A statistical analysis and inter-comparison of the reflected radiation at two sites in Cyprus representing two different climate regimes of the island (Athalassa-inland plain vs Larnaca-coastal location) covering the period January 2013-December 2015 is presented. Mean annual and mean monthly daily totals of the reflected radiation and ground albedo as well as their frequency distribution are computed and discussed. The monthly means of reflected irradiance range from 50 to 120 W m-2 at Athalassa, while at Larnaca they range between 50 and 140 W m-2. The mean annual albedo for Athalassa is 0.202 which coincides with the most common value given in the literature, while for Larnaca it is higher (0.221). The higher value at Larnaca is attributed to its alluvial (silty sandy) soil which has higher reflectivity compared to the calcarenite (calcium carbonate) soil at Athalassa. The lowest values of albedo at Athalassa, are observed around noon and the higher values in the morning and afternoon. The highest values are obtained in January and February and the lowest in May and June when global solar irradiance have its higher values. On the other hand, we observe an increasing trend from the morning towards the afternoon hours at Larnaca throughout the year. The maximum of daily reflected irradiation is about 6.5 MJ m-2 at Athalassa and about 7.5 MJ m-2 at Larnaca. The monthly mean daily values of albedo at Athalassa range between 0.175 to 0.224, while at Larnaca they are higher ranging from 0.198 to 0.239. As a result of the differences of the global and reflected radiation of the two sites, the net shortwave irradiation at Larnaca is slightly higher than that at Athalassa, almost throughout the year.

In the current study, we have experimentally and comparatively investigated and compared malignant human cancer cells and tissues before and after irradiating of synchrotron radiation using T hermal Spectroscopy, Photothermal Spectroscopy, Thermal Microspectroscopy, Photothermal Microspectroscopy, Thermal Macrospectroscopy and Photothermal Macrospectroscopy. It is clear that malignant human cancer cells and tissues have gradually transformed to benign human cancer cells and tissues under synchrotron radiation with the passage of time (Figures 1-6) [1-123]. It can be concluded that malignant human cancer cells and tissues have gradually transformed to benign human cancer cells and tissues under synchrotron radiation with the passage of time (Figures 1-6) [1-123].

This paper presents a new methodology for jointly identifying bent cable phasic patterns and mixture of progressors and non-progressors of human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) patients based on longitudinal and time-to-event data. Using the longitudinal data, the bentcable model gives an estimate of a gradual transition period for the development of drug resistance to Antiretroviral (ARV) drug for treating HIV patients. In addition to finding such an estimate (phasic pattern identification), a two-part modeling is carried out to incorporate a relatively large percentage of left-censored data in the framework of joint analysis of time to event and longitudinal data. Even though there are some methods for separately analyzing time to event and longitudinal data, those methods may not be appropriate when time to event is dependent on the longitudinal outcome. A better approach is to extend a bent-cable To bit model that jointly incorporates patients who are potentially progressors to AIDS from those patients who do not, phasic changes of trajectories of viral load, and the association between the time to a decline of CD4/CD8 ratio and rates of change in viral load. The proposed methods are illustrated using real data from an AIDS clinical study.

Cotton yield is a function of growth rates, flower production rates and flower and boll retention during the fruiting period. This study, predicting effects of climatic factors during different convenient intervals (in days) on cotton flower and boll production compared with daily observations and collect information about the nature of the relationship between various climatic factors and cotton boll development and the 15-day period both prior to and after initiation of individual bolls. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h and maximum air temperature, are the important climatic factors that significantly affect flower and boll production. Evaporation; minimum humidity and sunshine duration were the most effective climatic factors during preceding and succeeding periods on boll production and retention. There was a negative correlation between f lower and boll production and either evaporation or sunshine duration, while that correlation with minimum relative humidity was positive.

Determination of the appropriate sample size to use when performing inference for a single Pearson correlation coefficient ρ is usually based on achieving sufficient power for the test of H0 : ρ = 0. However, sample sizes found using this method can yield confidence intervals that are so wide that they provide very little useful information about the magnitude of the population correlation. Alternative approaches for determining the appropriate sample size are proposed and compared to the "usual" method.

A new continuous numerical approach based on the approximation of polynomials is here proposed for solving the equation arising from heat transfer along a large flat steel plate subject to initial and boundary conditions. The method results from discretization of the heat equation which leads to the production of a system of algebraic equations. By solving the system of algebraic equations we obtain the problem approximate solutions.

Hourly global and diffuse irradiance data are used to estimate Linke turbidity factor (TL ) at two sites in Cyprus, representing two different climate regimes of the island (Athalassa-inland plain vs Larnaca-coastal location) for the period January 2013-December 2015. The annual mean values of TL are 3.40 at both stations. The daily and monthly variation of atmospheric turbidity has been studied. It was found that atmospheric turbidity decreases during the winter season (rainy season) and increases during the summer season. The higher diurnal variation is observed in spring and summer months at both stations. TL is increased from morning to afternoon. The increase of TL from morning to afternoon can be attributed to the fact that higher traffic activities are observed during the day at both stations. The results presented are comparable to those recorded in different places in Mediterranean region. The Cumulative Frequency Distributions (CDF) of TL shows that about 37% of TL values are less than 3, 50% are between 3 to 5 and only 13% are greater than 5. This indicates that the skies on cloudless days are clean and clear. The short-term variation of atmospheric turbidity depends on local weather conditions (temperature, vapour pressure, wind speed and wind direction) and its long-term one on the climate of the area. It was found that the Linke turbidity factor is increased linearly with air temperature at both stations. TL is also increased with increasing the vapour pressure at both stations. The prevailing winds, which may transport moisture or aerosol particles from distant sources, play a major role in the spatio-temporal variation of turbidity. Wind speed also plays a significant role in the transport of moisture or aerosol particles. The diffuse irradiance is increased with the increase of atmospheric turbidity. Linear relationships were established between diffuse irradiance and atmospheric turbidity. Comparing TL values of different locations over the island, it was found that the high altitude station has the lowest values, since this station has clearer atmosphere than the other sites.

The study objective is to establish an interactive Big Data Platform Analytics (BDA) platform with Hadoop/ MapReduce technologies distributed over HBase (key-value NoSQL database storage) and to generate hospitalization metadata on the platform. Performance tests retrieved results from simulated patient records with Apache tools in Hadoop’s ecosystem. At optimized iteration, the Hadoop distributed file system (HDFS) ingestion with HBase exhibited sustained database integrity over hundreds of iterations; however, the platform required a month to complete its bulk loading via MapReduce to HBase and validate queries required a month. To generate HBase datafiles, the framework took a week for one billion (10TB) files and a month for three billion (30TB) files. Inconsistencies of MapReduce limited the capacity to generate/replicate data efficiently. Dependencies among the data elements system could be expressed via “family” primary keys set in code via Apache Phoenix as database generator. Modeling a hospital system based on a patient encounter-centric database was very difficult because data profiles were fully representative of complex relationships. Apache Spark and Apache Drill showed high performance. Recommendations regarding key-value storage should be considered when analyzing large volumes of healthcare data securely.

Object Oriented Bayesian Networks (OOBNs) are a semi-quantitative modeling approach that can be utilized to represent complexities of management tradeoffs and spillovers within a conservation and ecological context. However, computation expense and gradual learning curve result in their underutilization in ecological and environmental disciplines. This is despite the reoccurring need for decision-makers to adapt wildlife management protocols while constraint by limited resources and scarce data. We provide guidelines to identifying and prioritizing uncertainties surrounding complex ecological processes. Empirical data and expert explicit understanding of uncertainties are utilized. We put forth two OOBNs, each accurately representing a snapshot of the moving parts in the complex wildebeest hybridization conservation case study in South Africa. We identifying and clustered key variables impacting the probability of hybridization in either spatial, biological, or market domains. Specifically, (i) blue wildebeest male to black wildebeest male ratio, and (ii) spatial connectivity. Ecologists facing similar constraints worldwide may utilize our stepwise procedural framework so that resources are maximized. This study promotes global collegially research by bridging the boundaries of applications across disciplines, so that their advantages may be extrapolated. The construction of the suggested prototypes is explained in detail so that they may be adapted modified to quantify similar ecology-related uncertainties worldwide.

Slutsky’s Theorem has important applications in biostatistics. Several generalizations of Slutsky’s Theorem are presented. For instance, we study the limiting distribution of n n Y X / when 0 n X→ in distribution. Then the sequence of random varibles tends to an extended random variable

In the field of biostatistics it is often required to develop inferential tools dealing with the presence of nuisance parameters. The most adopted solution is to resort to pseudo-likelihood functions, having properties similar to the ones of a genuine likelihood. A possible choice is to use the integrated likelihood where the nuisance parameters are eliminated by means of integration with respect to a weight function. The selection of the weight function turns out to be crucial since it could have a strong impact on the properties of the resulting integrated likelihood. After having introduced the concept of pseudo-likelihood, the definition and the properties of the integrated likelihood, the focus will be on reviewing the main alternatives to choose the weight function according to different inference paradigms.

Let Pi (N) be the number of primes less than or equal to N, for any real number N, the New Prime Number Theorem can be expressed by the formulas as follows: Pi (N) = R (N) + K × ( Li (N) - R (N) ), 1 ≥ K ≥ -1 P (K) = 1.99471140200716338969973029967…×EXP (-12.5×K×K) Where the R (N) is the Riemann Prime Counting Function, the Li (N) is the logarithmic integral function; the P (K) is the Normal Distribution N (μ=0, σ=0.2).

Let Pi(N) be the number of primes less than or equal to N, Pi (2≤Pi≤Pm) be taken over the primes less than or equal to √N, then exists the formula as follows: Pi(N) = INT { N×∏ (1-1/Pi) } + m-1 = Li (N) - 0.5×Li (N^0.5) ± 0.5×Li (N^0.5) Li (N^0.5) ≥ Li (N) - Pi (N) ≥ 0 : ( The Riemann Hypothesis is proved ) Pi (N) = R (N) + K × (Li (N) - R (N) ), 1 ≥ K ≥ -1 . P (K) = 1.99471140200716338969973029967…×EXP (-12.5×K×K) Where the INT { } expresses the taking integer operation of formula spread out type in { }, the Li (N) is the logarithmic integral function, the R (N) is the Riemann Prime Counting Function, the P (K) is the Normal Distribution N (μ=0,σ=0.2).

The Exponential Transmuted Exponential distribution (ETE) appeared in [1] and in this paper we present a new generalization of the ETE distribution based on a Box-Cox transformation of the form ( ) 2 X 1 Z λ−µλ+ −σλµ = Where 2 0 , , R, 0,andZ is ETE distributed. We also show the new distribution is a good fit to some real-life data, indicating practical significance.