Level Repulsion in Constrained Gaussian Random-Matrix Ensembles

Introducing sets of constraints, we define new classes of random-matrix ensembles, the constrained Gaussian unitary (CGUE) and the deformed Gaussian unitary (DGUE) ensembles. The latter interpolate between the GUE and the CGUE. We derive a sufficient condition for GUE-type level repulsion to persist in the presence of constraints. For special classes of constraints, we extend this approach to the orthogonal and to the symplectic ensembles. A generalized Fourier theorem relates the spectral properties of the constraining ensembles with those of the constrained ones. We find that in the DGUEs, level repulsion always prevails at a sufficiently short distance and may be lifted only in the limit of strictly enforced constraints.Comment: 20 pages, no figures. New section adde

Performance Analysis Of Amplify And Forward Based Cooperative Diversity with Multiple Transmit Antennas

In todays world,wireless communication has a tremendous impact on the human civilization.There has been a sea changes in modern day living and the credit goes to the development in Wireless communication technology.But Wireless communication is highly challenging due to complex,time varying propagation medium which causes multi path fading,co-channel interferences, cross talk,etc..,Diversity techniques are widely used in wireless communication to mitigate these effects

Knowledge, attitude, perceptions and assessment of effectiveness of educational intervention on Pharmacovigilance among undergraduate medical students at Gulbarga Institute of Medical Sciences, Kalaburagi, India

Background: The Study was designed to assess the awareness of Pharmacovigilance and to evaluate the impact of an educational intervention.Methods: This was a questionnaire-based pre- and post-test educational interventional study. Students were given handouts containing information about pharmacovigilance one month before the educational intervention. A pre-validated 20-point questionnaire on (KAP) Knowledge, attitude, perception about Pharmacovigilance was distributed to second year medical students (n=115). An interactive educational intervention (Power point presentation) was designed. The chi-square test and unpaired paired t-test was used for statistical calculation.Results: The overall response rates were expressed as percentages, Mean±SD. The knowledge, attitude and perceptions of pharmacovigilance when compared before (pre-KAP) and after (post-KAP) the educational intervention, the correct response rates were found to be statistically significant (P<0.001). The feedback from the students was encouraging, handouts before the lecture classes helped them to easily grasp the pharmacovigilance concepts better during lectures.Conclusions: The study concluded that imparting the knowledge about pharmacovigilance and ADR reporting promotes drug safety and rational use of medicines in future

Near infrared fluorescent imaging of choline kinase alpha expression and inhibition in breast tumors

Choline kinase alpha (ChoKα) overexpression is associated with an aggressive tumor phenotype. ChoKα inhibitors induce apoptosis in tumors, however validation of their specificity is difficult in vivo. We report the use of optical imaging to assess ChoKα status in cells and in vivo using JAS239, a carbocyanine-based ChoKα inhibitor with inherent near infrared fluorescence. JAS239 attenuated choline phosphorylation and viability in a panel of human breast cancer cell lines. Antibody blockade prevented cellular retention of JAS239 indicating direct interaction with ChoKα independent of the choline transporters and catabolic choline pathways. In mice bearing orthotopic MCF7 breast xenografts, optical imaging with JAS239 distinguished tumors overexpressing ChoKα from their empty vector counterparts and delineated tumor margins. Pharmacological inhibition of ChoK by the established inhibitor MN58b led to a growth inhibition in 4175-Luc+ tumors that was accompanied by concomitant reduction in JAS239 uptake and decreased total choline metabolite levels as measured using magnetic resonance spectroscopy. At higher therapeutic doses, JAS239 was as effective as MN58b at arresting tumor growth and inducing apoptosis in MDA-MB-231 tumors, significantly reducing tumor choline below baseline levels without observable systemic toxicity. These data introduce a new method to monitor therapeutically effective inhibitors of choline metabolism in breast cancer using a small molecule companion diagnostic

Negative moments of characteristic polynomials of random GOE matrices and singularity-dominated strong fluctuations

We calculate the negative integer moments of the (regularized) characteristic polynomials of N x N random matrices taken from the Gaussian Orthogonal Ensemble (GOE) in the limit as $N \to \infty$. The results agree nontrivially with a recent conjecture of Berry & Keating motivated by techniques developed in the theory of singularity-dominated strong fluctuations. This is the first example where nontrivial predictions obtained using these techniques have been proved.Comment: 13 page

Detection and Predicting Air Pollution Level in a Specific City using Deep Learning

Air pollution affects millions of people worldwide, making it a growing issue. Deep learning can identify and forecast metropolitan air pollution. Deep learning needs a massive dataset of air quality measurements and meteorological factors to predict city air pollution levels. Government monitoring stations and citizen scientific programs collect this data. Once we have our dataset, we can apply deep learning to develop a model that predicts air pollution levels. Temperature, humidity, wind speed, and air quality data will be used to predict future air pollution levels. Predicting air pollution using the LSTM network is popular. This neural network works well with air quality time-series data. The LSTM network's long-term data learning is essential for accurate air pollution predictions. We would pre-process our data to prepare it for an LSTM network to predict air pollution. Scaling, splitting, and encoding data may be needed. Train the LSTM network using backpropagation and gradient descent on our dataset. Adjusting the network's weights and biases would lessen the air pollution gap. After training, the network can predict city air quality. Inputting current meteorological and environmental factors may help accomplish this aim and deliver timely predictions. Deep learning can detect and predict urban air pollution. LSTM neural network algorithms may accurately forecast complex air quality data patterns, providing vital information about our planet's health

National Physical Laboratory demonstrates 1 g Kibble balance: Linkage of macroscopic mass to Planck constant

Mass is the only base unit, which is represented as a primary standard in the form of artifact for more than 125 years. International prototype of kilogram (IPK) is kept at the Bureau International des Poids et Mesures (BIPM), Paris and serves as the international standard of kilogram. It is made of 90% platinum and 10% iridium and as a cylinder of 39 mm diameter and 39 mm height. Replicas of the IPK are made of the same material and used at BIPM as reference or working standards and national prototype of kilogram (NPK), kept at different National Metrology Institutes (NMIs). NPK-57, kept at CSIR-National Physical Laboratory, is sent periodically to BIPM for calibration