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With the rapid growth of ICT (Information and Communication Technologies), the traditional educational system has been restructured into e-learning systems. E-learning adoption and acceptance is in rudimentary stages of development in Pakistan. Therefore, it is imperative to understand and evaluate the critical factors of technology adoption and acceptance considering the moderating effect of individual differences (gender and experience) based on Technology Acceptance Model. Quantitative analysis was conducted on 356 learners from the Virtual University of Pakistan. Structural Equation Modelling was used to test research models. The results illustrate that the perception of and intention to adopt technology are highly dependent on gender and experience in using e-learning systems. Computer anxiety and social influence are significant predictors of elearning adoption in female learners whereas social influence and enjoyment is significant predictors in less experienced learners than more experienced learners.

Knowledge Management (KM) has become a dynamic concept for inquiry in research. The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline, several KM frameworks have been designed to serve this purpose. This research aims to propose a Collaborative Knowledge Management (CKM) Framework that bridges gaps and overcomes weaknesses in existing frameworks. The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan. A software LCWU aKMS was developed which serves as a practical implementation of the concepts behind the proposed CKMF framework. LCWU aKMS served as an effective system for rice leaf disease detection and identification. It aimed to enhance CKM through knowledge sharing, lessons learned, feedback on problem resolutions, help from co-workers, collaboration, and helping communities. Data were collected from 300 rice crop farmers by questionnaires based on hypotheses. Jennex Olfman model was used to estimate the effectiveness of CKMF. Various tests were performed including frequency measures of variables, Cronbach's alpha reliability, and Pearson's correlation. The research provided a KMS depicting KM and collaborative features. The disease detection module was evaluated using the precision and recall method and found to be 94.16% accurate. The system could replace the work of extension agents, making it a cost and time-effective initiative for farmer betterment.

The future smart grid would help to benefit both the users and the electricity providing companies from smart pricing techniques. In addition, smart pricing can be used to achieve social objectives and would in turn fluctuate wholesale market into demand side. Collecting abundant information regarding the users electricity consumption pattern is a challenging task for utility providing companies. That is, users may not be willing to expose their indigenous information without any incentive. In this paper an Optimal Energy Consumption Scheduling (OECS) mechanism is proposed to tackle this problem. An agent-based forecasting method is designed, which is capable of predicting energy consumption of each consumer with a lead-time of one hour. This forecasting is exploited to estimate the cost of buying required amount of energy from multiple suppliers. Consequently, based on the estimated required energy and cost, an auction mechanism is proposed to optimize the energy traded between consumers and multiple suppliers within a smart grid. The objectives include increased efficiency and cost reduction of electricity usage by the end users. The results and properties of the proposed OECS mechanism are studied, and it is shown that the auction technique is budget balanced for distribution of electrical energy among consumers from diverse renewable generation resources. Extensive numerical simulations are also conducted to show and prove the beneficial properties of OECS mechanism.