The demand for lightweight, high-performance, and multifunctional structures has driven rapid advances in multi-material joining technologies across aerospace, automotive, and electronics industries. Traditional joining methods often struggle with challenges such as thermal distortion, brittle intermetallic formation, and residual stresses when bonding dissimilar materials. This review critically examines three advanced additive manufacturing techniques—Ultrasonic Consolidation (UC), Cold Spray (CS), and Electron Beam Melting (EBM)—that offer promising solutions for multi-material fabrication. The mechanisms, material compatibility, microstructural evolution, and mechanical performance of joints produced by each process are systematically discussed. UC and CS, as solid-state processes, minimize thermal damage and oxidation, enabling strong joints between metals with dissimilar properties. EBM, operating in a high-vacuum environment, allows precise control over microstructure and enables the fabrication of complex, high-performance components. The novelty of this review lies in its integrative comparison of solid-state and fusion-based techniques, with a specific focus on their effectiveness in multi-material structural applications. It emphasizes interface behavior, residual stress development, and scalability challenges, while highlighting underexplored directions such as hybrid processing, interface engineering, tailored material feedstocks, and in-situ monitoring strategies. However, challenges such as bonding efficiency, residual stress management, and scalability remain. Future research directions are proposed, including process optimization, interface engineering, expanded material libraries, and integrated real-time monitoring to fully realize the potential of these emerging technologies for multi-material structural applications.

Joining dissimilar materials, such as aluminum and titanium, through fusion-based techniques presents difficulties because of their considerable differences in physical properties and metallurgical incompatibility. This research employed a hybrid bonding method for joining aluminum and titanium by integrating adhesive bonding with metallurgical joining at the solid titanium and liquid aluminum interface, utilizing resistance spot welding. The experimental findings indicated that this hybrid bonding method greatly enhances the load-bearing capacity and energy absorption performance of AA6061/Ti-6Al-4V joints compared to conventional resistance spot welding.

Background: Lumbar foraminal stenosis (LFS) is a prevalent degenerative condition associated with significant radicular pain and impaired quality of life. Advances in minimally invasive and fusion-based surgical techniques have introduced new strategies for decompressing the neural elements. However, comparative data correlating volumetric foraminal expansion with functional outcomes remain limited. Methods: This retrospective cohort study analyzed 256 patients treated surgically for symptomatic LFS between December 2017 and December 2023. Patients were categorized into four surgical subgroups: endoscopic decompression, anterior lumbar interbody fusion (ALIF), microsurgical decompression, and transforaminal lumbar interbody fusion (TLIF). Preoperative and postoperative assessments included magnetic resonance imaging (MRI) to calculate foraminal volume and standardized clinical scales: the Oswestry Disability Index (ODI), Visual Analogue Scale (VAS) for back and leg pain, and SF-36 health-related quality-of-life scores. Statistical significance was determined using p-values, and inter-observer agreement was evaluated via κ-statistics. Results: Postoperative imaging demonstrated a significant increase in foraminal canal volume across all surgical groups: endoscopy (29.9%), ALIF (71.8%), microsurgery (48.06%), and TLIF (67.0%). ODI scores improved from a preoperative mean of 55.25 to 18.27 at 24 months post-surgery (p < 0.001). VAS scores for back pain decreased from 6.37 to 2.1 (p < 0.001), while leg pain scores declined from 6.85 to 2.05 (p < 0.001). Functional improvement reached or exceeded the minimal clinically important difference (MCID) threshold in over 66% of patients. Conclusions: Modern surgical strategies for LFS, particularly fusion-based techniques, yield significant volumetric decompression and durable clinical improvement. Volumetric gain in the foraminal canal is closely associated with pain reduction and enhanced functional outcomes. These findings support a tailored surgical approach based on anatomical pathology and segmental stability.

This study aims to combat health misinformation by enhancing the retrieval of credible health information using effective fusion-based techniques. It focuses on clustering-based subset selection to improve data fusion performance. Five clustering methods - two K-means variants, Agglomerative Hierarchical (AH) clustering, BIRCH, and Chameleon - are evaluated for selecting optimal subsets of information retrieval systems. Experiments are conducted on two health-related datasets from the TREC challenge. The selected subsets are used in data fusion to boost retrieval quality and credibility. AH and BIRCH outperform other methods in identifying effective IR subsets. Using AH-based fusion of up to 20 systems results in a 60% gain in MAP and over a 30% increase in NDCG_UCC, a credibility-focused metric, compared to the best single system. Clustering-based fusion strategies significantly enhance the retrieval of trustworthy health content, helping to reduce misinformation. These findings support incorporating advanced data fusion into health information retrieval systems to improve access to reliable information. The source code of this research is publicly available at https://github.com/Gary752752/DataFusion.

Aluminum alloys are widely used in the transportation sector due to their favorable characteristics and mechanical properties, which meet the growing demand for high-performance vehicles with enhanced autonomy. The 7XXX series is particularly notable among these alloys, especially in the aeronautical industry. The AA7075-T651 alloy was chosen for this study because of its high mechanical strength, low density, and superior corrosion resistance in comparison to other aluminum alloys typically recommended for aeronautical and aerospace applications. However, its use is constrained when employing conventional welding techniques, mainly due to weldability challenges. To broaden the applicability of this alloy to other structural components, the development of solid-state joining methods is essential. The low weldability of these alloys under fusion-based techniques is linked to the rapid vaporization of zinc and magnesium, key alloying elements, that results in pore formation in the weld metal, thereby compromising joint integrity. Friction Stir Welding (FSW), a solid-state joining process, offers a promising alternative, effectively addressing these limitations. Since the mechanical properties of welded joints in heat-treatable aluminum alloys are significantly influenced by thermal exposure during welding, it is crucial to assess the temperature distribution throughout the FSW process and establish correlations with the thermal cycles experienced by the joints. In this study, AA7075-T651 alloy plates were welded under various process parameters: welding speeds of 117 mm/min and 47 mm/min, along with tool rotation speeds of 1585 rpm and 470 rpm, using a threaded cylindrical tool made of H13 steel. Uniaxial tensile testing, macro- and microstructural characterization, Vickers hardness measurements, and thermal cycle monitoring using K-type thermocouples were performed. The highest peak temperatures, nearing 375 °C, were recorded under elevated welding and tool rotation speeds (117 mm/min and 1585 rpm, respectively). In contrast, joints produced at lower welding (47 mm/min) and tool rotation speeds (470 rpm) displayed lower peak temperatures (~324 °C), reduced cooling rates (~3 °C/s), and enhanced mechanical performance, with ultimate tensile strength (UTS) reaching 353 MPa. Conversely, joints manufactured under higher energy input conditions exhibited brittle behavior and significantly lower UTS values (~176 MPa), attributed to excessive heat input and the accelerated thermal cycles involved.

Recent urbanization has posed challenges for the global navigation satellite system (GNSS) to provide accurate navigation solutions. This is especially true in GNSS-denied environments, where the clear line of sight (LOS) path between the satellites and receiver is lacking. For such environments, fusion-based techniques relying on external sensors and/or other signals are widely used. However, such external sensors and signals may not be feasible and/or cost-effective every time. To overcome these limitations, this work proposes a system that makes explicit use of past available measurements, under certain assumptions, to generate new synthetic measurements. For this purpose, two functions are proposed in this work: a geometrically decaying series and a linear combination of past measurements. To enhance the overall performance of the system, an inertial measurement unit (IMU) is used as an additional measurement source in the extended Kalman filter (EKF). In addition, an approach to adapt the noise co-variances that support the generation of synthetic measurements is proposed. Furthermore, we derive the optimal gain under specific assumptions for a concrete theoretical understanding of the proposed algorithm. The proposed algorithm is tested and validated through two real-world datasets collected in Hong Kong, one corresponding to a moving vehicle inside a significantly long sea tunnel and another set in a harsh urban area, involving complex trajectories. A detailed analysis of the results has been performed with respect to all the aforementioned contributions. Additionally, the proposed algorithm has been compared with other existing algorithms. Experimental results show that a mean error of about 4 m is attained inside the tunnel, while it is around 4.6 m for the second scenario set in a harsh urban environment.

In view of the fact that the development of convolutional neural networks (CNN) and other deep learning techniques, scientists have become more interested in the scene categorization of remotely acquired images as well as other algorithms and datasets. The spatial geometric detail information may be lost as the convolution layer thickness increases, which would have a significant impact on the classification accuracy. Fusion-based techniques, which are regarded to be a viable way to express scene features, have recently attracted a lot of interest as a solution to this issue. Here, we suggested a convolutional feature fusion network that makes use of canonical correlation, which is the linear correlation between two feature maps. Then, to improve scene classification accuracy, the deep features extracted from various pre-trained convolutional neural networks are efficiently fused. We thoroughly evaluated three different fused CNN designs to achieve the best results. Finally, we used the support vector machine for categorization (SVM). In the analysis, two real-world datasets UC Merced and SIRI-WHU were employed, and the competitiveness of the investigated technique was evaluated. The improved categorization accuracy demonstrates that the fusion technique under consideration has produced affirmative results when compared to individual networks.

Multi-layer friction surfacing (MLFS) is a layer deposition technique that allows building structures from metals in solid state. As approach for additive manufacturing, the re-heating during subsequent deposition processes is significantly lower compared to fusion-based techniques. Available research work presents promising properties of MLFS structures from aluminum alloys, reporting no significant directional dependency in terms of tensile strength. The present study focuses on the fatigue crack propagation behavior and the role of layer-to-substrate (LTS) as well as layer-to-layer (LTL) interfaces. Compact tension specimens were extracted in different orientations from the MLFS stacks built from AA5083. The crack propagation parallel and perpendicular to the LTL interfaces as well as from the substrate material across LTS interface into the MLFS deposited material was investigated. The results show that LTL interfaces play no significant role for the crack propagation, i.e. specimens with LTL interfaces perpendicular and parallel to the crack presented no significant differences in terms of their fatigue crack propagation behavior. The specimens where the crack propagated from the substrate material across the LTS interface into the MLFS deposited material showed higher fatigue life than the specimens with crack propagation in the MLFS deposited material only. Crack retardation can be observed as long as the crack propagates within the substrate material, which is associated with compressive residual stresses introduced in the substrate during the layer deposition process.

The inherent properties of magnesium (Mg) make it one of the most challenging metals to process with additive manufacturing (AM), especially with fusion-based techniques. Binder jetting is a two-step AM method in which green Mg objects print near room temperature, then the as-printed green object sinters at a high temperature. Thus far, a limited number of studies have been reported on the binder jetting of Mg powder. This study aimed to push the knowledge base of binder jetting and sintering for AZ91D powder. To this end, the principle of capillary-mediated binderless printing was used to determine the ink saturation level (SL) required for the binder jetting of a green AZ91 object. The effects of various SLs on forming interparticle bridges between AZ91 powder particles and the dimensional accuracy of the resultant as-printed objects were investigated. Green AZ91 objects sintered at different temperatures ranging from 530 °C to 575 °C showed a marginal increment in density with an increase in sintering temperature (i.e., 1.5% to 5.1%). The root cause of such a low sintering densification rate in the presence of up to 54.5 vol. % liquid phase was discussed in the context of the powder packing density of as-printed objects and swelling occurring at sintering temperatures ≥ 45 °C. Overall, this work demonstrates the great potential of binderless printing for AM of Mg powder and the need for pushing sintering boundaries for further densification of as-printed Mg components.

The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.

Recent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient and lightweight compared to deep architecture-based transfer learning techniques. This research has identified the competence of classifying breast cancer images using feature engineering and representation learning over the established and contemporary notion of using transfer learning techniques. Moreover, it has revealed superior feature learning capacity with feature fusion in contrast to the conventional belief of understanding unknown feature patterns better with representation learning alone. Experiments have been conducted on two different and popular breast cancer image datasets, namely, KIMIA Path960 and BreakHis datasets. A comparison of image-level accuracy is performed on these datasets using the above-mentioned feature extraction techniques. Image level accuracy of 97.81% is achieved for KIMIA Path960 dataset using individual features extracted with handcrafted (color histogram) technique. Fusion of uniform Local Binary Pattern (uLBP) and color histogram features has resulted in 99.17% of highest accuracy for the same dataset. Experimentation with BreakHis dataset has resulted in highest classification accuracy of 88.41% with color histogram features for images with 200X magnification factor. Finally, the results are contrasted to that of state-of-the-art and superior performances are observed on many occasions with the proposed fusion-based techniques. In case of BreakHis dataset, the highest accuracies 87.60% (with least standard deviation) and 85.77% are recorded for 200X and 400X magnification factors, respectively, and the results for the aforesaid magnification factors of images have exceeded the state-of-the-art.

Although significant actions have been taken towards the utilization of poly(vinyl alcohol) (PVA) in the preparation of drug amorphous solid dispersions (ASDs) using fusion-based techniques (such as melt-quench cooling and hot-melt extrusion), several drawbacks regarding its rather high melting temperature and its thermal degradation profile make the use of the polymer extremely challenging. This is especially important when the active pharmaceutical ingredient (API) has a lower melting temperature (than PVA) or when it is thermally labile. In this vein, a previous study showed that newly synthesized polyester-based plasticizers may improve the processability and the thermal properties of PVA. However, the effects of such polyester-based plasticizers on the drug’s physicochemical and pharmaco-technical properties are yet unknown. Hence, the aim of the present study is to extend our previous findings and evaluate the use of poly(propylene succinate) (PPSu, i.e., the most promising plasticizer in regard to PVA) in the preparation of drug-loaded PVA-based ASDs. Dronedarone (DRN), a poorly water-soluble API, was selected as a model drug, and drug ASDs (using either neat PVA or PVA-PPSu) were prepared using the melt-mixing/quench cooling approach at low melting temperatures (i.e., 170 °C). DSC and pXRD analysis showed that a portion of the API remained crystalline in the ASDs prepared only with the use of neat PVA, while the samples having PPSu as a plasticizer were completely amorphous. Further evaluation with ATR-FTIR spectroscopy revealed the formation of significant intermolecular interactions between the API and the PVA-PPSu matrix, which could explain the system’s physical stability during storage. Finally, dissolution studies, conducted under nonsink conditions, revealed that the use of PVA-PPSu is able to maintain DRN’s sustained supersaturation for up to 8 h.

With deep learning playing a crucial role in biomedical image segmentation, multi-modality fusion-based techniques have gained rapid growth. For any radiologist, identification and segmentation of brain tumor (gliomas) via multi-sequence 3D volumetric MRI scan for diagnosis, monitoring, and treatment, are complex and time-consuming tasks. The brain tumor segmentation (BraTS) challenge offers 3D volumes of high-graded gliomas (HGG), and low-graded gliomas (LGG) MRI scans with four modalities: T1, T1c, T2 and FLAIR. In this article, the tumor segmentation is performed on the preprocessed multi-modalities by proposed 3D deep neural network components: multi-modalities fusion, tumor extractor, and tumor segmenter. The multi-modalities fusion component uses the deep inception based encoding to merge the multi-modalities. Tumor extractor component is passed with the fused images to recognise the tumor patterns using the 3D inception U-Net model. Finally, tumor segmenter utilises the progressive approach to decode the extracted feature maps into the tumor regions. The architecture segments each lesion region into the whole tumor (WT), core tumor (CT), and enhancing tumor (ET) using the five target classes: background, necrosis, edema, enhancing tumor and non-enhancing tumor. To tackle the class imbalance problem, the weighted segmentation loss function is proposed based on the dice coefficient and the Jaccard index. This article illustrates the significance of each component on the BraTS 2017 and 2018 datasets by achieving better segmentation results. The performance of the proposed approach is also compared with the other state-of-the-art methods.

It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.

Molecular modelling and simulation of fusion-based amorphous drug dispersions in polymer/plasticizer blends

Fusion additive technologies have pushed the boundaries beyond what was previously envisaged in the metal community.Championed by laser-based and electron beam technologies, metal additive manufacturing has captivated the interests of aerospace, defence, energy, automotive, and medical sectors.Though game-changing, these fusion-based techniques suffer from solidification related issues which play a critical role in applications where high premium is placed on materials' performance.Furthermore, only limited number of alloys can be built due to complexities associated with melting.To address these drawbacks, parallel work on solid state technologies was initiated in the last decade.An outcome of these efforts has been the development of additive manufacturing technologies based on friction which has now reached a stage where compilation is possible.In this article, fundamental principle and features of these friction-based additive technologies are reviewed with special emphasis on their individual advantages and differences between them.In addition, further scope, challenges and potential of these technologies are highlighted.

The recent development of petawatt-class lasers with kilojoule-picosecond pulses, such as OMEGA EP [L. Waxer et al., Opt. Photonics News 16, 30 (2005)], provides a new diagnostic capability to study inertial-confinement-fusion (ICF) and high-energy-density (HED) plasmas. Specifically, petawatt OMEGA EP pulses have been used to backlight OMEGA implosions with energetic proton beams generated through the target normal sheath acceleration (TNSA) mechanism. This allows time-resolved studies of the mass distribution and electromagnetic field structures in ICF and HED plasmas. This principle has been previously demonstrated using Vulcan to backlight six-beam implosions [A. J. Mackinnon et al., Phys. Rev. Lett. 97, 045001 (2006)]. The TNSA proton backlighter offers better spatial and temporal resolution but poorer spatial uniformity and energy resolution than previous D(3)He fusion-based techniques [C. Li et al., Rev. Sci. Instrum. 77, 10E725 (2006)]. A target and the experimental design technique to mitigate potential problems in using TNSA backlighting to study full-energy implosions is discussed. The first proton radiographs of 60-beam spherical OMEGA implosions using the techniques discussed in this paper are presented. Sample radiographs and suggestions for troubleshooting failed radiography shots using TNSA backlighting are given, and future applications of this technique at OMEGA and the NIF are discussed.

Thermal processing of a poorly water-soluble drug substance exhibiting a high melting point: The utility of KinetiSol ® Dispersing

Analysis of human blastomere chromosomes following nuclear injection into mature mouse oocytes.