Due to growing concerns of global warming, reducing carbon emissions has become one of the major tasks for developing countries to meet the national demand for energy policies. The objective of this study is to measure the energy consumption, carbon emission and economic-environmental efficiency in terms of the environmental performance of the top 20 industrial countries by employing a data envelopment analysis (DEA) model from 2013 to 2017. This study used the trilemma of energy efficiency, CO2 emission efficiency, and environmental efficiency, and also the contribution included the quantitative analysis of 20 industrial countries The results show that the energy efficiency of Australia, China, Japan, Saudi Arabia, and Poland are the best performing countries, whereas Mexico, Indonesia, Russia, and Brazil are identified as least efficient among all 20 countries. Furthermore, Russia’s energy intensity has a maximum score while Poland has a minimum score. Additionally, in the case of CO2 emission efficiency, Brazil, France, and Saudi Arabia are considered as efficient while nine country’s scores were less than 0.5. The results show that most countries exhibit higher performance in economic efficiency than environmental efficiency. The study provides valuable information for energy policy-makers.
From the last few decades' radio frequency (RF) spectrum remained attractive for wireless/wire-line applications. RF spectrum becomes dense and occupied, which makes its availability difficult to other broadband channels. Free space optical (FSO) communication, which was used for messaging/indication in the ancient days, now becomes more popular and attractive in the new era. The popularity of FSO is due to its freely available large spectrum, inherited secure links and ease of deployment. However, FSO link has to face certain challenges, e.g., line-of-sight (LOS) requirement, link attenuation due to atmospheric turbulence and severe weather conditions. To overcome the issues of FSO communication, hybrid optical-radio transmission system is proposed by which we can get maximum benefits of both spectrum. Considering the proposed transmission system link quality, link budget analysis is done in the presented work. Link budget design of transmission system is important to analyze and give recommendations. INDEX TERMS Free space communication (FSO), link budget analysis (LBA), radio frequency (RF), signal dependent Gaussian noise (SDGN), signal independent Gaussian noise (SIGN).
Practitioners of virtual laboratory confront issues on how to fulfill individuals' needs, motivate them to participate and use the tools, and to enhance their performance using virtual tools. Therefore, this study aims at examining the effects of usability and learning objective factors in evaluating students' performance impact from using virtual laboratory. The study proposes a theoretical model based on usability factors of technology acceptance model (perceived ease of use and perceived usefulness) and laboratory learning objectives (instruments, creativity and innovation) to capture the entire patterns of students' perceptions and use outcomes from using the simulation-based virtual laboratory. The study collected survey data from 116 first year Electrical Engineering students from the University of Queensland in Australia, reflecting their personal experience in using virtual laboratory tools. Partial least square approach using structural equation modeling technique (PLS-SEM) was used for statistical analysis and model testing. The results confirm that the proposed model provides a comprehensive understanding of students' perceptions and the understudy factors were truly significant in reflecting their performance impact from using such laboratory tools. More specifically, instrumentation and perceived usefulness of virtual laboratory were found to be the most significant influencing factors that have impacts on students' performance. Also, the findings shed light on the mediation roles of laboratory learning objectives between usability factors and use outcomes. Overall, this study contributes to literature by demonstrating the beneficial use of laboratory learning objectives in creating realistic and credible simulation tool, that can expedite the learning process and foster students' learning outcomes.
Blind Source Separation (BSS) application is a delinquent issue in a complex reverberant environment with changing room geometric dimensions and an increasing number of speech sources. The BSS application issue is determined by the independent component analysis that usually manipulates higher-order statistical approaches. However, the permutation between desired speech sources remains a challenging issue for BSS applications. The permutation problem is been rectified by Independent Vector Analysis (IVA) for BSS applications in the frequency domain. The performance dependency of the IVA approach solely relies on the selection of appropriate source-prior to preserving the inter-frequency dependencies between the same speech source amongst different frequency bins. Therefore, a hybrid model for the IVA method is presented, which comprises of multivariate generalized Gaussian and super-Gaussian distribution source priors to model low as well as high amplitudes speech signals. The weights of the hybrid model between multivariate Gaussian and generalized Gaussian are assigned following the energy of the observed non-stationary speech mixture signal. In the simulations, different speech mixtures are generated from various speech sources by simulated room model. The proposed approach evaluates the blind separation performance in terms of signal-to-distortion ratio (SDR) and is compared with well-known BSS methods. The results show an improvement of the proposed methodology for non-stationary speech signals over the state-of-the-art IVA models having a fixed source prior.
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