Introduction to multifunctional polymer nanocomposites

The nano era, similar to the mid-industrial steel era, not only stands for great technical innovations but also indicates the future trend of existing technologies. It is believed that this period will dominate and transform people's daily lives. 'Nano' is a unit of length defined as 10-9 m. To give you an idea of how small it is, the width of a human hair is 10-6 nm, and the size of an atom is 0.1 nm. In recent decades, the development of microscopes has enabled scientists to observe the structures of the materials at nanoscale and investigate their novel properties. In the early 1980s, IBM (Zurich) invented the scanning tunneling microscope, which was the first instrument that could 'see' atoms. In order to expand the types of materials that could be studied, scientists invented the atomic force microscope. Now, these instruments can be used to observe the structures and different properties of materials at nanometer scale. Physics reveals big differences at the nanometer scale, and the properties observed on a microscopic scale are novel and important. For example, quantum mechanical and thermodynamic properties have pushed forward the development of science and technology in the 20th century. Nanotechnology means the study and application of materials with structures between 1 and 100 nfn in size. Unlike bulk materials, one can work with individual atoms and molecules and learn about an individual molecule's properties. Also, we can arrange atoms and molecules together in well-defined ways to produce new materials with amazing characteristics. For example, nanotechnology has produced huge increases in computer speed and storage capacity. That is why 'nano' has attracted attention in the research fields of physics, chemistry, biology, and even engineering. This word has entered the popular culture and can be found in television, movie, and commercial advertisements. Politicians and leaders around the world have realized the importance and urgency of developing nanoscience and nanotechnology, so countries have promoted research in nanoscience and nanotechnology in their universities and laboratories. With the huge increase in funding, scientists are pursuing nano research intensively, and the rate of discovery is increasing dramatically

On the preprocessing and postprocessing of HRTF individualization based on sparse representation of anthropometric features

Individualization of head-related transfer functions (HRTFs) can be realized using the person's anthropometry with a pretrained model. This model usually establishes a direct linear or non-linear mapping from anthropometry to HRTFs in the training database. Due to the complex relation between anthropometry and HRTFs, the accuracy of this model depends heavily on the correct selection of the anthropometric features. To alleviate this problem and improve the accuracy of HRTF individualization, an indirect HRTF individualization framework was proposed recently, where HRTFs are synthesized using a sparse representation trained from the anthropometric features. In this paper, we extend their study on this framework by investigating the effects of different preprocessing and postprocessing methods on HRTF individualization. Our experimental results showed that preprocessing and postprocessing methods are crucial for achieving accurate HRTF individualization

Active Semisupervised Clustering Algorithm with Label Propagation for Imbalanced and Multidensity Datasets

The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced

Computational Studies on the Potency and Selectivity of PUGNAc Derivatives Against GH3, GH20, and GH84 β-N-acetyl-D-hexosaminidases

β-N-acetyl-D-hexosaminidases have attracted significant attention due to their crucial role in diverse physiological functions including antibacterial synergists, pathogen defense, virus infection, lysosomal storage, and protein glycosylation. In particular, the GH3 β-N-acetyl-D-hexosaminidase of V. cholerae (VcNagZ), human GH20 β-N-acetyl-D-hexosaminidase B (HsHexB), and human GH84 β-N-acetyl-D-hexosaminidase (hOGA) are three important representative glycosidases. These have been found to be implicated in β-lactam resistance (VcNagZ), lysosomal storage disorders (HsHexB) and Alzheimer's disease (hOGA). Considering the profound effects of these three enzymes, many small molecule inhibitors with good potency and selectivity have been reported to regulate the corresponding physiological functions. In this paper, the best-known inhibitors PUGNAc and two of its derivatives (N-valeryl-PUGNAc and EtBuPUG) were selected as model compounds and docked into the active pockets of VcNagZ, HsHexB, and hOGA, respectively. Subsequently, molecular dynamics simulations of the nine systems were performed to systematically compare their binding modes from active pocket architecture and individual interactions. Furthermore, the binding free energy and free energy decomposition are calculated using the MM/GBSA methods to predict the binding affinities of enzyme-inhibitor systems and to quantitatively analyze the contribution of each residue. The results show that PUGNAc is deeply-buried in the active pockets of all three enzymes, which indicates its potency (but not selectivity) against VcNagZ, HsHexB, and hOGA. However, EtBuPUG, bearing branched 2-isobutamido, adopted strained conformations and was only located in the active pocket of VcNagZ. It has completely moved out of the pocket of HsHexB and lacks interactions with HsHexB. This indicates why the selectivity of EtBuPUG to VcNagZ/HsHexB is the largest, reaching 968-fold. In addition, the contributions of the catalytic residue Asp253 (VcNagZ), Asp254 (VcNagZ), Asp175 (hOGA), and Asp354 (HsHexB) are important to distinguish the activity and selectivity of these inhibitors. The results of this study provide a helpful structural guideline to promote the development of novel and selective inhibitors against specific β-N-acetyl-D-hexosaminidases

Tetrahedral mesh improvement by shell transformation

Existing flips for tetrahedral meshes simply make a selection from a few possible configurations within a single shell (i.e., a polyhedron that can be filled up with a mesh composed of a set of elements that meet each other at one edge), and their effectiveness is usually confined. A new topological operation for tetrahedral meshes named shell transformation is proposed. Its recursive callings execute a sequence of shell transformations on neighboring shells, acting like composite edge removal transformations. Such topological transformations are able to perform on a much larger element set than that of a single flip, thereby leading the way towards a better local optimum solution. Hence, a new mesh improvement algorithm is developed by combining this recursive scheme with other schemes, including smoothing, point insertion and point suppression. Numerical experiments reveal that the proposed algorithm can well balance some stringent and yet sometimes even conflict requirements of mesh improvement, i.e., resulting in high-quality meshes and reducing computing time at the same time. Therefore, it can be used for mesh quality improvement tasks involving millions of elements, in which it is essential not only to generate high-quality meshes, but also to reduce total computational time for mesh improvement

Time-shifting based primary-ambient extraction for spatial audio reproduction

One of the key issues in spatial audio analysis and reproduction is to decompose a signal into primary and ambient components based on their directional and diffuse spatial features, respectively. Existing approaches employed in primary-ambient extraction (PAE), such as principal component analysis (PCA), are mainly based on a basic stereo signal model. The performance of these PAE approaches has not been well studied for the input signals that do not satisfy all the assumptions of the stereo signal model. In practice, one such case commonly encountered is that the primary components of the stereo signal are partially correlated at zero lag, referred to as the primary-complex case. In this paper, we take PCA as a representative of existing PAE approaches and investigate the performance degradation of PAE with respect to the correlation of the primary components in the primary-complex case. A time-shifting technique is proposed in PAE to alleviate the performance degradation due to the low correlation of the primary components in such stereo signals. This technique involves time-shifting the input signal according to the estimated inter-channel time difference of the primary component prior to the signal decomposition using conventional PAE approaches. To avoid the switching artifacts caused by the varied time-shifting in successive time frames, overlapped output mapping is suggested. Based on the results from our experiments, PAE approaches with the proposed time-shifting technique are found to be superior to the conventional PAE approaches in terms of extraction accuracy and spatial accuracy.Accepted versio

Primary-ambient extraction using ambient phase estimation with a sparsity constraint

Spatial audio reproduction addresses the growing commercial need to recreate an immersive listening experience of digital media content, such as movies and games. Primary-ambient extraction (PAE) is one of the key approaches to facilitate flexible and optimal rendering in spatial audio reproduction. Existing approaches, such as principal component analysis and time-frequency masking, often suffer from severe extraction error. This problem is more evident when the sound scene contains a relatively strong ambient component, which is frequently encountered in digital media. In this Letter, we propose a novel PAE approach by estimating the ambient phase with a sparsity constraint (APES). This approach exploits the equal magnitude of the uncorrelated ambient components in the two channels of a stereo signal and reformulates the PAE problem as an ambient phase estimation problem, which is then solved using the criterion that the primary component is sparse. Our experimental results demonstrate that the proposed approach significantly outperforms existing approaches, especially when the ambient component is relatively strong.Accepted versio

Primary-Ambient Extraction Using Ambient Spectrum Estimation for Immersive Spatial Audio Reproduction

The diversity of today’s playback systems requires a flexible, efficient, and immersive reproduction of sound scenes in digital media. Spatial audio reproduction based on primary-ambient extraction (PAE) fulfills this objective, where accurate extraction of primary and ambient components from sound mixtures in channel-based audio is crucial. Severe extraction error was found in existing PAE approaches when dealing with sound mixtures that contain a relatively strong ambient component, a commonly encountered case in the sound scenes of digital media. In this paper, we propose a novel ambient spectrum estimation (ASE) framework to improve the performance of PAE. The ASE framework exploits the equal magnitude of the uncorrelated ambient components in two channels of a stereo signal, and reformulates the PAE problem into the problem of estimating either ambient phase or magnitude. In particular, we take advantage of the sparse characteristic of the primary components to derive sparse solutions for ASE based PAE, together with an approximate solution that can significantly reduce the computational cost. Our objective and subjective experimental results demonstrate that the proposed ASE approaches significantly outperform existing approaches, especially when the ambient component is relatively strong.Accepted versio