Assessment of Calendula officinal growing in Iraq

Evaluation of Iraqi medicinal plants is very crucial to help people avoid the use of herbs without prior knowledge which results in many side effects and sometimes even leads to death. The plant constituents vary according to season, weather and type of soil, therefore it is necessary to evaluate the chemical constituents and determine the time of collection of medicinal plants. In this research evaluation of the medicinal plant Calendula officinal grown in Iraq was performed by measuring the quantity of hyperoside found in the plant together with macroscopical and microscopical evaluation of the plant.

Towards a Unified and Efficient Command Selection Mechanism for Touch-Based Devices Using Soft Keyboard Hotkeys

We advocate for the usage of hotkeys on touch-based devices by capitalising on soft keyboards through four studies. First, we evaluated visual designs and recommended icons with command names for novices while letters with command names for experts. Second, we investigated the discoverability by asking crowdworkers to use our prototype, with some tasks only doable upon successfully discovering the technique. Discovery rates were high regardless of conditions that vary the familiarity and saliency of modifier keys. However, familiarity with desktop hotkeys boosted discoverability. Our third study focused on how prior knowledge of hotkeys could be leveraged and resulted in a 5% selection time improvement and identified the role of spatial memory in retention. Finally, we compared our soft keyboard layout with a grid layout similar to FastTap. The latter offered a 12–16% gain on selection speed, but at a high cost in terms of screen estate and low spatial stability.

Estimation of Parameters of PTRC SRGM using Non-informative Priors

Reliability is an essentially important characteristic of software. The reliability of software has been assessed by considering Poisson Type occurrence of software failures and the failure intensity of one parameter say (η_1 ) Rayleigh class. Here, it is assumed that the software contains fixed number of inherent faults say (η_0 ). The scale parameter of Rayleigh density (η_1 ) and fixed number of inherent faults contained in software are the parameters of interest. The failure intensity and mean failure function of this Poisson Type Rayleigh Class (PTRC) Software Reliability Growth Model (SRGM) have been studied. The estimates of above parameters can be obtained by using maximum likelihood method. Bayesian technique has been used to about estimates of η_0 and η_1 if prior knowledge about these parameters is available. The prior knowledge about these parameters is considered in the form of non- informative priors for both the parameters. The proposed Bayes estimators are compared with their corresponding maximum likelihood estimators on the basis of risk efficiencies under squared error loss. The Monte Carlo simulation technique is used for calculating risk efficiencies. It is seen that both the proposed Bayes estimators can be preferred over corresponding MLEs for the proper choice of the values of execution time.

A CNN-ABiGRU method for Gearbox Fault Diagnosis

As a key part of modern industrial machinery, there has been a lot of fault diagnosis methods for gearbox. However, traditional fault diagnosis methods suffer from dependence on prior knowledge. This paper proposed an end-to-end method based on convolutional neural network (CNN), Bidirectional gated recurrent unit (BiGRU), and Attention Mechanism. Among them, the application of BiGRU not only made perfect use of the time sequence of signal, but also saved computing resources more than the same type of networks because of the low amount of calculation. In order to verify the effectiveness and generalization performance of the proposed method, experiments are carried out on two datasets, and the accuracy is calculated by the ten-fold crossvalidation. Compared with the existing fault diagnosis methods, the experimental results show that the proposed model has higher accuracy.

Pengaruh Model Eliciting Activities (MEA’s) terhadap Higher Order Thinking Skills (HOTS) dan Self Esteem Siswa Sekolah Menengah Pertama

This research aims to know the effect of Model Eliciting Activities (MEAs) and Conventional Learning Models to Higher Order Thinking Skills (HOTS) in mathematics and students self-esteem.The research was conducted at SMPN 200 and SMPN 53 Jakarta in the academic year 2020/2021. Design of this research is a post-test only experiment and control group design. The method used in this research was a quasi experiment. The instruments consist of the Higher Order Thinking Skills test, prior knowledge of mathematics test, and a questionnaire of mathematics self-esteem. The instruments have been checked in terms of validity and reliability. Two-way Anova was used for data analysis. The results show that: (1) Higher Order Thinking Skills of students who are given MEA’s is higher than the students who are given conventional Learning Models. (2) There is an enhancement of Higher Order Thinking Skills of students effected by prior knowledge of mathematics and Mathematics Learning Models. (3) Higher Order Thinking Skills of students with high KAM who are given MEA is higher than the students who are given Conventional Learning Model. (4) There is no significant difference in terms of Higher Order Thinking Skills between students with low KAM given MEA treatment and students given Conventional Learning Model treatment. (5) Mathematics Self Esteem of students given MEA treatment is higher than those given Conventional Learning Model treatment.

A Forward-Backward-Forward Algorithm for Solving Quasimonotone Variational Inequalities

In this paper, we continue to investigate the convergence analysis of Tseng-type forward-backward-forward algorithms for solving quasimonotone variational inequalities in Hilbert spaces. We use a self-adaptive technique to update the step sizes without prior knowledge of the Lipschitz constant of quasimonotone operators. Furthermore, we weaken the sequential weak continuity of quasimonotone operators to a weaker condition. Under some mild assumptions, we prove that Tseng-type forward-backward-forward algorithm converges weakly to a solution of quasimonotone variational inequalities.