Exploring the role of Entrepreneurial Education, Technology and Teachers’ Creativity in excelling Sustainable Business Competencies

Encouraging entrepreneurs and transforming entrepreneurship education is a need to a time, which can assist in excelling the economic development, wealth and employment generation. However development of Business Entrepreneurial Competencies remains crucial as it enables the entrepreneurs to excel in the highly competitive market. Moreover, the transformation of Entrepreneurial Education through vocational colleges is an under studied area. Hence the current study aims to explore the role of Entrepreneurial Education, Technology and Teachers’ Creativity in excelling Entrepreneurial Competencies through vocational colleges. Based on a thorough discussion and literature exploration, the current study propose the hypothesized relationships which were statistically evaluated through the application of Partial Least Square-Structural Equation Modeling on the data of 357 of potential future entrepreneurs. The results reported a significant association of the said predictors in excelling the Entrepreneurial Competencies. Based on this findings, several policy recommendations were proposed including re-adjustment and timely update of the teaching material whereas facilitators and instructors are also directed to be creative and innovative while integrating the use of technology efficiently

9,9-Dimethyl-12-(3-nitro­phen­yl)-7,8,9,10,11,12-hexa­hydro­benz[a]acridin-11-one

The title compound, C25H22N2O3, was synthesized by the reaction of 3-nitro­benzaldehyde, dimedone and 2-naphthyl­amine in ethanol. In the mol­ecular structure, the cyclo­hexenone ring adopts an envelope conformation, whereas the piperidine ring has a boat conformation. The crystal packing is stabilized by inter­molecular N—H⋯O hydrogen bonds

Worst-case delay control in multigroup overlay networks

This paper proposes a novel and simple adaptive control algorithm for the effective delay control and resource utilization of end host multicast (EMcast) when the traffic load becomes heavy in a multigroup network with real-time flows constrained by (sigma, rho) regulators. The control algorithm is implemented at the overlay networks and provides more regulations through a novel (sigma, rho, lambda) regulator at each group end host who suffers from heavy input traffic. To our knowledge, it is the first work to incorporate traffic regulators into the end host multicast to control heavy traffic output. Our further contributions include a theoretical analysis and a set of results. We prove the existence and calculate the value of the rate threshold rho* such that for a given set of K groups, when the average rate of traffic entering the group end hosts rho macr > rho*, the ratio of the worst-case multicast delay bound of the proposed (sigma, rho, lambda) regulator over the traditional (sigma, rho) regulator is O(1/Kn) for any integer n. We also prove the efficiency of the novel algorithm and regulator in decreasing worst-case delays by conducting computer simulations

Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field

Prior works on joint Information Extraction (IE) typically model instance (e.g., event triggers, entities, roles, relations) interactions by representation enhancement, type dependencies scoring, or global decoding. We find that the previous models generally consider binary type dependency scoring of a pair of instances, and leverage local search such as beam search to approximate global solutions. To better integrate cross-instance interactions, in this work, we introduce a joint IE framework (CRFIE) that formulates joint IE as a high-order Conditional Random Field. Specifically, we design binary factors and ternary factors to directly model interactions between not only a pair of instances but also triplets. Then, these factors are utilized to jointly predict labels of all instances. To address the intractability problem of exact high-order inference, we incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method, which achieves consistent learning and inference. The experimental results show that our approach achieves consistent improvements on three IE tasks compared with our baseline and prior work