Prediction Of Different Parameters Research Articles

Functional annotation of uncharacterized proteins from Fusobacterium nucleatum: identification of virulence factors.

Fusobacterium nucleatum is a gram-negative bacteria associated with diverse infections like appendicitis and colorectal cancer. It mainly attacks the epithelial cells in the oral cavity and throat of the infected individual. It has a single circular genome of 2.7 Mb. Many proteins in F. nucleatum genome are listed as "Uncharacterized." Annotation of these proteins is crucial for obtaining new facts about the pathogen and deciphering the gene regulation, functions, and pathways along with discovery of novel target proteins. In the light of new genomic information, an armoury of bioinformatic tools were used for predicting the physicochemical parameters, domain and motif search, pattern search, and localization of the uncharacterized proteins. The programs such as receiver operating characteristics determine the efficacy of the databases that have been employed for prediction of different parameters at 83.6%. Functions were successfully assigned to 46 uncharacterized proteins which included enzymes, transporter proteins, membrane proteins, binding proteins, etc. Apart from the function prediction, the proteins were also subjected to string analysis to reveal the interacting partners. The annotated proteins were also put through homology-based structure prediction and modeling using Swiss PDB and Phyre2 servers. Two probable virulent factors were also identified which could be investigated further for potential drug-related studies. The assigning of functions to uncharacterized proteins has shown that some of these proteins are important for cell survival inside the host and can act as effective drug targets.

Enhancing the Security of Cloud Computing via Unscented Kalman Filter; with Statistical Analysis About Power Consumption

To migrate to cloud computing or other new generation of computational and communicational network we should making some laws about limitations on such networks. We should clarify clearly the level of accessions to resources and/or the ability to sending or receiving files through such network. We can avoid unpredicted and undesired works by using of law makings. Furthermore, besides of law making we can use evolutionary and/or intelligence techniques for estimation and prediction about parameters that deal with controlling of our network – for example in this paper we introduce one type of such intelligence algorithms which named as unscented Kalman filter. By giving some raw inputs about treatments and resources of such networks we’re able to estimate and predict about lots of different conditions of these networks. For example, we should have an estimation and prediction algorithm to be able to track and trace the hackers or crackers, if they’re permeating to our network. Or we should estimate the amount of users that will use particular software or application on a specific hour through such network. Also, if we study users treatment on a specific network, like cloud computing, we’re able to estimate and predict their keen and eagerness about new released software or application; because if we know how people treat and face and react with new emerging software we’re able to construct and program our applications more precisely. In this paper, at first we tell about cloud computing – we define it completely and discuss about all aspects of this new generation of internet – also in the first section we discuss about efficiency of cloud computing in saving of energy and tell differences between the amount of energy that cloud resources use and the amount of energy that other computerized frames were use. We proof our suggestions and ideas by the means of analytical and mathematical analysis; after that when we understand about all of its concepts, a brief review about Microsoft decisions on cloud computing were present, we review new systems and software’s of Microsoft that equipped with cloud computing and study the advantages and disadvantages of these new services which are equipped with cloud computing. In the next section by the knowing of whole concepts and key features of this network and also by knowing the critical uses of this network on different industries we introduce fixedinterval smoothers; which can be used for estimation and prediction of different parameters in such network. For example, we can use this for estimation and prediction about permeating of hackers or for predicting the amount of users that will use special part of our network in a particular period of time or by tracking and tracing the packets or even users. By performing such activities we’re able to eliminate malicious and spyware treatments from the beginning points of our network. Also by the means of mathematical analysis in this paper, we demonstrate all of our suggestions and according to these proofs we conclude about workability and liability of such network.

Spread Mapping of Covid-19 in Morocco Using Geospatial Approach

Most of the people infected in Morocco are triggered by the outbreak of COVID-19. The number of affected cases is currently rising day by day. As of July 16th, 2020, In Morocco, 15,165 cases were tested positive for COVID-19, including 239 deaths and 11,417 patients cured the highest number of Corona virus cases reported as Ministry of Health Department in Morocco. The COVID-19 virus threatens the health, economy, development and social life of individuals. The city needs to be conscious of the fight against this epidemic. GIS technology has played an important role in many aspects, including geospatial perception, geostatistical simulation and spatial knowledge enabling decision-making, mitigation and prediction including COVID-19. GIS has evolved reasonably rapidly and has a full technical route for data processing, modeling and map creation. However, in the battle against the popular endemic, the key challenge is to find ways of controlling conventional technological methods and to increase the quality and accuracy of the knowledge provided for social management. As a consequence, IDW and computational approaches are used to forecast potential cases in the region. Prediction of different parameters existing confirmed events, death and recovery of COVID-19. See reports have been used to take proactive measures in order to penetrate regions. The suggested method of understanding is effective within a certain context and would be a valuable tool for both governments and health authorities.

Autotuning of configuration for program execution in GPUs

SummaryGraphics Processing Units (GPUs) are used as accelerators for improving performance while executing highly data parallel applications. The GPUs are characterized by a number of Streaming Multiprocessors (SM) and a large number of cores within each SM. In addition to this, a hierarchy of memories with different latencies and sizes is present in the GPUs. The program execution in GPUs is thus dependent on a number of parameter values, both at compile time and runtime. To obtain the optimal performance with these GPU resources, a large parameter space is to be explored, and this leads to a number of unproductive program executions. To alleviate this difficulty, machine learning–based autotuning systems are proposed to predict the right configuration using a limited set of compile‐time parameters. In this paper, we propose a two‐stage machine learning–based autotuning framework using an expanded set of attributes. The important parameters such as block size, occupancy, eligible warps, and execution time are predicted. The mean relative error in prediction of different parameters ranges from of 16% to 6.5%. Dimensionality reduction for the features set reduces the features by up to 50% with further increase in prediction accuracy.

Investigation of the performance and accuracy of multivariate timeseries models in predicting EC and TDS values of the rivers of Urmia Lake Basin

Considering the complexity of hydrological processes, it seems that multivariate methods may enhance the accuracy of time series models and the results obtained from them by taking more influential factors into account. Indeed, the results of multivariate models can improve the results of description, modeling, and prediction of different parameters by involving other influential factors. In this study, univariate models (ARMA) and auto-correlated multivariate models with the simultaneous autoregressive moving average model (CARMA) were evaluated for modeling Electrical Conductivity and Total Dissolved Solid parameters of the western stations of Urmia Lake Basin. To use the CARMA models, annual flow rate time series, EC, TDS, SAR, and pH values measured across seventeen hydrometric stations between 1992 and 2013 were used. In the studied statistical period, the river flow in the west of Urmia Lake Basin decreased and experienced an incremental increase compared to the EC and TDS values in river flow. By applying influential parameters in CARMA models, the mean error value of the model in training and experimental stages reduces by 32% and 44% for EC values and 34% and 36% for TDS values, respectively.

Neural network modeling as an efficient approach to predict the density of ionic liquids/ethanol binary systems

Ionic liquids (ILs) especially their mixtures are of high interest within the different scientific societies due to their amazing properties. In this regard, a number of attempts have been made to measure, correlate, estimate and calculate the properties of ILs in the neat or mixture forms. Among the different possible predictive methods, artificial neural networks (ANNs) are widely used because of their unique and amazing capabilities for prediction of different parameters. With respect to this paper, a feed-forward ANN model is proposed to model the densities of different binary mixtures of ILs/ethanol. The proposed network is trained and tested with 1078 binary data points gathered by mining into the different published literatures. The data gathered from previously published literatures are separated into two different subsets namely training and testing. The statistical error analysis has shown that the proposed neural network correlated the binary densities with the overall mean absolute percentage error (MAPE), average relative deviation percentage error (ARD%), minimum relative deviation percent (RDmin%), maximum relative deviation Percent (RDmax%) and correlation coefficient ([Formula: see text] of 1.5%, [Formula: see text]0.1%, [Formula: see text]13.0%, 15.0% and 0.9712, respectively.

Application of Bayesian Inference Technique for the reconstruction of an isothermal hot spot inside a circular disc from peripheral temperature measurement – A critical assessment

In this communication an internal hot spot in a two dimensional circular domain has been reconstructed using Bayesian Inference Technique. A semi-analytical forward model based on boundary collocation method has been used for the determination of temperature distribution. The posterior mean and the maximum a posteriori estimates have been computed using the Markov Chain Monte Carlo sampling technique. Based on the problem definition the reconstruction of the hot spot requires the estimation of the location, size and boundary temperature of the spot. Estimation of each of these parameters has been done individually (single parameter estimation) as well as taking different combinations of them (multiple parameter estimation). Synthetic measurement data with and without uncertainty have been used first to critically examine the scheme of reconstruction and to judge the goodness of the scheme of estimation for each of the unknowns. An experimental scheme has also been devised to generate data for reconstruction. Estimation using data from both of these sources reveal that the uncertainty involved in the prediction of different parameters vary widely. It has been observed that the uncertainty involved in the prediction of eccentricity is much more compared to that for the prediction of the temperature or radius of the hot spot. The reason for this has been pinpointed through the sensitivity analysis. The sensitivity analysis further reveals that simultaneous estimation of all the attributes of the internal hotspot is difficult to make in the present scenario. Finally, the limitation of the present scheme has been discussed.