Hybrid Fusion Research Articles

AI-TFNet: Active Inference Transfer Convolutional Fusion Network for Hyperspectral Image Classification

The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral–spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this paper, we established a novel active inference transfer convolutional fusion network (AI-TFNet) for HSI classification. First, in order to reveal and merge the local low-level and global high-level spectral–spatial contextual features at different stages of extraction, an end-to-end fully hybrid multi-stage transfer fusion network (TFNet) was designed to improve classification performance and efficiency. Meanwhile, an active inference (AI) pseudo-label propagation algorithm for spatially homogeneous samples was constructed using the homogeneous pre-segmentation of the proposed TFNet. In addition, a confidence-augmented pseudo-label loss (CapLoss) was proposed in order to define the confidence of a pseudo-label with an adaptive threshold in homogeneous regions for acquiring pseudo-label samples; this can adaptively infer a pseudo-label by actively augmenting the homogeneous training samples based on their spatial homogeneity and spectral continuity. Experiments on three real HSI datasets proved that the proposed method had competitive performance and efficiency compared to several related state-of-the-art methods.

A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber

Recently, deep learning methods have become one of the hottest topics in predicting material properties, however, one bottleneck in current research is the simultaneous analysis of heterogeneous data. In this study, a deep learning fusion model is developed for the first time to predict the material properties of carbon fiber monofilament using textual (macroscopic properties of composites and matrix) and visual (two-point statistics of microstructures) data. For this, 1200 stochastic microstructures are generated using the greedy-based generation (GBG) algorithm. Then, the statistical representations of microstructures are determined using two-point statistics and the macroscopic properties are calculated based on a micro-scale finite element (FE) simulation. Finally, the visual and textual data are fed into the convolutional neural network (CNN) and multi-layer perceptron (MLP) fusion model for predicting the mechanical properties of carbon fibers. The developed hybrid CNN-MLP fusion model achieves encouraging average testing R2 of longitudinal modulus, transverse modulus, in-plane shear modulus, major Poisson’s ratio, and out-of-plane shear modulus of carbon fibers with values of 0.991, 0.969, 0.984, 0.903, and 0.955, respectively. Thus, the proposed strategy provides a promising framework for predicting material properties via multisource heterogeneous data and is expected to accelerate the smart design and optimization of materials.

Multiverse fractional calculus based hybrid deep learning and fusion approach for detecting malicious behavior in cloud computing environment

The tremendous development and rapid evolution in computing advancements has urged a lot of organizations to expand their data as well as computational needs. Such type of services offers security concepts like confidentiality, integrity, and availability. Thus, a highly secured domain is the fundamental need of cloud environments. In addition, security breaches are also growing equally in the cloud because of the sophisticated services of the cloud, which cannot be mitigated efficiently through firewall rules and packet filtering methods. In order to mitigate the malicious attacks and to detect the malicious behavior with high detection accuracy, an effective strategy named Multiverse Fractional Calculus (MFC) based hybrid deep learning approach is proposed. Here, two network classifiers namely Hierarchical Attention Network (HAN) and Random Multimodel Deep Learning (RMDL) are employed to detect the presence of malicious behavior. The network classifier is trained by exploiting proposed MFC, which is an integration of multi-verse optimizer and fractional calculus. The proposed MFC-based hybrid deep learning approach has attained superior results with utmost testing sensitivity, accuracy, and specificity of 0.949, 0.939, and 0.947.

A Hybrid Multimodal Fusion Framework for sEMG-ACC-Based Hand Gesture Recognition

Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and accelerometer (ACC) signals has attracted increasing attention. The design of an effective multimodal fusion strategy plays a vital role. Existing studies fuse sEMG and ACC either in a low-level data space or in a high-level decision space. These fusion strategies result in insufficiently representing intramodal specificity and intermodal association simultaneously. Meanwhile, current fusion methods tend to neglect multiscale characteristics and hierarchical relationships and rarely specifically take into account the possible low discrimination in similar gestures. To address these issues, we propose a novel and practical hybrid fusion (HyFusion) model with multiscale attention and metric learning, to improve the performance of HGR based on sEMG and ACC. In HyFusion, a fusion framework including parallel intramodal and intermodal branches is first designed to adequately explore intramodal specificity and intermodal association simultaneously. Furthermore, the fusion of multiscale hierarchical features in each branch is conducted through a multiscale spatial attention (SA) module to generate more effective features. Finally, with the joint supervision of softmax and metric learning loss, HyFusion extracts features that maximize intraclass aggregation and interclass differentiation, thus particularly facilitating distinguishing similar gestures. The proposed HyFusion is validated on three public datasets (Ninapro DB2, DB3, and DB7), involving transradial amputees and healthy subjects. The results demonstrate that HyFusion achieves state-of-the-art performance.

Role of complexin-lipid interactions in controlling snare-mediated membrane fusion.

Fast and reliable neurotransmitter release is essential for signaling fidelity at neuronal synapses. Synaptic exocytosis is a tightly controlled process accomplished through Ca2+ triggered fusion of neurotransmitter-laden vesicles with the presynaptic plasma membrane. This process is mediated by SNARE (soluble N-ethylmaleimide sensitive factor attachment protein receptor) proteins that zipper into a four-helix bundle to drive membrane fusion. The neuronal SNARE complex, consisting of Syntaxin-1a, SNAP-25, and Synaptobrevin-2, is intrinsically capable of catalyzing slow and Ca2+ independent fusion in reconstituted fusion assays in vitro. A single model of how interactions with SNARE regulatory proteins impart physiological properties seen in neurons, such as Ca2+ specificity, has yet to be established. Complexin-1 (Cpx) is a small, soluble regulatory protein that binds SNAREs and lipid membranes. Cpx is proposed to facilitate fusion in the presence of Ca2+ as well as suppress fusion in the absence of Ca2+ through interactions of its central domain with SNARE complexes in their prefusion and postfusion states. Additionally, the Cpx C-terminal domain interacts with membranes, an interaction that has been proposed to facilitate function. Recent work has shown that Cpx's lipid-binding domains can differentiate between membranes of different compositions, though the functional relevance of this finding has yet to be established. Our group has previously developed a hybrid fusion assay that combines synthetic, reconstituted, and native components to reproduce key features of synaptic exocytosis. This method mimics intracellular conditions more closely than a reconstituted system with only purified components while maintaining the ability to easily alter lipid, protein, and ion concentrations. We utilize this system with wild-type and mutant Cpx, along with other protein and lipid regulators of SNAREs, to investigate how interaction with and composition of the fusing membranes controls Cpx's regulatory function.

Scoping Studies for a Lead-Lithium-Cooled, Minor-Actinide-Burning, Fission-Fusion Hybrid Reactor Design

A feasibility study of a subcritical fission-fusion hybrid reactor using lead-lithium eutectic as a coolant and minor actinides (MAs) as fuel is presented. Such a reactor could support the fission community by transmuting MAs and the fusion community by breeding tritium. The feasibility of such a reactor for the burnup of MAs is assessed in terms of burnup performance, tritium breeding, and safety characteristics. Tandem mirrors are a promising neutron source technology, and a deuterium-tritium tandem mirror is considered here for the neutron source with power Psource = 1.13 MW assumed for scoping purposes. Subcritical reactivities from keff = 0.9800 to keff = 0.9950 were considered, representing the initial reference for subcritical reactivity and the assessed upper limit, respectively. Stability analyses indicated the reactivity would be stable under perturbations of fuel, coolant, and inlet temperatures, with a positive reactivity insertion expected during reactor shutdown. This range corresponded to nuclear heating values of 150 to 650 MW and mass burn rates of 53 to 216 kg/year. The upper mass burn rate limit would require 1110 reactor years with a capacity factor of 0.9 to fission the global supply of MAs and could offset the annual U.S. MA production with eight reactors. Tritium breeding was assessed for keff = 0.9800 and 3.795% 6Li enrichment in the coolant, and a tritium breeding ratio of 1.602 0.017 was tallied, suggesting the reactor could, without elevated 6Li enrichment, produce tritium to both sustain operation and supply tritium for other fusion devices. Time-series modeling of fuel burnup was conducted for a four-batch loading scheme and three different fuel residence times at keff = 0.9800, which showed system performance would drop with burnup, and that the rate of this drop was lower for longer fuel residence times, motivating a means of reactivity control. Last, changes in fuel composition with burnup were assessed for relative concentrations of MAs, transmutation products, and fission products. The breeding of plutonium in the blanket was calculated and found to be of minimal proliferation concern.

Adaptive denoising of photoacoustic signal and image based on modified Kalman filter.

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal-to-noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real-time estimation due to its fixed model. Moreover, KF-based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch-Tung-Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in-band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm.

Total Pancreatectomy with Spleen Preservation for Degenerated IPMN: A Hybrid Fusion between the Kimura Technique and the Whipple Procedure

Total Pancreatectomy with Spleen Preservation for Degenerated IPMN: A Hybrid Fusion between the Kimura Technique and the Whipple Procedure

Multi-group analysis of Minor Actinides transmutation in a Fusion Hybrid Reactor

New nuclear technologies are currently being study to face High Level Waste treatment and disposal issues. Generally, GEN-IV fission Fast Reactors (FR) are considered the waste-burners of the future. In fact, a fast flux turns out to be the best choice for actinides irradiation in critical reactors because of favorable cross section conditions. Differently, Fusion Fission Hybrid Reactors (FFHR) are futuristic devices based on the combination of fusion and fission systems and could represent an alternative to FRs. In such systems, the choice spectrum of the neutron flux that irradiates HLW may be non-obvious due to some operational constraints which have to be considered. To design and optimize these systems as waste-burners, one should fully understand the transmutation dynamics occurring into the fission region. A multi-energy-group analysis by FISPACT-II code has been set to analyze the conversion processes in scenarios characterized by different neutron energy spectra and fluences. The results of this study show that, despite fast fluxes are characterized by better behaviors in terms of radiotoxicity treatment, the difficulties of reaching high reaction yields may require solutions involving moderators or broadened neutron fluxes to increase the reactions probabilities and, consequently, actinides mass conversion yield.

Infrared and visible image fusion with deep wavelet-dense network

We propose a high-quality infrared and visible image fusion method based on a deep wavelet-dense network (WT-DenseNet). The WT-DenseNet includes three network layers, the hybrid feature extraction layer, fusion layer, and image reconstruction layer. The hybrid feature extraction layer is composed of a wavelet and dense network. The wavelet network decomposes the feature map of the visible and infrared images into low-frequency and high-frequency components, respectively. The dense network extracts the salient features. A fusion layer is designed to integrate low-frequency and salient features. Finally, the fusion images are outputted by an image reconstruction layer. The experimental results demonstrate that the proposed method can realize high-quality infrared and visible image fusions, and the performance of the proposed method is better than that of the six recently published fusion methods in terms of contrast and detail performance.

Сhannelized Karyotype Evolution of the Common Shrew <i>Sorex araneus</i> (Mammalia)

A model of channelized evolution of the S. araneus L. karyotype in the processes of replacement of ten pairs of acrocentric chromosomes by five pairs of metacentric chromosomes has been proposed. The channelized evolution of the karyotype arises due to the inability of Rb fusions with incomplete (monobrachial) homology to spread in the same population. Therefore, an Rb fusion, due to some random event first appearing in a population, largely determines the further evolution of the karyotype of that population. After the third replacement of acrocentric chromosomes by metacentric ones, the replacement of the remaining 6 pairs of acrocentrics allows the formation of no more than three karyotypes with five diagnostic metacentrics, which can be predicted, regardless of which rearrangements result in metacentric chromosomes (Rb fusion or WART). The channelized karyotypic evolution greatly increases the likelihood of parallel karyotype formation, in cases where evolution begins with identical metacentrics in geographically distant populations. An example of parallel evolution that began with the gk metacentric is the identical karyotypes of the new Mogilev race from Belarus and the Tomsk race from Western Siberia. The evolution of Eastern European chromosomal races shows hybrid fusion processes between the karyotypes of the East European karyotypic group (EEKG) and the West European karyotypic group (WEKG).

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Radiation and Nuclear Physics Aspects of the Use of the Thorium Fuel Cycle in a Hybrid Fusion Facility

This paper presents the results of the experimental determination and computational simulation of the ambient dose equivalent rate for a metallic thorium cylindrical miniblock and the (n,2n), (n,f), and (n,γ) reaction rates in a thin 232Th metal foil irradiated with neutrons of the NG-24M generator spectrum. The ambient dose equivalent rate was determined by dosimeters-radiometers. The reaction rates were determined by the activation method using Ge spectrometers without destroying the irradiated samples. Computational simulations of ambient dose equivalent and reaction rates were performed, respectively, using the radiation transport codes PHITS, MCNP5, and KIR2, which use various nuclear data libraries: JEFF-3.2 and -3.3; JENDL4.0; ENDF/B-VII.0, -VII.1, and -VIII.0; ROSFOND; FENDL; and TENDL. The authors give an estimate of the 232U/233U relative accumulation upon natural thorium irradiation in a fusion facility blanket with defined neutron spectrum. The nonirradiated and irradiated thorium nuclide composition change simulation and visualization were performed using analytical solutions of an ordinary system of homogeneous linear differential equations describing nuclide transmutations.

Intelligence Fusion for the European Union’s Common Security and Defence Policy

The data fusion methods, techniques and tools are regarded as a remedy for shortcomings of information/knowledge management and intelligence production. They also address current needs of the holistic, all-source approach to intelligence. Their implementation means the creation of new organizational elements − ‘fusion centers’. The concept of a fusion center has been introduced to and tested in the European Union for years. This paper examines data fusion processes and elements within the EU and focuses on intelligence fusion capabilities developed under the Common Security and Defence Policy (CSDP). The examples of the Single Intelligence Analysis Capacity (SIAC) and EU Hybrid Fusion Cell in the Intelligence and Situation Center (INTCEN) are examined to evaluate challenges, opportunities and limitations of EU intelligence fusion elements. This paper is also an effort to indicate that there are still many elements to be improved within the EU intelligence establishment, including the area of data and information fusion – with the overall aim to effectively and timely support CSDP. Intelligence sharing by Member States with the EU remains one of the main impediments.

FUSION RESEARCH IN STELLARATOR DEPARTMENT OF IPP NSC KIPT

This paper briefly describes intrinsic and collaborative scientific activities in the Stellarator Department of the Institute of Plasma Physics of the National Science Center “Kharkov Institute of Physics and Technology” in last two years. These activities include experiments on JET tokamak, stellarators Wendelstein 7-X and Uragan-2M, TOMAS toroidal device and theoretical studies related to modeling of radio-frequency fields in plasma and conceptual development of the stellarator-mirror fission-fusion hybrid.

Pharmacological Modulation of the Crosstalk between Aberrant Janus Kinase Signaling and Epigenetic Modifiers of the Histone Deacetylase Family to Treat Cancer.

Hyperactivated Janus kinase (JAK) signaling is an appreciated drug target in human cancers. Numerous mutant JAK molecules as well as inherent and acquired drug resistance mechanisms limit the efficacy of JAK inhibitors (JAKi). There is accumulating evidence that epigenetic mechanisms control JAK-dependent signaling cascades. Like JAKs, epigenetic modifiers of the histone deacetylase (HDAC) family regulate the growth and development of cells and are often dysregulated in cancer cells. The notion that inhibitors of histone deacetylases (HDACi) abrogate oncogenic JAK-dependent signaling cascades illustrates an intricate crosstalk between JAKs and HDACs. Here, we summarize how structurally divergent, broad-acting as well as isoenzyme-specific HDACi, hybrid fusion pharmacophores containing JAKi and HDACi, and proteolysis targeting chimeras for JAKs inactivate the four JAK proteins JAK1, JAK2, JAK3, and tyrosine kinase-2. These agents suppress aberrant JAK activity through specific transcription-dependent processes and mechanisms that alter the phosphorylation and stability of JAKs. Pharmacological inhibition of HDACs abrogates allosteric activation of JAKs, overcomes limitations of ATP-competitive type 1 and type 2 JAKi, and interacts favorably with JAKi. Since such findings were collected in cultured cells, experimental animals, and cancer patients, we condense preclinical and translational relevance. We also discuss how future research on acetylation-dependent mechanisms that regulate JAKs might allow the rational design of improved treatments for cancer patients. SIGNIFICANCE STATEMENT: Reversible lysine-ɛ-N acetylation and deacetylation cycles control phosphorylation-dependent Janus kinase-signal transducer and activator of transcription signaling. The intricate crosstalk between these fundamental molecular mechanisms provides opportunities for pharmacological intervention strategies with modern small molecule inhibitors. This could help patients suffering from cancer.

Effect of a Hybrid Fusion Plant Operating in a Fission and Fusion Reactor System on the Fuel Cycle

Effect of a Hybrid Fusion Plant Operating in a Fission and Fusion Reactor System on the Fuel Cycle

Bacillus subtilis Stressosome Sensor Protein Sequences Govern the Ability To Distinguish among Environmental Stressors and Elicit Different σB Response Profiles.

Bacteria use a variety of systems to sense stress and mount an appropriate response to ensure fitness and survival. Bacillus subtilis uses stressosomes-cytoplasmic multiprotein complexes-to sense environmental stressors and enact the general stress response by activating the alternative sigma factor σB. Each stressosome includes 40 RsbR proteins, representing four paralogous (RsbRA, RsbRB, RsbRC, and RsbRD) putative stress sensors. Population-level analyses suggested that the RsbR paralogs are largely redundant, while our prior work using microfluidics-coupled fluorescence microscopy uncovered differences among the RsbR paralogs' σB response profiles with respect to timing and intensity when facing an identical stressor. Here, we use a similar approach to address the question of whether the σB responses mediated by each paralog differ in the presence of different environmental stressors: can they distinguish among stressors? Wild-type cells (with all four paralogs) and RsbRA-only cells activate σB with characteristic transient response timing irrespective of stressor but show various response magnitudes. However, cells with other individual RsbR paralogs show distinct timing and magnitude in their responses to ethanol, salt, oxidative, and acid stress, implying that RsbR proteins can distinguish among stressors. Experiments with hybrid fusion proteins comprising the N-terminal half of one paralog and the C-terminal half of another argue that the N-terminal identity influences response magnitude and that determinants in both halves of RsbRA are important for its stereotypical transient σB response timing. IMPORTANCE Bacterial survival depends on appropriate responses to diverse stressors. The general stress-response system in the environmental model bacterium Bacillus subtilis is constantly poised for an immediate response and uses unusual stress-sensing protein complexes called stressosomes. Stressosomes typically contain four different types of putative sensing protein. We asked whether each type of sensor has a distinct role in mediating response dynamics to different environmental stressors. We find that one sensor type always mediates a transient response, while the others show distinct response magnitude and timing to different stressors. We also find that a transient response is exceptional, as several engineered hybrid proteins did not show strong transient responses. Our work reveals functional distinctions among subunits of the stressosome complex and represents a step toward understanding how the general stress response of B. subtilis ensures its survival in natural environmental settings.

Research on the Application of Multi-Source Information Fusion in Multiple Gait Pattern Transition Recognition.

Multi-source information fusion technology is a kind of information processing technology which comprehensively processes and utilizes multi-source uncertain information. It is an effective scheme to solve complex pattern recognition and improve classification performance. This study aims to improve the accuracy and robustness of exoskeleton gait pattern transition recognition in complex environments. Based on the theory of multi-source information fusion, this paper explored a multi-source information fusion model for exoskeleton gait pattern transition recognition in terms of two aspects of multi-source information fusion strategy and multi-classifier fusion. For eight common gait pattern transitions (between level and stair walking and between level and ramp walking), we proposed a hybrid fusion strategy of multi-source information at the feature level and decision level. We first selected an optimal feature subset through correlation feature extraction and feature selection algorithm, followed by the feature fusion through the classifier. We then studied the construction of a multi-classifier fusion model with a focus on the selection of base classifier and multi-classifier fusion algorithm. By analyzing the classification performance and robustness of the multi-classifier fusion model integrating multiple classifier combinations with a number of multi-classifier fusion algorithms, we finally constructed a multi-classifier fusion model based on D-S evidence theory and the combination of three SVM classifiers with different kernel functions (linear, RBF, polynomial). Such multi-source information fusion model improved the anti-interference and fault tolerance of the model through the hybrid fusion strategy of feature level and decision level and had higher accuracy and robustness in the gait pattern transition recognition, whose average recognition accuracy for eight gait pattern transitions reached 99.70%, which increased by 0.15% compared with the highest average recognition accuracy of the single classifier. Moreover, the average recognition accuracy in the absence of different feature data reached 97.47% with good robustness.

A complementary study on the thermophysical and gas-dynamic characteristics of a hybrid fusion–fission reactor facility during operation

A hybrid fusion–fission facility, which features an asymmetric assembly of fuel blocks placed in a high-temperature gas-cooled reactor and an extended plasma source of deuterium–tritium (D–T) neutrons, is investigated to evaluate its thermophysical and gas-dynamic characteristics. The proposed installation generates a power in the ∼10–100 MWth range, has small dimensions, and produces a relatively low level of radioactive waste. Thus, it is suitable for regional energy production. An optimization of the thermophysical and gas-dynamic characteristics is presented. The objective is to reduce the emerging offsets of the radial energy formed in the volume of the multiplying part of the facility. These offsets are caused by the pulsed operation of the D–T neutron plasma source.