Integrated atomistic process and device simulation of decananometre MOSFETs

In this paper we present a methodology for the integrated atomistic process and device simulation of decananometre MOSFETs. The atomistic process simulations were carried out using the kinetic Monte Carlo process simulator DADOS, which is now integrated into the Synopsys 3D process and device simulation suite Taurus. The device simulations were performed using the Glasgow 3D statistical atomistic simulator, which incorporates density gradient quantum corrections. The overall methodology is illustrated in the atomistic process and device simulation of a well behaved 35 nm physical gate length MOSFET reported by Toshiba

Effect of Toluene and Dioctylphthalate on the Rebar Corrosion of Medium Carbon Steel in Seawater and Cassava Fluid

Chemical mitigation is regularly used as one of the principal prevention and control techniques in reinforcement corrosion. Hence this study presents the effect of toluene and dioctylphthalate on the rebar corrosion of medium carbon steel in seawater and cassava fluid with a view to determining inhibitive potentials of the different in-hibitors in the two media. Gravimetric and voltametric techniques were employed in this study and a total of forty-five corrosion coupons of different dimensions were produced. Forty coupons were used for gravimetry and the remaining five for corrosion potentials measurements. Eight of the samples were used as control; while other eight samples were admixed with dioctylphthalate and toluene in concrete cubes. It was later immersed in seawater and cassava fluid for a total duration of 32 days and the measurements were taken at interval of 4 days in order to determine the corrosion rates in mils per year (mmpy). Two controls and admixed samples were later immersed in seawater and cassava fluid, respectively, for durations of thirty-two days to determine the corrosion potentials using a voltmeter and a Copper-Copper Sulphate Electrode (Cu/CuSO4). The pH of each medium was also measured throughout the period of exposure. The results obtained showed that all the samples except the control samples, displayed some degree of inhibition. The inhibition levels for the admixed samples in seawater were on the higher side compared with those in cassava fluid. The inhibition efficiencies for different inhibitors followed different trends in different environment. The inhibition efficiencies for toluene in cassava fluid and seawater were 21.64% and 45.78% respectively. The study concluded that organic inhibitors were effective in inhibiting corrosion in cyanide and chloride contaminated concrete cubes

Linear Subspaces of Solutions Applied to Hirota Bilinear Equations

- Linear subspace of solution is applied to Boussinesq and Kadomtseve-Petviashvili (KP) equations using Hirota bilinear transformation. A sufficient and necessary condition for the existence of linear subspaces of exponential travelling wave solutions to Hirota bilinear equations is applied to show that multivariate polynomials whose zeros form a vector space can generate the desire Hirota bilinear equations with given linear subspaces of solutions and formulate such multivariate polynomials by using multivariate polynomials which have one and only one zero

Effect of Calcium Nitrate and Sodium Nitrite on the Rebar Corrosion of Medium Carbon Steel in Seawater and Cassava Fluid

Inhibitors are regularly used as one of the principal prevention and control techniques in reinforcement corrosion. Hence this study investigates the effect of calcium nitrate and sodium nitrite inhibitors on the rebar corrosion of medium carbon steel in seawater and cassava fluid with a view to determining inhibitive potentials of the different inhibitors in the two media. Gravimetric and voltametric techniques were employed in this study and a total of forty-five corrosion coupons of different dimensions were produced. Forty coupons were used for gravimetric analysis and the remaining five for corrosion potentials measurements. Eight of the samples were used as control; while other eight samples each were admixed with calcium nitrate and sodium nitrite in concrete cubes. It was later immersed in seawater and cassava fluid for a total duration of 32 days and the measurements were taken at the interval of 4 days in order to determine the corrosion rates in mils per year (mmpy). Two controls and admixed samples each were later immersed in seawater and cassava fluid, respectively, for durations of 32 days to determine the corrosion potentials using a voltmeter and a Copper-Copper Sulphate Electrode (Cu/CuSO4). The pH of each medium was measured throughout the period of exposure. The results obtained expressed that all the samples except the control samples, displayed some degree of inhibition. The inhibition levels for the admixed samples in seawater were higher compared with those in cassava fluid. Inhibition efficiencies for various inhibitors followed different trends in different environment. The inhibition efficiencies for calcium nitrate in cassava fluid and seawater were 26.81% and 64.85% respectively. The study concluded that inorganic inhibitors were effective in inhibiting corrosion in cyanide and chloride contaminated concrete cubes

Data in support of high rate of pregnancy related deaths in Maiduguri,Borno State,Northeast Nigeria

Pregnancy relateddeaths(PRD)arepublichealthconcerninmost developing countriesandNigeriainparticular.Despitetheefforts put inbytheconcernedauthorities,PRDremainsanintegralpart of maternalmortalityormaternaldeathsinNigeriaingeneraland Borno stateinparticular,asevidencedfromtherecordsobtained from UmaruShehuHospital,Maiduguri(astatehospitalinthe state capital.ThedatacontainsfrequencyofPRDinmonthsand grouped intogynaecology,ante-natalandpost-natal,andlabour obtained frommid-2009tomid-2017.Thestatisticalanalysisof the datamayrevealtheextentofincidenceorepidemiologyof PRD isinthestat

Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm

Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data. Findings – Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. Research limitations/implications – One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm. Practical implications – The authors conducted some of the experiments at individual residence which may affect environmental constraints. Originality/value – The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot

Variability Study of High Current Junctionless Silicon Nanowire Transistors

Silicon nanowires have numerous potential applications, including transistors, memories, photovoltaics, biosensors and qubits [1]. Fabricating a nanowire with characteristics required for a specific application, however, poses some challenges. For example, a major challenge is that as the transistors dimensions are reduced, it is difficult to maintain a low off-current (Ioff) whilst simultaneously maintaining a high on-current (Ion). This can be the result of quantum mechanical tunnelling, short channel effects or statistical variability [2]. A variety of new architectures, including ultra-thin silicon-on-insulator (SOI), double gate, FinFETs, tri-gate, junctionless and gate all-around (GAA) nanowire transistors, have therefore been developed to improve the electrostatic control of the conducting channel. This is essential since a low Ioff implies low static power dissipation and it will therefore improve power management in the multi-billion transistor circuits employed globally in microprocessors, sensors and memories

Quantile Approximation of the Chi–square Distribution using the Quantile Mechanics

In the field of probability and statistics, the quantile function and the quantile density function which is the derivative of the quantile function are one of the important ways of characterizing probability distributions and as well, can serve as a viable alternative to the probability mass function or probability density function. The quantile function (QF) and the cumulative distribution function (CDF) of the chi-square distribution do not have closed form representations except at degrees of freedom equals to two and as such researchers devise some methods for their approximations. One of the available methods is the quantile mechanics approach. The paper is focused on using the quantile mechanics approach to obtain the quantile density function and their corresponding quartiles or percentage points. The outcome of the method is second order nonlinear ordinary differential equation (ODE) which was solved using the traditional power series method. The quantile density function was transformed to obtain the respective percentage points (quartiles) which were represented on a table. The results compared favorably with known results at high quartiles. A very clear application of this method will help in modeling and simulation of physical processes

Machine Learning Heuristic for Solving Multi-Mode Resource-Constrained Project Scheduling Problems

The non-preemptive resource-constrained project scheduling problem is considered in this work. It is assumed that each activity has many ways of execution and the objective is to find a schedule that minimizes the project’s completion time (multi-mode RCPSP). Methods that are based on priority rules do not always give the needed very good results when used to solve multi-mode RCPSP. In solving large real-life problems quickly though, these methods are absolutely necessary. Hence good methods based on priority rules to get the primary results for metaheuristic algorithms are needed. This work presents a novel method based on priority rules to calculate the primary solutions for metaheuristic algorithms. It is a machine learning approach. This algorithm first of all uses Preprocessing to reduce the project data in order to speed up the process. It then employs a mode assignment procedure to obtain the mode of each job. After which the algorithm uses machine learning priority rule to get the precedence feasible activity list of the project’s tasks. Finally, it then uses the Serial Schedule Generation Scheme to get the total completion time of the project. In our experiments, we use our algorithm to solve some problems in the literature that was solved with metaheuristic procedures. We compared our results with the initial solutions the authors started with, and our results competes favorably with the initial solutions, making our algorithm a good entry point for metaheuristic procedures