk of an incursion. The development of next-generation vaccines, including recombinant protein, viral-vectored, and mRNA, has been limited due to the lack of understanding of the protective antigen(s) of capripoxviruses. The complexity of capripoxviruses, with up to 156 open reading frames, makes the identification of protective antigen(s) difficult. This paper identifies the most promising antigens by first considering the membrane-associated proteins and then further selecting proteins based on immunogenicity and their role in immunity by comparing them to known orthopoxvirus homologues. From the 156 potential antigens, 13 have been identified as being the most likely to be protective. Further evaluation of these proteins, as immunogens, would be required to identify the optimal combination of immunodominant antigen(s) for the development of next-generation capripoxvirus vaccines. Full article

Multi-temporal remote sensing image matching is crucial for tasks such as drone positioning under satellite-denial conditions, natural disaster monitoring, and land-cover-change detection. However, the significant differences between multi-temporal images often lead to the reduced accuracy or even failure of most image matching methods in these scenarios. To address this challenge, this paper introduces a Discrete Cosine Transform (DCT) for frequency analysis tailored to the characteristics of remote sensing images, and proposes a network that combines the DCT with attention mechanisms for multi-scale feature matching. First, DCT-enhanced channel attention is embedded in the multi-scale feature extraction module to capture richer ground object information. Second, in coarse-scale feature matching, DCT-guided sparse attention is proposed for feature enhancement, which suppresses the impact of temporal differences on matching while making the amount of computation controllable. The coarse-scale matching results are further refined in the fine-scale feature map to obtain the final matches. Our method achieved correct keypoint percentages of 81.92% and 88.48%, with average corner errors of 4.27 and 2.98 pixels on the DSIFN dataset and LEVIR-CD dataset, respectively, while maintaining a high inference speed. The experimental results demonstrate that our method outperformed the state-of-art methods in terms of both robustness and efficiency in the multi-temporal scenarios. Full article

Throughout its service life, an aero-engine will experience a series of health conditions due to the inevitable performance degradation of its major components, and characteristics will deviate from their initial states. For improving tracking accuracy of the self-tunning on-board engine model on the engine output variables throughout the engine service life, a new method based on the separability index and reverse search algorithm was proposed in this paper. By using this method, a qualified training set of neural networks was created on the basis of eSTORM (enhanced Self Tuning On-board Real-time Model) database, and the problem that the accuracy of neural networks is reduced or even that the training process is not convergent can be solved. Compared with the method of introducing sample memory factors, the method proposed in this paper makes the self-tunning on-board model maintain higher tracking accuracy in the whole engine life, and the algorithm is simple enough for implementation. Finally, the training set center generated in the calculation process of the proposed method could be used for the real-time monitoring of the engine gas path parameters without additional calculations. Compared with the commonly used sliding window method, the proposed method avoids the problem of low algorithm efficiency caused by fewer abnormal data samples. Full article

In the development, evaluation, and application of medical flat-panel detectors, the modulation transfer function (MTF) is crucial, as it reflects the device’s ability to restore detailed information. Medical flat-panel detectors encompass both image data acquisition and digitization processes, and detectors with varying pixel sizes exhibit differing capabilities for observing details. Accurately quantifying MTF is a critical challenge. The complexity of MTF calculation, combined with unclear principles and details, may result in erroneous outcomes, thereby misleading research and decision-making processes. This paper presents an improved MTF oversampling method based on the slit model. MTF testing is conducted under various sample conditions and using different focal spot diameters of the X-ray tube to analyze the impact of focal spot size. High-precision tungsten plates and fixtures are designed and fabricated, and MTF results with varying line spread function (LSF) sampling intervals are compared. The results demonstrate that the improved slit model offers distinct advantages, with MTF measurements achieving 92.4% of the ideal value. Compared to traditional tungsten edge and point (aperture) model testing methods, the accuracy of the proposed method is improved by 5–13%. The optimal sampling interval is approximately 1/29 of the pixel pitch, offering a more accurate method for evaluating detector performance. Full article

The most commonly used optical oxygen sensing materials are phosphorescent molecules and functionalized nanocrystals. Many exploration studies on oxygen sensing have been carried out using the fluorescence or phosphorescence of semiconductor nanomaterials. Lead halide perovskite nanocrystals, a new type of ionic semiconductor, have excellent optical properties, making them suitable for use in optoelectronic devices. They also show promising applications in analytical sensing and biological imaging, especially manganese-doped perovskite nanocrystals for optical oxygen sensing. As a class of materials with diverse sources, copper iodide cluster semiconductors have rich structural and excellent luminescent properties, and have attracted attention in recent years. These materials have adjustable optical properties and sensitive stimulus response properties, showing great potential for optical sensing applications. This review paper provides a brief introduction to traditional oxygen sensing using organic molecules and introduces research on oxygen sensing using novel luminescent semiconductor materials, perovskite metal halides and copper iodide hybrid materials in recent years. It focuses on the mechanism and application of these materials for oxygen sensing and evaluates the future development direction of these materials for oxygen sensing. Full article

A digital infrastructure has the potential to mitigate the digital exclusion in rural areas, offering a pathway to alleviate the financial vulnerability of rural households. This paper investigates the impact of the Broadband China pilot policy—an important government initiative—on rural household financial vulnerability, utilizing data from five waves of the China family panel studies (CFPS) spanning from 2012 to 2020. By leveraging the quasi-natural experiment provided by the Broadband China initiative, this study makes a novel contribution to understanding how a digital infrastructure affects financial sustainability in rural households. The findings show that the Broadband China pilot policy significantly reduces rural household financial vulnerability, with particularly strong effects on female-headed households, spousal-headed households, and those in regions with a limited traditional or advanced digital finance infrastructure. Further analysis reveals that a digital infrastructure enhances rural household financial resilience by increasing land transfer opportunities through an ‘income effect’ and by fostering non-farm employment and financial literacy through a ‘security effect’. This paper contributes to the literature by shedding light on the specific mechanisms through which a digital infrastructure enhances the financial sustainability of rural households and offers valuable insights into policies aimed at bridging the rural–urban divide. Full article

Accurately predicting corn market prices is crucial for ensuring corn production, enhancing farmers’ income, and maintaining the stability of the grain market. However, corn price fluctuations are influenced by various factors, exhibiting non-stationarity, nonlinearity, and high volatility, making prediction challenging. Therefore, this paper proposes a comprehensive, efficient, and accurate method for predicting corn prices. First, in the data processing phase, the seasonal and trend decomposition using LOESS (STL) algorithm was used to extract the trend, seasonality, and residual components of corn prices, combined with the GARCH-in-mean (GARCH-M) model to delve into the volatility clustering characteristics. Next, the kernel principal component analysis (KPCA) was employed for nonlinear dimensionality reduction to extract key information and accelerate model convergence. Finally, a BiGRU-Attention model, optimized by the grey wolf optimizer (GWO), was constructed to predict corn market prices accurately. The effectiveness of the proposed model was assessed through cross-sectional and longitudinal validation experiments. The empirical results indicated that the proposed STLG-KPCA-GWO-BiGRU-Attention (SGKGBA) model exhibited significant advantages in terms of MAE (0.0159), RMSE (0.0215), MAPE (0.5544%), and R² (0.9815). This model effectively captures price fluctuation features, significantly enhances prediction accuracy, and offers reliable trend forecasts for decision makers regarding corn market prices. Full article

In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse samples. This paper explores their application in improving datasets for indoor positioning and brainwave monitoring-based attention tracking. The goal is to develop an intelligent lighting system that adjusts settings based on users’ brainwave states and positions. GANs enhance brainwave monitoring and positioning datasets, with Principal Component Analysis (PCA) applied for dimensionality reduction. machine learning and deep learning models train on these augmented datasets, enabling dynamic lighting adjustments to optimize user experience. GANs undergo parameter fine-tuning to improve dataset quality. Various classification models, including neural networks (NN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM), are used for brainwave monitoring, attention, and positioning. Fuzzy logic enhances system stability. The trained models are integrated with hardware components, such as the Raspberry Pi 4, to implement an “Indoor Positioning Deep Learning Brainwave Monitoring and Attention Monitoring System Based on the Grey Wolf Optimizer Algorithm”. Experimental results demonstrate a positioning accuracy of 15 cm and significant improvements in brainwave monitoring and attention tracking. Full article

The civil engineering sector operates within a complex ecosystem of stakeholders, requiring efficient management and maintenance of structural and infrastructural assets. In this context, there is an increasing need for robust tools to track critical events (e.g., alerts, unusual behaviors) and support decision-making processes related to maintenance and interventions. At the same time, ensuring secure and prompt payments is essential for timely and effective responses. This paper investigated the potential of smart contracts, integrated with blockchain technology, to automate and optimize asset management and maintenance processes. The proposed framework examines how these technologies can enhance operational efficiency, security, and event traceability, providing a structured approach for both routine operations and emergency interventions. Although smart contracts have been widely applied in the construction phase of infrastructure projects, their use in long-term asset management remains largely unexplored. As a conceptual study, this work does not present a quantitative analysis but instead lays the groundwork for future research and real-world applications of blockchain-based smart contracts in infrastructure management and safety procedures. Full article

Enhancing the dynamic response of Flux-Switching Permanent Magnet Synchronous Machines (FSPMSMs) is crucial for high-performance applications such as electric vehicles, renewable energy systems, and industrial automation. Conventional Proportional Integral (PI) controllers within model predictive current control (MPCC) frameworks often struggle to meet the demands of rapid transient response and precise speed tracking, particularly under dynamic operating conditions. To address these challenges, this paper presents a hybrid control strategy that integrates Sliding Mode Control (SMC) into the speed loop of MPCC, aiming to significantly improve the dynamic response and control robustness of FSPMSMs. The feasibility and effectiveness of the proposed approach are validated through high-fidelity real-time simulations using OPAL-RT Technologies’ OP5707XG simulator. Two control schemes are compared: MPCC with a PI controller in the speed loop (MPCC-PI) and MPCC with SMC in the speed loop (MPCC-SMC). Testing was conducted under various operating scenarios, including starting tests, load variations, speed ramping, and speed reversals. The results demonstrate that the MPCC-SMC strategy achieves superior dynamic performance, faster settling times, smoother transitions, and enhanced steady-state precision compared to the MPCC-PI scheme. The comparative results confirm that the MPCC-SMC method outperforms conventional MPCC strategies, making it a compelling solution for advanced motor drive applications requiring enhanced dynamic control. Full article

Generating accurate SQL queries from natural language is critical for enabling non-experts to interact with complex databases, particularly in high-stakes domains like healthcare. This paper presents an extensive evaluation of state-of-the-art large language models (LLM), including LLaMA 3.3, Mixtral, Gemini, Claude 3.5, GPT-4o, and Qwen for transforming medical questions into executable SQL queries using the MIMIC-3 and TREQS datasets. Our approach employs LLMs with various prompts across 1000 natural language questions. The experiments are repeated multiple times to assess performance consistency, token efficiency, and cost-effectiveness. We explore the impact of prompt design on model accuracy through an ablation study, focusing on the role of table data samples and one-shot learning examples. The results highlight substantial trade-offs between accuracy, consistency, and computational cost between the models. This study also underscores the limitations of current models in handling medical terminology and provides insights to improve SQL query generation in the healthcare domain. Future directions include implementing RAG pipelines based on embeddings and reranking models, integrating ICD taxonomies, and refining evaluation metrics for medical query performance. By bridging these gaps, language models can become reliable tools for medical database interaction, enhancing accessibility and decision-making in clinical settings. Full article