Purpose The purpose of this paper is to investigate the impact of family background, big five personality traits and self-efficacy on entrepreneurial intentions (EIs) of business students in private universities in Pakistan. Design/methodology/approach Data were collected with the help of structured questionnaires, 500 questionnaires were distributed among the students and 306 useable questionnaires were received and analyzed. Structural equation modeling was used to investigate the relationship among the study variables. SmartPLS was utilized to run the analysis. Findings The findings revealed a strong relationship between the exogenous and endogenous variables. The variance accounted by the independent variables was 74.3 percent in the EIs of the students. Family background was found to have a positive impact on the EIs of students. The findings also showed a positive relationship between self-efficacy and EIs. Consciousness, extroversion and openness to experience are positively linked with EIs while neuroticism and agreeableness did not show any relationship. Originality/value The study’s findings attract the attention of the academicians to take note of the factors examined while training the students the art of entrepreneurship. This is because this study has revealed that if these factors are not present the intention of the students to start a business venture may prove to be weak. Entrepreneurial activities are one of the biggest ways to reduce unemployment, thus, it is suggested that academicians should develop psychological plans and training to motivate the students to convert their intentions into actions.

Aims Many authors have reported a shorter treatment time when using trifocal bone transport (TFT) rather than bifocal bone transport (BFT) in the management of long segmental tibial bone defects. However, the difference in the incidence of additional procedures, the true complications, and the final results have not been investigated. Patients and Methods A total of 86 consecutive patients with a long tibial bone defect (≥ 8 cm), who were treated between January 2008 and January 2015, were retrospectively reviewed. A total of 45 were treated by BFT and 41 by TFT. The median age of the 45 patients in the BFT group was 43 years (interquartile range (IQR) 23 to 54). Results The size of the bone defect was significantly longer (p = 0.005), the number of previous operations was significantly higher (p < 0.001), the operating time was significantly longer (p < 0.001), and the bone transport distance was significantly increased (p = 0.017) in the TFT group. However, the external fixation time (p < 0.001), the healing index (p < 0.001), the number of additional procedures (p = 0.013), and the number of true complications (p < 0.001) were significantly reduced in this group. Both groups achieved highly satisfactory bone and functional results. Conclusion TFT can significantly reduce the treatment time, the number of additional surgical procedures, and true complications compared with BFT in the treatment of long segmental tibial bone defects.

Latest report of NASA claims that planet average surface temperature has risen about 1.62 F since the late 19th century because of increased human-made emissions into atmosphere. Due to recent COVID-19 pandemic, this planet is getting relief from pollution, as Wright stated that the sudden fall in pollution is observed in India, a country having world's 30 most polluted cities, similarly Gohd stated that satellite track emissions drop over china during this coronavirus outbreak. These facts showing, human is responsible to increase the pollution in this planet, therefore current study aims to test a model which can help in changing consumer behavior into consumer green behavior. A significant trend has been observed towards the recognition of the strategic importance of protecting the environment by enforcing laws. These kind of efforts are in favor of reducing consumption of plastic bags, and the need for practical and rapid measures to reduce the amount of plastic waste has increased, especially nylon bags, has increased. However, plastic bags are still the most common and used by many individuals, who prefer them to others to put their needs and belongings and their various purchases, or to save their food and drink because of its ease. Data were collected from shoppers at Cyberjaya (Malaysia) and Bangkok (Thailand). Findings indicate that knowledge and attitude have a significant and positive impact on consumer green behavior; the study also identified that the ban on plastic bags also has a significant and positive influence on consumer green behavior. This more considerable influence shows the need for such governmental interventions to moderate consumers' environmentally unfriendly behavior, and we cannot merely rely on consumers' acknowledgement of understanding the danger.

Ilizarov ring fixation is a viable option for infected non-union of the tibia. Adequate assessment of bone union is crucial before removal of fixator to prevent re-fracture.

MapReduce has become a major computing model for data intensive applications. Hadoop, an open source implementation of MapReduce, has been adopted by an increasingly growing user community. Cloud computing service providers such as Amazon EC2 Cloud offer the opportunities for Hadoop users to lease a certain amount of resources and pay for their use. However, a key challenge is that cloud service providers do not have a resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user's responsibility to estimate the required amount of resources for running a job in the cloud. This paper presents a Hadoop job performance model that accurately estimates job completion time and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model builds on historical job execution records and employs Locally Weighted Linear Regression (LWLR) technique to estimate the execution time of a job. Furthermore, it employs Lagrange Multipliers technique for resource provisioning to satisfy jobs with deadline requirements. The proposed model is initially evaluated on an in-house Hadoop cluster and subsequently evaluated in the Amazon EC2 Cloud. Experimental results show that the accuracy of the proposed model in job execution estimation is in the range of 94.97% and 95.51%, and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model.