The research aims to know the availability of supra-cognitive thinking skills in the questions and activities of the computer book for the fifth grade preparatory scientific and literary branches in Iraq for the academic year 2018/2019, as the researcher has prepared a list of supra-cognitive thinking skills included two areas and (6) key skills and (27) A sub - skill, where by the questions and activities of the aforementioned authors were analyzed. The researcher followed the descriptive analytical approach "method of content analysis", and adopted the explicit and implicit unit of analysis, as was verified the validity and stability of the analysis, and the results showed unevenness and imbalance in the distribution of supra-cognitive thinking skills in the evaluation questions and activities contained in the book, where it focused on ( Concept Awareness Skill) significantly relative to other skills and omission of many skills (giving reasoning, strategic switching skill, context optimization skill, problem solving skill, re-planning skill, product modification skill, process manipulation skill, thinking organizing skill) Of relevance, then The researcher presented a number of recommendations and suggestions according to the research findings.
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This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being 0.66975075, 0.470
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The current study investigated the stability and the extraction efficiency of emulsion liquid membrane (ELM) for Abamectin pesticide removal from aqueous solution. The stability was investigated in terms of droplet emulsion size distribution and emulsion breakage percent. The proposed ELM included a mixture of corn oil and kerosene (1:1) as a diluent, Span 80 (sorbitan monooleate) as a surfactant and hydrochloric acid (HCl) as a stripping agent without utilizing a carrier agent. Parameters such as homogenizer speed, surfactant concentration, emulsification time and internal to organic volume ratio (I/O) were evaluated. Results show that the lower droplet size of 0.9 µm and higher stable emulsion in terms of breakage percent of 1.12 % were
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In the presence of deep submicron noise, providing reliable and energy‐efficient network on‐chip operation is becoming a challenging objective. In this study, the authors propose a hybrid automatic repeat request (HARQ)‐based coding scheme that simultaneously reduces the crosstalk induced bus delay and provides multi‐bit error protection while achieving high‐energy savings. This is achieved by calculating two‐dimensional parities and duplicating all the bits, which provide single error correction and six errors detection. The error correction reduces the performance degradation caused by retransmissions, which when combined with voltage swing reduction, due to its high error detection, high‐energy savings are achieved. The res
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