Search Results (14,706)

The critical challenge of achieving precise geometric control in laser directed energy deposition (L-DED) of Inconel 690 for nuclear applications is addressed by this study. We established a data-driven optimization framework that reduces time-consuming trial-and-error experiments. A comprehensive process-geometry dataset was generated through full-factor experiments. Pearson correlation analysis revealed significant correlations: strong positive correlations between laser power and bead width (r = 0.82) and depth (r = 0.85), and between powder feed rate and height (r = 0.70). A hybrid machine learning model was subsequently developed. It used a Backpropagation Neural Network (BPNN) to achieve excellent prediction of width, height, and depth (R² ≤ 0.962). It also generated 100 uniformly distributed Pareto optimal process parameter sets via the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Experimental validation confirmed the model’s high predictive accuracy—relative error ≤ 5% for width/depth, and a maximum relative error of 5.34% for height. This demonstrates the framework’s effectiveness for reliable multi-objective process optimization in high-precision deposition tasks. It also highlights its potential for use in nuclear component repair and other material systems. Full article

Background: Oxidative stress and inflammation are major drivers of metabolic inflammatory diseases, and natural antioxidant peptides represent promising therapeutic agents. Antioxidant peptides derived from Cyperus protein (CAOP) exhibit high digestibility and bioavailability, but their antioxidant and anti-inflammatory mechanisms remain unclear. Methods: We employed in vitro experiments, non-targeted metabolomics, peptide omics, and molecular docking techniques to explore how CAOP exerts dual antioxidant and anti-inflammatory effects. Results: The in vitro experiments showed that in LPS-induced RAW264.7 cells, CAOP not only significantly increased the levels of superoxide dismutase (SOD) and catalase (CAT) but also significantly reduced the gene expression and secretion of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), as well as the phagocytic ability of cells. Metabolomics studies indicate that CAOP protects cells from LPS-induced damage by enhancing intracellular glutathione metabolism pathways, glyceraldehyde and dicarboxylic acid metabolism pathways, pantothenic acid and coenzyme A biosynthesis metabolism pathways, and thiamine metabolism pathways while inhibiting the ferroptosis pathway. CAOP was purified using Sephadex G-25 column chromatography, and its amino acid sequence was determined using LC-MS/MS technology. Subsequently, 25 peptide sequences were screened through bioinformatics analysis. These peptides can target Keap1. Among them, DLHMFVWS (-ICE = 62.8072) and LGHPWGNAPG (-ICE = 57.4345) are most likely to activate the Nrf2-Keap1 pathway. Conclusions: CAOP exerts antioxidant and anti-inflammatory effects by regulating the key metabolic networks, demonstrating its therapeutic promise for associated with oxidative damage and metabolic inflammation disorders. Full article

Background and Objectives: The use of Artificial Intelligence (AI) in the medical field is rapidly expanding. This review aims to explore and summarize all published research on the development and validation of deep learning (DL) models in gynecologic laparoscopic surgeries. Materials and Methods: MEDLINE, IEEE Xplore, and Google scholar were searched for eligible studies published between January 2000 and May 2025. Selected studies developed a DL model using datasets derived from gynecologic laparoscopic procedures. The exclusion criteria included non-gynecologic datasets, non-laparoscopic datasets, non-Convolutional Neural Network (CNN) models, and non-English publications. Results: A total of 16 out of 621 studies met our inclusion criteria. The findings were categorized into four main application areas: (i) anatomy classification (n = 6), (ii) anatomy segmentation (n = 5), (iii) surgical instrument classification and segmentation (n = 5), and (iv) surgical action recognition (n = 5). Conclusions: This review emphasizes the growing role of AI in gynecologic laparoscopy, improving anatomy recognition, instrument tracking, and surgical action analysis. As datasets grow and computational capabilities advance, these technologies are poised to improve intraoperative guidance and standardize surgical training. Full article

Reliable estimation of operational modal parameters is essential in structural health monitoring (SHM), particularly when these parameters serve as damage-sensitive features. Modern distributed monitoring systems, often employing digital MEMS accelerometers, must account for timing uncertainties across sensor networks. Clock irregularities can lead to non-deterministic sampling, introducing uncertainty in the identification of modal parameters. In this paper, the effects of timing variability throughout the network are propagated to the final modal quantities through a Monte-Carlo-based framework. The modal parameters are identified using the covariance-driven stochastic subspace identification (SSI-COV) algorithm. A finite element model of a steel cantilever beam serves as a test case, with timing irregularities modeled probabilistically to simulate variations in sensing node clock stability. The results demonstrate that clock variability at both intra-node and inter-node levels significantly influences mode shape estimation and introduces systematic biases in the identified natural frequencies and damping ratios. The confidence intervals are calculated, showing increased uncertainty with greater timing irregularity. Furthermore, the study examines how clock variability impacts damage detection, offering metrological insights into the limitations of distributed vibration-based SHM systems. Overall, the findings offer guidance for designing and deploying monitoring systems with independently timed nodes, aiming to enhance their reliability and robustness. Full article

Liver cirrhosis represents the end-stage of chronic hepatic injury, arising from a diverse range of etiologies including viral hepatitis, alcohol abuse and non-alcoholic fatty liver disease. A key driver of cirrhosis is hepatic fibrogenesis, a multifaceted process involving hepatic stellate cell activation, inflammatory signaling and extracellular matrix accumulation. MicroRNAs (miRNAs), a class of small non-coding RNAs, have emerged as pivotal regulators in this context, modulating gene expression networks that govern inflammation, fibrosis and hepatocarcinogenesis. This review synthesizes current evidence on the role of miRNAs in liver cirrhosis, emphasizing specific miRNAs such as miR-21, miR-122, miR-125, miR-146 and miR-155. These miRNAs influence pathways involving TGF-β, NF-κB and PI3K/Akt signaling, contributing to either fibrogenic progression or its suppression. The unique expression profiles and stability of miRNAs in biological fluids position them as promising non-invasive biomarkers for cirrhosis diagnosis and monitoring. Moreover, therapeutic modulation of miRNA activity through mimics or inhibitors holds future potential, though delivery and safety challenges remain. Advancing our understanding of miRNA-mediated regulation in cirrhosis could transform current diagnostic and therapeutic strategies, enabling more precise and personalized liver disease management. Full article

To address the issue of low trajectory tracking accuracy in six-degree-of-freedom robotic arms, this study proposes a trajectory tracking control strategy that integrates a Radial Basis Function Neural Network (RBFNN) with non-singular fast terminal sliding mode (NFTSM) control. (1) The Lagrangian method is utilized to develop the dynamic model of the robotic arm. At the same time, a non-singular fast terminal sliding surface is designed to accelerate trajectory convergence and resolve the singularity problem commonly associated with traditional sliding mode control by integrating nonlinear and fast terminal terms. (2) The RBF neural network is employed to globally approximate and compensate for uncertainties in the model and variations in the parameters of the robotic arm. (3) To confirm the overall stability of the control system with the proposed NFTSM control strategy, the Lyapunov stability theory is applied to formulate a Lyapunov function. (4) The six-degree-of-freedom robotic manipulator is simulated in the MATLAB/Simulink environment to assess the effectiveness of the proposed control method. In addition, experimental validation is carried out on a real robotic manipulator to verify the effectiveness of the proposed method. The simulation and experimental results show that, compared with NFTSM and RBFNN-SMC, the proposed control strategy significantly enhances the trajectory tracking accuracy of the six-degree-of-freedom robotic manipulator, thereby offering an effective and practical solution for its trajectory tracking control. Full article

We investigate binary sequences generated by non-Markovian rules with memory length $\mu$, similar to those adopted in elementary cellular automata. This generation procedure is equivalent to a shift register, and certain rules produce sequences with maximal periods, known as de Bruijn sequences. We introduce a novel methodology for generating de Bruijn sequences that combines (i) a set of derived properties that significantly reduce the space of feasible generating rules and (ii) a neural-network-based classifier that identifies which rules produce de Bruijn sequences. The experiments for some values of $\mu$ demonstrate the approach’s effectiveness and computational efficiency. Full article

In factory-controlled recirculating aquaculture systems, precise regulation of water velocity is crucial for optimizing shrimp feeding behavior and improving aquaculture efficiency. However, quantitative analysis of the impact of water velocity on shrimp behavior remains challenging. This study developed an innovative multi-objective behavioral analysis framework integrating detection, tracking, and behavioral interpretation. Specifically, the YOLOv8 model was employed for precise shrimp detection, ByteTrack with a dual-threshold matching strategy ensured continuous individual trajectory tracking in complex water environments, and Kalman filtering corrected coordinate offsets caused by water refraction. Under typical recirculating aquaculture system conditions, three water circulation rates (2.0, 5.0, and 10.0 cycles/day) were established to simulate varying flow velocities. High-frequency imaging (30 fps) was used to simultaneously record and analyze the movement trajectories of Litopenaeus vannamei during feeding and non-feeding periods, from which two-dimensional behavioral parameters—velocity and turning angle—were extracted. Key experimental results indicated that water circulation rates significantly affected shrimp movement velocity but had no significant effect on turning angle. Importantly, under only the moderate circulation rate (5.0 cycles/day), the average movement velocity during feeding was significantly lower than during non-feeding periods (p < 0.05). This finding reveals that moderate water velocity constitutes a critical hydrodynamic window for eliciting specific feeding behavior in shrimp. These results provide core parameters for an intelligent Litopenaeus vannamei feeding intensity assessment model based on spatiotemporal graph convolutional networks and offer theoretically valuable and practically applicable guidance for optimizing hydrodynamics and formulating precision feeding strategies in recirculating aquaculture systems. Full article

We aimed to evaluate the non-invasive photoplethysmography waveform as a means to predict mean arterial pressure using artificial intelligence models. This was performed using datasets captured in large animal hemorrhage and resuscitation studies. An initial deep learning model trained using a subset of large animal data and was then evaluated for real-time blood pressure prediction. With the successful proof-of-concept experiment, we further tested different feature extraction approaches as well as different machine learning and deep learning methodologies to examine how various combinations of these methods can improve the accuracy of mean arterial pressure predictions from a non-invasive photoplethysmography sensor. Different combinations of feature extraction and artificial intelligence models successfully predicted mean arterial pressure throughout the study. Overall, manual feature extraction fed into a long short-term memory network tracked the mean arterial pressure through hemorrhage and resuscitation with the highest accuracy. Full article

Ultrasound array imaging frequently employs a coupling medium to facilitate wave transmission from the transducer to the target component. Surface echoes, identified by their high-amplitude peaks, are crucial for determining the Time of Flight (TOF) in each channel, which is essential for deriving imaging focal laws. Accurate TOF measurement is vital in numerous applications, such as Non-Destructive Testing (NDT) and medical imaging. Conventional methods, such as threshold crossing and peak search, are highly sensitive to noise and spurious signals, therefore, more robust estimation techniques are needed. This study explores the application of a deep 3D Convolutional Neural Network (CNN) to detect surface echoes in Full Matrix Capture (FMC) ultrasound data. The CNN was trained on signals obtained with a matrix array and a set of reference components, utilizing a robotic arm setup to ensure precise probe positioning. Theoretical TOFs were computed based on the setup geometry to generate labeled training data. Test results indicated that the CNN model, which we have called DeepEcho3D, closely aligned with the ground truth and significantly reduced TOF estimation outliers (up to 98%) compared to traditional methods, demonstrating its potential for improved accuracy in surface echo detection. Full article

Effective rural governance is essential for fully advancing rural revitalization and achieving sustainable development in rural areas. The construction and operation of rural governance networks are intrinsically tied to governance effectiveness. Focusing on the Tianlong Tunpu community in Guizhou Province, China, this research applies Actor–Network Theory (ANT) to analyze the transformation of rural governance networks. It introduces the “administrative–social–market” threefold empowerment mechanism to explain the underlying mechanism of this process. The findings indicate that the successful construction and operation of a stable rural governance network hinge on the ability of key actors to continuously mobilize administrative, social, and market resources during translation processes, thereby achieving stable “administrative–social–economic” threefold empowerment. This mechanism is dynamic, adapting through reallocation and adjustment to meet the changing realities of rural development. The study also highlights the combined influence of human and non-human actors in the rural governance network. Among non-human factors, Tunpu culture stands out for its cultural and economic value, serving as a shared foundation for collaboration between local governments, rural elites, villagers, and businesses. This cultural element acts as a cornerstone, ensuring the network’s stability and adaptability over time. Full article

Modern networked industrial applications often require low-latency communication. Some applications evolve over time, however, are tied to yet existing infrastructures, like power grids spanning across large areas. For instance, medium voltage direct current (MVDC) grids are evolving to a promising alternative to traditional medium voltage alternating current (MVAC) grids due to their efficiency and suitability for novel use cases like electric mobility. MVDC grids, however, require an active control and fault handling strategy. Some strategies demand for a continuous state exchange of the converter substations via a low-latency communication channel with less than 1 millisecond. While some communication approaches for MVDC grids are described in the literature, none of them is inherently designed to be secure. In this paper, we present a protocol for ultra-low-latency secure state exchange (PULLSE) based on conventional non-deterministic Ethernet and AES-GCM. We chose Ethernet in order to not limit the approaches usability in terms of hardware requirements or communication patterns. PULLSE is designed to prevent traffic eavesdropping, replay, and manipulation attacks. Full article

Target-oriented opinion words extraction (TOWE) is a novel subtask of aspect-based sentiment analysis (ABSA), which aims to extract opinion words corresponding to a given opinion target within a sentence. In recent years, neural networks have been widely used to solve this problem and have achieved competitive results. However, when faced with complex and long sentences, the existing methods struggle to accurately identify the semantic relationships between distant opinion targets and opinion words. This is primarily because they rely on literal distance, rather than semantic distance, to model the local context or opinion span of the opinion target. To address this issue, we propose a neural network model called DTOWE, which comprises (1) a global module using Inward-LSTM and Outward-LSTM to capture general sentence-level context, and (2) a local module that employs BiLSTM combined with DT-LCF to focus on target-specific opinion spans. DT-LCF is implemented in two ways: DT-LCF-Mask, which uses a binary mask to zero out non-local context beyond a dependency tree distance threshold, α, and DT-LCF-weight, which applies a dynamic weighted decay to downweigh distant context based on semantic distance. These mechanisms leverage dependency tree structures to measure semantic proximity, reducing the impact of irrelevant words and enhancing the accuracy of opinion span detection. Extensive experiments on four benchmark datasets demonstrate that DTOWE outperforms state-of-the-art models. Specifically, DT-LCF-Weight achieves F1-scores of 73.62% (14lap), 82.24% (14res), 75.35% (15res), and 83.83% (16res), with improvements of 2.63% to 3.44% over the previous state-of-the-art (SOTA) model, IOG. Ablation studies confirm that the dependency tree-based distance measurement and DT-LCF mechanism are critical to the model’s effectiveness, validating their ability to handle complex sentences and capture semantic dependencies between targets and opinion words. Full article

Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of various biological processes in plants, including development, stress responses, and pathogen defense. Advances in multi-omics sequencing analysis and molecular biology methods have significantly expanded our understanding of the plant lncRNA landscape, revealing novel lncRNAs across diverse species. In this review, we provided an overview of the essential roles of lncRNAs in multilevel regulatory functions in plant growth, development, and stress responses. Moreover, we bridged the module network among these different conditions. One of the most important functions of lncRNA is gene expression regulation. Thus, we summarized the plant lncRNAs acting in cis/trans and as endogenous target mimics (eTMs) to influence the expression of target genes in transcription and post-transcription regulation. This review also sheds light on several application values in agricultural production and development of plant-specific databases and bioinformatic tools. These datasets facilitated the exploration of lncRNA function, enabling the identification of their expression patterns, phylogenetic relationships, and molecular interactions. As research progresses, multi-omics approaches will provide deeper insights into the regulatory mechanisms of lncRNAs, offering promising strategies for enhancing crop resilience and productivity in response to climate change. Full article

Under cool and moist climates, timely implementation of ditch network maintenance (DNM) is crucial for sustaining productivity of drained forests, thus reducing operational costs, while mitigating environmental risks. This underscores the need to understand tree growth responses to DNM. This study evaluated the effects of DNM on tree radial increment in sites with both organic and mineral drained soils, focusing on regionally commercially important species: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and silver birch (Betula pendula). Responses of relative growth changes over eight years post-DNM to site and tree characteristics were assessed using a linear mixed-effects model. Species- and site-specific growth responses to DNM were indicated by significant interactions between tree species, site type, and distance from the drainage ditch. While growth responses were generally neutral (non-significant), variability among sites and species suggests that both organic and mineral soils might be prone to site-level moisture depletion near drainage infrastructure in the post-DNM period. The effect of stand age and density suggested higher responsiveness of older and less dense stands, hence positive effects of thinning to resilience of stands to DNM. These findings highlight the importance of adapting DNM strategies to local site conditions and stand characteristics to minimize drought-related growth limitations. Full article