• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.551-556
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• 2022

The desalinated water obtained by the water treatment process based on the membrane is attracting a lot of attention as a promising technology that can solve the global water shortage problem. Reverse osmosis membrane-based desalination, one of the most widely used desalination processes, is a technology that desalinates abundant seawater on Earth, thus having great potential in the desalination industry. To improve the performance of the desalination process, it is necessary to understand the reverse osmosis mechanism of the membrane at the atomic/molecular level. In this review, we introduce molecular simulation, which plays an important role in material research today, and the roles of computational simulation at the atomic/molecular level in the development of reverse osmosis membranes.

• Current Optics and Photonics
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• v.5 no.6
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• pp.699-710
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• 2021

In this study, we propose a biomimetic optical structure design methodology for investigating micro-optical mechanisms associated with the compound eyes of insects. With these compound eyes, insects can respond fast while maintaining a wide field of view. Also, considerable research attention has been focused on the insect compound eyes to utilize these benefits. However, their nano micro-structures are complex and challenging to demonstrate in real applications. An effectively integrated design methodology is required considering the manufacturing difficulty. We show that photorealistic ray-traced visualization is an effective method for designing the biomimetic of a micro-compound eye of an insect. We analyze the image formation mechanism and create a three-dimensional computer-aided design model. Then, a ray-trace visualization is applied to observe the optical image formation. Finally, the segmented images are stitched together to generate an image with a wide-angle; the image is assessed for quality. The high structural similarity index (SSIM) value (approximately 0.84 to 0.89) of the stitched image proves that the proposed MATLAB-based image stitching algorithm performs effectively and comparably to the commercial software. The results may be employed for the understanding, researching, and design of advanced optical systems based on biological eyes and for other industrial applications.

• Applied Chemistry for Engineering
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• v.33 no.5
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• pp.502-522
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• 2022

Clay minerals are natural materials that show widespread applications in various branches of science, including environmental sciences, in particular the remediation of water contaminated with various water pollutants. Modified clays and minerals have attracted the attention of researchers in the recent past since the modified materials are seemingly more useful and efficient for removing emerging water contaminants. Therefore, modified engineered materials having multi-functionalities have received greater interest from researchers. The advanced clay-based materials are highly effective in the remediation of water contaminated with organic and inorganic contaminants, and these materials show enhanced selectivity towards the specific pollutants. The review inherently discusses various methods employed in the modification of clays and addresses the challenges in synthesizing the advanced engineered materials precursor to natural clay minerals. The changes in physical and chemical properties, as investigated by various characterization techniques before and after the modifications, are broadly explained. Further, the implications of these materials for the decontamination of waterbodies as contaminated with potential water pollutants are extensively discussed. Additionally, the insights involved in the removal of organic and inorganic pollutants are discussed in the review. Furthermore, the future perspectives and specific challenges in the scaling up of the treatment methods in technology development are included in this communication.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.59-74
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• 2022

During the last decade, the security of digital images has received considerable attention in various multimedia transmission schemes. However, many current cryptosystems tend to adopt a single-layer permutation or diffusion algorithm, resulting in inadequate security. A hierarchical bilateral diffusion architecture for color image encryption is proposed in response to this issue, based on a hyperchaotic system and DNA sequence operation. Primarily, two hyperchaotic systems are adopted and combined with cipher matrixes generation algorithm to overcome exhaustive attacks. Further, the proposed architecture involves designing pixelpermutation, pixel-diffusion, and DNA (deoxyribonucleic acid) based block-diffusion algorithm, considering system security and transmission efficiency. The pixel-permutation aims to reduce the correlation of adjacent pixels and provide excellent initial conditions for subsequent diffusion procedures, while the diffusion architecture confuses the image matrix in a bilateral direction with ultra-low power consumption. The proposed system achieves preferable number of pixel change rate (NPCR) and unified average changing intensity (UACI) of 99.61% and 33.46%, and a lower encryption time of 3.30 seconds, which performs better than some current image encryption algorithms. The simulated results and security analysis demonstrate that the proposed mechanism can resist various potential attacks with comparatively low computational time consumption.

• Steel and Composite Structures
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• v.44 no.3
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• pp.325-337
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• 2022

In this research, an earthquake-resistant structural system consisting of a pin-connected steel frame and a bracing with metallic fuses is proposed. Contrary to the conventional braced frames, the main structural elements are deemed to remain elastic under earthquakes and the seismic energy is efficiently dissipated by the damper-braces with an amplification mechanism. The superiority of the proposed damping system lies in easy manufacture, high yield capacity and energy dissipation, and an effortless replacement of damaged fuses after earthquake events. Furthermore, the stiffness and the yield capacity are almost decoupled in the proposed damper-brace which makes it highly versatile for performance-based seismic design compared to most other dampers. A special attention is paid to derive the theoretical formulation for nonlinear behavior of the proposed damper-brace, which is verified using analytical results. Next, a direct displacement-based design procedure is provided for the proposed system and an example structure is designed and analyzed thoroughly to check its seismic performance. The results show that the proposed system designed with the provided procedure satisfies the given performance objective and can be used for developing highly efficient low-damage structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4024-4041
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• 2022

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

• Journal of Electrochemical Science and Technology
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• v.13 no.3
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• pp.398-406
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• 2022

To satisfy the growing demand for high-performance batteries, diverse novel anode materials with high specific capacities have been developed to replace commercial graphite. Among them, cobalt hydroxides have received considerable attention as promising anode materials for lithium-ion batteries as they exhibit a high reversible capacity owing to the additional reaction of LiOH, followed by conversion reaction. In this study, we introduced graphene in the fabrication of Co(OH)₂-based anode materials to further improve electrochemical performance. The resultant Co(OH)₂/graphene composite exhibited a larger reversible capacity of ~1090 mAh g^-1, compared with ~705 mAh g^-1 for bare Co(OH)₂. Synchrotron-based analyses were conducted to explore the beneficial effects of graphene on the composite material. The experimental results demonstrate that introducing graphene into Co(OH)₂ facilitates both the conversion and reaction of the LiOH phase and provides additional lithium storage sites. In addition to insights into how the electrochemical performance of composite materials can be improved, this study also provides an effective strategy for designing composite materials.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4275-4291
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• 2021

With the unprecedented growth of textual information on the Internet, an efficient automatic summarization system has become an urgent need. Recently, the neural network models based on the encoder-decoder with an attention mechanism have demonstrated powerful capabilities in the sentence summarization task. However, for paragraphs or longer document summarization, these models fail to mine the core information in the input text, which leads to information loss and repetitions. In this paper, we propose an abstractive document summarization method by applying guidance signals of key sentences to the encoder based on the hierarchical encoder-decoder architecture, denoted as KI-HABS. Specifically, we first train an extractor to extract key sentences in the input document by the hierarchical bidirectional GRU. Then, we encode the key sentences to the key information representation in the sentence level. Finally, we adopt key information representation guided selective encoding strategies to filter source information, which establishes a connection between the key sentences and the document. We use the CNN/Daily Mail and Gigaword datasets to evaluate our model. The experimental results demonstrate that our method generates more informative and concise summaries, achieving better performance than the competitive models.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.