Fusion Process Research Articles

On the Creation of a Material Bond between L-PBF-Manufactured AZ91 and Ti-6Al-4V Components in the Context of Medical Applications.

Within the scope of these investigations, the feasibility of a material bond between Ti-6Al-4V and the magnesium alloy AZ91 is analyzed. Ti-6Al-4V is frequently used for implants due to its biocompatibility, corrosion resistance, and specific strength. However, depending on the surface quality, the attachment behavior of the bone to the implant varies. Magnesium implants promote the regeneration of bone tissue and biodegrade as the bone tissue heals. Combining the properties of both materials in one implant enables a reduced implant volume and increased stability. For this reason, this study aims to demonstrate the feasibility of creating a material bond between the materials Ti-6Al-4V and AZ91. For this purpose, Ti-6Al-4V truncated cones and AZ91 sleeves were produced using the additive manufacturing process of laser powder bed fusion (L-PBF). The as-built sleeves were then pressed onto machined truncated cones. Since zinc serves as a lubricant and has good diffusion properties with the materials used as a result of heat treatment, a comparison was made between zinc-coated and the as-built Ti-6Al-4V samples. This showed that a bond was created after hot isostatic pressing and that the push-out force could be increased by more than 4.5 times. Consequently, a proof of feasibility was demonstrated, and a high potential for applications in medical technology was shown.

On the effect of the processing parameters in microstructure and thermomechanical properties of LPBF NiTi shape memory alloys

Recent studies have shown that variations in the parameters of the laser powder bed fusion (LPBF) process significantly impact the microstructure and thermomechanical properties of NiTi shape memory alloys (SMAs). Understanding how parameter selection influences the final NiTi SMA properties enables the strategic design of additively manufactured (AM) components with tailored mechanical and transformation behavior, suitable for specific applications in a plethora of scientific fields. However, the connection between processing and thermomechanical properties of NiTi SMAs is still not completely understood. This study investigates the effects of AM parameters laser power (72–185 W), scanning speed (668-1739 mm/s), and hatch spacing (28–85 μm) on a near equiatomic NiTi powder. Low porosity levels are found in the samples additively manufactured (AM) with the different LPBF parameter combinations. The findings also reveal that transformation temperatures and nickel evaporation trends correlate directly with volumetric energy density (VED). Additionally, the amount of Ti2Ni precipitates increases with higher VED. Thermal cycles under constant stress, training, and two-way shape memory effect tests have been performed; the results demonstrate a strain recovery ratio of 2–3.5% for most of the samples. The results arising from this study pave the way for the engineering of customized materials with tailored properties from the same alloy metal powder.

Diverse soliton wave profile analysis in ion-acoustic wave through an improved analytical approach

In engineering and applied sciences, several physical phenomena can be more precisely characterized by employing nonlinear fractional partial differential equations. The primary goal of this research is to examine the traveling wave solution in closed form for the nonlinear acoustic wave propagation model known as the time fractional simplified modified Camassa–Holm equation, which is used to explain the unidirectional propagation of shallow-water waves with non-hydrostatic pressure and explains the dispersion properties of numerous phenomena like fluid flow, control theory, liquid drop patterning in plasma, acoustics, fusion, and fission processes, etc. The utmost potential approach, namely the new auxiliary equation technique, is applied for analyzing the time nonlinear fractional simplified modified Camassa-Holm equation in the logic of the newest established truncated M-fractional derivative. The fractional partial differential equations have been transformed to the ordinary differential equation using the complex wave transformation in the sense of truncated M-fractional derivative. A variety of soliton solutions, including anti-kink, single soliton, anti-bell, bell, kink, multiple soliton, double soliton, singular-kink, compacton shape, periodic shape, and so many, are displayed in the diagram of 3D and contour plots by taking into account a number of various parameters. It is essential to point out that all derived outcomes are directly compared to the original solutions to certify their exactness. Results show that the used scheme is capable, simple, and straightforward and can be useful to a variety of complex phenomena. The acquired results are unique for the model equation and could be applied to the analysis of several nonlinear study fields.

STK40 inhibits trophoblast fusion by mediating COP1 ubiquitination to degrade P57Kip2

BackgroundThe syncytiotrophoblast (SCT) layer in the placenta serves as a crucial physical barrier separating maternal-fetal circulation, facilitating essential signal and substance exchange between the mother and fetus. Any abnormalities in its formation or function can result in various maternal syndromes, such as preeclampsia. The transition of proliferative villous cytotrophoblasts (VCT) from the mitotic cell cycle to the G0 phase is a prerequisite for VCT differentiation and their fusion into SCT. The imprinting gene P57Kip2, specifically expressed in intermediate VCT capable of fusion, plays a pivotal role in driving this key event. Moreover, aberrant expression of P57Kip2 has been linked to pathological placental conditions and adverse fetal outcomes.MethodsValidation of STK40 interaction with P57Kip2 using rigid molecular simulation docking and co-immunoprecipitation. STK40 expression was modulated by lentivirus in BeWo cells, and the effect of STK40 on trophoblast fusion was assessed by real-time quantitative PCR, western blot, immunofluorescence, and cell viability and proliferation assays. Co-immunoprecipitation, transcriptome sequencing, and western blot were used to determine the potential mechanisms by which STK40 regulates P57Kip2.ResultsIn this study, STK40 has been identified as a novel interacting protein with P57Kip2, and its expression is down-regulated during the fusion process of trophoblast cells. Overexpressing STK40 inhibited cell fusion in BeWo cells while stimulating mitotic cell cycle activity. Further experiments indicated that this effect is attributed to its specific binding to the CDK-binding and the Cyclin-binding domains of P57Kip2, mediating the E3 ubiquitin ligase COP1-mediated ubiquitination and degradation of P57Kip2. Moreover, abnormally high expression of STK40 might significantly contribute to the occurrence of preeclampsia.ConclusionsThis study offers new insights into the role of STK40 in regulating the protein-level homeostasis of P57Kip2 during placental development.

High accuracy patient-specific 3D coronary reconstruction from angiography and intravascular optical coherence tomography.

The integration of digital subtraction angiography (DSA) with intravascular optical coherence tomography (IVOCT) offers a comprehensive 3D arterial model, which is invaluable for the analysis of vascular anatomy and biomechanics. However, the process of image fusion is often hindered by the challenge of accurately orienting IVOCT images. This paper introduces a novel, to our knowledge, dual-path 3D reconstruction method that leverages the guidewire and the vessel's centerline to establish cross sectional direction vectors within the IVOCT images and spatial direction vectors along the guidewire's trajectory. This approach minimizes the accumulation of reconstruction errors by ensuring the precise orientation of each vascular cross section. The efficacy of the proposed method is validated through vascular phantom experiments and the reconstruction of patient-specific 3D coronary models.

Target Recognition Based on Infrared and Visible Image Fusion and Improved YOLOv8 Algorithm.

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives.

Grain boundary behaviour and impact fractography of cryogenic treated AISI 316l manufactured by laser powder bed fusion

AISI 316L manufactured using laser powder bed fusion process was subjected to heat and cryogenic treatments. The effect of microstructural and mechanical properties, such as hardness, thermal residual stress, and Charpy impact, were investigated. The results indicate that after the heat and cryogenic treatment, the amount of austenite (FCC) phase increased, and the residual stress was removed after the heat and cryogenic treatment. The increase in deformation twins (Σ3) was observed after heat and cryogenic treatment. The cryogenic treatment altered the crystal lattice and orientation of crystal grains, resulting in grain refinement.

SARS-CoV-2 Delta and Omicron variants resist spike cleavage by human airway trypsin-like protease.

Soluble host factors in the upper respiratory tract can serve as the first line of defense against SARS-CoV-2 infection. In this study, we described the identification and function of a human airway trypsin-like protease (HAT), capable of reducing the infectivity of ancestral SARS-CoV-2. Further, in mouse models, HAT analogue expression was upregulated by SARS-CoV-2 infection. The antiviral activity of HAT functioned through the cleavage of the SARS-CoV-2 spike glycoprotein at R682. This cleavage resulted in inhibition of the attachment of ancestral spike proteins to host cells, which inhibited the cell-cell membrane fusion process. Importantly, exogenous addition of HAT notably reduced the infectivity of ancestral SARS-CoV-2 in vivo. However, HAT was ineffective against the Delta variant and most circulating Omicron variants, including the BQ.1.1 and XBB.1.5 subvariants. We demonstrate that the P681R mutation in Delta and P681H mutation in the Omicron variants, adjacent to the R682 cleavage site, contributed to HAT resistance. Our study reports what we believe to be a novel soluble defense factor against SARS-CoV-2 and resistance of its actions in the Delta and Omicron variants.

MIDF-DMAP: Multimodal information dynamic fusion for drug molecule activity prediction

BackgroundComputer-aided drug development can alleviate limitations in drug research and development processes, such as long cycles, high costs, and limited targeting. Deep learning models can be used to predict the activity of drug compound molecules. However, existing models still need to adequately address semantic redundancy, conflicts, and noise between different data modalities. It results in low accuracy in predicting molecular activity and many false positive results that need further resolution. ObjectiveThis study aims to design a parallel deep learning model for processing multimodal molecular information fusion, dynamically adjusting the weights of different modal features during the fusion process. This approach ensures that high-quality modal information is more significant in molecular activity prediction, improving the model’s predictive accuracy. MethodsFirst, three modal features of drug molecules are extracted: one-dimensional fingerprints, two-dimensional topological structures, and three-dimensional geometric structures. Then, a dynamic weighting mechanism is proposed to fuse these three modal features organically. Finally, the fused features are input into the model classifier to obtain molecular activity prediction results. ResultsExperimental results show that the proposed model improved the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) value by 29.96% compared to the Weave model and 26.38% compared to the GC model on the BBBP dataset. Compared to the GraphMVP model and the multi-channel substructure graph model MSGG, the ROC-AUC value increased by 20.44% and 15.80%, respectively. This method also achieved good results on imbalanced samples and small molecule datasets. ConclusionThis study provides an effective multimodal dynamic fusion method for virtual drug screening, enriching computer-aided drug development theory and improving drug research and development efficiency.

QCD corrections to Higgs boson production and H→bb¯ decay in weak boson fusion

We study QCD corrections to the process where a Higgs boson is produced in weak boson fusion and then decays into a pair of massive b quarks. We find that typical experimental criteria used to identify b jets in this process affect QCD corrections to the decay, making it necessary to account for them in the proper description of this process. Indeed, if corrections to the production and decay are combined, the fiducial cross section of the weak boson fusion process pp→H(→bb¯)jj is reduced by about 40% relative to leading-order predictions, compared to just about 8% if only corrections to the production process are considered. We investigate the origin of these large corrections through next-to-next-to-leading order and conclude that they appear because a number of independent moderately large effects conspire to significantly reduce the fiducial cross section for this process. Published by the American Physical Society 2024

Development and Validation of a One‐Dimensional Finite Difference Simulation Scheme for Polymer Laser Powder Bed Fusion with Application to the Effect of the Inter Layer Time

The division of the polymer laser powder bed fusion process polymers (PBF‐LB/P) into temporal regimes originating from laser motion, thermal diffusion, viscous flow, crystallization kinetics, and powder application is exploited by considering only the building direction. The reduction of dimensionality enables fast simulations which are used to investigate the influence of the inter layer time on the final part density. Numerical simulation results are validated by experiments using polyamide 12 (PA 12) with good agreement in terms of part density. It is shown that an inter layer time of 90 s leads to nearly dense PA 12 parts while a time of 45 s leads to less dense parts. The cooling effect of the applied powder is identified as a cause for insufficient densification of the previous layer. The simulation tool is quantitatively validated against experimental results for the surface temperature of PA 12 as function of scan speed and hatch distance, for the melt pool depth of polyamide 6 as function of scan speed and for the melt pool depth of polyetherketoneketone as function of laser power. The presented simulation method enables rapid process parameter adjustment for new polymer materials in the PBF‐LB/P process.

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents’ movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity status

Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.

Microstructural Stability of IN625 Reinforced by the Addition of TiC Produced by Laser Powder Bed Fusion after Prolonged Thermal Exposure.

This paper deals with the development and characterization of an Inconel 625 (IN625) reinforced with 2 wt.% of sub-micrometrical TiC particles produced by the laser powder bed fusion (LPBF) process. IN625 and IN625 2 wt.% TiC microstructural evolution was evaluated in the as-built, solution-annealed (2 h at 1150 °C), and prolonged heat-treated (2 h at 1150 °C + 100 h at 1000 °C) conditions. The IN625 and IN625 + TiC samples were successfully produced with low residual porosity (<0.15%). In the as-built conditions, both materials developed mainly columnar grains elongated to the building direction with melt pools, fine dendric structures, and small fractions of recrystallized grains. Some TiC segregations were observed in the composite, preferentially located at the melt pool boundaries. The heat treatments led to a different microstructural evolution between the base alloy and the composite. After solution annealing, the IN625 alloy was subjected to full recrystallization with a drastic reduction in hardness. Afterward, the prolonged thermal exposures for 100 h at 1000 °C provoked the formation of carbides, increasing the hardness. On the contrary, the composite retained the as-built microstructure with columnar grains in the solution-annealed and prolonged heat-treated conditions, revealing a limited formation and growth of carbides, thus resulting in a reduced hardness variation. The addition of TiC inside the IN625 enhanced the microstructural stability of the composite, preventing the recrystallization and the growth of phases occurring under prolonged thermal exposures. The current study therefore reported the effect of TiC particles on the microstructural stabilization of LPBFed IN625, with a peculiar focus on the prolonged thermal exposure at 1000 °C.

Polarization effects of hot nuclear matter on the fusion of heavy ions

Within the framework of the modified density-dependent Seyler–Blanchard approach in the [Formula: see text] approximation, the polarization effects of hot nuclear matter (PEHNM) are investigated on the fusion process of 16 different colliding systems in the mass range [Formula: see text]. In order to calculate the nucleus–nucleus potential, we use the double-folding (DF) model supplemented with the thermal effects of compound nucleus and also a repulsive potential that takes into account the incompressibility of the nuclear matter. The calculations of the fusion cross-sections are performed based on the couplings to the low-lying [Formula: see text] and [Formula: see text] vibrational states in both target and projectile. The sensitivity of sub-barrier fusion cross-sections to the polarization effects is evident from this study. The obtained results reveal that the modified form of the DF potential in the presence of the PEHNM appropriately reproduces the energy-dependent behavior of the experimental fusion cross-sections for the studied reactions. The role of the polarization effects on the fusion barriers as well as the inner part of the potential is also discussed.

BFFN: A novel balanced feature fusion network for fair facial expression recognition

Facial expression recognition (FER) technology has become increasingly mature and applicable in recent years. However, it still suffers from the bias of expression class, which can lead to unfair decisions for certain expression classes in applications. This study aims to mitigate expression class bias through both pre-processing and in-processing approaches. First, we analyze the output of existing models and demonstrate the existence of obvious class bias, particularly for underrepresented expressions. Second, we develop a class-balanced dataset constructed through data generation, mitigating unfairness at the data level. Then, we propose the Balanced Feature Fusion Network (BFFN), a class fairness-enhancing network. The BFFN mitigates the class bias by adding facial action units (AU) to enrich expression-related features and allocating weights in the AU feature fusion process to improve the extraction ability of underrepresented expression features. Finally, extensive experiments on datasets (RAF-DB and AffectNet) provide evidence that our BFFN outperforms existing FER models, improving the fairness by at least 16%.

Mechanical response of LPBFed TI64 thickness graded Voronoi lattice structures

The possibility to realize Additively Manufactured functionally graded lattice structure based on Voronoi tessellation enormously increases the possibility in tailoring the stiffness, mechanical properties and energy absorption capacity of the samples. The work presents the design and mechanical characterization of functionally thickness graded Voronoi lattice structures in comparison with constant thickness lattice structures for the evaluation of mechanical performance and energy absorption capacity. Firstly, the design and laser power bed fusion process are detailed. The dimensional deviation between designed models and Ti6Al4V specimens is quantified to assess the samples’ quality. Their mechanical performance is analyzed by quasi-static compression experimental tests, supported by numerical analysis for the evaluation of local stress distributions and deformation modes. The average dimensional deviation between CAD models and fabricated samples is 0.09 mm, likeminded with the literature optimum. The structures exhibit Young Modulus values ranging between 10 MPa and 21 MPa, compatible with biomedical applications. The compressive force for thickness graded structures tends to increase up to densification, while uniform thickness structures present an almost constant value of force in the platform stage. Additionally, the energy storage changes according to the presence of thickness gradient: the larger the thickness gradient, the larger the energy absorption capacity.

Logistics distribution model and storage planning design based on multi-source information positioning in smart city development

The research mainly focuses on the inefficient planning of autonomous distribution and storage modes of distribution vehicles in smart city logistics and distribution, which in turn leads to poor customer experience, rising distribution costs, and wasted distribution resources. From the perspective of information processing in the logistics distribution process, the study takes multi-angle and multi-source information collection and fusion processing strategy as the main basis to help smart delivery robots realize map construction and autonomous positioning in the distribution process, and then facilitate real-time logistics distribution and storage mode planning by robots in combination with their own states. The research results show that the algorithm accuracy, standard error, and average running time of the extended Kalman filter localization model designed in the study are 0.96, 1.52, and 105s, respectively, with the algorithm accuracy being the highest and the other two values being the lowest in its class. Meanwhile, in the simulation logistics planning, the research-designed logistics planning model has the strongest information capturing ability, and the planning of distribution routes and storage modes is more reasonable, which can provide more efficient autonomous planning solutions.

Investigating the process parameter window for laser powder bed fusion of copper chrome zirconium

ABSTRACT Copper-based alloy UNS C18150 has become an attractive material for laser powder bed fusion (LPBF) processing. However, the complexity of LPBF mandates that unique process parameters be devised for each alloy as these are highly sensitive to material chemistry. This work aims to clarify the general boundaries of the process window for the most influential parameters alongside an exploration into the effects of scan strategy on the density. A series of statistical design of experiments were utilised to model density as a response and predict process parameters that would result in minimised residual porosity. Using optimised parameters, specimen with up to 99.1% of theoretical density were produced, and a window of process parameters was established that yielded similarly dense products. Post-build aging of the LPBF parts was also explored. Here, direct aging of the as-built product at 390°C for 100 h resulted in a peak hardness of 83.3 ± 0.6 HRB.

ER membrane remodeling by targeting RTN4 induces pyroptosis to facilitate antitumor immune.

Pyroptosis is an identified programmed cell death that has been highly linked to endoplasmic reticulum (ER) dynamics. However, the crucial proteins for modulating dynamic ER membrane curvature change that trigger pyroptosis are currently not well understood. In this study, a biotin-labeled chemical probe of potent pyroptosis inducer α-mangostin (α-MG) was synthesized. Through protein microarray analysis, reticulon-4 (RTN4/Nogo), a crucial regulator of ER membrane curvature, was identified as a target of α-MG. We observed that chemically induced proteasome degradation of RTN4 by α-MG through recruiting E3 ligase UBR5 significantly enhances the pyroptosis phenotype in cancer cells. Interestingly, the downregulation of RTN4 expression significantly facilitated a dynamic remodeling of ER membrane curvature through a transition from tubules to sheets, consequently leading to rapid fusion of the ER with the cell plasma membrane. In particular, the ER-to-plasma membrane fusion process is supported by the observed translocation of several crucial ER markers to the "bubble" structures of pyroptotic cells. Furthermore, α-MG-induced RTN4 knockdown leads to PKM2-dependent conventional caspase-3/GSDME cleavages for pyroptosis progression. In vivo, we observed that chemical or genetic RTN4 knockdown significantly inhibited cancer cells growth, which further exhibited an antitumor immune response with anti-PD-1. In translational research, RTN4 high expression was closely correlated with the tumor metastasis and death of patients. Taken together, RTN4 plays a fundamental role in inducing pyroptosis through the modulation of ER membrane curvature remodeling, thus representing a prospective druggable target for anticancer immunotherapy.