Moments of inertia, nucleon axial-vector coupling, the {\bf 8}, {\bf 10}, 10ˉ\bar{\bf 10} and 273/2{\bf 27}_{3/2} mass spectrums and the higher SU(3)_f representation mass splittings in the Skyrme model

The broad importance of a recent experimental discovery of pentaquarks requires more theoretical insight into the structure of higher representation multiplets. The nucleon axial-vector coupling, moments of inertia, the {\bf 8}, {\bf 10}, $\bar{\bf 10}$, and ${\bf 27}_{3/2}$ absolute mass spectra and the higher SU(3)$_f$ representation mass splittings for the multiplets ${\bf 8}$, ${\bf 10}$, $\bar{\bf 10}$, ${\bf 27}$, ${\bf 35}$, $\bar{\bf 35}$, and $\bf 64$ are computed in the framework of the minimal ${\rm SU(3)_f}$ extended Skyrme model by using only one free parameter, i.e., the Skyrme charge $e$. The analysis presented in this paper represents simple and clear theoretical estimates, obtained without using any experimental results for higher ($\bar{\bf 10}$,...) multiplets. The obtained results are in good agreement with other chiral soliton model approaches that more extensively use experimental results as inputs.Comment: 22 pages, 12 figures, 9 tables, version accepted in JHE

Classification of lubricant oil adulteration level using case-based reasoning

The main purpose of this paper is to classify lubricant oil odor-profile using Case-based Reasoning (CBR) classifier. Electronic nose was used for the purpose of taking data readings for each lubricant oil smell sample. The data that have been collected will be normalized so that the data can be evaluated in a smaller scale to establish an odor-profile for each sample. Then, the odor-profiles were classified using Case based Reasoning (CBR) classifier. The classification performance resulting 100% successfully correct classification.Keywords: lubricant oil; odor-profile; electronic nose; case-based reasoning

SSM-iCrop2 : A simple model for diverse crop species over large areas

Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senescence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straight-forward. The resultant model (SSM-iCrop2) was parameterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.</p