Polyphosphazenes—A Promising Candidate for Drug Delivery,Bioimaging, and Tissue Engineering: A Review

Synthetic polymer materials have been surged to the forefront of research in the fields of tissue engineering, drug delivery, and biomonitoring in recent years. Biodegradable synthetic polymers are increasingly needed as transient substrates for tissue regeneration and medicine delivery. In contrast to commonly used polymers including polyesters, polylactones, polyanhydrides, poly(propylene fumarates), polyorthoesters, and polyurethanes, biodegradable polyphosphazenes (PPZs) hold great potential for the purposes indicated above. PPZ's versatility in the synthetic process has enabled the production of a variety of polymers with various physico-chemical, and biological properties have been produced, making them appropriate for biomedical applications. Biocompatible PPZs are often used as scaffolds in the regeneration of skeleton, bones, and other tissues. PPZs have also received special attention as potential drug vehicles of high-value biopharmaceuticals such as anticancer drugs. Additionally, by incorporating fluorophores into the PPZ backbone to produce photoluminescent biodegradable PPZs, the utility of polyphosphazenes is further expanded as they are used in tracking the regeneration of the target tissue as well as the fate of PPZ based scaffolds or drug delivery vehicles. This review provides a summary of the evolution of PPZ applications in the fields of tissue engineering, drug delivery, and bioimaging in recent 5 years.     

Phase stability and transformations in NiTi from density functional theory calculations

We used density functional theory to characterize various crystalline phases of NiTi alloys: (i) high-temperature austenite phase B2; (ii) orthorhombic B19; (iii) the monoclinic martensite phase B19′; and (iv) a body-centered orthorhombic phase (BCO), theoretically predicted to be the ground state. We also investigated possible transition pathways between the various phases and the energetics involved. We found B19 to be metastable with a 1 meV energy barrier separating it from B19′. Interestingly, we predicted a new phase of NiTi, denoted B19′′, that is involved in the transition between B19′ and BCO. B19′′ is monoclinic and can exhibit shape memory; furthermore, its presence reduces the internal stress required to stabilize the experimentally observed B19′ structure, and it consequently plays a key role in NiTi’s properties.     

Particle size effect on the magnetic properties of NiO nanoparticles prepared by a precipitation method

Nickel oxide (NiO) nanoparticles of nominal size range 16 nm and 25 nm were obtained by controlling the calcination temperature. These particles were prepared by the precipitation method. Structural, optical and morphological characterizations were done by X-ray powder diffraction, UV-vis diffuse reflectance spectroscopy and scanning electron microscopy. Studies of magnetic measurements (up to 60 kOe) and temperature variations from 2 K to 300 K of the NiO nanoparticles were investigated. Particles of 16 nm exhibited a weak ferromagnetic component and hysteresis loop. There is a increase in coercivity H_c and the remanence M_r at 8 K accompanied by an exchange bias H_E. H_E monotonically tends to zero as the particles size varied from 16 nm to 25 nm. The hysteresis loop and the size dependent χ are interpreted with the uncompensated surface spins, whereas the transition at 30 K is suggested to be Néel temperature T_N of the spins in the core of the 16 nm particles. In addition, the increasing temperature cannot showed an approach to saturation in the magnetization curve, it indicates the possibility of an asperomagnetism and/or spin glass behavior of the NiO nanoparticles.     

Nonlinear thermoacoustics of ducted premixed flames: The influence of perturbation convection speed

When a premixed flame is placed within a duct, acoustic waves induce velocity perturbations at the flame’s base. These travel down the flame, distorting its surface and modulating its heat release. This can induce self-sustained thermoacoustic oscillations. Although the phase speed of these perturbations is often assumed to equal the mean flow speed, experiments conducted in other studies and Direct Numerical Simulation (DNS) conducted in this study show that it varies with the acoustic frequency. In this paper, we examine how these variations affect the nonlinear thermoacoustic behaviour. We model the heat release with a nonlinear kinematic G-equation, in which the velocity perturbation is modelled on DNS results. The acoustics are governed by linearised momentum and energy equations. We calculate the flame describing function (FDF) using harmonic forcing at several frequencies and amplitudes. Then we calculate thermoacoustic limit cycles and explain their existence and stability by examining the amplitude-dependence of the gain and phase of the FDF. We find that, when the phase speed equals the mean flow speed, the system has only one stable state. When the phase speed does not equal the mean flow speed, however, the system supports multiple limit cycles because the phase of the FDF changes significantly with oscillation amplitude. This shows that the phase speed of velocity perturbations has a strong influence on the nonlinear thermoacoustic behaviour of ducted premixed flames.     

Tensile properties of modified 9Cr-1Mo steel by shear punch testing and correlation with microstructures

Modified 9Cr-1Mo ferritic steel (P91) is subjected to a series of heat treatments consisting of soaking for 5 min at the selected temperatures in the range 973 K–1623 K (below Ac₁ to above Ac₄) followed by oil quenching and tempering at 1033 K for 1 h to obtain different microstructural conditions. The tensile properties of the different microstructural conditions are evaluated from small volumes of material by shear punch test technique. A new methodology for evaluating yield strength, ultimate tensile strength and strain hardening exponent from shear punch test by using correlation equations without employing empirical constants is presented and validated. The changes in the tensile properties are related to the microstructural changes of the steel investigated by electron microscopic studies. The steel exhibits minimum strength and hardness when soaked between Ac₁ and Ac₃ (intercritical range) temperatures due to the replacement of original lath martensitic structure with subgrains. The finer martensitic microstructure produced in the steel after soaking at temperatures above Ac₃ leads to a monotonic increase in hardness and strength with decreasing strain hardening exponent. For soaking temperatures above Ac₄, the hardness and strength of the steel increases marginally due to the formation of soft δ ferrite.     

Why do Firms Locate Across Multiple Clusters? Cluster Density,Capabilities and Ethnic Ties

It is well-accepted that firms locate in clusters to benefit from spillover effects from similar firms in the location. However, some firms choose to locate in multiple clusters. In this paper, we focus on the phenomenon of multi-cluster presence. Through an empirical investigation of 95 firms from the information technology enabled service industry within India, we analyze the drivers of membership across multiple clusters. Our findings indicate first that firms that are located in lower density clusters show a tendency to locate in a larger number of clusters. Second, firms that are looking for people-based creative capabilities also tend to locate in a larger number of clusters. Finally, the firms that are not founded at the location of ethnic origin of the founder CEO also tend to locate in a larger number of clusters.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

基于NPU和NSE系统的控制平面分析

典型的网络系统包括一个或多个用于监视数据平面分组处理的控制平面CPU。通过CPU进行的控制更新将导致存储于网络搜索引擎(NSE)和相联SRAM中的路由表和政策表的更新。在数据平面中，NSE和SRAM通过诸如网络处理机论坛的LookAside-1(LA-1)接口等高速接口连接至网络处理单元(NPU)。从外部CPU至NSE和SRAM的控制平面更新信息可以经由带内线路(即在NPU内部并通过NPU的LA-1接口)来传送。     

Reproducible,biocompatible medical materials from biologically derived polysaccharides: Processing and Characterization

Natural polysaccharides like chitosan and dextran have garnered considerable interest in biomedical applications due to their biocompatibility, biodegradability, and nontoxicity. Nonetheless, the development of a reproducible class of medical devices from these materials is challenging and has had limited success. Chitosan and dextran are inherently variable and synthesis using these materials is prone to inconsistencies. In this study, we put forward a robust product development regimen that allows these natural materials to be developed into a reproducible class of biomaterials. First, an array of validated characterization methods (Proton Nuclear Magnetic Resonance, titrations, Ultraviolet spectroscopy, Size Exclusion Chromatography—Multi-Angle Light Scattering, Size Exclusion Chromatography—Refractive Index, and proprietary methods) were developed that allowed rigorous specifications to be set for unprocessed chitosan and dextran, chitosan and dextran intermediates, and chemically modified materials—acrylated chitosan (aCHN) and oxidized dextran (oDEX). Second, a robust and reproducible synthesis scheme involving various in-process controls was developed to chemically modify the unprocessed polysaccharides. Third, purification methods to remove byproducts and low-molecular-weight impurities for both aCHN and oDEX were developed. The study presents a viable strategy for converting variable, natural materials into a reproducible class of biomaterials that can be applied in various biomedical applications. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48454.