A Study of Manganese and Cobalt Incorporated Nickel Oxide Based Core-Shell Magnetic Nanoparticles

The synthesis along with the structural and magnetic properties of manganese (Mn) and cobalt (Co) -incorporated nickel oxide (NiO) inverted core-shell nanoparticles (CSNs) were investigated. The primary objective of this study was to determine the effect of substitution of nickel (Ni) by transition metal ions (Mn2+/Co2+) in affecting the magnetic properties of the resultant CSNs. The core of the CSNs is comprised of NiO and the shell constitutes a Nix(Mn/Co)1-xO phase. The synthesis of the CSNs was accomplished in two steps: first, NiO nanoparticles were synthesized using a thermal decomposition method. In the second step, our hydrothermal nanophase epitaxy method was used to create the core-shell structure. Rietveld refinement of X-ray diffraction (XRD) data show rock salt structure throughout in the Mn/Co incorporated CSNs. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images show a combination of pseudo-spherical and faceted shapes of CSNs whereas energy dispersive spectroscopy (EDS) indicates transition metal incorporation in the CSNs. The high-resolution (HR) TEM images confirmed the formation of distinct core and shell regions. Magnetic characterization shows that the Mn- and Co-substituted nickel oxide-based CSNs possess an inverted magnetic structure, with an antiferromagnetic core and a ferro- or ferrimagnetic shell. The coercivity and exchange bias properties are of larger magnitude in Mn-incorporated than in Co-incorporated CSNs

Recovering motifs from biased genomes: application of signal correction

A significant problem in biological motif analysis arises when the background symbol distribution is biased (e.g. high/low GC content in the case of DNA sequences). This can lead to overestimation of the amount of information encoded in a motif. A motif can be depicted as a signal using information theory (IT). We apply two concepts from IT, distortion and patterned interference (a type of noise), to model genomic and codon bias respectively. This modeling approach allows us to correct a raw signal to recover signals that are weakened by compositional bias. The corrected signal is more likely to be discriminated from a biased background by a macromolecule. We apply this correction technique to recover ribosome-binding site (RBS) signals from available sequenced and annotated prokaryotic genomes having diverse compositional biases. We observed that linear correction was sufficient for recovering signals even at the extremes of these biases. Further comparative genomics studies were made possible upon correction of these signals. We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique. Within this reduced average distance, we can find examples of class-specific RBS signals. Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters

Propagation of New Innovations: An Approach to Classify Human Behavior and Movement from Available Social Network Data

It is interesting to observe new innovations, products, or ideas propagating into the society. One important factor of this propagation is the role of individual's social network; while another factor is individual's activities. In this paper, an approach will be made to analyze the propagation of different ideas in a popular social network. Individuals' responses to different activities in the network will be analyzed. The properties of network will also be investigated for successful propagation of innovations

Understanding the Loss in Community Resilience due to Hurricanes using Facebook Data

Significant negative impacts are observed in productivity, economy, and social wellbeing because of the reduced human activity due to extreme events. Community resilience is an important and widely used concept to understand the impacts of an extreme event to population activity. Resilience is generally defined as the ability of a system to manage shocks and return to a steady state in response to an extreme event. In this study, aggregate location data from Facebook in response to Hurricane Ida are analyzed. Using changes in the number of Facebook users before, during, and after the disaster, community resilience is quantified as a function of the magnitude of impact and the time to recover from the extreme situation. Based on the resilience function, the transient loss of resilience in population activity is measured for the affected communities in Louisiana. The loss in resilience of the affected communities are explained by three types of factors, including disruption in physical infrastructures, disaster conditions due to hurricanes, and socio-economic characteristics. A greater loss in community resilience is associated with factors such as disruptions in power and transportation services and disaster conditions. Socioeconomic disparities in loss of resilience are found with respect to median income of a community. Understanding community resilience using decreased population activity levels due to a disaster and the factors associated with losses in resilience will enable us improve hazard preparedness, enhance disaster management practices, and create better recovery policies towards strengthening infrastructure and community resilience

Crisis Communication Patterns in Social Media during Hurricane Sandy

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises

A Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability during Hurricane Evacuation

Hurricane evacuation has become a major problem for the coastal residents of the United States. Devastating hurricanes have threatened the lives and infrastructure of coastal communities and caused billions of dollars in damage. There is a need for better traffic management strategies to improve the safety and mobility of evacuation traffic. In this study hurricane evacuation traffic was simulated using SUMO a microscopic traffic simulation model. The effects of Connected and Autonomous Vehicles (CAVs) and Autonomous Vehicles (AVs) were evaluated using two approaches. (i) Using the state-of-the-art car-following models available in SUMO and (ii) a co-simulation study by integrating the microscopic traffic simulation model with a separate communication simulator to find the realistic effect of CAVs on evacuation traffic. A road network of I-75 in Florida was created to represent real-world evacuation traffic observed in Hurricane Irma s evacuation periods. Simulation experiments were performed by creating mixed traffic scenarios with 25, 50, 75 and 100 percentages of different vehicle technologies including CAVs or AVs and human-driven vehicles. HDV Simulation results suggest that the CACC car-following model, implemented in SUMO and commonly used in the literature to represent CAVs, produces highly unstable results On the other hand the ACC car following model, used to represent AVs, produces better and more stable results. However, in a co-simulation study, to evaluate the effect of CAVs in the same evacuation traffic scenario, results indicate that with 25 percentage of CAVs the number of potential collisions decrease up to 42.5 percentage

Emerging Next Generation Solar Cells Route to High Efficiency and Low Cost

Generation of clean energy is one of the main challenges of the 21st century. Solar energy is the most abundantly available renewable energy source which would be supplying more than 50 of the global electricity demand in 2100. Solar cells are used to convert light energy into electrical energy directly with an appeal that it does not generate any harmful bi products, like greenhouse gasses. The manufacturing of solar cells is actually based on the types of semiconducting or non semiconducting materials used and commercial maturity. From the very beginning of the terrestrial use of Solar Cells, efficiency and costs are the main focusing areas of research. The definition of so called emerging technologies sometimes described as including any technology capable of overcoming the Shockley-Queisser limit of power conversion efficiency 33.7 percent for a single junction device. In this paper, few promising materials for solar cells are discussed including their structural morphology, electrical and optical properties. The excellent state of the art technology, advantages and potential research issues yet to be explored are also pointed out. Md. Samiul Islam Sadek | Dr. M Junaebur Rashid | Dr. Zahid Hasan Mahmood "Emerging Next Generation Solar Cells: Route to High Efficiency and Low Cost" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 201

Spatiotemporal Patterns of Urban Human Mobility

The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility