On dynamical net-charge fluctuations within a hadron resonance gas approach

The dynamical net-charge fluctuations (${\nu}_{dyn}$) in different particle ratios $K/{\pi}$, $K/p$, and $p/{\pi}$ are calculated from the hadron resonance gas (HRG) model and compared with STAR central Au+Au collisions at $\sqrt{s_{NN}}=7.7-200~$GeV and NA49 central Pb+Pb collisions at $\sqrt{s_{NN}}=6.3-17.3~$GeV. The three charged-particle ratios ($K/{\pi}$, $K/p$, and $p/{\pi}$) are determined as total and average of opposite and average of same charges. We find an excellent agreement between the HRG calculations and the experimental measurements, especially from STAR beam energy scan (BES) program, while the strange particles in the NA49 experiment at lower Super Proton Synchrotron (SPS) energies are not reproduced by the HRG approach. We conclude that the utilized HRG version seems to take into consideration various types of correlations including strong interactions through the heavy resonances and their decays especially at BES energies.Comment: 8 pages, 1 figure, accepted for publication in Advances in High Energy Physic

Exploiting the knowledge engineering paradigms for designing smart learning systems

Knowledge engineering (KE) is a subarea of artificial intelligence (AI). Recently, KE paradigms have become more widespread within the fields of smart education and learning. Developing of Smart learning Systems (SLS) is very difficult from the technological perspective and a challenging task. In this paper, three KE paradigms, namely: case-based reasoning, data mining, and intelligent agents are discussed. This article demonstrates how SLS can take advantage of the innovative KE paradigms. Therefore, the paper addresses the pros of such smart computing approaches for the industry of SLS. Moreover, we concentrate our discussion on the challenges faced by knowledge engineers and software developers in developing and deploying efficient and robust SLS. Overall, this study introduces the reader the KE techniques, approaches and algorithms currently in use and the open research issues in designing the smart learning systems.Инженерия знаний (ИЗ) – это подобласть искусственного интеллекта (ИИ). В последнее время парадигмы ИЗ и умных вычислений получают все более широкое распространение в сфере умного образования и обучения. Разработка систем умного обучения (СУО) является очень трудной с технологической точки зрения и сложной задачей. В данной статье мы изучили три парадигмы ИЗ, а именно рассуждения на основе прецедентов, интеллектуальный анализ данных и интеллектуальные агенты. Наше исследование указывает на то, что такие парадигмы могут эффективно использоваться для СУОІнженерія знань (ІЗ) – це пiдобласть штучного інтелекту (ШІ). Останнім часом парадигми ШІ та розумних обчислень отримують все більш широке поширення в сферi розумної освіти i навчання. Розробка систем розумного навчання (СРН) є дуже важким з технологічної точки зору і складним завданням. У даній статті ми вивчили три парадигми ШІ, а саме міркування на основі прецедентів, інтелектуальний аналіз даних та інтелектуальні агенти. Наше дослідження вказує на те, що такі парадигми можуть ефективно використовуватися для СР

Dynamic Analysis Of A Novel Manpowered Transportation Vehicle With High Mechanical Efficiency

This paper evaluates the dynamics of a novel manpowered transportation vehicle. The vehicle has a novel mechanism that maximizes the mechanical input work and utilizes the weight of the rider for propulsion. The rider applies reciprocating stepping linear forces to drive chain and ratchet mechanism. The later transfer the reciprocating motion into a unidirectional rotational motion at the rear wheel to propel the vehicle. We analyzed the dynamics of the driving and transmission mechanism and derived the equations of motion, at first. Then, we evaluated the performance of the vehicle. Results show significant advantages of the novel driving mechanism

Coronavirus Classification using Deep Convolutional Neural Network, Models. and Chest ,X-ray images

The COVID-2019 virus, which was discovered for the first time in December 2019 in the city of Wuhan, China, went on to become a pandemic after rapidly spreading around the globe. As there are currently no reliable automated toolkits on the market, there has been an increase in the demand for supplementary diagnostic tools for COVID19 patients. It may be possible to improve the accuracy of the diagnosis of covid19 disease by making use of more recent developments in artificial intelligence (AI) approaches and radiological imaging. In this research, three different convolution neural networks were applied to raw chest x-rays before the histogram filter was used for the basic pre-processing. The goal was to automatically detect COVID-19. The results that we obtained using the three suggested models indicate that the ResNet50 model provides the greatest classification performance with 96% accuracy , while the InceptionV3 model only achieves 95% accuracy, and the Inception-ResNetV2 model only achieves 82% accuracy