Comparative Study of the Dynamics of Cosmic Rays for the Pakistan and China Atmospheric Regions

This paper presents an analysis of cosmic ray intensity in Pakistan air space using spatial interpolation, comparing it with Chinese cosmic ray records from 1984 to 1993. The Exploratory Data Analytic (EDA) approach was applied to compare the cosmic ray fluctuations in both countries. The time series plot of the monthly cosmic rays showed relatively flatter counts in Pakistan than in China. The cosmic ray data for the years 1984 to 1993 fell within Solar Cycle 22, which lasted from 1986 to 1996, with its maximum phase in 1989 to 1991. The cosmic radiation varies between the atmospheric regions of Pakistan and China due to modulations in intensity that are accessible accordingly. It can be explained by purely astrophysical phenomena: (1) the source of emission of cosmic radiation may be different, (2) the rate at which emanation takes place depends on bursts of deep space dynamical objects from their sources that may be affected by solar wind and other solar radiations. Therefore, modulations in intensity are not only due to different geophysical locations. This study will help government organizations to predict and forecast cosmic rays values.

MODEL DINAMIS AUTOREGRESSIVE DISTRIBUTED LAG (STUDI KASUS: PENGARUH KURS DOLAR AMERIKA DAN INFLASI TERHADAP HARGA SAHAM TAHUN 2014-2018)

The aim of this research is to determine the dynamic model equation of autoregressive distributed lag by using koyck method, to find out the effect of log US dollar exchange rate and log inflation on log stock price in 20142018, and to forecast value of log stock price on January 2019August 2019. The data used in 20142018. The data was transformed into logarithm format. Time series plot of log US dollar exchange rate, log inflation, and log stock price suggest that the fluctuation in the data, for instance, both upward and downward trends, during the period. We obtained that the Koyck transformation could changed the lag distribution model into autoregressive distributed lag (ARDL) dynamic model. Furthermore, the log of US dollar exchange rate and log inflation have negative effect on log stock price in particular period. We measured forecasting accuracy using mean absolute prediction error (MAPE) and concluded that ARDL forecasting using Koyck model shows a significant increase in stock price.

Visually Communicating Future Climate in a Web Environment

While there is growing demand for use of climate model projections to understand the potential impacts of future climate on resources, there is a lack of effective visuals that convey the range of possible climates across spatial scales and with uncertainties that potential users need to inform their impact assessments and studies. We use usability testing including eye tracking to explore how a group of resource professionals (foresters) interpret and understand a series of graphical representations of future climate change, housed within a web-based decision support system (DSS), that address limitations identified in other tools. We find that a three-map layout effectively communicates the spread of future climate projections spatially, that location-specific information is effectively communicated if depicted both spatially on a map and temporally on a time series plot, and that model error metrics may be useful for communicating uncertainty and in demonstrating the utility of these future climate datasets.

Evaluation of Two Global Rainfall Models for Water Resources Planning and Management in Nigeria

Given the sparseness of weather stations in Nigeria, there is an increasing need for alternative sources of rainfall data such as satellite measurements, numerical models and reanalysis. Nevertheless, the complexity of such data requires proper evaluation and validation. Therefore, this study evaluated two globally available rainfall products from Climate Research Unit (CRU) and University of Delaware (UNIDEL) using rain gauge data obtained from Nigerian Meteorological Agency (NIMET), over a period of twenty years (1980-1999) covering 24 stations. Time series plot and statistical tools were used to evaluate the products on annual, seasonal and zonal basis. The results show that the two products demonstrated comparable ability and sufficiently captured the spatial and temporal patterns of rainfall over the country. However, the products overestimated and underestimated during the dry and rainy seasons, respectively. Although, correlation was comparatively high between 0.3 and 0.8, but negative in few instances, mean bias error (MBE) and root mean square error (RMSE) were generally high depicting high random error. The performance of the products was best in the Sahel, followed by the Savannah and Forest zones, with UNIDEL showing better performance in most cases. Consequently, we recommend further studies to validate the present results on the use of gridded data in the country.Keywords— evaluation, CRU, UNIDEL, rain gauge, rainfall products

Lessons from Severe Acute Respiratory Syndrome Coronavirus 2003 Pandemic as Evidence to Advocate for Stroke Public Education During the Current Coronavirus Disease 2019 Pandemic

Introduction: The coronavirus disease 2019 (COVID-19) outbreak is affecting hospital admissions of stroke patients. This, in turn, will reduce the use of proven stroke treatments, which will result in poorer stroke outcomes. We examined local stroke admissions before, during, and after the 2003 outbreak of the severe acute respiratory syndrome (SARS) (these periods being defined in both the Singapore and worldwide contexts), to extrapolate stroke admission patterns in Singapore during the current COVID-19 crisis. Materials and Methods: National inpatient admission data from the Ministry of Health (MOH), Singapore, and death data from the Registry of Births and Deaths (RBD), Singapore, were analysed. Trends of local stroke admissions and stroke-related mortality pre-SARS, during SARS, and post-SARS periods, both in the Singapore and worldwide contexts, were analysed using time series plot in monthly time units. Differences between periods were presented as percentage change between: (1) SARS and pre-SARS periods, and (2) post-SARS and SARS periods and compared using two-sample t-tests. Results: There was a 19% decline in stroke admissions into all local hospitals during the Singapore SARS period (P = 0.002) and a 13% reduction during the worldwide SARS period (P = 0.006). Stroke admissions increased by 18% after the Singapore SARS period was over (P = 0.003) and rose by a further 8% when the worldwide SARS period ended (P = 0.046). Stroke-related mortality remained stable throughout. Conclusions: During the SARS pandemic, there was a reduction in the number of stroke admissions, and this was apparent during both the local SARS and worldwide SARS outbreak periods. We should take appropriate steps through public education to minimise the expected reduced stroke admissions during the COVID-19 pandemic, inferred from the findings during the SARS pandemic. Key words: Care-seeking behaviour, COVID-19, Inpatient admission, Pandemic, SARS

Bifurcation analysis of the propagation of femtosecond pulses for the Triki-Biswas equation in monomode optical fibers

Bifurcation analysis of the propagation of femtosecond pulses for the Triki–Biswas (TB) equation in monomode optical fibers is reported for the first time. Bifurcation of phase plots of the dynamical system is dispensed using phase plane analysis through symbolic computation. It is observed that the TB equation supports femtosecond solitary pulse, periodic pulse, superperiodic pulse, kink and anti-kink pulses, which are presented through time series plot numerically. Analytical forms of the femtosecond solitary pulses are obtained. This contribution may be applicable to interpret the dynamical behavior of various femtosecond pulses in monomode optical fibers beyond the Kerr limit.

Mining single-cell time-series datasets with Time Course Inspector

Summary Thanks to recent advances in live cell imaging of biosensors, microscopy experiments can generate thousands of single-cell time-series. To identify sub-populations with distinct temporal behaviours that correspond to different cell fates, we developed Time Course Inspector (TCI)—a unique tool written in R/Shiny to combine time-series analysis with clustering. With TCI it is convenient to inspect time-series, plot different data views and remove outliers. TCI facilitates interactive exploration of various hierarchical clustering and cluster validation methods. We showcase TCI by analysing a single-cell signalling time-series dataset acquired using a fluorescent biosensor. Availability and implementation https://github.com/pertzlab/shiny-timecourse-inspector. Supplementary information Supplementary data are available at Bioinformatics online.

A NEW METHODOLOGY TO ANALYZE THE DYNAMIC OF DAILY POWER DEMAND WITH ATTRACTORS INTO THE MANDELBROT SET

The time series plot of electricity daily load demand is seasonal as shown by its regular repetitive pattern during the same period each year. Its behavior is determined by phase-space diagrams that are able to identify any of the following states of the series: fixed point, periodic, or chaotic. The first two deal with predictable systems. This paper focuses on presenting a new methodology to analyze the dynamics of the series in reference by using the curve formed by attractors that move in the complex plane over the Mandelbrot set according to the law dictated by the load curve. Because electrical power is a variable, it is also defined in the complex plane with the components of active power on the real axis and reactive power on the imaginary axis. Therefore, electrical power facilitates a new field of analysis in Mandelbrot fractal space. The obtained temporal curve confirms that the profile of the electric power demand is also mapped with the new fractal geometric space of the Mandelbrot set, thus providing a new contribution that extends knowledge about the dynamics of systems in fractal geometry.

NLP Based Text Analytics and Visualization of Political Speeches

This paper presents a case study of implementing computational methods like Natural Language Processing (NLP) to perform Text Analytics and Visualization on political speech transcripts. The speech transcripts are published on websites, social media, and documents in large volumes and multiple languages. These transcripts are available in unstructured textual format and thus they are a part of big-data requiring analytics to derive insights from it. In this experiment, a significantly large volume of speech transcripts are analyzed and graphical visualizations are generated such as Lexical Dispersion Plot, Time Series Plot, WordCloud, Bar-Graphs using various Python libraries. The study has been useful in identifying issues highlighted across a large number of speech transcripts. So far, the linguists have tried to perform analysis using manual linguistic approaches which are extremely time-consuming and complex to understand the Political Discourse. Our experiment of applying NLP based text analytics proves to be a very efficient technique for Political Discourse Analysis (PDA).

Estimates and Analysis of the Reference Evapotranspiration in Gassol, Taraba State

Reference evapotranspiration is very important because it correlates with the amount of water required by crops and also plays very key role in the hydrological cycle. Evaporation is the process of water loss from the earth surface in which temperature effect is significant while transpiration is water loss from plants. Studying evapotranspiration is also important because of the link between climate change and water scarcity. The reference evapotranspiration for Gassol was estimated and analysis done to observe its trend and variation. In this paper, the FAO Penmann-Monteith model was used to estimate the reference evapotranspiration for Gassol town located in the Sudan Savannah vegetation belt of Nigeria. The annual monthly estimates show a generally recurring seasonal pattern of variation from 1985 to 1991. January through June had lower ET0 compared to July through December. The time series plot of the ET0 estimates from 1985 to 1991 in monthly renditions gives a cyclical pattern of variability with most of the years showing bimodal peaks. Also, an evenly spread data was presented by the normal distribution curve. The periodogram of the estimated reference evapotranspiration gave a dominant periodicity of 9.33 months cycle. The estimates of and the pattern of variation of the reference evapotranspiration as observed for Gassol in this study will very likely experience a continuous downward trend. For proper irrigation management, January to March and October to December should be properly planned.