Analysis of Intended Farmers’ Response to CAP Scenarios: Environmental considerations

This research is a result of the CAP-IRE project which objective is the understanding farmer’s reactions under CAP scenarios by 2020. In particular this research aims to analyze the role of the current CAP design on the farmer’s decision process focusing on several environmental issues. The analysis is based on 2,360 observations of household farmers across 11 cases study in 9 EU countries. Intended responses of farmers to the CAP reforms are analyzed by logistic model regression. According to the results CAP scenarios would influence farmer’s decision on fertilizers and pesticides, as well as water use, while the highest effect is found for decisions on number of animal rearing on the farm. Factors determining reaction to the CAP scenario are monetary and non-monetary, as well as structural and spatial. CAP role appears to be non univocal and strongly case-specific, as it substantially differs across regions according to their socio-economic structureEnvironmental sustainability, Farmer’s intended behaviour, Logistic regression, Agricultural policy, Agricultural and Food Policy, Environmental Economics and Policy, Q18,

How would big data support societal development and environmental sustainability? Insights and practices

The theme of this Special Volume (SV) focuses on improving natural resource management and human health to ensure sustainable societal development. Natural resources have been exploited unduly regardless of the consequences, which has resulted in inappropriate management natural resources and has caused severe environmental degradation. Contributions in this SV addressed improved environmental management, utilization, and allocation of natural resources; evaluation of sustainable natural resource management; pollution prevention and treatment; and evaluation and suggestions for improved natural resource-related policies. The authors presented an inspiring panorama of the initiatives that have been developed throughout the world for sustainable natural resource management and improve societal development. Theoretically, new approaches to bridge the gaps between the economic development and environmental protection were increasingly dominant. Empirically, many of the papers provided case studies of regions in China and other regions. The authorship reflected growing collaboration between researchers from many different countries or universities. While the great diversity of contributions on the topic reflected the wealth of insights generated on the topic in recent years, there is much more that must be done to achieve societal sustainability in natural resource management.No Full Tex

A Mixed Geographically Weighted Approach to Decoupling and Rural Development in the EU-15

The CAP reform and the recent EC communication aimed at preparing its Health Check emphasise the need for interventions locally based where agricultural policy integrates with a broader policy for rural areas growth. In this context, the paper investigates the possible different sets policy indicators affecting agricultural productivity at the regional level considering spatial heterogeneity by means of a Mixed Geographically Weighted Regression approach. The analysis is based on a set of policy sensitive indicators selected according to the key component of the CAP reform and referred to a sample of 164 EU-15 regions at NUTS2 level. The methodology adopted, new for the empirical literature on the topic, allows for a more accurate understanding of spatial relationship of the agricultural and socio-economic factors affecting agricultural productivity at the local level providing useful information for policy making.CAP reform, agricultural productivity, spatial analysis, cluster analysis, Agricultural and Food Policy, Community/Rural/Urban Development, Research Methods/ Statistical Methods,

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Smart Metering System: Developing New Designs to Improve Privacy and Functionality

This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase thepredictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project

Farmers’ stated responses towards the chemicals use under the CAP liberalization

Th e research aims to analyze the farmers’ preferences towards the chemical input use in the case of the Common Agricultural Policy (CAP) being removed after 2013. Th e analysis is based on a survey of European farmers carried out in 2009. Th e intended responses of farmers to the CAP liberalization are analyzed by the logit model regressions. Although for the majority of respondents there would be no change in their intentions if the CAP were suppressed, about 20% would intend to decrease the amount of chemicals. Th e eff ects of the CAP liberalization appear not to be univocal and strongly case-specifi c, as it substantially diff ers across the European regions, farm locations and socio-economic structures

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Productivity Improvement with Generative AI Framework for Data Enrichment in Agriculture

The improvement in agricultural sector is essential for ensuring food security. Sector faces a multitude of challenges like climate change, resource limitations, and heightened food demand. To meet these challenges, there is an increasing demand for innovative solutions to enhance agricultural productivity, sustainability, and efficiency. This study presents an innovative framework that harnesses Generative Artificial Intelligence (GAI) to revolutionize agriculture. The objective is to conceptualize framework that integrates state-of-the-art AI techniques, encompassing deep learning and generative models, to provide farmers and stakeholders with data-driven insights and decision support tools. By leveraging GAI capabilities, study aims to address key agricultural issues, providing prototype implementation. Study concludes with various possible solution including crop yield prediction, disease identification, soil analysis, and resource optimization and future direction