摘要：Using the supercavitation phenomenon is necessary to reach high velocities underwater. Supercavitation can be achieved in two ways: natural and artificial. In this article, the simulation of flows around a torpedo was studied naturally and artificially. The validity of simulation using theoretical and practical data in the natural and artificial phases was evaluated. Results showed that the simulations were consistent with the laboratory results. The results in different injection coefficient rates, injection angles, and cavitation numbers were studied. The obtained results showed the importance of cavitation number, injection rate coefficient, and injection angle in cavity shape. At the final level, determining the performance conditions using the Design of Experiment (DOE) method was emphasized, and the performance of cavitation number, injection rate coefficient, and injection angle in drag and lift coefficient was studied. The increase in injection angle in the low injection rate coefficient resulted in a diminished drag coefficient and that in the high injection rate coefficient resulted in an enhanced drag coefficient.

摘要：A storage system is the core of a computer,and plays an important role in the sustainable development of emerging strategic industries,such as artificial intelligence,big data,cloud computing,and the Internet of *** stack access is a major factor restricting the performance of data-intensive systems because of the increasing performance of processors and network devices.

摘要：This paper motivated and inspired by an interdisciplinary critical educational issue adopted for a research work approach. It concerned with application of realistic artificial Neural Networks (ANNs) models integrating reading brain function with multi-sensory cognitive learning theory. Specifically, these models adopted to improve tutoring quality (academic achievement) while teaching children “how to read?” considering the analysis and evaluation of phonics methodology. Herein, quantitative analysis and evaluation of this issue performed by considering two computer aided learning (CAL) packages concerned with a specific selected mathematical topic namely: long division process. Via realistic modeling of packages using (ANNs) based upon associative memory learning paradigm. In more details, at educational field practice; both CAL packages have been applied for teaching children algorithmic steps performing long division processes. Moreover, learning performance evaluation of presented packages considers children outcomes’ achievement after tutoring for suggested Mathematical Topic either with or without associated tutor’s voice. Interestingly, statistical analysis of obtained educational case study results at children classrooms (for both applied packages) versus classical tutoring proved to be in well agreement with obtained after ANNs computer simulation results.

摘要：Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a widerange of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, wereview the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. Wefirst briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis fordata-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are alsopresented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factorydevelopment using examples from recent studies, including the prediction of protein function, improvement of metabolicmodels, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway *** the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methodsshould be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains forindustrial biomanufacturing.

摘要：This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10-7?for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10-4?using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%;and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%;and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column.

摘要：Iranian Plateau between the Lesser Caucasus-Alborz Mountains on the north and Zagros-Makran Ranges on the south has several inter-mountainous depressions which were filled by Quaternary deposits. Geologic evidence implied that, the last marine conditions in some depressions such as the Dasht-e Kavir, Dasht-e Lut and Jazmourian basins, had been changed to land conditions in middle Miocene. Based on shape and elevation of the Dasht-e Kavir, Dasht-e Lut and Jazmourian plains related to sea level and geomorphology of Iranian plateau, three semi-connective artificial lakes can be constructed upon the mentioned plains by consideration of many geologic and geotechnical parameters. These artificial lakes can feed by pumping of water from Oman Sea and form a triple artificial bay which they must be connected together by two gated straits. Therefore, a possible morphotectonic idea with many advantages has suggested that it can be present as an international geotechnologic design. This design has the important environmental impacts which can be changed desert to lake sedimentary basins.

摘要：Weani ng from mechanical ventilation in the in tensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complications that increases length of stay and costs as well as contributes to morbidity and even mortality and long-term quality of life. The need to reduce MV is both clinical and economical. artificial intelligence or machine learning (ML) methods are promising opportunities to positively influence patient outcomes. ML methods have been proposed to enhance clinical decisions processes by using the large amount of digital information generated in the ICU setting. There is a particular interest in empirical methods (such as ML) to improve management of "difficult-to-wean" patients, due to the associated costs and adverse events associated with this population. A systematic literature search was performed using the OVID, IEEEXplore, PubMed, and Web of Science databases. All publications that included (1) the application of ML to weaning from MV in the ICU and (2) a clinical outcome measurement were reviewed. A checklist to assess the study quality of medical ML publications was modified to suit the critical assessment of ML in MV weaning literature. The systematic search identified nine studies that used ML for weaning management from MV in critical care. The weaning management application areas included (1) prediction of successful spontaneous breathing trials (SBTs),(2) prediction of successful extubation,(3) prediction of arterial blood gases, and (4) ventilator setting and oxygenation-adjustment advisory systems. Seven of the nine studies scored seven out of eight on the quality index. The remaining two of the nine studies scored one out of eight on the quality index. This scoring may, in part, be explained by the publications' focus on technical novelty, and therefore focusing on issues most important to a technical audience, instead of issues most impor

摘要：*** Given the emergence of intelligent manufacturing capabilities,we may wonder whether design for manufacturing(DFM)practices should be reconceptualized to take advantage of these ***,if so,what should design for intelligent manufacturing(DFIM)be?The integration of cloud computing,data analytics,artificial intelligence(AI),and the Internet of Things(IoT)with advanced manufacturing technologies has enabled the emergence of what has been called new-generation intelligent manufacturing[1].Such new-generation intelligent manufacturing capabilities will enable transformational new products and services with unprecedented levels of quality,responsiveness,and efficiency.

摘要：三维交通仿真是对可视化交通管理系统进行分析和评价的一个有效方法,也是智能交通系统的一个重要研究方向.本文使用artificial Engines这种托管3D引擎技术,通过由道路数据自动生成大规模三维路网场景,以及车辆运动算法,建立起一种可视化、实时高效的三维交通管理系统.

摘要：A major advancement has recently occurred in the ability to predict protein secondary structure from sequence using artificialneural networks. This new accessibility to high-quality predicted structures provides a big opportunity for the protein designcommunity. It is particularly welcome for membrane protein design, where the scarcity of solved structures has been a majorlimitation of the field for decades. Here, we review the work done to date on the membrane protein design and set outestablished and emerging tools that can be used to most effectively exploit this new access to structures.