Determination of monolayer-protected gold nanoparticle ligand–shell morphology using NMR

It is accepted that the ligand shell morphology of nanoparticles coated with a monolayer of molecules can be partly responsible for important properties such as cell membrane penetration and wetting. When binary mixtures of molecules coat a nanoparticle, they can arrange randomly or separate into domains, for example, forming Janus, patchy or striped particles. To date, there is no straightforward method for the determination of such structures. Here we show that a combination of one-dimensional and two-dimensional NMR can be used to determine the ligand shell structure of a series of particles covered with aliphatic and aromatic ligands of varying composition. This approach is a powerful way to determine the ligand shell structure of patchy particles; it has the limitation of needing a whole series of compositions and ligands' combinations with NMR peaks well separated and whose shifts due to the surrounding environment can be large enough

Indocyanine Green Angiography in Uveitis

The visualization of the choroidal vasculature with the use of indocyanine green angiography (ICGA) has led to an improved understanding of choroid involving uveitic entities and their pathogenesis. ICGA has also emerged as having an important role in the diagnosis and follow-up of uveitic diseases, as well as in evaluating the full extent of choriocapillaris and choroidal stromal involvement in these diseases. This chapter reviews ICGA findings in the different chorioretinal uveitic diseases

Age Classification for work sustainability using SVM using Co-occurrence features on Fibonacci Weighted Neighborhood Pattern Matrix

Computer vision systems are increasingly focusing on age recognition from facial images. To solve this problem, In this paper, proposed a method that computes the Fibonacci Weighted Neighborhood Pattern on an image to obtain local neighborhood information, then evaluates Co-occurrence features for work sustainability age classification with SVM classifier. These characteristics show how people’s ages differ. The proposed method has been tested on the FG-Net facial images dataset as well as other scanned images. Experiments showed that the proposed approach outperformed other currently existing methods

Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm

Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm

Ensemble Framework of Artificial immune system based on Network Intrusion Detection System for Network Security Sustainability

The popularity and rapid growth of the internet have reemphasized the importance of intrusion detection systems (IDS) significance in the network security. IDS decreases hacking, data theft risk, privacy intrusion, and others. To save the system from external and internal intruders, the primary approaches of IDS are used. Many techniques[13], like genetic algorithms, artificial neural networks, and artificial immune systems, have been applied to IDS. This paper describes an Ensemble Framework of Artificial Immune System (AIS) based on Network Intrusion Detection System. Without placing a significant additional load on networks and monitoring systems, the large volume of data is analysed by a network-based Intrusion Detection System (NIDS). For determining the connection type, data from KDD Cup 99 competitions is utilized. To differentiate between attacks and valid connections, IDS can be utilized. Optimized feature selection is used to speed up the time-consuming rough set. The results obtained from the IDS system indicate that it can effectively identify the attacking connections with a high success rate

Anomalous excess noise behavior in thick Al0.85Ga0.15As0.56Sb0.44 avalanche photodiodes

Abstract Al0.85Ga0.15As0.56Sb0.44 has recently attracted significant research interest as a material for 1550 nm low-noise short-wave infrared (SWIR) avalanche photodiodes (APDs) due to the very wide ratio between its electron and hole ionization coefficients. This work reports new experimental excess noise data for thick Al0.85Ga0.15As0.56Sb0.44 PIN and NIP structures, measuring low noise at significantly higher multiplication values than previously reported (F = 2.2 at M = 38). These results disagree with the classical McIntyre excess noise theory, which overestimates the expected noise based on the ionization coefficients reported for this alloy. Even the addition of ‘dead space’ effects cannot account for these discrepancies. The only way to explain the low excess noise observed is to conclude that the spatial probability distributions for impact ionization of electrons and holes in this material follows a Weibull–Fréchet distribution function even at relatively low electric-fields. Knowledge of the ionization coefficients alone is no longer sufficient to predict the excess noise properties of this material system and consequently the electric-field dependent electron and hole ionization probability distributions are extracted for this alloy