Search Results (1,423)

In this paper, SiC and Si/SiC ceramics were fabricated using direct laser powder bed fusion with chemical vapor infiltration. Their microstructure, mechanical properties and the impacts of silicon addition were analyzed. The incorporation of silicon led to an increase in the relative density of the silicon carbide ceramics from 76.4% to 78.3% and the compression strength increased from 39 ± 13 MPa to 90 ± 8 MPa after laser powder bed fusion with chemical vapor infiltration. The melting and re-solidification of silicon allows the silicon to encapsulate the silicon carbide grains, changing the microstructure and the failure mechanism of the silicon carbide ceramics, resulting in a small amount of silicon residue. In the LPBF-CVI SiC ceramic specimen, the LPBF-formed SiC exhibits a microhardness of 24.2 ± 1.0 GPa. In LPBF-CVI Si/SiC, the spherical dual-phase structure displays a moderately increased hardness (25.9 ± 4.4 GPa), and the CVI-formed SiC exhibits a hardness of 55.3 ± 9.3 GPa. Full article

In this narrative review, we explore the role of artificial intelligence (AI) in managing lumbar degenerative conditions, a topic that has recently garnered significant interest. The use of AI-based solutions in spine surgery is particularly appealing due to its potential applications in preoperative planning and outcome prediction. This study aims to clarify the impact of artificial intelligence models on the diagnosis and prognosis of common types of degenerative conditions: lumbar disc herniation, spinal stenosis, and eventually spinal fusion. Additionally, the study seeks to identify predictive factors for lumbar fusion surgery based on a review of the literature from the past 10 years. From the literature search, 96 articles were examined. The literature on this topic appears to be consistent, describing various models that show promising results, particularly in predicting outcomes. However, most studies adopt a retrospective approach and often lack detailed information about imaging features, intraoperative findings, and postoperative functional metrics. Additionally, the predictive performance of these models varies significantly, and few studies include external validation. The application of artificial intelligence in treating degenerative spine conditions, while valid and promising, is still in a developmental phase. However, over the last decade, there has been an exponential growth in studies related to this subject, which is beginning to pave the way for its systematic use in clinical practice. Full article

This study systematically investigates the homogenization-induced Laves phase dissolution kinetics and recrystallization mechanisms in laser powder bed fusion (L-PBF) processed IN718 superalloy. The as-built material exhibits a characteristic fine dendritic microstructure with interdendritic Laves phase segregation and high dislocation density, featuring directional sub-grain boundaries aligned with the build direction. Laves phase dissolution demonstrates dual-stage kinetics: initial rapid dissolution (0–15 min) governed by bulk atomic diffusion, followed by interface reaction-controlled deceleration (15–60 min) after 1 h at 1150 °C. Complete dissolution of the Laves phase is achieved after 3.7 h at 1150 °C. Recrystallization initiates preferentially at serrated grain boundaries through boundary bulging mechanisms, driven by localized orientation gradients and stored energy differentials. Grain growth kinetics obey a fourth-power time dependence, confirming Ostwald ripening-controlled boundary migration via grain boundary diffusion. Such a study is expected to be helpful in understanding the microstructural development of L-PBF-built IN718 under heat treatments. Full article

This study aims to determine the optimal low-temperature stress relieving heat treatment that minimizes residual stresses while preserving corrosion resistance in Laser Powder Bed Fusion (L-PBF) processed Ti6Al4V alloy. Specifically, it investigates the effects of stress relieving at 400 °C, 600 °C, and 800 °C on microstructure, residual stress, and electrochemical performance. Specimens were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and electrochemical techniques. A novel microcapillary electrochemical method was employed to precisely assess passive layer stability and corrosion behaviour under simulated oral conditions, including fluoride contamination and tensile loading. Results show that heat treatments up to 600 °C effectively reduce residual stress with minimal impact on corrosion resistance. However, 800 °C treatment leads to a phase transformation from α′ martensite to a dual-phase α + β structure, significantly compromising passive film integrity. The findings establish 600 °C as the optimal stress-relieving temperature for balancing mechanical stability and electrochemical performance in biomedical and aerospace components. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors, with a five-year overall survival rate below 10%. While the introduction of multi-agent chemotherapy regimens has improved outcomes marginally, most patients with advanced disease continue to have limited therapeutic options. Molecular profiling has uncovered actionable genomic alterations in select subgroups of PDAC, yet the clinical impact of targeted therapies remains modest. This review aims to provide a clinically oriented synthesis of emerging molecular targets in PDAC, their therapeutic relevance, and practical considerations for biomarker testing, including current FDA and EMA indications. Methods: A narrative review was conducted using data from PubMed, Embase, Scopus, and international guidelines (NCCN, ESMO, ASCO). The selection focused on evidence published between 2020 and 2025, highlighting molecularly defined PDAC subsets and the current status of targeted therapies. Results: Actionable genomic alterations in PDAC include KRAS G12C mutations, BRCA1/2 and PALB2-associated homologous recombination deficiency, MSI-H/dMMR status, and rare gene fusions involving NTRK, RET, and NRG1. While only a minority of patients are eligible for targeted treatments, early-phase trials and real-world data have shown promising results in these subgroups. Testing molecular profiling is increasingly standard in advanced PDAC. Conclusions: Despite the rarity of targetable mutations, systematic molecular profiling is critical in advanced PDAC to guide off-label therapy or clinical trial enrollment. A practical framework for identifying and acting on molecular targets is essential to bridge the gap between precision oncology and clinical management. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

The anode assembly, as a key component in the electrolytic aluminum process, is composed of steel claws and aluminum guide rods. The connection quality between the steel claws and guide rods directly affects the current conduction efficiency, energy consumption, and operational stability of equipment. Achieving high-quality joining between the aluminum alloy and steel has become a key process in the preparation of the anode assembly. To join the guide rods and steel claws, this work uses Cold Metal Transfer (CMT) technology to clad aluminum on the steel surface and employs machine vision to detect surface forming defects in the cladding layer. The influence of different currents on the interfacial microstructure and mechanical properties of aluminum alloy cladding on the steel surface was investigated. The results show that increasing the cladding current leads to an increase in the width of the fusion line and grain size and the formation of layered Fe₂Al₅ intermetallic compounds (IMCs) at the interface. As the current increases from 90 A to 110 A, the thickness of the Al-Fe IMC layer increases from 1.46 μm to 2.06 μm. When the current reaches 110 A, the thickness of the interfacial brittle phase is the largest, at 2 ± 0.5 μm. The interfacial region where aluminum and steel are fused has the highest hardness, and the tensile strength first increases and then decreases with the current. The highest tensile strength is 120.45 MPa at 100 A. All the fracture surfaces exhibit a brittle fracture. Full article

To address the challenges of unstable vibration signals, indistinct fault features, and difficulties in feature extraction during rolling bearing operation, this paper presents a novel fault diagnosis method based on the fusion of PSR-CRP and DenseNet. The Phase Space Reconstruction (PSR) method transforms one-dimensional bearing vibration data into a three-dimensional space. Euclidean distances between phase points are calculated and mapped into a Color Recurrence Plot (CRP) to represent the bearings’ operational state. This approach effectively reduces feature extraction ambiguity compared to RP, GAF, and MTF methods. Fault features are extracted and classified using DenseNet’s densely connected topology. Compared with CNN and ViT models, DenseNet improves diagnostic accuracy by reusing limited features across multiple dimensions. The training set accuracy was 99.82% and 99.90%, while the test set accuracy is 97.03% and 95.08% for the CWRU and JNU datasets under five-fold cross-validation; F1 scores were 0.9739 and 0.9537, respectively. This method achieves highly accurate diagnosis under conditions of non-smooth signals and inconspicuous fault characteristics and is applicable to fault diagnosis scenarios for precision components in aerospace, military systems, robotics, and related fields. Full article

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article

Selective laser melting (SLM) offers a novel approach for manufacturing intricate structures, broadening the application of titanium alloy parts in the aerospace industry. After the build period, heat treatments of annealing (AT) and hot isostatic pressing (HIP) are often implemented, but a comparison of their mechanical performances based on the specimen orientation is still lacking. In this study, horizontally and vertically built Ti6Al4V SLM specimens that underwent the aforementioned treatments, together with their microstructural and defect characteristics, were, respectively, investigated using metallography and X-ray imaging. The mechanical properties and failure mechanism, via fracture analysis, were obtained. The critical factors influencing the mechanical properties and the correlation of the fatigue lives and failure origins were also estimated. The results demonstrate that the mechanical performances were determined by the α-phase morphology and defects, which included micropores and fewer large lack-of-fusion defects. Following the coarsening of the α phase, the strength decreased while the plasticity remained stable. With the discrepancy in the defect occurrence, anisotropy and scatter of the mechanical performances were introduced, which was significantly alleviated with HIP treatment. The fatigue failure origins were governed by defects and the α colony, which was composed of parallel α phases. Approximately linear relationships correlating fatigue lives with the X-parameter and maximum stress amplitude were, respectively, established in the AT and HIP states. The results provide an understanding of the technological significance of the evaluation of mechanical properties. Full article

Introduction: Brain tumor, marked by abnormal and rapid cell growth, poses severe health risks and requires accurate diagnosis for effective treatment. Classifying brain tumors using deep learning techniques applied to Magnetic Resonance Imaging (MRI) images has attracted the attention of many researchers, and specifically, reducing the bias of models and enhancing robustness is still a very pertinent active topic of attention. Methods: For capturing diverse information from different feature sets, we propose a Features Concatenation-based Brain Tumor Classification (FCBTC) Framework using Hybrid Deep Learning Models. For this, we have chosen three pretrained models—ResNet50; VGG16; and DensetNet121—as the baseline models. Our proposed hybrid models are built by the fusion of feature vectors. Results: The testing phase results show that, for the FCBTC Model-3, values for Precision, Recall, F1-score, and Accuracy are 98.33%, 98.26%, 98.27%, and 98.40%, respectively. This reinforces our idea that feature diversity does improve the classifier’s performance. Conclusions: Comparative performance evaluation of our work shows that, the proposed hybrid FCBTC Models have performed better than other proposed baseline models. Full article

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

Ergonomic risk classification during manual lifting tasks is crucial for the prevention of workplace injuries. This study addresses the challenge of classifying lifting task risk levels (low, medium, and high risk, labeled as 0, 1, and 2) using multi-modal time-series data comprising raw facial landmarks and bio-signals (electrocardiography [ECG] and electrodermal activity [EDA]). Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. To overcome these challenges, this paper proposes a Multi-Adaptive Functional Neural Network (Multi-AdaFNN), a novel method that integrates functional data analysis with deep learning techniques. The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. The Multi-AdaFNN approach was evaluated across five distinct dataset configurations: (1) facial landmarks only, (2) bio-signals only, (3) full fusion of all available features, (4) a reduced-dimensionality set of 12 selected facial landmark trajectories, and (5) the same reduced set combined with bio-signals. Performance was rigorously assessed using 100 independent stratified splits (70% training and 30% testing) and optimized via a weighted cross-entropy loss function to manage class imbalance effectively. The results demonstrated that the integrated approach, fusing facial landmarks and bio-signals, achieved the highest classification accuracy and robustness. Furthermore, the adaptive basis functions revealed specific phases within lifting tasks critical for risk prediction. These findings underscore the efficacy and transparency of the Multi-AdaFNN framework for multi-modal ergonomic risk assessment, highlighting its potential for real-time monitoring and proactive injury prevention in industrial environments. Full article

Accurate phenological information on sugarcane is crucial for guiding precise cultivation management and enhancing sugar production. Remote sensing offers an efficient approach for large-scale phenology retrieval, but most studies have primarily focused on staple crops. The methods for retrieving the sugarcane phenology—the germination, tillering, elongation, and maturity stages—remain underexplored. This study addresses the challenge of accurately monitoring the sugarcane phenology in complex terrains by proposing an optimized strategy integrating spatiotemporal fusion data. Ground-based validation showed that the change detection method based on the Double-Logistic curve significantly outperformed the threshold-based approach, with the highest accuracy for the elongation and maturity stages achieved at the maximum slope points of the ascending and descending phases, respectively. For the germination and tillering stages with low canopy cover, a novel time-windowed change detection method was introduced, using the first local maximum of the third derivative curve (denoted as Point A) to establish a temporal buffer. The optimal retrieval models were identified as 25 days before and 20 days after Point A for germination and tillering, respectively. Among the six commonly used vegetation indices, the NDVI (normalized difference vegetation index) performed the best across all the phenological stages. Spatiotemporal fusion using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) significantly improved the monitoring accuracy in heterogeneous agricultural landscapes, reducing the RMSE (root-mean-squared error) by 21–46%, with retrieval errors decreasing from 18.25 to 12.97 days for germination, from 8.19 to 4.41 days for tillering, from 19.17 to 10.78 days for elongation, and from 19.02 to 15.04 days for maturity, highlighting its superior accuracy. The findings provide a reliable technical solution for precision sugarcane management in heterogeneous landscapes. Full article

Infrared and visible image fusion plays a critical role in multimodal perception systems, particularly under challenging conditions such as low illumination, occlusion, or complex backgrounds. However, existing approaches often struggle with global feature modelling, cross-modal dependency learning, and preserving structural details in the fused images. In this paper, we propose a novel adversarial fusion framework driven by a state-space modelling paradigm to address these limitations. In the feature extraction phase, a computationally efficient state-space model is utilized to capture global semantic context from both infrared and visible inputs. A cross-modality state-space architecture is then introduced in the fusion phase to model long-range dependencies between heterogeneous features effectively. Finally, a multi-class discriminator, trained under an adversarial learning scheme, enhances the structural fidelity and detail consistency of the fused output. Extensive experiments conducted on publicly available infrared–visible fusion datasets demonstrate that the proposed method achieves superior performance in terms of information retention, contrast enhancement, and visual realism. The results confirm the robustness and generalizability of our framework for complex scene understanding and downstream tasks such as object detection under adverse conditions. Full article