Artificial Bee Colony (ABC) is one of the recently introduced optimization methods based on intelligent behavior of honey bees. In this work, we propose an Adaptive Multi-Objective Artificial Bee Colony (A-MOABC) Optimizer which uses Pareto dominance notion and takes advantage of CROWDING DISTANCE and windowing mechanisms. The employed bees use an adaptive windowing mechanism to select their own leaders and alter their positions. Besides, onlookers update their positions using food sources presented by employed bees. Pareto dominance notion is used to show the quality of the food sources. Those employed or onlooker bees which find food sources with poor quality turn into scout bees in order to search other areas. The suggested method uses CROWDING DISTANCE technique in conjunction with the windowing mechanism in order to keep diversity in the external archive. The experimental results indicate that the proposed approach is not only thoroughly competitive compared to other algorithms considered in this work, but also finds the result with satisfactory precision.

DISTANCE-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density DISTANCE (GDD) approach was proposed to optimize local criteria in terms of DISTANCE and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and DISTANCE properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The DISTANCE metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different DISTANCE measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

Periodonlal diseases are considered as one of the most prevalent problems among oral diseases. Conflicting hypothesis have been presented on the role of CROWDING on periodontal diseases and plaque accumulation. This study was. Therefore designed to evaluate the prevalence of periodontal diseases associated , with CROWDING. A total of 360 patients, age ranged 15-19 years were evaluated in Tehran province in a randomized multi-stage sampling manner. Those cases whose the DISTANCE between right and left cupids was smaller than the sum of the sizes of four anterior incisors were considered as "Cases", while "Controls" were those with no such CROWDING. Periodonlal status of the cases was evaluated using bleeding on gentle probing, calculus. And pockets depth measurement in the teeth 46, 31, 36, 26, 11, 16. Plaque accumulation was also compared in both groups using Ramfjord Index without considering proximal surfaces in the teeth 45, 44, 37, 36, 25, 24. 21. 17, 16.Results showed no relationship between the CROWDING and periodontal disease whereas pockets with the depth of more than 3.5 mm were found in anterior teeth in about 11.2% of the case group and 3.8% of the control group. Anterior teeth absence of plaque accumulation was seen in 15.5% control and 5.4% of the case group.

Aim: With regard to the importance of CROWDING and the factors effecting on CROWDING. this study was done to estimate the correlation between vertical facial growth and CROWDING of lower anterior teeth.Method and Materials: This descriptive study was done on one hundred orthodontic patients over 12 years old, with more than 2 mm CROWDING of lower anterior teeth. The patients were divided into four groups due to the amount of their CROWDING. jarabak index was measured by lateral cephalograms. The amount, intensity and direction of correlation was calculated by Pearson test in each group of CROWDING and totally in all groups.Results: In 100 patients (68 females, 32 males) of 15.6±3.4 years of age, there was no correlation between lower anterior CROWDING and jarabak index in the four groups.Conclusions: To obtain the better result it is recommended to choose the patients with vertical and horizontal grow patterns (high and low jarabak index) and then measure the amount of CROWDING and inclination of lower anterior teeth.

Economic theorists have different approaches on the function of governmental investment over economic activities of private section, so that according to the stand point of Keynesian, governmental investment intrigues private investment. On the contrary, classists argue that governmental investment causes private sectors to be driven out from economic activities. Ricardianeconomics is also believed the neutral and non- effective role of governmental investment on instigation of private activities. The present paper is carried out according to the annual data of the Central Bank through the years 1959-2012, using annual data via Johansen co integration test. The results have indicated that governmental investment has positive and meaningful significance based on Johansen common cumulative approach which has been normalized by private investment. It also presented that increasing in governmental investment would certainly lead to the increasing of private investment which represents a supplementary relation. The stated condition is in accordance with Keynesian’s theory. Government expenditure has also have negative as well as meaningful effect and the production of civil gross according to the expectation of a theorem has a positive symptom. The results have shown that inflation has insignificant impact on private investment which is not significant.

The main objective of this paper is to analyze empirically the effects of public investment on private investment, evaluating the existence of CROWDING-out/-in effects, in Iran for the 1970-2019 periods. I evaluate the macroeconomic effects of public and private investment through VECM analysis. From impulse response functions, we are able to assess the extent of CROWDING-in or CROWDING-out of both components of investment. I also compute the associated macroeconomic rates of return of public and private investment for Iran Economy. The results point to the existence of positive effects of public and private investment on output. On the other hand, results show the CROWDING-in effects of public investment on private investment. Based on these results, it is suggested that the government provide the conditions for private investment by creating infrastructure, helping to finance investment projects and creating a secure economic environment. I also compute the associated macroeconomic rates of return of public and private investment for Iran Economy. The results point to the existence of positive effects of public and private investment on output. On the other hand, results show the CROWDING-in effects of public investment on private investment.

Dear Editor: I read with great interest the article entitled “Application of Queuing Analytic Theory to Decrease Waiting Times in Emergency Department: Does it Make Sense?” in the autumn issue of Archives of Trauma Research (1). As an emergency physician in the United States, I can share the frustration of the authors that throughput of patients in the emergency department can often be hindered by numerous medical and logistical factors. In the United States, unlike most countries, emergency department care is not reserved to patients with acute severe conditions alone. Rather, under US law, emergency departments regularly provide care to patients who often do not have access to health care in other settings (2).