A constructive method for decomposing real representations

A constructive method for decomposing finite dimensional representations of semisimple real Lie algebras is developed. The method is illustrated by an example. We also discuss an implementation of the algorithm in the language of the computer algebra system {\sf GAP}4.Comment: Final version; to appear in "Journal of Symbolic Computation

Carrier Status of Methicillin-Resistant Staphylococcus Aureus (MRSA)

To investigate nasal carriage of Methicillin-Resistant Staphylococcus aureus (MRSA) among dental healthcare workers (HCWs) , as the carriers could be the potential risk factor for the transmission of nosocomial infection when exposed to hospital setting during clinical posting. Methods: One hundred HCWs including postgraduate trainees, house physicians, staff nurses and technicians participated in the study. Nasal specimens were obtained by using cotton swabs moistened in sterile saline. The nasal specimens collected were processed as per (CLSI, 2008). Specimens were inoculated on blood agar to look for β-hemolysis of Staphylococcus aureus. Nutrient agar was used for the direct colony identification of Staphylococcus aureus. Mannitol salt agar (MSA) and DNAse were used as selective media for the isolation of Staphylococcus aureus and incubated at 35˚C for 48 hrs.Resistance to methicillin was detected with cefoxitin(30 μg) through Disk Diffusion Test and interpreted according to (CLSI, 2009). A diameter of ≥22 mm was considered as susceptible and ≤21 mm as resistant as per (CLSI, 2010).Results: Out of 100 nasal swabs collected, 71 nasal swabs were from the dental surgeons and 29 were from the nursing staff, 35 (35%) showed a growth of Staphylococcus aureus. Among those who were positive for Staphylococcus aureus 62.85%were positive for MRSA. Overall 22 (22%) out of a 100 individuals came out to be positive for MRSA.Conclusion: Health care workers (HCWs) were the potential colonizers of methicillin resistant Staphylococcus aureus and may serve as reservoirs or disseminators of MRSA

A Miniature Implanted Inverted-F Antenna for GPS Application

A miniature implanted Hilbert inverted-F antenna design at the 1.575 GHz; global positioning system (GPS) frequency is proposed, which can be used to track the location of its user, e.g., the elderly with declining mental capacity (alzheimer\u27s disease). A detailed parametric study of the antenna design is performed by considering it to be implanted within a human muscle model. The effects of the substrate, superstrate, and the nearby muscle tissue on antenna performance are carefully investigated. A laboratory prototype of the antenna was built and tested within a muscle equivalent fluid indicating good VSWR performance. Finally the said antenna when placed within the tissue equivalent fluid was also tested to track GPS satellites with the help of a low noise amplifier (LNa). Such field measurements demonstrate that the antenna can easily lock into six or more satellites which are more than enough to determine the location of a person

Transmission of low-energy scalar waves through a traversable wormhole

We study the scattering of low-energy massless and massive minimally coupled scalar fields by an asymptotically flat traversable wormhole. We provide a comprehensive treatment of this problem offering analytic expressions for the transmission and reflection amplitudes of the corresponding effective potential and the absorption cross section of the wormhole. Our results, which are based on a recently developed dynamical formulation of time-independent scattering theory, apply to a large class of wormhole spacetimes including a wormhole with a sharp transition, the Ellis wormhole, and a family of its generalizations.Comment: 21 pages, 3 figures, references added, accepted for publication in European Physical Journal

Big Data and Internet of Behaviors (IoB): Its Nature and Importance in the World of KYC

With rapid digitalization, a novel concept called "Internet of Behaviors" (IoB) is emerging, where businesses can leverage large data gathered through IoT as a tool to affect people's actions. By the end of 2025, more than half of the world's population, according to Gartner, will be enrolled in the IoB program. The research below identifies how the Internet of Behaviors (IoB) can be integrated and used to change the world of KYC (Know Your Customer). The analysis identifies that KYC is a critical process, especially in finance. Financial organizations can use KYC to effectively manage the funds they receive by protecting the market from money obtained through fraud. The theoretical foundation shows that the research follows the TAM model. IoT, People, and IoE are identified as critical external factors that influence the way IoB can change the world of KYC. The study comes to the conclusion that KYC is directly impacted by IoB through the massive big data it gathers, which has a substantial impact on success in many modern firms

Application of artificial neural network for prediction of halogenated refrigerants vapor pressure

Application of Artificial Neural Network (ANN) for modeling of vapor pressure for some halogenated refrigerants (halogenated methanes and ethanes) is presented. Neural network training structure was feed forward with back-propagation algorithm. The optimized number of hidden layer and neurons between layers were determined by a trial and error procedure. Neural network parameters were obtained through a learning phase by Levenberg-Marquardt algorithm. The vapor pressure at different temperatures obtained from open literatures was considered as the neural model target. ANN predictions of vapor pressure are more accurate for a wider range of temperature. The ANN modeling reduced the average error for the refrigerants from 0.69% to 0.31% for low temperature range and from 1.39% to 0.99% for high temperature range. Finally, ANN modeling reduced the average error in comparison to theAntoine equation by 47.88% and 32.18% for low and high temperature range, respectively

Fault-Tolerant Spatio-Temporal Compression Scheme for Wireless Sensor Networks

Wireless sensor networks are often deployed for environmental sampling and data gathering. A typical wireless sensor network consists, from hundreds to thousands, of battery powered sensor nodes fitted with various sensors to sample the environmental attributes, and one or more base stations, called the sink. Sensor nodes have limited computing power, memory and battery. Sensor nodes are wirelessly interconnected and transmit the sampled data in a multihop fashion to the sink. The sheer number of sensor nodes and the amount of sampled data can generate enormous amount of data to be transmitted to the sink, which subsequently can transform into network congestion problem resulting into data losses and rapid battery drain. Hence, one of the main challenges is to reduce the number of transmissions both to accommodate to the network bandwidth and to reduce the energy consumption. One possibility of reducing the data volume would be to reduce the sampling rates and shutdown sensor nodes. However, it can affect the spatial and temporal data resolution. Hence, we propose a compression scheme to minimize the transmissions instead of reducing the sampling. The sensor nodes are vulnerable to external/environmental effects and, being relatively cheap, are susceptible to various hardware faults, e.g., sensor saturation, memory corruption. These factors can cause the sensor nodes to malfunction or sample erroneous data. Hence, the second biggest challenge in data gathering is to be able to tolerate such faults. In this thesis we develop a spatio-temporal compression scheme that detects data redundancies both in space and time and applies data modeling techniques to compress the data to address the large data volume problem. The proposed scheme not only reduces the data volume but also the number of transmissions needed to transport the data to the sink, reducing the overall energy consumption. The proposed spatio-temporal compression scheme has the following major components: Temporal Data Modeling: Models are constructed from the sampled data of the sensor nodes, which are then transmitted to the sink instead of the raw samples. Low computing power, limited memory and battery force us to avoid computationally expensive operations and use simple models, which offer limited data compressibility (fewer samples are approximated). However, we are able to extend the compressibility in time through our model caching scheme while maintaining simple models. Hierarchical Clustering: The data sampled by the sensor nodes is often not only temporally correlated but also spatially correlated. Hence, the sensor nodes are initially grouped into 1-hop clusters based on sampled data. Only a single model is constructed for one cluster, essentially reducing the sampled data of all the sensor nodes to a single data model. However, we also observed through experiments that the data correlations often extend beyond 1-hop clusters. Hence, we devised a hierarchical clustering scheme, which uses the model of one 1-hop cluster to also approximate the sampled data in the neighboring clusters. All the 1-hop clusters approximated by a given model are grouped into a larger cluster. The devised scheme determines the clusters that can construct the data models, the dissimilation of the model to the neighboring clusters and finally the transmission of the data model to the sink. The accuracy of data to the single sensor node level is maintained through outliers for each sensor node, which are maintained by the cluster heads of the respective 1-hop clusters and cumulatively transmitted to the sink. The proposed spatio-temporal compression scheme reduces the total data volume, is computationally inexpensive, reduces the total network traffic and hence minimizes the overall energy consumption while maintaining the data accuracy as per the user requirements. This thesis also addresses the second problem related to data gathering in sensor networks caused by the faults that results into data errors. We have developed a fault-tolerance scheme that can detect the anomalies in the sampled data and classify them as errors and can often correct the resulting data errors. The proposed scheme can detect data errors that may arise from a range of fault classes including sporadic and permanent faults. It is also able to distinguish the data patterns that may occur due to both the data errors and a physical event. The proposed scheme is quite light weight as it exploits the underlying mechanisms already implemented by spatio-temporal compression scheme. The proposed fault-tolerance scheme uses the data models constructed by the compression scheme to additionally detect data errors and subsequently correct the erroneous samples

Efficiency in BRICS banking under data vagueness:a two-stage fuzzy approach

This study analyzes the efficiency levels of the banking industry in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 2010 to 2014, using an integrated two-stage fuzzy approach. Very often the reliability of data collected from BRICS is questionable. In this research, we first use fuzzy TOPSIS to capture vagueness in the relative efficiency of BRICS banking over time. In the second stage, we adopt fuzzy regressions based on different rule-based systems to enhance the power of significant socioeconomic, regulatory, and demographic variables to predict banking efficiency. These variables are previously identified by using bootstrapped truncated regressions with conditional α-levels, as proposed by Wanke, Barros, and Emrouznejad (2015a). The results reveal that efficiency in the banking industry is positively associated with country gross savings and the GINI index ratio, but negatively associated with relatively high inflation ratios. Fuzzy regressions proved far more accurate than bootstrapped truncated regressions with conditional α-levels. We derive policy implications