The purpose of this paper is to study the properties of the
partial level density ( ) l g and the total level density g ( ),
numerically obtained as a l sum of ( ) l g up to 34 max l  , for
a Harmonic – Oscillator potential well. This method applied the
quantum – mechanical phase shift technique and concentrated
on the continuum region. Also a discussion of peculiarities of
quantal calculation for single particle level density of energy –
dependent potential

Dengke Naniura is a traditional food from Sumatera Utara, Indonesia, that is produced through fermenting process, and this food is believed to contain high probiotics. The objective of the current research is to determine the potential of LAB as a probiotic that has been obtained from Dengke Naniura. Dengke Naniura was traditionally prepared from Cyprinus carpio. Four LABs have been successfully isolated from Dengke Naniura, such as D7DA3, D7B3, D7DBF and D7DN3. Those four LAB isolates were identified as Lactobacillus sp. This result has been confirmed by the non-spore forming bacterium, non-motile, and Gram-positive. Also, it has been supported by biochemical test, for the example Voges Proskauer, catalase test, Methyl

The research discusses one of the most critical issues of corporate finance which is related to asset utilization efficiency. Researchers used internal growth rate as independent variable (Proxy of asset utilization efficiency) and sustainable growth rate-dependent variable (proxy of stockholders wealth). According to these two variables, researchers formulate major hypotheses (There is no significant effect of internal growth rate on sustainable growth rate), as well as two sub-hypotheses, examine the components of major variables. Sample of Iraqi industrial companies which listed in the Iraqi stock exchange selected to test and examine main hypotheses. Result of simple and multiple regressions explain there is a significant effect of i

The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

Recently, a great rise in the population and fast manufacturing processes were noticed. These processes release significant magnitudes of waste. These wastes occupied a notable ground region, generating big issues for the earth and the environment. To enhance the geotechnical properties of fine-grained soil, a sequence of research projects in the lab were conducted to analyze the impacts of adding sludge waste (SW). The tests were done on both natural and mixed soil with SW at various proportions (2%, 4%, 6%, 8%, and 10%) based on the dry mass of the soil used. The experiments conducted focused on consistency, compaction, and shear strength. With the addition of 10% of SW, the values of LL and PI decreased by 29.7% and 3

This study was conducted on the effect of the sedimentary source (the sediments coming from both the Iraqi-Iranian borderline and the Tigris river) on the optical and textural features, especially sphericity and roundness of feldspar minerals (potassium and plagioclase types) in soils of the southern part of the alluvial plain. Eight pedons were selected to represent the study area, five of them represented sediments coming from the borderline, which included pedons of (Badra, Taj Al-Din, Al-Shihabi, Jassan, and Galati), while two of them represent the sediments of the Tigris River (Essaouira, Al-Dabouni), the pedon of Ali Al-Gharbi was represented the mixing area of sediments of all the floods coming from the borderline and the sediments o

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo