Search Results (15,444)

Background: Identification of targetable biomarkers to improve early disease detection and overall patient outcomes is becoming an urgent need in clinical oncology. Ovarian cancer (OC) has one of the highest mortality rates among gynecological cancers. It is asymptomatic and almost always diagnosed at an advanced stage (III or IV), leading to a 5-year survival rate of approximately 35%. Methods: Current therapeutic approaches for OC are very limited and mainly consist of cytoreductive surgery and cisplatin plus taxane-based chemotherapy. No gender and tumor specific biomarkers are known. Exosomes, lipid bilayer vesicles of endocytic origin secreted by most cell types, represent sources of information for their involvement in the onset and progression of many diseases. Hence, research on exosome contents as tools and targets in precise oncology therapy provides knowledge essential to improving diagnosis and prognosis of the disease. Results: This review attempts to give an overview of how exosomes are implicated in ovarian carcinoma pathogenesis to trigger further cancer exosome-based investigations aimed at developing ovarian cancer fine-tuning diagnostic methodologies. Conclusions: It is essential to investigate exosome-based cancer drugs to advance understanding, improve treatment plans, create personalized strategies, ensure safety, and speed up clinical translation to increase patients’ overall survival and quality of life. Papers published in PubMed and Web of Science databases in the last five years (2020–2024) were used as a bibliographic source. Full article

As a type of sustainable and renewable natural source, biomass-derived 5-hydroxymethyl furfural (HMF) can be converted into high-value chemicals. This study investigated the interactions between silver (Ag) and oxide supports with varied reducibility and their contributions to tuning catalytic performance in the selective oxidation of HMF. Three representatives of manganese dioxide (MnO₂), zirconium dioxide (ZrO₂), and silicon dioxide (SiO₂) were selected to support the Ag active sites. The catalysts were characterized by techniques such as STEM (TEM), Raman, XPS, H₂-TPR, and FT-IR spectroscopy to explore the morphology, Ag dispersion, surface properties, and electronic states. The catalytic results demonstrated that MnO₂ with the highest reducibility exhibited superior catalytic performance, achieving 75.4% of HMF conversion and 41.6% of selectivity for 2,5-furandicarboxylic acid (FDCA) at 120 °C. In contrast, ZrO₂ and SiO₂ exhibited limited oxidation capabilities, mainly producing intermediate products like FFCA and/or HMFCA. The oxidation ability of these catalysts was governed by support reducibility, because it determined the density of oxygen vacancies (Ov) and surface hydroxyl groups (O_OH), and eventually influenced the catalytic activity, as demonstrated by the reaction rate: Ag/MnO₂ (3214.5 mol_HMF·g_Ag⁻¹·h⁻¹), Ag/ZrO₂ (2062.3 mol_HMF·g_Ag⁻¹·h⁻¹), and Ag/SiO₂ (1394.4 mol_HMF·g_Ag⁻¹·h⁻¹). These findings provide valuable insights into the rational design of high-performance catalysts for biomass-derived chemical conversion. Full article

The disambiguation of word senses (Word Sense Disambiguation, WSD) plays a crucial role in various natural language processing (NLP) tasks, such as machine translation, sentiment analysis, and information retrieval. Due to the complex morphological structure and polysemy of the Korean language, the meaning of words can change depending on the context, making the WSD problem challenging. Since a single word can have multiple meanings, accurately distinguishing between them is essential for improving the performance of NLP models. Recently, large-scale pre-trained models like BERT and GPT, based on transfer learning, have shown promising results in addressing this issue. However, for languages with complex morphological structures, like Korean, the tokenization mismatch between pre-trained models and fine-tuning data prevents the rich contextual and lexical information learned by the pre-trained models from being fully utilized in downstream tasks. This paper proposes a novel method to address the tokenization mismatch issue during the fine-tuning of Korean WSD, leveraging BERT-based pre-trained models and the Sejong corpus, which has been annotated by language experts. Experimental results using various BERT-based pre-trained models and datasets from the Sejong corpus demonstrate that the proposed method improves performance by approximately 3–5% compared to existing approaches. Full article

Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE. Full article

In the engineering domain, the detection of damage in high-strength bolts is critical for ensuring the safe and reliable operation of equipment. Traditional manual inspection methods are not only inefficient but also susceptible to human error. This paper proposes an automated bolt damage identification method leveraging AIGC (Artificial Intelligence Generated Content) technology and object detection algorithms. Specifically, we introduce the application of AIGC in image generation, focusing on the Stable Diffusion model. Given that the quality of bolt images generated directly by the Stable Diffusion model is suboptimal, we employ the LoRA fine-tuning technique to enhance the model, thereby generating a high-quality dataset of bolt images. This dataset is then used to train the YOLO (You Only Look Once) object detection algorithm, demonstrating significant improvements in both accuracy and recall for bolt damage recognition. Experimental results show that the LoRA fine-tuned Stable Diffusion model significantly enhances the performance of the YOLO algorithm, providing an efficient and accurate solution for automated bolt damage detection. Future work will concentrate on further optimizing the model to improve its robustness and real-time performance, thereby better meeting the demands of practical industrial applications. Full article

This study proposes an optimized approach to enhance the performance of the Weighted Geometric Center (WGC) method for stabilizing time-delay systems, which has applications in industrial process control, robotics, and high-order dynamic systems. The traditional WGC method determines controller parameters by calculating the Weighted Geometric Center of the stable region, but it often overlooks better-performing parameter pairs near the WGC point. To address this limitation, a goal function is formulated based on percentage overshoot, rise time, and settling time. The optimization process explores the vicinity of the WGC and selects controller parameters that minimize the goal function, ensuring improved performance. The proposed optimization is applied to PI and PI-PD controllers, and its effectiveness is demonstrated through multiple case studies. Simulation results indicate that the optimized method significantly improves control performance, particularly in reducing overshoot, enhancing settling time, and ensuring a more stable response compared to the conventional WGC method. For instance, the Optimized WGC method reduces overshoot by up to 15% and settling time by up to 20%. These findings highlight the practical benefits of integrating local optimization into the WGC framework for superior controller tuning in time-delay systems. Full article

In dynamic and unstructured environments, the obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs) are crucial for mission success. Traditional methods struggle with adaptability and effectiveness in unknown or changing scenes. In contrast, the commonly used deep reinforcement learning (DRL) ones suffer from slow convergence, reduced accuracy, and inadequate robustness due to the loss of sensitivity to outliers and parameter rigidity. To address these challenges, we propose an enhanced DRL framework that leverages a Dynamic Huber loss function tailored for UAV autonomous obstacle avoidance. By incorporating Soft updates for target network and dynamically tuning the Huber loss, the proposed method facilitates faster model convergence, superior control precision, and improved robustness. Both theoretical analysis and experimental simulation verify its effectiveness with superior planning success rate, shorter average path length, and faster model convergence over traditional approaches. Specifically, in static environments, the Dynamic Huber-loss-based DRL framework achieves a 98.85% success rate with an optimized average path length of 10.73; in dynamic environments, it attains a 74.20% success rate with an average path length of 37.04; adding wind disturbances in a dynamic environment, it attains a 70.95% success rate with an average path length of 40.40, highlighting its enhanced performance and adaptability. Full article

The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning exam questions with course objectives, improving clarity and difficulty, and generating new items guided by learning goals. The research spans four university courses—two theory-focused and two application-focused—covering diverse cognitive levels according to Bloom’s taxonomy. A balanced dataset ensures representation of question categories and structures. Three LLM-based agents—VectorRAG, VectorGraphRAG, and a fine-tuned LLM—are developed and evaluated against a meta-evaluator, supervised by human experts, to assess alignment accuracy and explanation quality. Robust analytical methods, including mixed-effects modeling, yield actionable insights for integrating generative AI into university assessment processes. Beyond exam-specific applications, this methodology provides a foundational approach for the broader adoption of AI in post-secondary education, emphasizing fairness, contextual relevance, and collaboration. The findings offer a comprehensive framework for aligning AI-generated content with learning objectives, detailing effective integration strategies, and addressing challenges such as bias and contextual limitations. Overall, this work underscores the potential of generative AI to enhance educational assessment while identifying pathways for responsible implementation. Full article

The current, continuous increase in attention toward preservation of the environment and natural resources is forcing resource-intensive industries like steelworks to investigate new solutions to improve resource efficiency and promote the growth of a circular economy. In this context, electric steelworks, which inherently implement circularity principles, are spending efforts to enhance valorization of their main by-product, namely slags. A reliable characterization of the slag’s composition is crucial for the identification of the best valorization pathway, but, currently, slag monitoring is often discontinuous. Furthermore, in the current period of transformation of steel production, preliminary knowledge of the effect of modifications of operating practices on slags composition is crucial to assessing the viability of these modifications. In this paper, a stationary flowsheet model of the electric steelmaking route is presented; this model enables joint monitoring of key variables related to process, steel and slags. For the estimation of the content of most compounds in slags, the average relative percentage error is below 20% for most of the considered steel families. Thus, the tool can be considered suitable for scenario analyses supporting slag valorization. Higher performance is achievable by exploiting more reliable data for model tuning. These data can be obtained via novel devices that gather more numerous and representative data on the amount and composition of slags. Full article

Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory monitoring, research on a deep learning-based analysis of breathing data remains limited. To address this research gap, we optimized CNN and ResNet through systematic hyperparameter tuning, enhancing classification accuracy and robustness. The optimized ResNet outperformed the CNN in accuracy (0.96 vs. 0.87) and precision for Class 4 (0.8 vs. 0.6), demonstrating its capability to capture complex breathing patterns. These findings highlight the importance of hyperparameter optimization in respiratory monitoring and suggest ResNet as a promising tool for real-time assessment in medical applications. Full article

This study proposes a novel approach to enhance the stability and performance of reinforcement learning (RL) in long-horizon tasks. Overestimation bias in value function estimation and high uncertainty within environments make it difficult to determine the optimal action. To address this, we improve the truncated quantile critics algorithm by managing uncertainty in robotic applications. Our dynamic method adjusts the discount factor based on policy entropy, allowing for fine-tuning that reflects the agent’s learning status. This enables the existing algorithm to learn stably even in scenarios with limited training data, ensuring more robust adaptation. By leveraging policy entropy loss, this approach effectively boosts confidence in predicting future rewards. Our experiments demonstrated an 11% increase in average evaluation return compared to traditional fixed-discount-factor approaches in the DeepMind Control Suite and Gymnasium robotics environments. This approach significantly enhances sample efficiency and adaptability in complex long-horizon tasks, highlighting the effectiveness of entropy-guided RL in navigating challenging and uncertain environments. Full article

Against the background of the development of control systems, Data Driven Control (DDC) methods are becoming more and more popular, given the system’s independence from physical models and the possibility of quickly tuning the controller. The usefulness of such tuning algorithms increases with the complexity of the plants. Nonlinear models are the main class of processes for which such laws are amenable. According to the literature, a class of DDC methods exist that perform online estimation of plant behavior with an unknown structure, which is generically called Model Free. This title is assumed by two types of algorithms, which contain it in the name. One is the gradient-based algorithm, Model Free Adaptive Control, defined by Hou, which uses the concept of dynamic linearization through pseudo partial derivatives (PPD) and pseudo gradient (PG). The other is a non-gradient based algorithm, Model Free Control, defined by Fliess and Join, which uses the concept of the ultralocal model and intelligent PID controllers (iPID). For the gradient-based methods, in the compact form of dynamic linearization (CFDL), i.e., partial form dynamic linearization (PFDL), two algorithms are proposed to determine the initial value of the time-varying parameters PPD and PG from the dynamic performance perspective as they offer the best responses. The CFDL and PFDL variants of the MFAC control law, which have parameters that result from the application of the proposed algorithms, are compared with iP and iPD controllers on nonlinear control systems. Full article

Owing to the demand for smoke and flame detection in natural scenes, this paper proposes a lightweight deep learning model, SF-YOLO (Smoke and Fire-YOLO), for video smoke and flame detection in such environments. Firstly, YOLOv11 is employed as the backbone network, combined with the C3k2 module based on a two-path residual attention mechanism, and a target detection head frame with an embedded attention mechanism. This combination enhances the response of the unobscured regions to compensate for the feature loss in occluded regions, thereby addressing the occlusion problem in dynamic backgrounds. Then, a two-channel loss function (W-SIoU) based on dynamic tuning and intelligent focusing is designed to enhance loss computation in the boundary regions, thus improving the YOLOv11 model’s ability to recognize targets with ambiguous boundaries. Finally, the algorithms proposed in this paper are experimentally validated using the self-generated dataset S-Firedata and the public smoke and flame virtual dataset M⁴SFWD. These datasets are derived from internet smoke and flame video frame extraction images and open-source smoke and flame dataset images, respectively. The experimental results demonstrate, compared with deep learning models such as YOLOv8, Gold-YOLO, and Faster-RCNN, the SF-YOLO model proposed in this paper is more lightweight and exhibits higher detection accuracy and robustness. The metrics mAP50 and mAP50-95 are improved by 2.5% and 2.4%, respectively, in the self-made dataset S-Firedata, and by 0.7% and 1.4%, respectively, in the publicly available dataset M⁴SFWD. The research presented in this paper provides practical methods for the automatic detection of smoke and flame in natural scenes, which can further enhance the effectiveness of fire monitoring systems. Full article

Controlling oil well pressure during drilling is one of the most complex and hazardous processes in the exploration stage. The drilling system undergoes constant variations, influenced by factors such as drilling depth, which in turn affects other process parameters. Consequently, applying a time-invariant control strategy becomes impractical. This study aimed to identify the PID parameters necessary to regulate bottom-hole pressure during drilling across different operating depths, with the goal of maintaining system stability and robustness. To achieve this, the parameters were tested using a Gain Scheduling (GS) controller, which adjusted the control gains according to various operating points. In the first section, the development of a mathematical model of the process, based on fluid mechanics, is presented. Linearizing this model introduced an integrating element, which complicated the process dynamics. In the second section, we present the design of the controller using the Internal Model Control (IMC) tuning methodology to address the integration challenges. Finally, PID parameters for different drilling depths were obtained and integrated into the GS controller via Matlab Simulink. The controller’s performance was then evaluated through simulations of typical drilling issues, such as simulated disturbances, confirming its viability. The GS-controlled system was compared to a system using an adaptive controller, demonstrating superior performance in the former. Full article

Feature engineering for whole-genome DNA sequences plays a critical role in predicting plant phenotypic traits. However, due to limitations in the models’ analytical capabilities and computational resources, the existing methods are predominantly confined to SNP-based approaches, which typically extract genetic variation sites for dimensionality reduction before feature extraction. These methods not only suffer from incomplete locus coverage and insufficient genetic information but also overlook the relationships between nucleotides, thereby restricting the accuracy of phenotypic trait prediction. Inspired by the parallels between gene sequences and natural language, the emergence of large language models (LLMs) offers novel approaches for addressing the challenge of constructing genome-wide feature representations with nucleotide granularity. This study proposes FE-WDNA, a whole-genome DNA sequence feature engineering method, using HyenaDNA to fine-tune it on whole-genome data from 1000 soybean samples. We thus provide deep insights into the contextual and long-range dependencies among nucleotide sites to derive comprehensive genome-wide feature vectors. We further evaluated the application of FE-WDNA in agronomic trait prediction, examining factors such as the context window length of the DNA input, feature vector dimensions, and trait prediction methods, achieving significant improvements compared to the existing SNP-based approaches. FE-WDNA provides a mode of high-quality DNA sequence feature engineering at nucleotide resolution, which can be transformed to other plants and directly applied to various computational breeding tasks. Full article