Search Results (24,592)

Abstract: Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized. Full article

RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Fourteen patients (6 RO and 8 ChRCC) were included in the prospective study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice. Full article

Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships, existing methods are unable to handle both dynamic and static relationships simultaneously. To address the limitations of prior research, we introduce a novel stock trend forecasting framework capable of mining the causal relationships that affect changes in companies’ stock prices and simultaneously extracts both dynamic and static features to enhance the forecasting performance. Extensive experimental results in the Chinese stock market demonstrate that the proposed framework achieves obvious improvement against multiple state-of-the-art approaches. Full article

Medical imaging plays an essential role in modern healthcare, providing non-invasive tools for diagnosing and monitoring various medical conditions. However, the resolution limitations of imaging hardware often result in suboptimal images, which can hinder the precision of clinical decision-making. Single image super-resolution (SISR) techniques offer a solution by reconstructing high-resolution (HR) images from low-resolution (LR) counterparts, enhancing the visual quality of medical images. In this paper, we propose an enhanced Residual Feature Learning Network (RFLN) tailored specifically for medical imaging. Our contributions include replacing the residual local feature blocks with standard residual blocks, increasing the model depth for improved feature extraction, and incorporating enhanced spatial attention (ESA) mechanisms to refine the feature selection. Extensive experiments on medical imaging datasets demonstrate that the proposed model achieves superior performance in terms of both quantitative metrics, such as PSNR and SSIM, and qualitative visual quality compared to existing state-of-the-art models. The enhanced RFLN not only effectively mitigates noise but also preserves critical anatomical details, making it a promising solution for high-precision medical imaging applications. Full article

The corrosion of oil and gas pipelines represents a significant factor influencing the safety of these pipelines. The extant research on intelligent algorithms for assessing corrosion rates in pipelines has primarily focused on static evaluation methods, which are inadequate for providing a comprehensive dynamic evaluation of the complex phenomenon of corrosion in buried oil and gas pipelines. This paper proposes a novel approach to predicting the corrosion rate of buried oil and gas pipelines. The method is based on the combination of an improved Beluga Optimization algorithm (IBWO) and Random Forest (RF) optimization with BiLSTM and gated cycle unit (GRU), which are used to classify corrosion rates as high or low. Initially, a feature screening of corrosion factors was conducted via RF, whereby variables exhibiting a strong correlation were extracted. Subsequently, IBWO was employed to optimize the feature selection process, with the objective of identifying the optimal feature subset to enhance the model’s performance. Ultimately, the BiLSTM method was employed for the purpose of predicting the occurrence of low corrosion. A GRU method was employed for prediction in the context of high corrosion conditions. The RF–IBWO-BiLSTM–GRU model constructed in this paper demonstrates high prediction accuracy for both high and low corrosion rates. The verification of 100 groups of experimental data yielded the following results: the mean square error of this model is 0.0498 and the R² is 0.9876, which is significantly superior to that of other prediction models. The combined model, which incorporates an intelligent algorithm, is an effective means of enhancing the precision of buried pipeline corrosion rate prediction. Furthermore, it offers a novel approach and insight that can inform subsequent research on the prediction of corrosion rates in buried oil and gas pipelines. Full article

Background and Objectives: Revumenib (SNDX-5613) is a powerful and specific inhibitor of the menin–KMT2A binding interaction. It is a small molecule that is currently being researched to treat KMT2A-rearranged (KMT2Ar) acute leukemias. Revumenib (RVB) has received Orphan Drug Designation from the US FDA for treating patients with AML. It has also been granted Fast Track designation by the FDA for treating pediatric and adult patients with R/R acute leukemias that have a KMT2Ar or NPM1 mutation. Materials and Methods: The target of this research was to create a fast, precise, green, and extremely sensitive UPLC-MS/MS technique for the estimation of the RVB level in human liver microsomes (HLMs), employing an ESI source. The validation procedures were carried out in accordance with the bioanalytical technique validation requirements established by the US Food and Drug Administration that involve linearity, selectivity, precision, accuracy, stability, matrix effect, and extraction recovery. The outcome data of the validation features of the UPLC-MS/MS approach were acceptable according to FDA guidelines. RVB parent ions were formed in the positive ESI source and its two fragment ions were estimated employing multiple reaction monitoring (MRM) mode. The separation of RVB and encorafenib was achieved using a C8 column (2.1 mm, 50 mm, and 3.5 µm) and isocratic mobile phase. Results: The RVB calibration curve linearity ranged from 1 to 3000 ng/mL (y = 0.6515x − 0.5459 and R² = 0.9945). The inter-day precision and accuracy spanned from −0.23% to 11.33%, while the intra-day precision and accuracy spanned from −0.88% to 11.67%, verifying the reproducibility of the UPLC-MS/MS analytical technique. The sensitivity of the developed methodology demonstrated its capability to quantify RVB levels at an LOQ of 0.96 ng/mL. The AGREE score was 0.77, confirming the greenness of the current method. The low in vitro t_1/2 (14.93 min) and high intrinsic clearance (54.31 mL/min/kg) of RVB revealed that RVB shares similarities with medications that have a high extraction ratio. Conclusions: The present LC-MS/MS approach is considered the first analytical approach with the application of metabolic stability assessment for RVB estimation in HLMs. These methods are essential for advancing the development of new pharmaceuticals, particularly in enhancing metabolic stability. Full article

As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to the alteration in color of rolled leaves. Herein, tea pigments are selected as the key quality indicators during rolling of black tea, aiming to establish rapid detection methods for them. A machine vision system is employed to extract nine color feature variables from the images of samples subjected to varying rolling times. Then, the tea pigment content in the corresponding samples is determined using a UV-visible spectrophotometer. In the meantime, the correlation between color variables and tea pigments is discussed. Additionally, Z-score and PCA are used to eliminate the magnitude difference and redundant information in original data. Finally, the quantitative prediction models of tea pigments based on the images’ color features are established by using PLSR, SVR, and ELM. The data show that the Z-score–PCA–ELM model has the best prediction effect for tea pigments. The Rp values for the model prediction sets are all over 0.96, and the RPD values are all greater than 3.50. In this study, rapid determination methods for tea pigments during rolling of black tea are established. These methods offer significant technical support for the digital production of black tea. Full article

Anxiety is a significant mental health concern that can be effectively monitored using physiological signals such as galvanic skin response (GSR). While the potential of machine learning (ML) algorithms to enhance the classification of anxiety based on GSR signals is promising, their effectiveness in this context remains largely underexplored. This study addresses this gap by investigating the performance of three commonly used ML algorithms, support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF), in classifying anxiety and stress activity using a benchmark dataset. We employed two feature extraction methods: traditional statistical feature extraction and an innovative automatic feature extraction approach utilizing a 14-layer autoencoder, aimed at improving classification performance. Our findings demonstrate the effectiveness of using GSR signals and the robust performance of the KNN algorithm in accurately classifying anxiety levels. The KNN algorithm achieved the highest accuracy in both the statistical and automatic feature extraction approaches, with results of 96.9% and 98.2%, respectively. These findings highlight the effectiveness of KNN for anxiety detection and emphasize the need for advanced feature extraction techniques to enhance classification outcomes in mental health monitoring. Full article

Background: Mechanical ventilation is a critical but resource-intensive treatment. Automated tools are common in screening diagnostics, whereas real-time, continuous trend analysis in mechanical ventilation remains rare. Current techniques for monitoring lung conditions are often invasive, lack accuracy, and fail to isolate respiratory resistance—making them impractical for continuous monitoring and diagnosis. To address this challenge, we propose an automated, non-invasive condition monitoring method to support pulmonologists. Methods: Our method leverages ventilation waveform time-series data in controlled modes to monitor lung conditions automatically and non-invasively on a breath-by-breath basis while accurately isolating respiratory resistance. Results: Using statistical classification and regression models, the approach achieves 99.1% accuracy for ventilation mode classification, 97.5% accuracy for feature extraction, and 99.0% for predicting mechanical lung parameters. The models are both computationally efficient (720 K predictions per second per core) and lightweight (24.5 MB). Conclusions: By storing breath-by-breath predictions, pulmonologists can access a high-resolution trend of lung conditions, gaining clear insights into sudden changes without speculation and streamlining diagnosis and decision-making. The deployment of this solution could expand domain knowledge, enhance the understanding of patient conditions, and enable real-time dashboards for parallel monitoring, helping to prioritize patients and optimize resource use, which is especially valuable during pandemics. Full article

Background: Parkinson’s disease (PD) diagnosis benefits significantly from advancements in artificial intelligence (AI) and image processing techniques. This paper explores various approaches for processing hand-drawn Archimedean spirals in order to detect signs of PD. Methods: The best approach is selected to be integrated in a neurodegenerative disease management platform called NeuroPredict. The most innovative aspects of the presented approaches are related to the employed feature extraction techniques that convert hand-drawn spirals into a frequency spectra, so that frequency features may be extracted and utilized as inputs for various classification algorithms. A second category of extracted features contains information related to the thickness and pressure of drawings. Results: The selected approach achieves an overall accuracy of 95.24% and allows acquiring new test data using only a pencil and paper, without requiring a specialized device like a graphic tablet or a digital pen. Conclusions: This study underscores the clinical relevance of AI in enhancing diagnostic precision for neurodegenerative diseases. Full article

In recent years, radar automatic target recognition (RATR) utilizing high-resolution range profiles (HRRPs) has received significant attention. Approaches based on deep learning have demonstrated remarkable efficacy in HRRP recognition tasks. However, the performance of neural networks is notably vulnerable to noise, leading to a detrimental effect on their recognition accuracy and overall robustness. To address this issue, a residual scattering attention network (RSAN) is proposed for HRRP target recognition, which comprises a residual scattering network, ResNet18, and a self-attention module. The residual scattering network is designed to suppress noise components and extract noise-robust features. It is derived from the improvement of a scattering network and does not need to learn parameters from the data. ResNet18 is employed for the purpose of extracting a deep representation of scattering features for HRRPs. Furthermore, a self-attention module is integrated into ResNet18, enabling the model to focus on target regions, thereby enhancing its feature-learning capability. The effectiveness and noise robustness of the proposed method are validated through experiments conducted on two measured datasets. Full article

Genomic selection serves as an effective way for crop genetic breeding, capable of significantly shortening the breeding cycle and improving the accuracy of breeding. Phenotype prediction can help identify genetic variants associated with specific phenotypes. This provides a data-driven selection criterion for genomic selection, making the selection process more efficient and targeted. Deep learning has become an important tool for phenotype prediction due to its abilities in automatic feature learning, nonlinear modeling, and high-dimensional data processing. Current deep learning models have improvements in various aspects, such as predictive performance and computation time, but they still have limitations in capturing the complex relationships between genotype and phenotype, indicating that there is still room for improvement in the accuracy of phenotype prediction. This study innovatively proposes a new method called DeepAT, which mainly includes an input layer, a data feature extraction layer, a feature relationship capture layer, and an output layer. This method can predict wheat yield based on genotype data and has innovations in the following four aspects: (1) The data feature extraction layer of DeepAT can extract representative feature vectors from high-dimensional SNP data. By introducing the ReLU activation function, it enhances the model’s ability to express nonlinear features and accelerates the model’s convergence speed; (2) DeepAT can handle high-dimensional and complex genotype data while retaining as much useful information as possible; (3) The feature relationship capture layer of DeepAT effectively captures the complex relationships between features from low-dimensional features through a self-attention mechanism; (4) Compared to traditional RNN structures, the model training process is more efficient and stable. Using a public wheat dataset from AGT, comparative experiments with three machine learning and six deep learning methods found that DeepAT exhibited better predictive performance than other methods, achieving a prediction accuracy of 99.98%, a mean squared error (MSE) of only 28.93 tones, and a Pearson correlation coefficient close to 1, with yield predicted values closely matching observed values. This method provides a new perspective for deep learning-assisted phenotype prediction and has great potential in smart breeding. Full article

Detailed evaluation of prostate cancer glands is an essential yet labor-intensive step in grading prostate cancer. Gland segmentation can serve as a valuable preliminary step for machine-learning-based downstream tasks, such as Gleason grading, patient classification, cancer biomarker building, and survival analysis. Despite its importance, there is currently a lack of a reliable gland segmentation model for prostate cancer. Without accurate gland segmentation, researchers rely on cell-level or human-annotated regions of interest for pathomic and deep feature extraction. This approach is sub-optimal, as the extracted features are not explicitly tailored to gland information. Although foundational segmentation models have gained a lot of interest, we demonstrated the limitations of this approach. This work proposes a prostate gland segmentation framework that utilizes a dual-path Swin Transformer UNet structure and leverages Masked Image Modeling for large-scale self-supervised pretaining. A tumor-guided self-distillation step further fused the binary tumor labels of each patch to the encoder to ensure the encoders are suitable for the gland segmentation step. We united heterogeneous data sources for self-supervised training, including biopsy and surgical specimens, to reflect the diversity of benign and cancerous pathology features. We evaluated the segmentation performance on two publicly available prostate cancer datasets. We achieved state-of-the-art segmentation performance with a test mDice of 0.947 on the PANDA dataset and a test mDice of 0.664 on the SICAPv2 dataset. Full article

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article

Cancer-related diseases are some of the major health hazards affecting individuals globally, especially breast cancer. Cases of breast cancer among women persist, and the early indicators of the diseases go unnoticed in many cases. Breast cancer can therefore be treated effectively if the detection is correctly conducted, and the cancer is classified at the preliminary stages. Yet, direct mammogram and ultrasound image diagnosis is a very intricate, time-consuming process, which can be best accomplished with the help of a professional. Manual diagnosis based on mammogram images can be cumbersome, and this often requires the input of professionals. Despite various AI-based strategies in the literature, similarity in cancer and non-cancer regions, irrelevant feature extraction, and poorly trained models are persistent problems. This paper presents a new Multi-Feature Attention Network (MFAN) for breast cancer classification that works well for small lesions and similar contexts. MFAN has two important modules: the McSCAM and the GLAM for Feature Fusion. During channel fusion, McSCAM can preserve the spatial characteristics and extract high-order statistical information, while the GLAM helps reduce the scale differences among the fused features. The global and local attention branches also help the network to effectively identify small lesion regions by obtaining global and local information. Based on the experimental results, the proposed MFAN is a powerful classification model that can classify breast cancer subtypes while providing a solution to the current problems in breast cancer diagnosis on two public datasets. Full article