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Abstract Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

Computational linguistics is the scientific and engineering discipline related to comprehending written and spoken language from a computational perspective and building artefacts that effectively process and produce language, either in bulk or in a dialogue setting. This paper develops a Chaotic Bird Swarm Optimization with deep ensemble learning based Arabic poem classification and dictarization (CBSOEDL-APCD) technique. The presented CBSOEDL-APCD technique involves the classification and dictarization of Arabic text into Arabic poetries and prose. Primarily, the CBSOEDL-APCD technique carries out data pre-processing to convert it into a useful format. Besides, the ensemble deep learning (EDL) model comprising deep belief network (DBN), gated recurrent unit (GRU), and probabilistic neural network (PNN) are exploited. At last, the CBSO algorithm is employed for the optimal hyperparameter tuning of the deep learning (DL) models to enhance the overall classification performance. A wide range of experiments was performed to establish the enhanced outcomes of the CBSOEDL-APCD technique. Comparative experimental analysis indicates the better outcomes of the CBSOEDLAPCD technique over other recent approaches. [ABSTRACT FROM AUTHOR]

This paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method is devised by assimilating the Wave Optimization algorithm (WWO), and the Conditional autoregressive value at risk (CAViaR) model. Here, the input images are subjected to Autoregressive WWO-based local polynomial approximation and intersection of confidence intervals (LPA-ICI) filter, and Deep Convolution neural network (Deep CNN) in a concurrent manner. The filter coefficients are obtained from the proposed Autoregressive WWO-based LPA-ICI filter and the residual image is obtained from Deep CNN. In order to create the demosaiced image, these two outputs are combined using an entropy measure. The proposed method offered superior performance with the highest Peak signal to noise ratio (PSNR) of 40.049dB, the highest Second derivative measure of enhancement (SDME) of 50.168dB, and highest Structural Index Similarity (SSIM) of 0.9056. [ABSTRACT FROM AUTHOR]

The entrained flow gasification has been identified as the most promising gasification technology. Serious environmental pollution and waste of land resources are caused by the increasing amount of storage and production of coal gasification slag. The aim of this work is to explore the feasibility of high-temperature combustion and melting technology for treating coal gasification fine slag and determine the important parameters of system operation. The flow properties and molten slag structure characteristics of three fine slags from different entrained flow gasifiers were studied. Depending on the melting mechanism of melt-dissolution, the melting time of fine slags is short. Three fine slags all produce glassy slags, which is conducive to slag discharge. The degree of polymerization of silicate melt is proportionate to the amount of SiO2 in the slag. A part of Al3+ exist in the form of [AlO4]5− because of the effect of CaO and Na2O, as the network former. Finally, the degree of polymerization of the three type molten slag was calculated by considering the role of Si and Al in molten slag and the property of each one. [ABSTRACT FROM AUTHOR]

Copyright of Clean Coal Technology is the property of Clean Coal Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The natural scenes are captured by digital cameras that adopt a single charged-coupled device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) sensor with a Color Filter Array (CFA). To reconstruct a full color image from the mosaiced CFA image, the missing components of color are recovered by a technique called color demosaicing. This research work introduces Deep Convolution Neural Network (DCNN)-based optimization assisted autoregressive method for image demosaicing. Here, the proposed optimization algorithm, termed Adaptive Autoregressive Water Wave Optimization algorithm (Adaptive Autoregressive-WWO), that is formed by combining Conditional autoregressive value at risk (CAViaR) model and Water Wave Optimization algorithm (WWO). Fusion process is carried out for residual images to generate the final demosaiced image based on entropy measure. Here, the output generated from DCNN is the first residual image. The LPA-ICI filter utilizes the optimization algorithm that generated second order polynomial coefficients to produce the second residual output image. Moreover, this proposed method is evaluated for its performance using metrics, such as Peak signal-tonoise ratio (PSNR) and Second Derivative like Measurement (SDME) and attained the highest PSNR values of 40.379dB and highest SDME values of 50.675dB. [ABSTRACT FROM AUTHOR]

Changes in land cover (LC) influence the geological, climatic, and biological environments of the earth. A precise LC map provides detailed information on resource management and intergovernmental collaboration to address global warming and biodiversity reduction. This article investigated the influence of atmospheric correction and four fusion techniques (Brovey, Hue-Saturation-Value (HSV), Principle Components (PC), and Gram-Schmidt (GS) spectral sharpening) on LC classification accuracy. Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) were used as LC mapping classification algorithms using panchromatic (PAN) and multispectral (MS) Landsat 8 OLI satellite imagery. This study indicated that the SVM technique led to greater accuracy than MLC by 17%, 4.48%, 4.4%, 3.97%, and 15.57% in Overall Accuracy (OA) for HSV, Brovey, PC, GS, and MS images alternatively. The GS sharpened image gave the highest outcome across all SVM categories by 85.14% with a Kappa coefficient (K) value of 0.81 compared with the GS-MLC classification of about 4% in OA and 0.04 in K value. The OA distinction between HSV, Brovey, PC, GS, and MS images with and without atmospheric correction was also about 5% on SVM classification occasions which is consistent with MLC outcomes. These findings have shown that atmospheric correction is not required for LC mapping based on DN image Nevertheless, fusion performs a more influential role in the accuracy of classification than atmospheric correction. A combination of atmospheric correction, pansharpening, and SVM could offer promising LC maps from Landsat 8 OLI multispectral and panchromatic images. [ABSTRACT FROM AUTHOR]

Emotion recognition is a strategy for social robots used to implement better Human-Robot Interaction and model their social behaviour. Since human emotions can be expressed in different ways (e.g., face, gesture, voice), multimodal approaches are useful to support the recognition process. However, although there exist studies dealing with multimodal emotion recognition for social robots, they still present limitations in the fusion process, dropping their performance if one or more modalities are not present or if modalities have different qualities. This is a common situation in social robotics, due to the high variety of the sensory capacities of robots; hence, more flexible multimodal models are needed. In this context, we propose an adaptive and flexible emotion recognition architecture able to work with multiple sources and modalities of information and manage different levels of data quality and missing data, to lead robots to better understand the mood of people in a given environment and accordingly adapt their behaviour. Each modality is analyzed independently to then aggregate the partial results with a previous proposed fusion method, called EmbraceNet+, which is adapted and integrated to our proposed framework. We also present an extensive review of state-of-the-art studies dealing with fusion methods for multimodal emotion recognition approaches. We evaluate the performance of our proposed architecture by performing different tests in which several modalities are combined to classify emotions using four categories (i.e., happiness, neutral, sadness, and anger). Results reveal that our approach is able to adapt to the quality and presence of modalities. Furthermore, results obtained are validated and compared with other similar proposals, obtaining competitive performance with state-of-the-art models.

The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.

• Fusion process between signals shows that traffic flow undergoes phase transition. • Traffic network is modelled as an Ising model with traffic flow taking on two states. • Transition from congestion becomes more apparent when control undergoes Parrondo switching. While increasing urban traffic can be an indicator of development, this inevitably results in traffic congestion in urban road networks. Is there a way to manage traffic flow through the control of traffic signals such that the overall network congestion is improved? Traffic light signals can be represented as two states of an Ising model. It is possible for traffic lights to "communicate" with each other through a fusion process from a remote management control system. This requires collection of information which can be fed to a centralized decision-making control mechanism. We first explore the fusion process between traffic signals and show that it is possible for traffic flow in a city to follow the phase transition as exhibited in the 2D Ising model. The model will be extended to show that a random switching between signalling control mechanisms can result in congested traffic being susceptible to transit out of congestion. [ABSTRACT FROM AUTHOR]

Copyright of Clean Coal Technology is the property of Clean Coal Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the fusion process of medical computed tomography (CT) and magnetic resonance image (MRI), traditional multiscale methods often reduce the contrast of fused images. Although sparse representation (SR) methods overcome this shortcoming, they are often too smooth along the strong edges of the fusion image. To overcome these shortcomings, CT and MRI image fusion based on multiscale decomposition method and hybrid approach is proposed. There are three main steps. First, the cartoon parts and texture parts of CT and MRI are obtained by the improved image decomposition method using global sparse gradients. Second, the large structure cartoon parts are fused using the specific cartoon dictionary and the 'L1‐max norm' principle. The textured parts are fused using non‐subsampled contourlet transformation (NSCT) and the maximum energy rule. Finally, the final result is obtained by superimposing the fused cartoon part and the fused texture part. The experimental results demonstrate that the proposed method outperforms the state‐of‐the‐art method SR and NSCT in terms of visual effect and objective quality. [ABSTRACT FROM AUTHOR]

Opinion dynamics is a fusion process of individual opinions, in which a group of interacting agents continuously fuse their opinions on the same issue based on established fusion rules to reach a consensus, polarization, or fragmentation in the final stage. To date, many studies have been conducted on opinion dynamics. To provide a clear perspective on the fusion process in opinion dynamics, this paper presents a review of the framework and formulation of opinion dynamics as well as some basic models, extensions, and applications. Based on the insights gained from prior studies, several open problems are proposed for future research. [ABSTRACT FROM AUTHOR]

The use of amorphous solid dispersions to improve the bioavailability of active ingredients from the BCS II and IV classifications continues to gain interest in the pharmaceutical industry. Over the last decade, methods for generating amorphous solid dispersions have been well established in commercially available products and in the literature. However, the amorphous solid dispersions manufactured by different technologies differ in many aspects, primarily chemical stability, physical stability, and performance, both in vitro and in vivo. This review analyzes the impact of manufacturing methods on those properties of amorphous solid dispersions. For example, the chemical stability of drugs and polymers can be influenced by differences in the level of thermal exposure during fusion-based and solvent-based processes. The physical stability of amorphous content varies according to the thermal history, particle morphology, and nucleation process of amorphous solid dispersions produced by different methods. The in vitro and in vivo performance of amorphous formulations are also affected by differences in particle morphology and in the molecular interactions caused by the manufacturing method. Additionally, we describe the mechanism of manufacturing methods and the thermodynamic theories that relate to amorphous formulations. [ABSTRACT FROM AUTHOR]

Automatic fruit classification is an interesting problem in retail supply chain processes and in fruit industry, since the process can help the supermarkets as well as the fruit growers find various fruits and their statuses from the containers or stock. In this background, the current research article designs an Automated Fruit Classification system using Enhanced Tunicate Swarm Algorithm with Fusion-based Deep Learning (AFC-ETSAFDL) technique. The goal of the proposed AFC-ETSAFDL technique is to recognize different types of fruits from the captured images. In the presented technique, a fusion-based feature extraction method is applied using three DL models namely, DenseNet, ResNet, and Inception v3 with ETSA as a hyperparameter optimizer. For fruit classification, the Extreme Gradient Boosting (XGBoost) model is used in this study. The proposed model was experimentally evaluated using an open-access fruit dataset and the results showcase the enhanced performance of the proposed technique over other existing models. [ABSTRACT FROM AUTHOR]

This paper deals with the formation of a bound dineutron in the outgoing channel of the 159Tb(n , n 2)158 g Tb nuclear reaction followed by assumed transformations of the reaction products 158 g Tb and n 2. Such nuclear processes were studied in detail from the point of view of 160Tb/160Dy/160Ho amount of nuclei versus time dependence. Some signs of fusion process between heavier nuclei (158Tb and/or 158Gd) and the deuteron, that is a bound dineutron decay product, were detected as unexpected increasing of 879.38 keV gamma-ray peak count rate due to 160Dy gamma-transitions. The mathematical model, including three systems of differential equations, was developed to describe the experimental data. This development requires a reasonable estimate of the half-life of a bound dineutron, which was found to be equal 5,877 s as an upper limit. We mathematically modeled the experimentally observed delayed in time build-up of the 160Tb radioactivity with a maximum at about 495 d since the neutron irradiation completion of the Tb sample, based on the similarity with the parent–daughter nuclei radioactivity decay and nuclear accumulation processes. • Formation of a bound dineutron in the outgoing channel of the 159Tb(n , n 2)158 g Tb reaction. • Decay of a bound dineutron into the deuteron in the singlet or triplet state with different half-lives. • Interplay of dineutron decay products with heavy nuclei via the strong and the weak interaction. • Fusion between heavy nuclei and deuterons at room temperature conditions. • Nuclear processes leading to some half-lives modification. [ABSTRACT FROM AUTHOR]

Opinion dynamics is a fusion process of individual opinions based on the established fusion rules. The existing opinion dynamics models assume that agents express and receive their opinions in a numerical way. In this paper, we focus on the opinion formation in linguistic environment (i.e., linguistic opinion formation), and propose a linguistic opinion dynamics (LOD) model in the framework of the bounded confidence and the 2-tuples linguistic model with numerical scales. In the LOD model, agents express and receive the opinions by using the simple terms in a linguistic term set with finite granularity at each time. Based on the LOD model, we present some theoretical analyses to reveal the conditions to form a consensus or splits in the linguistic opinion formation. Furthermore, we design some simulations to investigate the effects of the bounded confidence and the uniformities of linguistic term sets on the linguistic opinion formation. The simulation results show that: (i) with the increase in the bounded confidence and the uniformities of linguistic term sets, the opportunity for reaching a consensus will increase, and the time for reaching a consensus will become shorter in the dynamics of linguistic opinion formation; (ii) there exists a critical point in the evolution of linguistic opinions, which is called agreed confidence in this paper, and a consensus among the agents will be reached if the value of the bounded confidence is equal or greater than the agreed confidence. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel opinion dynamics model that is based on bounded confidence and termed interval opinion dynamics with the dynamic bounded confidence. In this opinion dynamics model, the agents express their opinions in numerical intervals, and the bounded confidences vary in a specified interval as time varies (i.e., dynamic bounded confidence). Based on several theoretical analyses of the proposed opinion dynamics, we propose conditions that are sufficient to form a consensus or fragmentations among the agents. Moreover, we also design several simulation experiments to investigate the effects of the dynamic bounded confidence and interval widths on the proposed opinion dynamics and to illustrate the differences between the proposed model and the original opinion dynamics with bounded confidence. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), decision makers who have different experiential, cultural and educational backgrounds will naturally provide their preference information by heterogeneous preference structures (e.g., utility values, preference orderings, numerical preference relations and multigranular linguistic preference relations). To date, many studies have discussed GDM problems with heterogeneous preference structures. To provide a clear perspective on the fusion process with heterogeneous preference structures in GDM, this paper presents a review of three types of fusion approaches: the indirect approach, the optimization-based approach and the direct approach. Moreover, with respect to insights gained from prior researches, several open problems are proposed for the future research. [ABSTRACT FROM AUTHOR]