Fusion Components Research Articles

Multimodal HSI Combined with Multiblock Data Fusion: A New Tool for the Study of Time-Dependent Alteration Processes in Dyed Textiles.

The present study describes an innovative approach for the study of time-dependent alteration processes. It combines an advanced hyperspectral imaging (HSI) system, to collect visible reflectance and fluorescence spectral data sets sequentially, with a tailored multiblock data processing method. This enables the modeling of chemical degradation maps and the early, spatially resolved detection of dye alteration in textiles. A chemometric method based on data fusion and principal component analysis was employed to identify spectral features of dye degradation, combining and enhancing information from reflectance and fluorescence HSI data. The most significant spectral profiles extracted were used to develop an asymmetric Gaussian-based pixel-by-pixel fitting model applied to the HSI fluorescence data set, enabling the reconstruction of degradation maps for rapid and intuitive visualization. In particular, changes in intensities and horizontal shift of dye emission peaks were pixel-by-pixel evaluated and fitted for the reconstruction of the degradation maps. Artificially aged wool samples tinted with indigo carmine (IC) dye served as a case study. IC is extensively used in textiles, and it is notable for its light sensitive. The results show that this approach effectively identifies spatial variations and chemical changes in dyed wool fibers, offering potential for sustainable conservation of historical textiles and other types of time-dependent processes. Thus, by amplifying variation in spectral profiles induced over time by aging, even minimal changes at early stages can be easily detected and localized, offering powerful tools for future studies on food and drug shelf life and stability, as well as forensic trace analysis.

A novel experimental approach to exploring process parameters for fabricating support-free horizontal overhangs in LPBF of Inconel 718

ABSTRACT The design freedom of Laser Powder Bed Fusion (LPBF) components has been confined by the minimum allowable overhang angle of 45°. It remains unclear how to achieve multiple layers of overhang at zero-degree overhang angle, which is the fundamental basis of overhang. Here, we have confirmed the possibility of manufacturing three layers of support-free 0° overhang, but also demonstrated the robustness of the present approach via the successful manufacture of an overhang with 18 mm span. The stable dross thickness reveals that the laser drilling effect did not penetrate beyond the three-layer overhang. Furthermore, this research also demonstrates that the failure to manufacture long bridge overhangs was caused by severe consolidation rather than the conventional understanding of residual stress of the first few layers. The high correlation between porosity and surface roughness, and the etched melt pool reveal the underlying mechanism for low surface roughness for the horizontal overhang. Highlights An experimental approach is proposed to efficiently explore the parameter space. The present approach enables the manufacture of three layers of support-free overhang. The mechanisms for the successful and failed prints are elucidated. The relationship of Ra and porosity is proposed to further refine the parameters.

Flooded Infrastructure Change Detection in Deeply Supervised Networks Based on Multi-Attention-Constrained Multi-Scale Feature Fusion

Flood disasters are frequent, sudden, and have significant chain effects, seriously damaging infrastructure. Remote sensing images provide a means for timely flood emergency monitoring. When floods occur, emergency management agencies need to respond quickly and assess the damage. However, manual evaluation takes a significant amount of time; in current, commercial applications, the post-disaster flood vector range is used to directly overlay land cover data. On the one hand, land cover data are not updated in time, resulting in the misjudgment of disaster losses; on the other hand, since buildings block floods, the above methods cannot detect flooded buildings. Automated change-detection methods can effectively alleviate the above problems. However, the ability of change-detection structures and deep learning models for flooding to characterize flooded buildings and roads is unclear. This study specifically evaluated the performance of different change-detection structures and different deep learning models for the change detection of flooded buildings and roads in very-high-resolution remote sensing images. At the same time, a plug-and-play, multi-attention-constrained, deeply supervised high-dimensional and low-dimensional multi-scale feature fusion (MSFF) module is proposed. The MSFF module was extended to different deep learning models. Experimental results showed that the embedded MSFF performs better than the baseline model, demonstrating that MSFF can be used as a general multi-scale feature fusion component. After FloodedCDNet introduced MSFF, the detection accuracy of flooded buildings and roads changed after the data augmentation reached a maximum of 69.1% MIoU. This demonstrates its effectiveness and robustness in identifying change regions and categories from very-high-resolution remote sensing images.

Hypothyroidism modulates mitochondrial dynamics and mitophagy in the heart of rats under fed and fasting conditions

AimsInvestigate the impact of hypothyroidism on mitochondrial dynamics and mitophagy in the heart under fed and fasting conditions. MethodsHypothyroidism was induced in male Wistar rats with methimazole (0.03 %) for 21 days. Half of the euthyroid and hypothyroid groups underwent a 48-h fasting. Mitochondrial number and ultrastructure were evaluated by transmission electron microscopy. Fusion, fission, mitophagy, oxidative stress, and mitochondrial oxidative phosphorylation system (OXPHOS) components were analyzed by Western Blot and qPCR. ResultsHypothyroidism increased DRP1 activation and the p-DRP1/OPA1 ratio, indicating a shift toward mitochondrial fission over fusion. Under fasting, hypothyroidism prevented the increases in mitochondrial size, elongation, OPA1, and OXPHOS seen in euthyroid fasted rats. Hypothyroidism also raised 4-HNE content, an oxidative stress product, increased mitochondrial injury, and exacerbated fasting-related mitochondrial damage. This was accompanied by elevated Parkin levels in both fed and fasted hypothyroid groups, but without changes in PINK1 levels or Parkin activation. While fasting upregulated Bnip3l and Map1lc3b expression in euthyroid rats, hypothyroidism suppressed this response, though it did not prevent fasting-induced Bnip3 increases. ConclusionsHypothyroidism increases the activation of mitochondrial fission machinery and oxidative stress, and induces mitochondrial damage without activation of mitophagy proteins, suggesting disrupted mitophagy signaling. It also interferes with fasting-induced mitochondrial dynamics adaptations, highlighting the essential role of thyroid hormones in metabolic adaptation to fasting.

Solution for heterogeneous data fusion based on autonomous classification

Variance component estimation (VCE) is an essential component of heterogeneous data fusion, however, VCE has problems with stability and reliability in random models. To progress data fusion research, this paper proposes a new solution that establishes a data fusion model based on the theory of Msplit estimation. To determine the classification number of heterogeneous data accurately, a median function was applied for autonomous classification using Msplit estimation, according to the basic characteristics of random error distribution, a method of autonomous classification is established for heterogeneous data. Especially, the fusion method presented in this paper does not rely on prior information of observation data and can effectively overcome the limitations of current methods for data fusion based on the theory of VCE. An example of coordinate affine transformation was used to verify that the model proposed in this paper could achieve effective fusion of heterogeneous data.

Exploring sample relationship for few-shot classification

Few-shot classification (FSC) is a challenging problem, which aims to identify novel classes with limited samples. Most existing methods employ vanilla transfer learning or episodic meta-training to learn a feature extractor, and then measure the similarity between the query image and the few support examples of novel classes. However, these approaches merely learn feature representations from individual images, overlooking the exploration of the interrelationships among images. This neglect can hinder the attainment of more discriminative feature representations, thus limiting the potential improvement of few-shot classification performance. To address this issue, we propose a Sample Relationship Exploration (SRE) module comprising the Sample-level Attention (SA), Explicit Guidance (EG) and Channel-wise Adaptive Fusion (CAF) components, to learn discriminative category-related features. Specifically, we first employ the SA component to explore the similarity relationships among samples and obtain aggregated features of similar samples. Furthermore, to enhance the robustness of these features, we introduce the EG component to explicitly guide the learning of sample relationships by providing an ideal affinity map among samples. Finally, the CAF component is adopted to perform weighted fusion of the original features and the aggregated features, yielding category-related embeddings. The proposed method is a plug-and-play module which can be embedded into both transfer learning and meta-learning based few-shot classification frameworks. Extensive experiments on benchmark datasets show that the proposed module can effectively improve the performance over baseline models, and also perform competitively against the state-of-the-art algorithms. The source code is available at https://github.com/Chenguoz/SRE.

Enhancing surface integrity of additively manufactured Inconel 718 through numerical modeling of hydrostatic ball burnishing parameters using TOPSIS and ANOVA

Abstract Surface enhancement of laser powder bed fusion components is pivotal for their suitability in end-use applications. Hydrostatic ball burnishing, a mechanical surface hardening technique, induces compressive residual stress on the surface by the application of hydraulic force resulting in improved fatigue life of the material. This present study focuses on determining the optimum burnishing parameter combinations for better surface integrity (roughness, hardness, and residual stress) utilizing ANOVA and TOPSIS analysis. The cumulative performance score of the TOPSIS analysis states that the best parameter combination for Inconel 718 is found to be 300 bar pressure, 0.2 mm burnishing width, and feed 800 mm min−1. The adjusted R2 values of the ANOVA model for all three responses are 0.96, 0.93, and 0.94, confirming their close agreement with experimental results. With optimum parameters, the roughness decreased from 2.217 μm to 0.305 μm, Microhardness increased from 318 9HV to 475 9HV and residual stress was converted from tensile 216 MPa to Compressive −54 MPa demonstrating the significance of this process on surface enhancement.

Accelerometer-assisted computer vision data fusion framework for structural dynamic displacement reconstruction

Accurate displacement measurement provides crucial information for evaluating the structural health condition. It is important to note that both direct and indirect displacement measurement methods have their own limitations, which can be remedied by leveraging each other’s strengths. In this study, a novel accelerometer-assisted computer vision data fusion framework is developed for accurately reconstructing structural dynamic displacement. The framework does not require a specific mathematical model or prior knowledge about the data’s characteristics, allowing it to be applied more broadly across different applications without the constraints of model assumptions. The core of this framework is to integrate successive variational mode decomposition (SVMD) and Bayesian optimization approaches to adaptively determine the weight factors of the fusion components. Importantly, an enhanced optical flow approach is presented for converting pixel movement to structural displacement from natural targets. This approach can effectively reduce the selection of mis-matched points within the ROI (Region of Interest), thereby reducing drift errors. The developed framework is verified via shaking table tests of a reinforced concrete frame structure under seismic excitation. Results indicate that the developed framework excels in accurately estimating structural dynamic displacement. Compared to single-vision displacement identification results, the proposed framework demonstrates lower peak error (< 1.65 mm) and normalized root mean square error (< 0.30). Meanwhile, the reconstructed displacement, by introducing dynamic displacement component from acceleration measurement, presents a wider frequency range than the vision-based displacement.

FNNGM: A neural-driven fractional-derivative multivariate fusion model for interpretable real-time CPI forecasts

Integrating models from diverse sources has attracted substantial interest in developing advanced time series forecasting technologies. However, current research lacks a comprehensive and deep fusion model to integrate multiple forecasting methodologies. To this end, this paper proposes a neural-driven fractional-derivative multivariate fusion model (FNNGM (p, n)) to assimilate the fractional-derivative dynamical system, the driving factor in grey multivariate models, and the neural network into a cohesive framework. Consequently, this fusion architecture can benefit from the synergy of the target system's dynamics, extensive exogenous information, and non-linear transformation. Additionally, FNNGM (p, n) fosters extra functionalities through its inherent memory layer and sequence decomposition, bolstering model interpretability with the visible memory mechanism and understandable model workflows. To showcase the utility of FNNGM (p, n), this paper conducts real-time monthly consumer price index (CPI) forecasts that span ten years (from 2013:08 to 2023:07), analyzing the interpretable results from FNNGM (p, n) and contrasting it against many prevailing benchmark models. The comparison results reveal FNNGM (p, n)’s highly concentrated error distributions and the minimum mean absolute percentage forecasting error (APFE), squared forecasting error (SFE), and absolute forecasting error (AFE) values of 0.22 %, 0.59, and 0.56, respectively. Furthermore, the ablation experiments are performed to explore the specific effects and compatibilities of the fusion components, validating the effectiveness of the proposed fusion approach.

Multi-modal Anaphora and Broadcasting of Information by Gestural Post-holds

This paper deals with three interrelated topics, linguistic anaphora, multi-modal anaphora and the top-down broadcasting of information using gestural post-holds in multimodal dialogue. Initially, a new solution for definite, pronominal and pro-adverbial anaphora is given based on the idea that an existentially quantified general term may output a definite reference. This approach is extended to multimodal anaphora, where part or all of an anaphor’s meaning is contributed by some sequence of iconic or deictic gestures. Anaphora exploit the semantic potential of their antecedents, they work, as tradition has it, “bottom-up”. An inverse relation, more general than cataphora, and investigated here for the first time, is “broadcasting”, where information is freely distributed top down and input to receiving sites (ports). Anaphora are modelled with the same top-down mechanism and the same applies for coherence relations in dialogue which generally show an anaphora-like behaviour. “Broadcasting” can be used in the context of anaphors, for example, to provide their gestural meaning parts but also for a verb’s multi-modal arguments for referring to a location, a direction or an area. As to multi-modal data, broadcasting is shown to be frequently tied up with gestural post-holds, the holding of a gesture’s stroke information independently of semantically alignable speech. This leads to considering post-holds from a new perspective, stressing their speech-independent function and their relevance for indicating topic-continuity. We show that multi-modal anaphora and especially broadcasting cross single contributions and turns. The data which let us develop these perspectives come from the SaGA (Speech and Gesture Alignment) corpus, a set of route-description dialogues generated in a VR-setting incorporating marker-based eye-tracking facilities. The calculus used to model the anaphora and broadcasting dynamics is the concurrent λΨ-calculus, a recently developed two-tiered machinery using a Ψ-calculus for input-output, data transport and broadcasting. The data transported are in a typed λ-calculus format incorporating Neo-Davidsonian representations; these data can be linguistic, gestural only or multi-modal. Multi-modal informational chunks are modelled as communicating agents sending and receiving information via input-output-channels. They are introduced incrementally on an empirically motivated construction or gesture-plus-construction or gesture only basis. The λΨ-calculus is also used for the multi-modal fusion component unifying gestural and linguistic information; hence, the paper is also a contribution to multi-modal fusion of linguistic and gestural input. Finally, it is shown how the presented algorithm can capture multi-modal coherence relations or a multi-modal anaphora resolution based on PTT ideas.

An evaluation of fusion status following lumbar fusion surgery utilizing multi-planar computed tomography.

To report the rate of fusion in a sample of patients undergoing lumbar fusion surgery and assess interrater reliability of computed tomography (CT)-based parameters for the assessment of fusion. All adult patients who underwent lumbar fusion surgery from 2017 to 2021 were retrospectively identified. Patient demographics and surgical characteristics were collected through chart review of the electronic medical records. CT scans were reviewed independently by two attending spine surgeons and two spine fellows. Fusion was defined as evidence of bone bridging in any one of (1) posterolateral gutters, (2) facets, or (3) interbody (when applicable) on any CT views. Evidence of screw haloing was indicative of nonunion. Interrater reliability was determined using cohen's kappa. Afterwards, a consensus agreement for each component of fusion was reached between participants. The overall fusion rate among all procedures was 63/69 (91.3%). Overall 22/25 (88.0%) TLIF, 16/19 (84.2%) PLDF, 3/3 (100%) LLIF, and 22/22 (100%) circumferential fusions experienced a successful fusion. Interrater reliability was good for interbody fusion (k = 0.734) and moderate for all other measures (k = 0.561 for posterolateral fusion; k = 0.471 for facet fusion; k = 0.458 for screw haloing). Overall, interrater reliability as to whether a patient had a fusion or nonunion was moderate (k = 0.510). There was only moderate interrater reliability across most radiographic measures used in assessing lumbar fusion status. Reliability was highest when evaluating the presence of interbody fusion. The majority of fusions occurred across the facet joints.

Surface Analysis of Stainless Steel Electrodes Cleaned by Atmospheric Pressure Plasma.

The Z-pinch device is a critical component in inertial confinement fusion, where stainless steel electrodes must withstand high current densities of up to MA/cm2. Gases and difficult-to-remove impurities adhering to the electrode surfaces can ionize, significantly impacting the device's electrical conductivity efficiency. In this paper, the surface of stainless steel electrodes was subjected to cleaning using a large-area plasma jet under atmospheric pressure. The wettability, chemical composition, and chemical state of the electrode surface were characterized using a water contact angle measuring instrument and X-ray photoelectron spectroscopy (XPS). The cleaning effect under different discharge parameters was systematically analyzed. The results revealed a significant reduction in the content of carbon pollutants on the surface of stainless steel electrodes, decreasing from 62.95% to a minimum of 37.68% after plasma cleaning. Moreover, the water contact angle decreased from 70.76° to a minimum of 29.31°, and the content of water molecules adsorbed on the surface decreased from 17.31% to a minimum of 5.9%. Based on the evolution process of micro-element content and chemical state on the surface of stainless steel electrode, the cleaning process of adhering substances on the surface by atmospheric pressure plasma was analyzed by the layered cleaning model for surface pollutants on stainless steel.

VEPO-S2S: A VEssel Portrait Oriented Trajectory Prediction Model Based on S2S Framework

The prediction of vessel trajectories plays a crucial role in ensuring maritime safety and reducing maritime accidents. Substantial progress has been made in trajectory prediction tasks by adopting sequence modeling methods, containing recurrent neural networks (RNNs) and sequence-to-sequence networks (Seq2Seq). However, (1) most of these studies focus on the application of trajectory information, such as the longitude, latitude, course, and speed, while neglecting the impact of differing vessel features and behavioral preferences on the trajectories. (2) Challenges remain in acquiring these features and preferences, as well as enabling the model to sensibly integrate and efficiently express them. To address the issue, we introduce a novel deep framework VEPO-S2S, consisting of a Multi-level Vessel Trajectory Representation Module (Multi-Rep) and a Feature Fusion and Decoding Module (FFDM). Apart from the trajectory information, we first defined the Multi-level Vessel Characteristics in Multi-Rep, encompassing Shallow-level Attributes (vessel length, width, draft, etc.) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, etc.). Subsequently, Multi-Rep was designed to obtain trajectory information and Multi-level Vessel Characteristics, applying distinct encoders for encoding. Next, the FFDM selected and integrated the above features from Multi-Rep for prediction by employing both a priori and a posteriori mechanisms, a Feature Fusion Component, and an enhanced decoder. This allows the model to efficiently leverage them and enhance overall performance. Finally, we conducted comparative experiments with several baseline models. The experimental results demonstrate that VEPO-S2S is both quantitatively and qualitatively superior to the models.

A Polymer‐Sol Binder Realizes Single‐Particle Binding for Nacre‐Like Piezoelectric Nanocomposites and Component‐Integrated Batteries

AbstractAchieving precisely defined and stable component binding is of great interest for composite materials and batteries, but very challenging owing to the lack of effective strategies to manipulate the binder itself. Here, a concept of single‐particle binding (SPB) strategy is proposed based on a polymer‐sol binder to conquer the above challenge. To do that, a polymer‐sol binder is first prepared by dispersing commercial poly(vinylidene fluoride) (PVDF) powder into a mixture of its solvent and non‐solvent with a rational weight ratio of 8:2. Then, by manipulating this PVDF‐sol microfluid, PVDF particles are uniformly and singly introduced onto the surface of other components, such as graphene oxide nanosheets and battery separators. Results further show that the temperature‐induced sol–gel transition of the microfluid finally generates single‐particle fusion and strong component binding with commercial separators (31.6 N m−1). Meanwhile, a surface‐swelling model is proposed to understand its binding mechanism. Finally, this unique SPB strategy has been employed to fabricate nacre‐like nanocomposites with advanced self‐polarized piezoelectricity (23.0 mV N−1), and component‐integrated batteries with robust separator/electrode interphases. This SPB strategy with the PVDF‐sol binder may inspire significant studies on polymer sol, microfluidics, nanocomposites, battery interfaces and beyond.

Study on hydrogen retention resistance of diamond film by HFCVD

To explore the potential application of diamond coating on the surface modification of graphite-based plasma-facing components in nuclear fusion, the retention of hydrogen irradiated into diamond film deposited by HFCVD was studied. After being attacked with 200 eV hydrogen ions for 10 h at 200 °C, this diamond layer was observed to maintain the stability of crystal structure. The C-H absorption peaks in the FTIR spectrum that were conspicuous in the sole graphite were absent in the diamond surface subjected to the same quantity of hydrogen irradiation, suggesting that the implanted hydrogen was difficult to penetrate or persist in the diamond covering. Further computational studies have demonstrated that hydrogen atoms exhibit a low propensity for retention within the diamond (111), in contrast to reside stably in graphite or graphite-like structures. The above shows that HFCVD diamond film has strong resistance to hydrogen retention and structural stability under hydrogen irradiation.

Shot peening coverage effect on laser powder bed fused steel

Developing effective strategies for enhancing the damage tolerance of laser powder bed fusion (LPBF) components remains a formidable challenge in the disruptive additive manufacturing field. The typical shot-peening (SP) is a promising and efficient post-treatment procedure owing to its capability to introduce compressive residual stress, enhance the surface integrity, shrinkage the subsurface lack-of-fusion (LoF) defects of LPBF components. However, there is still a significant gap in understanding the appropriate utilization of the SP procedure to enhance fatigue performance, hindering its full potential in practical applications. This study systematically investigated the influence of surface coverage (150 %, 300 %, and 450 %) on surface characteristics, subsurface characteristics, and mechanical properties of LPBF 304L austenitic steel. Though evaluating the feasibility of severe shot peening and observing post-deformed microstructures, the process-microstructure-performance relationship of shot-peened LPBF austenitic steel was preliminarily established for the first time. The results indicated that increasing surface coverage up to a threshold (300 % in this work) could effectively enhance the strength and fatigue performance without significantly sacrificing ductility, primarily attributed to the increased gradient strain generated by the gradient deformation structures as well as the shrinkage of detrimental near-the-surface defects. The excessive surface coverage may be detrimental to ductility and fatigue performance, as it directly increases the severe plastic deformation and near-saturated micro-defects in the gradient deformation layer, substantially weakening the strain gradient's capability to accommodate dislocation flow during deformation. The findings suggested careful selection of shot peening coverage to balance enhanced mechanical properties and minimal introduction of detrimental defects.

An innovative coil fabrication and assembly process for the VNS tokamak based on in-situ winding

The paper introduces a new approach to assemble tokamak machines applied to the plasma-based Volumetric Neutron Source (VNS) device, addressing challenges related to machine topology. The VNS, designed as a compact tokamak, serves as a qualification testbed for in-vessel fusion components, especially the breeding blanket. The project aims to bridge the technology gap between ITER and DEMO. The machine size has been minimized to maximize the Neutron Wall Load (NWL). This results in a small, dense plasma and, due to the massive neutron shielding structure, a comparably large distance between the plasma and the equilibrium magnets, challenging the primary stabilization and shaping of the plasma. To overcome this, a magnet system architecture is proposed for VNS in which the poloidal field (PF) coil system is placed inside the TF coils. An assembly procedure is proposed, where the superconducting PF coils are first completed and positioned around the vacuum vessel, followed by the in-situ winding and assembly of the toroidal field coil system. The main magnetic cage components of the machine are based on High-Temperature Superconductors (HTS) to avoid the need for heat treatment. The paper presents the advantages of the magnet system architecture, and the preliminary feasibility study of the machine assembly process.

Cancer fusion transcripts with human non-coding RNAs.

Cancer chimeric, or fusion, transcripts are thought to most frequently appear due to chromosomal aberrations that combine moieties of unrelated normal genes. When being expressed, this results in chimeric RNAs having upstream and downstream parts relatively to the breakpoint position for the 5'- and 3'-fusion components, respectively. As many other types of cancer mutations, fusion genes can be of either driver or passenger type. The driver fusions may have pivotal roles in malignisation by regulating survival, growth, and proliferation of tumor cells, whereas the passenger fusions most likely have no specific function in cancer. The majority of research on fusion gene formation events is concentrated on identifying fusion proteins through chimeric transcripts. However, contemporary studies evidence that fusion events involving non-coding RNA (ncRNA) genes may also have strong oncogenic potential. In this review we highlight most frequent classes of ncRNAs fusions and summarize current understanding of their functional roles. In many cases, cancer ncRNA fusion can result in altered concentration of the non-coding RNA itself, or it can promote protein expression from the protein-coding fusion moiety. Differential splicing, in turn, can enrich the repertoire of cancer chimeric transcripts, e.g. as observed for the fusions of circular RNAs and long non-coding RNAs. These and other ncRNA fusions are being increasingly recognized as cancer biomarkers and even potential therapeutic targets. Finally, we discuss the use of ncRNA fusion genes in the context of cancer detection and therapy.

A novel multimodal depression diagnosis approach utilizing a new hybrid fusion method

In recent years, research has found that the impact of depression status primarily lies in patients’ language expression and facial expressions. Furthermore, facial expressions and intonation in speech exhibit a natural coexistence, making facial and vocal information core recognition indicators in depression identification. It is imperative to explore the effective use of deep learning methods for multimodal depression detection. We have proposed a novel trilateral bimodal encoding model (MEN), attentional decision fusion (ADF), and feature extraction fusion strategy. We employed a hybrid fusion approach that combines early intra-modality fusion with late inter-modality fusion, for multimodal depression diagnosis. In the feature extraction fusion component, we combine different representations of the same modality before inputting them into the network for training, enhancing features relevant to depression in the data. Through our multimodal encoding network, we extract frame-level information using Convolutional Neural Networks (CNN) while considering long-term context information and dependencies with Bidirectional Long Short-Term Memory (BiLSTM). Finally, the three streams of information were effectively integrated through attention fusion representation in our Attention Decision Fusion module (ADF), for depression score regression prediction. Extensive experiments were conducted on two public datasets, AVEC2013 and AVEC2014. The average absolute error/ root mean squared error (MAE/RMSE) scores for predicting depression scores were 6.48/8.91 and 7.01/9.38, respectively. This demonstrated that our hybrid fusion method outperforms traditional early or late fusion methods in terms of performance.

CORE-ReID: Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-Identification

This study introduces a novel framework, “Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)”, to address an Unsupervised Domain Adaptation (UDA) for Person Re-identification (ReID). The framework utilizes CycleGAN to generate diverse data that harmonize differences in image characteristics from different camera sources in the pre-training stage. In the fine-tuning stage, based on a pair of teacher–student networks, the framework integrates multi-view features for multi-level clustering to derive diverse pseudo-labels. A learnable Ensemble Fusion component that focuses on fine-grained local information within global features is introduced to enhance learning comprehensiveness and avoid ambiguity associated with multiple pseudo-labels. Experimental results on three common UDAs in Person ReID demonstrated significant performance gains over state-of-the-art approaches. Additional enhancements, such as Efficient Channel Attention Block and Bidirectional Mean Feature Normalization mitigate deviation effects and the adaptive fusion of global and local features using the ResNet-based model, further strengthening the framework. The proposed framework ensures clarity in fusion features, avoids ambiguity, and achieves high accuracy in terms of Mean Average Precision, Top-1, Top-5, and Top-10, positioning it as an advanced and effective solution for UDA in Person ReID.