Effect of lead and cadmium on germination and seedling growth of Leucaena leucocephala

A study was conducted to determine the effect of different concentrations of lead and cadmium on seed germination and seedling growth of Leucaena leucocephala. Seed were grown under laboratory conditions at 25, 50, 75 and 100 ppm of metal ions of lead and cadmium. Both lead and cadmium treatments showed toxic effectson various growth indices of L. leucocephala. Increasing the concentration of lead to 75 ppm, significantly (

Adaptation in Atriplex griffithii and Prosopis juliflora plants in response to cement dust pollution

In the present study, we attempted to determine the effects of cement dust on the adaptations of plants growing in polluted area and to compare it with a leeward site (control) of the cement factory that was unpolluted. The emphasis was also given to observe the effects of cement dust on the soil characteristics of the factory area. The introduction of cement dust from a cement factory produced negative effects on the morphological traits of both plant species (Atriplex griffithii and Prosopis juliflora) growing at the polluted as compared to unpolluted area. Low seedling height and plant circumference for A. griffithii andi were observed at the polluted site of the cement factory. A. griffithii showed significant reduction in leaf area growing at the polluted site as compared to control site. Similarly, a significant (p<0.05) reduction in leaf area was also recorded for P. juliflora at the polluted sites. The growth pattern of A. griffithii and P. juliflora looked more greener, better in plant height and healthier as observed at unpolluted sites. No significant difference in vegetative growth for both plant species for plant height and circumference was seen at the polluted sites of the factory. We believe that the underlying edaphic factor and genotypic ability of both species helped to some extent in adaptation to the extreme habitat conditions at the polluted sites. The significance differences in soil pH level and organic matter contents were recorded from polluted area as compared to control site. © JASE

Paying for Likes? Understanding Facebook like fraud using honeypots

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior

Effect of auto exhaust emission on the phenology of Cassia siamea and Peltophorum pterocarpum growing in different areas of Karachi

The response of plant species to different seasons and environmental pollutants is of important interest to researchers. Observations were made on Cassia siamea and Peltophorum pterocarpum plantsgrowing in the polluted and other less polluted areas of Karachi. The phenology of C. siamea and P.pterocarpum was significantly (

API based discrimination of ransomware and benign cryptographic programs

Ransomware is a widespread class of malware that encrypts files in a victim’s computer and extorts victims into paying a fee to regain access to their data. Previous research has proposed methods for ransomware detection using machine learning techniques. However, this research has not examined the precision of ransomware detection. While existing techniques show an overall high accuracy in detecting novel ransomware samples, previous research does not investigate the discrimination of novel ransomware from benign cryptographic programs. This is a critical, practical limitation of current research; machine learning based techniques would be limited in their practical benefit if they generated too many false positives (at best) or deleted/quarantined critical data (at worst). We examine the ability of machine learning techniques based on Application Programming Interface (API) profile features to discriminate novel ransomware from benign-cryptographic programs. This research provides a ransomware detection technique that provides improved detection accuracy and precision compared to other API profile based ransomware detection techniques while using significantly simpler features than previous dynamic ransomware detection research. © 2020, Springer Nature Switzerland AG