Fabrication and Experimental Analysis of Axially Oriented Nanofibers

A novel design of a laboratory built axially rotating collector (ARC) having capability to align electrospun nanofibers have been described. A detailed morphological comparison of such nanofibers orientation and their geometry is done using scanning electron microscopy (SEM). For comparison various polymeric solutions were electrospun on conventional static collector as well as ARC. The average diameter of polyvinyl alcohol (PVA) nanofibers was found to be 250 nm while polycaprolactone (PCL) nanofibers were found to be within a range of 600–800 nm. Conducting nanoparticles such as graphene and multi-walled carbon nanotubes (MWNTs) mixed with polymer solutions shown to have a significant influence on the overall geometry of these nanofibers and their diameter distribution. It is evident from the SEM analysis that both graphene and MWNTs in polymer solution play a crucial role in achieving a uniform diameter of nanofibers. Lastly, the formation of the aligned nanofibers using ARC has been mathematically modeled and the electromagnetic field governing the process has been simulated

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Assessing the life cycle environmental impacts of titania nanoparticle production by continuous flow solvo/hydrothermal synthesis

Continuous-flow hydrothermal and solvothermal syntheses offer substantial advantages over conventional processes, producing high quality materials from a wide range of precursors. In this study, we evaluate the “cradle-to-gate” life cycle environmental impacts of alternative titanium dioxide (TiO₂) nanoparticle production parameters, considering a range of operational conditions, precursors, material properties and production capacities. A detailed characterisation of the nano-TiO₂ products allows us, for the first time, to link key nanoparticle characteristics to production parameters and environmental impacts, providing a useful foundation for future studies evaluating nano-TiO₂ applications. Five different titanium precursors are considered, ranging from simple inorganic precursors, like titanium oxysulphate (TiOS), to complex organic precursors such as titanium bis(ammonium-lactato)dihydroxide (TiBALD). Synthesis at the laboratory scale is used to determine the yield, size distribution, crystallinity and phase of the nanoparticles. The specifications and operating experience of a full scale plant (>1000 t per year) are used to estimate the mass and energy inputs of industrial scale production for the life cycle assessment. Overall, higher process temperatures are linked to larger, more crystalline nanoparticles and higher conversion rates. Precursor selection also influences nano-TiO₂ properties: production from TiOS results in the largest particle sizes, while TiBALD achieves the smallest particles and narrowest size distribution. Precursor selection is the main factor in determining cradle-to-gate environmental impacts (>80% in some cases), due to the production impact of complex organic precursors. Nano-TiO2 production from TiOS shows the lowest global warming potential (GWP) (<12 kg CO₂-eq. per kg TiO₂) and cumulative energy demand (CED) (<149 MJ kg¯¹ TiO₂) due to the low environmental impact of the precursor, the use of water as a solvent and its high yield even at lower temperatures. Conversely, the TiBALD precursor shows the highest impact (86 kg CO₂-eq. per kg TiO₂ and 1952 MJ kg¯¹ TiO₂) due to the need for additional post-synthesis steps and complexity of precursor manufacturing. The main purpose of this study is not a direct comparison of the environmental impacts of TiO₂ nanoparticles manufactured utilizing various precursors under different conditions, but to provide an essential foundation for future work evaluating potential applications of nano-TiO₂ and their life cycle environmental impacts

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

k-Degree Anonymity Model for Social Network Data Publishing

Publicly accessible platform for social networking has gained special attraction because of its easy data sharing. Data generated on such social network is analyzed for various activities like marketing, social psychology, etc. This requires preservation of sensitive attributes before it becomes easily accessible. Simply removing the personal identities of the users before publishing data is not enough to maintain the privacy of the individuals. The structure of the social network data itself reveals much information regarding its users and their connections. To resolve this problem, k-degree anonymous method is adopted. It emphasizes on the modification of the graph to provide at least k number of nodes that contain the same degree. However, this approach is not efficient on a huge amount of social data and the modification of the original data fails to maintain data usefulness. In addition to this, the current anonymization approaches focus on a degree sequence-based graph model which leads to major modification of the graph topological properties. In this paper, we have proposed an improved k-degree anonymity model that retain the social network structural properties and also to provide privacy to the individuals. Utility measurement approach for community based graph model is used to verify the performance of the proposed technique