Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets dagger

In This Paper, A Monitoring System Of Agricultural Production Is Modeled As A Data Fusion System (Data From Local Fairs And Meteorological Data). The Proposal Considers The Particular Information Of Sales In Agricultural Markets For Knowledge Extraction About The Associations Among Them. This Association Knowledge Is Employed To Improve Predictions Of Sales Using A Spatial Prediction Technique, As Shown With Data Collected From Local Markets Of The Andean Region Of Ecuador. The Commercial Activity In These Markets Uses Alternative Marketing Circuits (Cialco). This Market Platform Establishes A Direct Relationship Between Producer And Consumer Prices And Promotes Direct Commercial Interaction Among Family Groups. The Problem Is Presented First As A General Fusion Problem With A Network Of Spatially Distributed Heterogeneous Data Sources, And Is Then Applied To The Prediction Of Products Sales Based On Association Rules Mined In Available Sales Data. First, Transactional Data Is Used As The Base To Extract The Best Association Rules Between Products Sold In Different Local Markets, Knowledge That Allows The System To Gain A Significant Improvement In Prediction Accuracy In The Spatial Region Considered.This work was supported in part by Project MINECO TEC2017-88048-C2–2-R, Salesian Polytechnic University of Quito-Ecuador and by Commercial Coordination Network, Ministry of Agriculture and Livestock, Ecuado

Innovation brokers and their roles in value chain-network innovation: preliminary findings and a research agenda

Intervention approaches have been implemented in developing countries to enhance farmer's livelihoods through improving their linkages to markets and inclusiveness in agricultural value chains. Such interventions are aimed at facilitating the inclusion of small farmers not just in the vertical activities of the value chain (coordination of the chain) but also in the horizontal activities (cooperation in the chain). Therefore value addition is made by not just innovating products and services, but also by innovating social processes, which we define as Value Chain-Network Innovation. In Value Chain-Network Innovation, linkage formation among networks and optimisation is one of the main objectives of innovation enhancing interventions. Here some important roles for innovation brokers are envisaged as crucial to dynamise this process, connecting different actors of the innovation system, paying special attention to the weaker ones. However, little attention has been given to identify different innovation brokering roles in those approaches, and to the need that they facilitate innovation processes and open safe spaces for innovation and social learning at different organisational settings and levels, to have more effective and sustainable impacts. This paper offers some preliminary empirical evidence of the roles of innovation brokers in a developing country setting, recognising the context-sensitive nature of innovations. Two cases from work experience with intervention approaches are analysed in light of the theories of innovation brokering, presenting some empirical evidence of different types of arrangements made by innovation brokers. A third case was taken from the literature. Data from questionnaires, key informant interviews, participant observations of different types of activities and processes carried out in those approaches, SWOT analysis and project reports were used for the analysis of different types of brokering roles and to draw some lessons. One important outcome of this preliminary analysis was that Information and Communication Technologies (ICT) in integration with other media facilitate new ways of social organisation and interaction of innovation networks, which offer more possibilities for processes of innovation, aggregating value to the production and sharing of knowledge. There is already a transition of paradigm for approaching agricultural innovation to more participative and open approaches, which offers a promissory landscape for organising the value chain actors in a way that is more favourable for small farmers

Methodologies for innovation and best practices in Industry 4.0 for SMEs

Today, cyber physical systems are transforming the way in which industries operate, we call this Industry 4.0 or the fourth industrial revolution. Industry 4.0 involves the use of technologies such as Cloud Computing, Edge Computing, Internet of Things, Robotics and most of all Big Data. Big Data are the very basis of the Industry 4.0 paradigm, because they can provide crucial information on all the processes that take place within manufacturing (which helps optimize processes and prevent downtime), as well as provide information about the employees (performance, individual needs, safety in the workplace) as well as clients/customers (their needs and wants, trends, opinions) which helps businesses become competitive and expand on the international market. Current processing capabilities thanks to technologies such as Internet of Things, Cloud Computing and Edge Computing, mean that data can be processed much faster and with greater security. The implementation of Artificial Intelligence techniques, such as Machine Learning, can enable technologies, can help machines take certain decisions autonomously, or help humans make decisions much faster. Furthermore, data can be used to feed predictive models which can help businesses and manufacturers anticipate future changes and needs, address problems before they cause tangible harm

Building Efficient Smart Cities

Current technological developments offer promising solutions to the challenges faced by cities such as crowding, pollution, housing, the search for greater comfort, better healthcare, optimized mobility and other urban services that must be adapted to the fast-paced life of the citizens. Cities that deploy technology to optimize their processes and infrastructure fit under the concept of a smart city. An increasing number of cities strive towards becoming smart and some are even already being recognized as such, including Singapore, London and Barcelona. Our society has an ever-greater reliance on technology for its sustenance. This will continue into the future, as technology is rapidly penetrating all facets of human life, from daily activities to the workplace and industries. A myriad of data is generated from all these digitized processes, which can be used to further enhance all smart services, increasing their adaptability, precision and efficiency. However, dealing with large amounts of data coming from different types of sources is a complex process; this impedes many cities from taking full advantage of data, or even worse, a lack of control over the data sources may lead to serious security issues, leaving cities vulnerable to cybercrime. Given that smart city infrastructure is largely digitized, a cyberattack would have fatal consequences on the city’s operation, leading to economic loss, citizen distrust and shut down of essential city services and networks. This is a threat to the efficiency smart cities strive for

Efficient Digital Management in Smart Cities

The concept of smart cities puts the citizen at the center of all processes. It is the citizen who decides what kind of city they live in. Their opinions and attitudes towards technologies and the solutions they would like to see in their cities must be listened to. With Deep Intelligence, cities will be able to create more optimal citizen-centered services as, as the tool can collect data from multiple sources, such as databases and social networks, from which valuable information on citizens’ opinions and attitudes regarding technology, smart city services and urban problems, may be extracted

IoT and Blockchain for Smart Cities

Blockchain is a Distributed Ledger Technology (DLT) that makes it possible to secure any type of transaction. This is because the information stored on the Blockchain is immutable, impeding any type of fraud or modification of the data. It was first created for Bitcoin transactions; however, the research community has realized its potential quickly, and started using it for purposes other than cryptocurrency transactions. Blockchain may even be used to secure and provide reliability to the data being transmitted between computational systems, ensuring their immutability. Given the amount of data produced within a smart city, the use of Blockchain is imperative in smart cities, as it protects them from cyberattacks and fraud. Moreover, the transparency of the information stored on Blockchain means that it helps create a more just and democratic society

AIoT for Achieving Sustainable Development Goals

Artificial Intelligence of Things (AIoT) is a relatively new concept that involves the merging of Artificial Intelligence (AI) with the Internet of Things (IoT). It has emerged from the realization that Internet of Things networks could be further enhanced if they were also provided with Artificial Intelligence, enhancing the extraction of data and network operation. Prior to AIoT, the Internet of Things would consist of networks of sensors embedded in a physical environment, that collected data and sent them to a remote server. Upon reaching the server, a data analysis would be carried out which normally involved the application of a series of Artificial Intelligence techniques by experts. However, as Internet of Things networks expand in smart cities, this workflow makes optimal operation unfeasible. This is because the data that is captured by IoT is increasing in size continually. Sending such amounts of data to a remote server becomes costly, time-consuming and resource inefficient. Moreover, dependence on a central server means that a server failure, which would be imminent if overloaded with data, would lead to a halt in the operation of the smart service for which the IoT network had been deployed. Thus, decentralizing the operation becomes a crucial element of AIoT. This is done through the Edge Computing paradigm which takes the processing of data to the edge of the network. Artificial Intelligence is found at the edge of the network so that the data may be processed, filtered and analyzed there. It is even possible to equip the edge of the network with the ability to make decisions through the implementation of AI techniques such as Machine Learning. The speed of decision making at the edge of the network means that many social, environmental, industrial and administrative processes may be optimized, as crucial decisions may be taken faster. Deep Intelligence is a tool that employs disruptive Artificial Intelligence techniques for data analysis i.e., classification, clustering, forecasting, optimization, visualization. Its strength lies in its ability to extract data from virtually any source type. This is a very important feature given the heterogeneity of the data being produced in the world today. Another very important characteristic is its intuitiveness and ability to operate almost autonomously. The user is guided through the process which means that anyone can use it without any knowledge of the technical, technological and mathematical aspects of the processes performed by the platform. This means that the Deepint.net platform integrates functionalities that would normally take years to implement in any sector individually and that would normally require a group of experts in data analysis and related technologies [1-322]. The Deep Intelligence platform can be used to easily operate Edge Computing architectures and IoT networks. The joint characteristics of a well-designed Edge Computing platform (that is, one which brings computing resources to the edge of the network) and of the advanced Deepint.net platform deployed in a cloud environment, mean that high speed, real-time response, effective troubleshooting and management, as well as precise forecasting can be achieved. Moreover, the low cost of the solution, in combination with the availability of low-cost sensors, devices, Edge Computing hardware, means that deployment becomes a possibility for developing countries, where such solutions are needed most

Artificial Intelligence, social changes and impact on the world of education

The way in which humans acquire and share knowledge has been under constant evolution throughout times. Since the appearance of the first computers, education has changed dramatically. Now, as disruptive technologies are in full development, new opportunities arise for taking education to levels that have never been seen before. Ever since the coronavirus pandemic, the use of online teaching modalities has become widespread all over the world and the situation has caused the development of robust digital learning solutions an urgent need. At present, primary, secondary, third-level teaching and all sorts of courses may be delivered online, either in real-time or recorded for later viewing. Classes can be complemented with videos, documents or even interactive exercises. However, the institutions that used little or no technology prior to Covid-19 have found this situation overwhelming. The lack of knowledge regarding the digital teaching/learning tools available on the market and/or lack of knowledge regarding their use, means that educational institutions will not be able to take full advantage of the opportunities offered; poor use of technology in online classrooms may hinder the students’ progress

Last mile delivery

Last mile delivery is one of the most complex processes in the whole logistics process. This is because it involves many uncertainties, such as weather conditions, road conditions, traffic, car accidents, delivery vehicle anomalies, choice of route, avoiding parcel damage and delivery errors, and communication with the retailer or the recipient of the parcel; all this makes the successful delivery of parcels at the customers’ doorstep difficult. In addition, today’s consumers have much greater expectations regarding delivery services, they demand to receive their parcels much faster or be able to choose the time and place of delivery. All this increases the cost of last mile delivery, accounting for 40% of overall supply chain costs. E-commerce giants such as Amazon can invest a large number of resources into creating optimal last mile delivery solutions, establish numerous warehouses throughout countries which enable them to store the parcels as close to the end user as possible. However, companies that do not have as many resources may find it difficult to satisfy the delivery expectations of their customers; longer and inflexible waiting times, as well as additional payment for delivery may cause companies to quickly lose competitiveness on the market. This means that companies must turn to technological solutions that are going to help them to improve their last mile delivery effectively but at a reasonably low price. Big Data are the basis of all smart solutions. This is because collecting large amounts of data makes it possible to extract information and make future predictions on the basis of past patterns

Creating a climate for food security: the business, people & landscapes in food production

AbstractBalancing human and environmental needs is urgent where food security and sustainability are under pressure from population increases and changing climates. Requirements of food security, social justice and environmental justice exacerbate the impact of agriculture on the supporting ecological environment. Viability of the Australian rural economy is intrinsically linked to food production and food security requiring systematic evaluation of climate change adaptation strategies for agricultural productivity.This food-systems research drew on global climate change literature to identify risks and adaptation. The transdisciplinary team applied specialist experience through collaboration in social science, economics and land-management to provide comprehensive methods to engage researchers and decision-makers making decisions across the food-system. Research focus on the dairy and horticulture sectors in the SW-WA and SEQld provided a comparative context in food-systems and regional economies. Expert knowledge was engaged through a series of panel meetings to test and challenge existing practice applying conceptual and empirical approaches in Structural Equation, Value-Chain, Supply-Chain modelling and Analytical Hierarchy modelling. This iterative action-research process provided immediate generation and transfer of expert knowledge across the involved sectors. The scenarios and adaptive strategies provide evidence-based pathways to strengthen food-systems; account for climate change mitigation and adaptation; and weather-proof regional economies in the face of climate change. Balancing human and environmental needs is urgent where food security and sustainability are under pressure from population increases and changing climates. Requirements of food security, social justice and environmental justice exacerbate the impact of agriculture on the supporting ecological environment. Viability of the Australian rural economy is intrinsically linked to food production and food security requiring systematic evaluation of climate change adaptation strategies for agricultural productivity.This food-systems research drew on global climate change literature to identify risks and adaptation. The transdisciplinary team applied specialist experience through collaboration in social science, economics and land-management to provide comprehensive methods to engage researchers and decision-makers making decisions across the food-system. Research focus on the dairy and horticulture sectors in the SW-WA and SEQld provided a comparative context in food-systems and regional economies. Expert knowledge was engaged through a series of panel meetings to test and challenge existing practice applying conceptual and empirical approaches in Structural Equation, Value-Chain, Supply-Chain modelling and Analytical Hierarchy modelling. This iterative action-research process provided immediate generation and transfer of expert knowledge across the involved sectors. The scenarios and adaptive strategies provide evidence-based pathways to strengthen food-systems; account for climate change mitigation and adaptation; and weather-proof regional economies in the face of climate change. The triple-bottom-line provided a comprehensive means of addressing social, economic and ecological requirements, and the modelling showed the interacting dynamics between these dimensions. In response to climate change, the agricultural sector must now optimise practices to address the interaction between economic, social and environmental investment. Differences in positions between the industry sector, the government and research sectors demonstrate the need for closer relationships between industry and government if climate change interventions are to be effectively targeted. Modelling shows that capacity for adaptation has a significant bearing on the success of implementing intervention strategies. Without intervention strategies to build viability and support, farm businesses are more likely to fail as a consequence of climate change. A framework of capitals that includes social components - cultural, human and social capital-, economic components -economic and physical capital - and ecological components -ecological and environmental capital - should be applied to address capacities. A priority assessment of climate change intervention strategies shows that strategies categorised as ‘Technology & Extension’ are most important in minimising risk from climate change impacts. To implement interventions to achieve ‘Food Business Resilience’, ‘Business Development’ strategies and alternative business models are most effective. ‘Research and Development’ interventions are essential to achieve enhanced ‘Adaptive Capacity’.The individual components of TBL Adaptive Capacity can be achieved through ‘Policy and Governance’ interventions for building ‘Social Capital’ capacity, ‘Research and Development’ will develop ‘Economic Capital’, and ‘Business Development’ strategies will build ‘Ecological Capital’.These strategic interventions will promote food security and maintain resilience in local food systems, agricultural production communities and markets, global industrial systems, and developing world food systems. Climate change mitigation and adaptation interventions reflect a rich conceptualisation drawing from the Australian context, but also acknowledging the moral context of global association.Please cite this report as:Wardell-Johnson, A, Uddin, N, Islam, N, Nath, T, Stockwell, B, Slade, C 2013 Creating a climate for food security: the businesses, people and landscapes in food production, National Climate Change Adaptation Research Facility, Gold Coast, pp. 144.Balancing human and environmental needs is urgent where food security and sustainability are under pressure from population increases and changing climates. Requirements of food security, social justice and environmental justice exacerbate the impact of agriculture on the supporting ecological environment. Viability of the Australian rural economy is intrinsically linked to food production and food security requiring systematic evaluation of climate change adaptation strategies for agricultural productivity.This food-systems research drew on global climate change literature to identify risks and adaptation. The transdisciplinary team applied specialist experience through collaboration in social science, economics and land-management to provide comprehensive methods to engage researchers and decision-makers making decisions across the food-system. Research focus on the dairy and horticulture sectors in the SW-WA and SEQld provided a comparative context in food-systems and regional economies. Expert knowledge was engaged through a series of panel meetings to test and challenge existing practice applying conceptual and empirical approaches in Structural Equation, Value-Chain, Supply-Chain modelling and Analytical Hierarchy modelling. This iterative action-research process provided immediate generation and transfer of expert knowledge across the involved sectors. The scenarios and adaptive strategies provide evidence-based pathways to strengthen food-systems; account for climate change mitigation and adaptation; and weather-proof regional economies in the face of climate change. The triple-bottom-line provided a comprehensive means of addressing social, economic and ecological requirements, and the modelling showed the interacting dynamics between these dimensions. In response to climate change, the agricultural sector must now optimise practices to address the interaction between economic, social and environmental investment. Differences in positions between the industry sector, the government and research sectors demonstrate the need for closer relationships between industry and government if climate change interventions are to be effectively targeted. Modelling shows that capacity for adaptation has a significant bearing on the success of implementing intervention strategies. Without intervention strategies to build viability and support, farm businesses are more likely to fail as a consequence of climate change. A framework of capitals that includes social components - cultural, human and social capital-, economic components -economic and physical capital - and ecological components -ecological and environmental capital - should be applied to address capacities. A priority assessment of climate change intervention strategies shows that strategies categorised as ‘Technology & Extension’ are most important in minimising risk from climate change impacts. To implement interventions to achieve ‘Food Business Resilience’, ‘Business Development’ strategies and alternative business models are most effective. ‘Research and Development’ interventions are essential to achieve enhanced ‘Adaptive Capacity’.The individual components of TBL Adaptive Capacity can be achieved through ‘Policy and Governance’ interventions for building ‘Social Capital’ capacity, ‘Research and Development’ will develop ‘Economic Capital’, and ‘Business Development’ strategies will build ‘Ecological Capital’.These strategic interventions will promote food security and maintain resilience in local food systems, agricultural production communities and markets, global industrial systems, and developing world food systems. Climate change mitigation and adaptation interventions reflect a rich conceptualisation drawing from the Australian context, but also acknowledging the moral context of global association