The potential for aflatoxin predictive risk modelling in sub-Saharan Africa: a review

This review presents the current state of aflatoxin risk prediction models and their potential for value actors throughout the food chain in sub-Saharan Africa, with a specific focus on improving smallholder farmer management practices. Several empirical and mechanistic models have been developed either in academic research or by private sector aggregators and processors in high-income countries including Australia, the USA, and Southern Europe, but these models have been only minimally applied in sub-Saharan Africa, where there is significant potential and increasing need due to climate variability. Predictions can be made based on historic occurrence data using either a mechanistic microbiological framework for aflatoxin accumulation or an empirical model based on statistical correlations with climate conditions and local agronomic factors. Model results can then be distributed to smallholders through private, public, or mobile extension services, used by policymakers for strategy or policy, or utilised by private sector institutions for management decisions. Specific agricultural advice can be given during the three most critical points in the phenological cycle: preseason insight including sowing timing and crop varieties, preharvest advice about management and harvest timing, and postharvest optimal practices including storage, drying, and market information. Model development for sub-Saharan Africa is limited by a dearth of georeferenced aflatoxin occurrence data and real-time high resolution climate data; the wide diversity of farm typologies each with significant information and technology gaps; a prevalence of informal market structures and lack of economic incentives systems; and general lack of awareness around aflatoxins and best management practices to mitigate risk. Given advancements towards solving these challenges, predictive aflatoxin models can be integrated into decision support platforms to focus on optimisation of value for smallholders by minimising yield and nutritional losses, which can propagate value throughout the production and postharvest phases

Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)

In metropolitan areas, the exponential growth in quantity of vehicles has instigated gridlock, pollution, and delays in the transportation of freight. IoT is the modern revolution which pushes the world towards intelligent management systems and automated procedures. This makes a significant contribution to automation and intelligent societies. Traffic regulation and effective congestion management assist conserve many priceless resources. In order to recognize, collect and send data, autonomous vehicles are furnished with IoT powered Intelligent Traffic Management System (ITMS) having a set of sensors.  Moreover, machine learning (ML) algorithms can also be employed to enhance the transportation system.  Traffic jams, delays, and a high death rate are the results of the problems that the current transport management systems face.  In this paper, an active traffic control for VANET is proposed which merges Roaming Agents (RA) with deep neural networks (DNN). The effectiveness of the DNN with RA (RAD2N) routing method in VANETs is evaluated experimentally and compared with the traditional ML and other DL routing algorithms. Several traffic congestion indicators, including delay, packet delivery ratio (PDR) and throughput are used to validate RAD2N. The outcomes demonstrate that the proposed approach delivers lower latency and energy consumption

Challenges, issues and opportunities for the development of smart grid

The development smart grids have made the power systems planning and operation more efficient by the application of renewable energy resources, electric vehicles, two-way communication, self-healing, consumer engagement, distribution intelligence, etc. The objective of this paper is to present a detailed comprehensive review of challenges, issues and opportunities for the development of smart grid. Smart grids are transforming the traditional way of meeting the electricity demand and providing the way towards an environmentally friendly, reliable and resilient power grid. This paper presents various challenges of smart grid development including interoperability, network communications, demand response, energy storage and distribution grid management. This paper also reviews various issues associated with the development of smart grid. Local, regional, national and global opportunities for the development of smart grid are also reported in this paper