Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model,social motivation,and self-determination theory

Online learning courses have expanded the educational landscape to distant and disadvantaged areas. Although such courses have generated extensive interest, there is as yet sparse literature evaluating the determinants of online course acceptance, especially in developing countries. Seeking to close this gap, this study examines the factors influencing students’ adoption of Massive Open Online Courses (MOOCs) in a developing country by applying an integrated framework incorporating the task-technology fit model, social motivation, and self-determination theory. In addition, the study investigates the moderating effect of perceived reputation on students’ adoption behavior. A self-administered survey was employed for data collection and valid responses from 414 participants were used for testing the proposed model. The data was analyzed using structural equation modeling through Smart-PLS. The results establish the significant contribution of task characteristics and technology characteristics in facilitating task-technology fit, and that the fit positively influences behavioral intentions. Moreover, social recognition, perceived competence, and perceived relatedness have positive and significant effects on the behavioral intentions of the students. This research also reveals that perceived reputation has an important moderating effect on the students’ usage behavior. The study results provide both practical and theoretical insights to enrich the understanding of the paradigm shift due to MOOCs and online education.     

Survivability and scalability of space networks: a survey

Survivability and scalability are key terms for any network. The challenge is greater in space networks than in terrestrial networks. Satellites have various uses, some notable uses are communication, observation, meteorology, navigation etc. In future satellites will aid terrestrial users by providing Internet connectivity. With proliferation of Internet users, demand for satellite services will increase and soon we will suffer from bandwidth limitation. Hence, the scalability and survivability of space networks are very important issues to ensure smooth connectivity to all the users both mobile and immobile. Since, most of the existing works on network survivability and scalability are focused on terrestrial networks. We have categorized the existing works on survivability and scalability of terrestrial networks. In addition, we have performed a comprehensive survey on the popular products available in the market for network survivability and scalability. Finally, we have listed the major issues and challenges of space networks that needs to be considered before applying network scalability and survivability solutions on them. This work can help network engineers to select suitable solutions for space networks considering the available choices and challenges listed in this paper.     

A 2 GHz variable gain low noise amplifier in 0.18-μm CMOS

This paper describes a 2 GHz active variable gain low noise amplifier (VGLNA) in a 0.18-μm CMOS process. The VGLNA provides a 50-Ω input impedance and utilizes a tuned load to provide high selectivity. The VGLNA achieves a maximum small signal gain of 16.8 dB and a minimum gain of 4.6 dB with good input return loss. In the high gain and the low gain modes, the NFs are 0.83 dB and 2.8 dB, respectively. The VGLNA’s IIP3 in the high gain mode is 2.13 dBm. The LNA consumes approximately 4 mA of current from a 1.8-V power supply.     

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

     

Wire bonding characteristics of gold conductors for low temperature co-fired ceramic applications

Low temperature co-fired ceramic (LTCC) with gold conductors has been used for high reliability applications, such as satellite communications. When the gold metallization is co-fired with the LTCC tape, inorganic adhesion additives in the gold conductor interact with the LTCC glass. This interaction is essential to provide adequate adhesion for the gold metallization. However, this interaction can also reduce the softening point of the LTCC glass. The reaction product can migrate to the surface of the gold conductor and affect the wire bondability of the gold conductor.Effort has been made to develop surface gold conductors with optimized interaction with LTCC. The new gold conductor shows significant reduction of inorganic particles on the surface and improvement of wire bondability. A systematic wire bonding study has been performed on gold conductors under various wire bonding conditions. It is demonstrated that the wire bonding window can be improved significantly by reducing the interaction between LTCC and gold metallization. This paper reports the results of the wire bonding study.     

A Semantics-based Approach to Large-Scale Mobile Social Networking

Mobile ad hoc social networks are self-configuring social networks that connect users using mobile devices, such as laptops, PDAs, and cellular phones. These social networks facilitate users to form virtual communities of similar interests or commonalities. This paper proposes semantics-based mobile social network (SMSN), a novel framework of a fully functional mobile ad hoc social network that incorporates semantics of users’ social data. SMSN provides effective and efficient solutions to social network construction, semantics-based user profile matching, multi-hop semantics-based routing, and privacy management. Moreover, SMSN is rigorously benchmarked using an elaborate simulation setup and released as a prototype system that can be run on cellular phones. Due to its generality, SMSN can be applied to a wide range of critical applications, such as disaster-recovery, homeland security, and personnel control.     

Generalized newton multi-step iterative methods GMNp,m for solving system of nonlinear equations

A generalization of the Newton multi-step iterative method is presented, in the form of distinct families of methods depending on proper parameters. The proposed generalization of the Newton multi-step consists of two parts, namely the base method and the multi-step part. The multi-step part requires a single evaluation of function per step. During the multi-step phase, we have to solve systems of linear equations whose coefficient matrix is the Jacobian evaluated at the initial guess. The direct inversion of the Jacobian it is an expensive operation, and hence, for moderately large systems, the lower-upper triangular factorization (LU) is a reasonable choice. Once we have the LU factors of the Jacobian, starting from the base method, we only solve systems of lower and upper triangular matrices that are in fact computationally economical. The developed families involve unknown parameters, and we are interested in setting them with the goal of maximizing the convergence order of the global method. Few families are investigated in some detail. The validity and numerical accuracy of the solution of the system of nonlinear equations are presented via numerical simulations, also involving examples coming from standard approximations of ordinary differential and partial differential nonlinear equations. The obtained results show the efficiency of constructed iterative methods, under the assumption of smoothness of the nonlinear function.     

Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm

We design a grey wolf optimizer hybridized with an interior point algorithm to correct a faulty antenna array. If a single sensor fails, the radiation power pattern of the entire array is disturbed in terms of sidelobe level (SLL) and null depth level (NDL), and nulls are damaged and shifted from their original locations. All these issues can be solved by designing a new fitness function to reduce the error between the preferred and expected radiation power patterns and the null limitations. The hybrid algorithm has been designed to control the array’s faulty radiation power pattern. Antenna arrays composed of 21 sensors are used in an example simulation scenario. The MATLAB simulation results confirm the good performance of the proposed method, compared with the existing methods in terms of SLL and NDL.