While farmers sell their crops, middlemen provide a linkage between them, markets and buyers. Middlemen have good knowledge of working conditions of markets and have access to agricultural market information. Due to poor access to markets and agricultural market information by smallholders, there is a feeling that middlemen benefit more while farmers sell their crops. Good access to markets and market information may help farmers bypass middlemen while selling crops and thus benefit more. Thus, it is best to improve the informational capabilities (ICs) of farmers in agricultural marketing. Thus, this research measured ICs of farmers accessing market information, through a program NINAYO, while selling their crops. The research utilized the informational, psychological, social, and economic dimensions of the empowerment framework in identifying capability indicators to formulate survey questions. Data were collected from smallholders in six regions in Tanzania. The analysis utilized measures of life satisfaction and results showed that about half of the variation in the dependent variable, satisfaction with capabilities, was explained by the model. Backward elimination analysis confirmed that life satisfaction is multidimensional. Robustness test confirmed a positive relationship between satisfaction and capabilities. Overall, results confirmed ICs are multidimensions, their improvement empowers farmers in agricultural marketing.

Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

The rapid urbanization of developing countries coupled with explosion in construction of high rising buildings and the high power usage in them calls for conservation and efficient energy program. Such a programme require monitoring of end-use appliances energy consumption in real-time.The worldwide recent adoption of smart-meter in smart-grid, has led to the rise of Non-Intrusive Load Monitoring (NILM); which enables estimation of appliance-specific power consumption from building's aggregate power consumption reading. NILM provides households with cost-effective real-time monitoring of end-use appliances to help them understand their consumption pattern and become part and parcel of energy conservation strategy.This paper presents an up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem. This is followed by the review of the state-of-the art NILM algorithms. Furthermore, we review several performance metrics used by NILM researcher to evaluate NILM algorithms and discuss existing benchmarking framework for direct comparison of the state of the art NILM algorithms. Finally, the paper discuss potential NILM use-cases, presents an overview of the public available dataset and highlight challenges and future research directions.

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