Entropy and Multifractality in Relativistic Ion-Ion Collisions

Entropy production in multiparticle systems is investigated by analysing the experimental data on ion-ion collision at AGS and SPS energies and comparing the findings with those reported earlier for hadron-hadron, hadron-nucleus and nucleus-nucleus collisions. It is observed that the entropy produced in limited and full phase space, when normalised to maximum rapidity exhibits a kind of scaling which is nicely supported by Monte Carlo model \hij. Using the R\'{e}nyi's order-q information entropy, multifractal characteristics of particle production are examined in terms of Generalized dimensions, D$_{q}$. Nearly the same values of multifractal specific heat, $c$ observed in hadronic and ion-ion collisions over a wide range of incident energies suggest that the quantity $c$ may be used as a universal characteristic of multiparticle production in hadron-hadron, hadron-nucleus and nucleus-nucleus collisions.Comment: 17 pages, 5 figures, 2 table

The impact of information and communication technologies (ICTs) on academic performance of medical students: an exploratory study

Background: Information and Communication Technology (ICT) has a potential to improve teaching and learning process. There are conflicting reports on the effect of ICT on student`s outcome. Though there is an early indication of positive impact, but the technology has a potential to have a detrimental effect. The present study was taken up to explore the effects of ICT on medical student’s academic performance.Methods: All the second professional students were given the questionnaire. Only 75 students had filled up the questionnaires completely.Results: The study population consisted of 48.00% males and 52.00% females. 97.33% students had smart phones, 44.00% had a laptop too. 10.66% students got less than 50% marks in the second professional examination, 14.66% got 50-59% marks, 62.66% got 60-69% marks and 12.00% got 70% or more marks. A low negative correlation was found between academic performance and possession of a smart phone (r= -0.062), and between academic performance and possession of a laptop (r= -0.029). A moderate negative correlation was found between academic performance and the time spent on a smart phone or laptop (r = -0.309). The correlations between academic performance and gender, and academic performance and the time spent on mobile phones or laptops were found statistically significant (p=0.000 and 0.007 respectively).Conclusions: Though ICT has capabilities of improving student`s academic performance, but it has a potential to have a negative effect if not used rationally. There is a vital need to sensitize the students about the potential academic risks associated with improper use of ICT. Students should be assisted and guided on how to use it judiciously

Personalized drug concentration predictions with machine learning: an exploratory study

Background: The dose individualization by therapeutic drug monitoring (TDM) can be improved if population-based reference ranges are available, as there is large inter- and intrapatient variability. If these ranges are not available, dose individualization may not be optimal. Machine learning can help achieve accurate drug dose settings and predict the resultant levels.Methods: Two random forest models, a multi-class classifier to predict dose and a regression model to predict blood drug level were trained on 320 patients’ data, consisting of their age, sex, dose and blood drug level. The classifier consisted of 1000 estimators (decision trees) and the regression model consisted of 1300 estimators. The model was evaluated on randomly split test set having 10% of the total dataset size. The regression model was compared against k-Nearest neighbor and linear regression models. The classifier was evaluated using accuracy, precision, and F1 Score; the regression model was evaluated using R2, Root mean squared error, and mean absolute error.Results: The classifier had an out-of-sample accuracy of 68.75%, average precision of 0.7567, and an average F1 score of 0.6907. The regression model had an out-of-sample R2 value of 0.2183, root mean squared value of 3.7359, and a mean absolute error of 2.5156. These values signify an average classification performance, and a below-average regression performance due to small dataset.Conclusions: It is possible for machine learning algorithms to be used in therapeutic drug monitoring. With a well-structured, rich, and large dataset, a very accurate model can be built

Assessment of River Shahpur for Flood Risk in Northern Pakistan

During 2010 flood, great losses to human and properties were reported in the study area. In terms of land and property loss, 74.1 hectare agriculture land, 2719 to 3114 houses were fully and partially damaged. This study was carried out with an aim, to explore flood risk assessment of river Shahpur in District Shangla. For the future perspectives ‘River Habitat Survey was used for flood risk assessment and ‘Spot Check Key Models’ are used to derive guidelines.  Settlements and agriculture activities on the river banks were some of the major causes of the reported catastrophe in the study area. Most of the damages were reported on the right bank of river Shahpur due to the topography and structure of soil. This study recommends the construction of check dams and river embankment on river Shahpur to mitigate flood destruction in the future, coupled by flood prediction; early warning systems and risk assessment procedures should be integrated in both local and national planning so that both fertile land and precious lives can be saved. Keywords: Flood Risk, Flood Risk Assessment, River Habitat Survey, Spot Check key

Assessment of River Shahpur for Flood Risk in Northern Pakistan

During 2010 flood, great losses to human and properties were reported in the study area. In terms of land and property loss, 74.1 hectare agriculture land, 2719 to 3114 houses were fully and partially damaged. This study was carried out with an aim, to explore flood risk assessment of river Shahpur in District Shangla. For the future perspectives ‘River Habitat Survey was used for flood risk assessment and ‘Spot Check Key Models’ are used to derive guidelines.  Settlements and agriculture activities on the river banks were some of the major causes of the reported catastrophe in the study area. Most of the damages were reported on the right bank of river Shahpur due to the topography and structure of soil. This study recommends the construction of check dams and river embankment on river Shahpur to mitigate flood destruction in the future, coupled by flood prediction; early warning systems and risk assessment procedures should be integrated in both local and national planning so that both fertile land and precious lives can be saved. Keywords: Flood Risk, Flood Risk Assessment, River Habitat Survey, Spot Check key

Adsorptive removal ofPb(II) ion from aqueous solution using rice husk-based activated carbon

There are various methods can be used to remove heavy metals from waste water, such as chemical precipitation, ion exchange, electroplating and membrane separation. Nevertheless the most effective method would be adsorption using activated carbon due to its large number of pores. This project will be focusing primarily on rice husk as potential activated carbon as low cost adsorbent to remove Pb(II) from wastewater. Preparation of activated carbon involves three steps which are raw rice husk preparation, activation stage and carbonization stages. Preparation of rice husk involves grinding to 63 !liD which is then treated with 1.0 M of sodium hydroxide (NaOH). The treated rice husk is then carbonized at 500°C for 2 hours to remove volatile organic components. After the activated carbons are produced, extraction study will be carried out to further study the adsorption capacity. The pores development of the rice husk was analysed using Field Emission Scanning Electron Microscope (FESEM). Other analyses for rice husk based activated carbon were conducted using TGA, XRD, CHN Elemental Analyser and FTIR for characterisation study on the particles of rice husk. Pb(II) ion extraction from aqueous solution was done using rice husk based activated carbon which was conducted at different contact time at room temperature. The adsorption capacity of Pb(II) ion was determined using Atomic Adsorption Spectroscopy (AAS). Results obtained from adsorption study indicate that rice has the potential to be used as adsorbent for heavy metals removal

Comparative Study Of Congestion Control Techniques In High Speed Networks

Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially to handle bursty traffic of todays very high speed networks. Since late 90s numerous schemes i.e. [1]...[10] etc. have been proposed. This paper concentrates on comparative study of the different congestion control schemes based on some key performance metrics. An effort has been made to judge the performance of Maximum Entropy (ME) based solution for a steady state GE/GE/1/N censored queues with partial buffer sharing scheme against these key performance metrics.Comment: 10 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS November 2009, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Event-by-Event Fluctuations Clusterization and Entropy Production in AA Collisions at AGS and SPS Energies

Event-by-event (ebe) fluctuations in mean pseudorapidity values of relativistic charged particles in full phase space is studied by analysing experimental data on $^{16}O-AgBr$ collisions at 14.5A, 60A, and 200A GeV/c and $^{32}S-AgBr$ collisions at 200A GeV/c. The findings are compared with the prediction of A Multi-Phase Transport(\amm) model and those obtained from the analysis of correlation free Monte-Carlo events. Fluctuations in mean pseudorapidity distributions are noticed to be in excess to that expected from the statistically independent particle emission. The observed dependence of the fluctuation strength measure parameter,$\phi$ on the beam energy and number of participating target nucleons indicate that nucleus-nucleus collisions can not be treated as simple superposition of multiple nucleon-nucleon interactions. Presence of clusters or jet-like phenomena in multihadron final states are searched for on ebe basis by using the concept of Jaynes Shannon entropy. The findings indicate the presence of cluster like objects in the experimental data with their size and frequency increasing with increasing beam energy. These observations, in turn suggest that the clustering or jet-like algorithm adopted in the present study may be used as a tool for triggering different classes of events.Comment: International Journal of Modern Physics E, to be published(accepted