Status, Dispersal, and Breeding Biology of the Exotic Eurasian Collared-Dove (Streptopelia decaocto) in Arkansas

The exotic Eurasian Collared-Dove (Streptopelia decaocto) was first sighted in Arkansas at Harrison (Boone Co.) on 25 June 1989. Since this initial sighting the species has grown in numbers and is now present in 42 of 75 counties across the state. In the spring and summer of 2009 and 2010, 20 nests were observed in the urban areas of Fort Smith (Sebastian County). Fifteen of the 20 nests (75%) were located on human-made structures of which 13 (65%) were on an electrical substation and two (10%) were on utility poles. The remaining 5 nests (25%) were in trees. Mean nest height was 7.62 m (n = 20 nests), and the mean width of the nest site support was 40 cm (n = 6 nests). Thirteen of the 20 nests (65%) yielded fledgling(s). Three focal nests were chosen for intense observation. Nest building lasted 1 to 3 days (mean = 2 days); incubation period was 15 days; and fledging occurred 17-18 days after hatching (n = 3 nests). A total of 6 young fledged from these 3 nests

Placement Analysis using Data Mining

Education data mining is one of the growing fields of the present time as it grows many issues to improve system comes in the notice one of them is improvement of the placement. Placement is a very important issue for any educational organization. Every organization wants to improve its placement. Success of any educational institute is measured by the placed student of the organization. This paper actually deals with the application of neural network to the educational data to improve placement. In today’s world all organization faces one of the big problems is recruit right candidate for the suitable position, Organization ready to invest a huge amount to recruitment process but till now they failed to recruit. In this paper, we apply the data mining techniques for placement prediction. To predict the performance of a student’s is the great concern for the organization, as they seek knowledgeable, talented and qualified professionals to need to fill up their positions. According to the survey, the corporate companies spend a sum of 1800crores for choosing candidates to fill up their vacancies. The Majority of the companies recruiting the candidates via on-campus recruitment and to fill up them positions. Our method is very useful for corporate companies, consultancies. This method is the best way to get the right candidate at the right time in the corporate world. The industry gets the best talent candidates from different institutes/universities, and the students also get a chance to kick start their career with some of the best organization. But the students facing some difficulties in getting placements. To overcome the problem we apply the Improved Decision Tree classification algorithms on these data, we have predicted which students placed in Recruitment Drives. Corporate companies need only knowledgeable and skilled persons for the vacant position. To find that particularly skilled person there's a question that how can the companies identify them. In order to overcome this problem in this paper, we provide a complete solution to the recruitment process. Actual challenges appear when they will develop real-world software. Training develops confidence in whatever School, Colleges, Universities will train. But all corporate expect skilled, confidence with active persons. We are giving to find the right candidate for the right job in this development. After recruiting employees corporate feel much confident about their development. So as much as possible, clear them confusions and get new ideas about their project training and become confident about their work

3-({[(1-Phenyl­eth­yl)sulfan­yl]methane­thio­yl}sulfan­yl)propanoic acid

In the title compound, C12H14O2S3, a chain transfer agent (CTA) used in polymerization, the dihedral angle between the aromatic ring and the CS3 grouping is 84.20 (10)°. In the crystal, carb­oxy­lic acid inversion dimers linked by pairs of O—H⋯O hydrogen bonds generate R 2 2(8) loops

Determination of metformin and triclosan in sewage sludge using Liquid chromatography-mass spectrometry (LC-MS)

Pharmaceuticals and personal care products (PPCPs) are generally neither totally removed by sewage treatment nor completely destroyed in the environment. Metformin (MET) and triclosan (TRI) are two compounds in PPCPs that have the potential to be environmental pollutants. This research aimed to determine MET and TRI in sewage sludge using a liquid chromatograph-mass spectrometer (LCMS-8040) and a sewage sludge extraction method. The Milli-Q water and sewage sludge were also tested at three different MET and TRI concentrations (0.01, 0.02, and 0.03 mg L-1). As a result, the corresponding recoveries of MET and TRI in both matrixes ranged from 85.93 to 116.10 per cent and 90.50 to 116.30 per cent (n = 7, RSD < 10%). Then, the limit of detection (LOD) and the limit of quantification (LOQ) for MET and TRI were found to be 0.005 and 0.01 mg L-1. The amounts of MET and TRI in the sewage sludge samples from the Ukkadam sewage treatment plant (USTP), Coimbatore, ranged from BDL to 0.0587 mg L-1 and 0.0719 to 0.1851 mg L-1, respectively. Consequently, the amounts of MET and TRI in the sewage sludge samples from the Tamil Nadu Agricultural University sewage treatment plant (TSTP), Coimbatore, ranged from BDL to 0.0227 mg L-1 and 0.0393 to 0.1296 mg L-1, respectively. This exclusive use of the highly sensitive LCMS-8040 consumes less time than other analytical methods for measuring the amount of MET and TRI in sewage sludge by overcoming the risk of chemical degradation