Determination of Level of Knowledge on Modern Methods of Family Planning among Women of Reproductive Age (18 - 49 Years) at Mathare North Health Center in Nairobi County, Kenya

Introduction: Modern family planning methods (MFPM) prevent unwanted pregnancies, reduce fertility rate, and increase the interval between pregnancies. They prevent pregnancy by preventing fertilization or implantation of the fertilized ovum. MFPM include tubal ligation (TL), vasectomy, oral contraceptive pills, the intrauterine contraceptive device (IUCD), depot injections, sub-dermal implants, and male and female condoms. Objective: To determine the level of knowledge of modern family planning methods (MFPM) among women of reproductive age (18 - 49 years) at the Mathari North Health Center in Nairobi County, Kenya. Methods: The study conducted among women of reproductive age at Mathare North Health Center in Nairobi was a cross-sectional descriptive survey between March 2016 and November 2018. It provided both qualitative and quantitative data. The sample size comprised of 274 women of reproductive age, i.e. (18 - 49 years) attending antenatal and postnatal clinics at the facility. Those excluded were women below 18 years of age, as they could not give consent according to Kenyan Laws. The data were collected using an interviewer-administered structured questionnaire, which consisted of socio-demographic and characteristics, knowledge of modern family planning methods and distance from the facility. Likert scale was used to ensure that data was tabulated on daily basis and subjected to statistical manipulation using Statistical Package for Social Sciences (SPSS). Results: The four leading MFPM in use in order of acceptability were injectables, implants, intrauterine contraceptive device and pills in that order. 91% of respondents were aware or had heard about modern family planning methods. Level of education of mother and father were the two variables that influenced the uptake of MFPM with p-values of 0.0260 and 0.025, respectively. The study further found that knowledge of MFPM had a significant influence on their assimilation and utilization. All secondary variables considered in the research exhibit a substantial relationship concerning the use of MFPM. Conclusion: Communities around Mathari North Health Center need to be given information;education and counselling on MFPM to enable them make an informed decision and choice on their preferred method of family planning.

Book Review MODERN METHODS IN ANALYTICAL ACOUSTICS

This is a collection of lecture notes of five applied mathematicians and acousticians.The authors are all world-famous authorities in their fields,showing for senioracousticians in their lectures the applications and usefulness of various mathematicalmethods.It is not a book of mathematics,the emphesis is on its effectiveness to the ad-vanced study of dynamic mechanics problems as encountered in aeroacoustics andunderwater acoustics.And large number of references are cited for those who want to in-dulge further into the mathematical origins.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Decoupled Wiener state fuser for descriptor systems

By the modem time series analysis method, based on the autoregressive moving average （ARMA） innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness and correctness.

Discussion on the Reform of Secure Coding

Secure Coding is an indispensable part of the undergraduate training program for Information Security Major.As a basic course for undergraduates to carry out project engineering,its importance is self-evident.In the actual teaching activities,the program design courses have to be solved in terms of content update,practical ability improvement and scientific research project,so the curriculum reform is imperative.This paper analyses the main problems existing in the Secure Coding course,explores the solution,proposes teaching methods,and gives the evaluation method.The practice shows that the reform exploration can obtain good teaching results.

A new information fusion white noise deconvolution estimator

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.

INFORMATION FUSION STEADY-STATE WHITE NOISE DECONVOLUTION ESTIMATORS WITH TIME-DELAYED MEASUREMENTS AND COLORED MEASUREMENT NOISES

White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.

Research progress on non-drug treatment of diabetic kidney disease

Diabetic kidney disease is one of the most serious and common chronic complications of diabetes and one of the leading causes of death in diabetic patients.In the case of diabetic kidney disease,sustained proteinuria is irreversible until it develops into end-stage renal disease.Drug treatment of diabetic kidney disease is relatively limited.More and more evidences into the effectiveness and safety that related non-drug treatments not only have the characteristics of simple operation and high safety,but also can improve the clinical symptoms of patients with diabetic kidney disease,reduce laboratory indicators,and delay disease progression.This article summarizes the recent literature on non-drug treatment of diabetic kidney disease such as exercise therapy,acupuncture therapy,acupoint application,auricular acupoint pressing pill therapy,moxibustion therapy,in order to provide reference for clinical treatment.

Innovative Methods to Speed up the Modernization of Chinese Medicine——The Meeting Report of the Session on Methodology Research and Modernization of Chinese Medicine,International Conference and Exposition on Traditional Medicine 2009

As a kind of complex medical science,Chinese medicine（CM） has a long history of development and application and has demonstrated on evidence basis its efficacy in many diseases affecting multiple organ systems.In recent years,great progress in CM research has been achieved with the initiation of application of sustained and multifaceted use of modern organ systems. In recent years, great progress in CM research has been achieved with the initiation of application of sustained and multifaceted use of modern scientific methods. More and more innovative methods are widely used in CM modernization researches, and the application of new methods becomes the key to further develop CM modernization.

Distributed fusion white noise deconvolution estimators

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication and signal processing.By combining the Kalman filtering method with the modern time series analysis method,based on the autoregressive moving average(ARMA)innovation model,new distributed fusion white noise deconvolution estimators are presented by weighting local input white noise estimators for general multisensor systems with different local dynamic models and correlated noises.The new estimators can handle input white noise fused filtering,prediction and smoothing problems,and are applicable to systems with colored measurement noise.Their accuracy is higher than that of local white noise deconvolution estimators.To compute the optimal weights,the new formula for local estimation error cross-covariances is given.A Monte Carlo simulation for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performance.