Fusion Process Research Articles

The use of twice interval estimations in the calibration process of LIDAR-based data

Abstract The calibration of measuring instruments requires the calculation of inner and outer data errors. We use interval twins (twice intervals) to describe the data estimations. The linear regression problem uses inner estimates of the data to obtain more accurate estimates. On the one hand, inner estimates can give an empty set as a result, on the other hand, using only outer ones leads to huge solution sets. In such cases, using a combination of inner and outer evaluations is convenient. This paper discusses a solution of a Twin System of Linear Algebraic Equations and its application in the calibration process of Nuclear Fusion and Plasma Physics LIDAR-based data collection and analysis in nuclear fusion. Two algorithms for solving these systems are presented here: the first method replaces inner estimates with outer ones, and the second method includes additional intervals. The operation of the algorithms is demonstrated using examples with generated data. In addition, a histogram shows the number of unresolvable systems, with the data taken from the PSI DRS4 fast analog memory chip.

Microstructural analysis and defect characterization of additively manufactured AA6061 aluminum alloy via laser powder bed fusion

Microstructural analysis and defect characterization of additively manufactured AA6061 aluminum alloy via laser powder bed fusion

Influence of substrate surface morphology on powder spreading in laser powder bed fusion process

Influence of substrate surface morphology on powder spreading in laser powder bed fusion process

Demonstration of novel fiber-coupled phosphor thermometry in a laser powder bed fusion process

This work introduces a novel in situ approach to analyze the thermal history during the laser-based powder bed fusion of metals, utilizing phosphor thermometry. AlSi10Mg powder was mixed with the thermographic phosphor MFG:Mn, serving as a temperature marker. After calibration, this powder blend was employed in the PBF-LB/M process, where it was excited in situ by the third harmonic of an external pulsed Nd:YAG laser. The decay of the thermographic phosphor’s luminescence lifetime showed a notable decrease, indicating temperature increases associated with the process. These findings were compared to in situ pyrometer and thermocouple measurements, demonstrating the method’s efficacy. While the study discusses the current challenges and uncertainties associated with in situ phosphor thermometry, it ultimately presents the method’s benefits as a promising complement to conventional pyrometric and tactile measurement techniques for in situ process monitoring. This novel method enhances the thermal measurement range particularly towards lower temperatures, facilitating improved monitoring of thermal cycling in additive manufacturing processes.Graphical abstract

Learning Sequential Variation Information for Dynamic Facial Expression Recognition.

A multiscale sequence information fusion (MSSIF) method is presented for dynamic facial expression recognition (DFER) in video sequences. It exploits multiscale information by integrating features from individual frames, subsequences, and entire sequences through a transformer-based architecture. This hierarchical feature fusion process includes deep feature extraction at the frame level to capture intricate visual details, intrasubsequence fusion using self-attention mechanisms for analyzing adjacent frames, and intersubsequence fusion to synthesize long-term emotional dynamics across time scales. The efficacy of MSSIF is demonstrated through extensive evaluation on three video datasets: eNTERFACE'05, BAUM-1s, and AFEW, where it achieves overall recognition accuracies of 60.1%, 60.7%, and 58.8%, respectively. These results substantiate MSSIF's superior performance in accurately recognizing facial expressions by managing short and long-term dependencies within video sequences, making it a potent tool for real-world applications requiring nuanced dynamic facial expression detection.

The microstructure and anisotropy of compressive behavior in NiTiCu alloy fabricated by laser powder bed fusion processing

The microstructure and anisotropy of compressive behavior in NiTiCu alloy fabricated by laser powder bed fusion processing

Partial Discharge Fault Detection of Substation GIS Based on CEEMDAN Fusion Processing Algorithm of Multi‐Frequency Signals

ABSTRACTPartial discharge is a common fault mode of GIS equipment, and timely and accurate detection of its status is of great significance for ensuring the safe operation of the power system. Therefore, a partial discharge fault detection method for substation GIS based on multi‐frequency signal CEEMDAN fusion processing algorithm is proposed. By analyzing the typical GIS partial discharge fault state structure, segmented collection of substation GIS partial discharge data is carried out; Based on the window function method and nonlinear gain adjustment method, a limited impulse response filter with precise linear phase characteristics is selected for multi‐frequency signal enhancement processing; Simultaneously combining wavelet reconstruction technology and Fisher criterion to improve the CEEMDAN algorithm, obtaining a fused signal containing frequency feature information; Using CNN network model to fuse feature signals as input, achieve accurate detection of partial discharge faults in substation GIS. The experimental results show that the detection accuracy of typical substation GIS partial discharge faults such as suspended discharge, hole discharge, metal particle discharge, and corona discharge obtained by the design method is higher than 95%. It can capture the partial discharge characteristics of GIS equipment, accurately judge its partial discharge state, accurately detect fault types, better generalize GIS equipment of different types and states, and have good robustness and practical detection effect.

Formation and influence mechanism of metal vapor on the powder spatter and denudation in laser additive manufacturing of Al alloys

Abstract The ejection of high-flux metal vapor and its entrained flow are primary factors causing the formation of spatter and denudation phenomena in the laser additive manufacturing process. This work develops a multiphase flow model by bi-directionally coupling computational fluid dynamics–discrete element methods to reveal the influence mechanism of metal vapor from different Al alloys on the stability of the powder bed in the laser powder bed fusion (LPBF) process. The simulation results indicate that the metal vapor generated from the evaporable Mg constituent dominates the formation of powder spatter and denudation in the LPBF processing of Al alloys. The number of spattered powder particles and the width of powder bed denudation are ∼991 and 800 μm for the AlMg5 alloy and ∼904 and 720 μm for the AlMn5 alloy, respectively. This is closely correlated to the composition, diffusion and dynamic behaviors of the metal vapor formed from these two alloys. Investigations on the corresponding surface morphology and roughness are conducted to indirectly verify the simulation results. Good surface quality with a surface roughness of ∼6.7 μm for the AlMn5 alloy is obtained with respect to that of the AlMg5 alloy (∼13.5 μm). This corresponds well to significantly reduced spattered powder and denudation phenomena for the AlMn5 alloy, further confirming the accuracy of the proposed model.

Mitochondrial dynamics in autoimmune diseases

Abstract Mitochondria, as the “energy factories” of cells, are vital for maintaining cell life and function. Mitochondria are highly mobile organelles within cells, constantly changing their morphology through fusion and fission processes to achieve dynamic transitions between interconnected network structures and fragmented states. This phenomenon is known as mitochondrial dynamics. Disorders in mitochondrial dynamics contribute to the onset of autoimmune conditions such as rheumatoid arthritis, multiple sclerosis, myasthenia gravis, and systemic lupus erythematosus, among others. This article aims to review the roles of mitochondrial dynamics disorders in autoimmune diseases and small molecule drugs targeting mitochondrial dynamic proteins, with the ultimate goal of aiding the development of new clinical treatments.

Research on Image Stitching Based on an Improved LightGlue Algorithm

In traditional centralized steel plant production monitoring systems, there are two major problems. On the one hand, the limited shooting angles of cameras make it impossible to capture comprehensive information. On the other hand, using multiple cameras to display monitoring screens separately on a large screen leads to clutter and easy omission of key information. To address the above-mentioned issues, this paper proposes an image stitching technique based on an improved LightGlue algorithm. First of all, this paper employs the SuperPoint (Self-Supervised Interest Point Detection and Description) algorithm as the feature extraction algorithm. The experimental results show that this algorithm outperforms traditional algorithms both in terms of feature extraction speed and extraction accuracy. Then, the LightGlue (Local Feature Matching at Light Speed) algorithm is selected as the feature matching algorithm, and it is optimized and improved by combining it with the Agglomerative Clustering (AGG) algorithm. The experimental results indicate that this improvement effectively increases the speed of feature matching. Compared with the original LightGlue algorithm, the matching efficiency is improved by 26.2%. Finally, aiming at the problems of parallax and ghosting existing in the image fusion process, this paper proposes a pixel dynamic adaptive fusion strategy. A local homography matrix strategy is proposed in the geometric alignment stage, and a pixel difference fusion strategy is proposed in the pixel fusion stage. The experimental results show that this improvement successfully solves the problems of parallax and ghosting and achieves a good image stitching effect.

Class-weighted Dempster-Shafer in dual-level fusion for multimodal fake real estate listings detection.

Detecting fake multimodal property listings is a significant challenge in online real estate platforms due to the increasing sophistication of fraudulent activities. The existing multimodal data fusion methods have several limitations and strengths in identifying fraudulent listings. Single-level fusion models whether at the feature, decision, or intermediate level struggle with balancing the contributions of different modalities leading to suboptimal decision-making. To address these problems, a dual-level fusion from multimodal for fake real estate listings detection is proposed. The dual-level fusion allows the integration of detailed features from text and image data to be performed at an early stage, followed by the metadata fusion at the decision stage in order to obtain a more comprehensive final classification. Furthermore, a new weighting scheme is introduced to optimize Dempster-Shafer in decision fusion to help the model achieve optimal performance and as a result, our method improves the classification. The Dempster-Shafer without class weightage lacks the flexibility to adapt to varying levels of uncertainty or importance across different classes. In Class Weighted Dempster-Shafer in Dual Level Fusion (CWDS-DLF), we employ advanced models (XLNet for text and ResNet101 for images) for feature extraction and use the Dempster-Shafer theory for decision fusion. A new weighting scheme, based on Bayesian optimization, was used to assign optimal weights to the 'fake' and 'not fake' classes, thereby enhancing the Dempster-Shafer theory in the decision fusion process. The CWDS-DLF was evaluated on the property listing website dataset and achieved an F1 score of 96% and an accuracy of 93%. A t-test confirms the significance of these improvements (p < 0.05), demonstrating the effectiveness of our method in detecting fake property listings. Compared to other models, including 2D-convolutional neural network (CNN), XGBoost, and various multimodal approaches, our model consistently outperforms in precision, recall, and F1-score. This underscores the potential of integrating multimodal analysis with sophisticated fusion techniques to enhance the detection of fake property listings, ultimately improving consumer protection and operational efficiency in online real estate platforms.

Modeling two-ways fluid-solid phase transitions in lava using Smoothed Particle Hydrodynamics

Phase transitions are a key phenomenon in the evolution of lava flows, significantly influencing their emplacement and the formation of geological features such as lava channels and lava tubes. Here, a numerical model employing Smoothed Particle Hydrodynamics (SPH) to simulate two-way phase transitions between solid and fluid states in lava is presented. By accurately representing the solidification dynamics and incorporating a temperature range that accounts for the solidus and liquidus temperatures, the model addresses limitations in previous approaches that relied on oversimplified phase transition assumptions. We validate the model against Stefan’s benchmark test case and apply it to two illustrative volcanic scenarios using artificial volcanic-like environments, demonstrating how the model effectively captures the processes of solidification and fusion within the modeled lava flow. The results underscore the importance of phase transition modeling in understanding the complex behavior of lava flows in real-world volcanic contexts.

Laser Additive Manufacturing of Oxide‐Dispersion‐Strengthened Steels: A Simulation‐Based Comparison Between Powder Bed Fusion and Direct Energy Deposition

Oxide‐dispersion‐strengthened steels, characterized by oxide nanoparticles dispersed in a metallic matrix, exhibit high strength at elevated temperatures. Controlling the size and distribution of these dispersoids is crucial for achieving desired properties. Using multiphysics phase‐field simulation and associated nanoparticle tracing, this work investigates the thermo‐microstructural evolution, melt flow, and nanoparticle dispersion and agglomeration of the manufactured Fe20Cr alloy with 0.5 (wt%) ZrO2 nanoparticle by powder bed fusion (PBF) and direct energy deposition (DED) processes. Results present evident differences in melt residence time and flow patterns between DED and PBF. Dispersion and agglomeration are influenced by their positioning relative to the melt pool geometry. Regions closer to the beam spot center experience longer melt residence times, leading to a higher degree of dispersion, greater interparticle distances, and lower estimated Orowan stress. For the same volumetric energy density, simulations indicate that DED supports better nanoparticle dispersion due to the larger melt pool and consistent flow patterns, distinguished from the erratic melt dynamics in PBF, leading to greater nanoparticle agglomeration. Higher Orowan stress is resolved in the sample by DED as compared to that by PBF.

Rola mitochondriów jako celów terapeutycznych w leczeniu gruczolakoraka przewodowego trzustki: możliwości i wyzwania

Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of cancer-related deaths worldwide. This is due to its complex microenvironment and high resistance to treatment. One of the key factors contributing to this resistance is the altered function of mitochondria in tumor cells, including high levels of oxidative phosphorylation (OXPHOS), a predominance of mitochondrial fusion processes, and increased autophagy, all of which are closely linked to cancer cell metabolism. The literature suggests three main therapeutic approaches: OXPHOS inhibition, modulation of mitochondrial dynamics, and autophagy suppression. Studies indicate that both reducing OXPHOS activity and inhibiting autophagy sensitize tumors to chemotherapy. Moreover, modulating mitochondrial dynamics effectively suppresses further tumor growth. Despite promising research on targeting mitochondria as a therapeutic strategy for PDAC, further studies are needed to determine the efficacy and safety of these approaches in humans.

Performance Comparison of Surface Sensitizers for Diode Laser Powder Bed Fusion of Polyamide 12

The efficient processing of polyamide 12 (PA12) in laser‐based additive manufacturing with near‐infrared (NIR) diode lasers requires modification with NIR‐absorbing additives. This study presents the absorbance‐normalized investigation, where the loading of copper sulfide (CuS), lanthanum hexaboride (LaB6), and carbon black (CB) on PA12 is adjusted to achieve identical absorbance at the diode laser printing wavelength of 808 nm. This approach enables a direct comparison of their effects on the processability, mechanical properties, and thermal stability, independent of the energy input. Interestingly, sub‐vol% sensitizer amounts are sufficient for processing. CuS, at only 0.04 vol%, achieves the highest mechanical strength (≈41 MPa) of the printed parts, good powder flowability, and thermal stability. LaB6 achieves the best flowability, but requires three times higher loading, affecting fusion behavior, while CB needs increased amounts due to its low density. All nanoparticle‐modified powders maintain stable optical properties during laser powder bed fusion processing, which is a basic requirement for a refresh rate of 0%, outperforming conventional organic dyes in thermal stability. These findings pave the way for high‐performance polymer feedstocks in diode laser‐based additive manufacturing.

Spatio-Temporal Fusion of Landsat and MODIS Data for Monitoring of High-Intensity Fire Traces in Karst Landscapes: A Case Study in China

The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions.

Anterior Cervical V-Shaped Osteotomy and Fusion (ACVF) for Retro-Corporeal Decompression Bypassing Anterior Corpus Vertebra: A Technical Note.

Anterior cervical discectomy and fusion (ACDF) is a trans-intervertebral space procedure with limited ability to treat compression at the back of the vertebral body. Anterior cervical corpectomy and fusion (ACCF) can be applied in this case, but the higher complication rates restrict its usage. This study aims to describe an ACDF-based procedure named anterior cervical V-shaped osteotomy and fusion (ACVF) with a long axial decompression range while preserving the intact anterior half of the vertebral body. Four patients with contiguous two-level degenerative cervical myelopathy who underwent ACVF with 12-month follow-up were retrospectively reviewed. Intraoperatively, an electrode penetration test was conducted to verify complete decompression. Clinical outcomes were evaluated using the modified Japanese Orthopedic Association (mJOA) score and the Visual Analog Scale (VAS) score. Radiological examinations, including computed tomography (CT) and magnetic resonance imaging (MRI), were used to assess spinal cord decompression and intervertebral fusion. All surgeries were successfully completed with an average operative time of 182 min. Both the mJOA score and the VAS score improved at the follow-ups postoperatively. Postoperative imaging showed thorough decompression of the spinal cord, unimpaired fusion process, and acceptable vertebral body height loss. ACVF may be safe and effective for spinal cord direct decompression in the case of retro-corporeal compression, with the potential to serve as a substitute for ACCF and avoid the complications associated with long-strut grafts.

PNNs4: a parallel quadruple neural network model for urban intersection turning movement prediction

Accurately predicting the turning traffic flow at urban intersections is crucial for optimizing traffic management strategies and alleviating congestion. Existing traffic flow prediction models often fail to effectively capture the inherent complexity and dynamic variability of intersections, particularly the nonlinear characteristics of turning movements. To address these limitations, this paper proposes a novel parallel quadruple neural networks (PNNs4) model that leverages deep learning and multi-source data feature fusion techniques in a parallel architecture, enabling comprehensive processing of multi-source traffic data. The innovative feature fusion technology enhances the learning capabilities and accuracy in predicting complex traffic patterns. The fusion process involves an innovative combination of spatial and temporal feature extraction, effectively synchronized through element-wise multiplication, enriching the model's input representation and robustness. Experimental validation across 49 intersections in Milton, Ontario, Canada, demonstrates that the PNNs4 model outperforms existing state-of-the-art methods in terms of both accuracy and stability.

An adaptive feature fusion strategy using dual-layer attention and multi-modal deep reinforcement learning for all-media similarity search

This paper proposes a novel adaptive feature fusion strategy that combines a dual-layer attention mechanism and Multi-modal deep reinforcement learning (DRL) to optimize cross-modal information retrieval. The dual-layer attention mechanism enhances the model's ability to capture deep semantic relationships between different modalities, while DRL optimizes the feature extraction and fusion process, improving adaptability in complex environments. Experimental results demonstrate that this strategy outperforms traditional CNN and RNN methods in terms of accuracy, recall, and efficiency across a range of cross-modal retrieval tasks, particularly in multi-modal data environments such as text-image, text-video, and image-video. The proposed approach offers a promising solution for improving the accuracy and efficiency of cross-modal information retrieval.

Impact of hatching strategies on overhang displacement in PBF-LB/M

One of the most widely used additive manufacturing processes for metals is the powder bed fusion process, using a laser beam to melt powder layer upon layer into complex three-dimensional structures. While this manufacturing technique offers considerable design versatility, it is limited by overhang features that necessitate support structures, with the required angles for these supports varying based on the material, process, and machine used. Overhang features are susceptible to distortion, which compromises process stability. If the overhang feature penetrates into the next layer of powder, the recoater that distributes the loosened powder can be damaged, which can lead to a total loss of the parts in severe cases. In this work, the effect of the laser path, the hatching, on the overhang displacement was investigated. Overhang specimens were built using different hatching strategies, orientations of the hatching sequence toward the gas flow, hatch lengths, and altered hatching in differently sized areas in the overhang. Among other implications, it was found, for the hatching, that the hatch length should be maximized to reduce overheating effects and displacement in overhang regions. The findings and their practical implications offer an overview of the overhang displacement and the influence of various hatching strategies and parameters. This research serves as a foundation for more advanced optimized hatching strategies that will reduce overhang displacement, increase process stability, and even further increase the freedom of design in additive manufacturing.