A SWOT analysis for offshore wind energy assessment using remote-sensing potential

The elaboration of a methodology for accurately assessing the potentialities of blue renewable energy sources is a key challenge among the current energy sustainability strategies all over the world. Consequentially, many researchers are currently working to improve the accuracy of marine renewable assessment methods. Nowadays, remote sensing (RSs) satellites are used to observe the environment in many fields and applications. These could also be used to identify regions of interest for future energy converter installations and to accurately identify areas with interesting potentials. Therefore, researchers can dramatically reduce the possibility of significant error. In this paper, a comprehensive SWOT (strengths, weaknesses, opportunities and threats) analysis is elaborated to assess RS satellite potentialities for offshore wind (OW) estimation. Sicily and Sardinia-the two biggest Italian islands with the highest potential for offshore wind energy generation-were selected as pilot areas. Since there is a lack of measuring instruments, such as cup anemometers and buoys in these areas (mainly due to their high economic costs), an accurate analysis was carried out to assess the marine energy potential from offshore wind. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome this limitation. Undoubtedly, with the advent of new technologies for measuring renewable energy sources (RESs), there could be a significant energy transition in this area that requires a proper orientation of plans to examine the factors influencing these new technologies that can negatively affect most of the available potential. Satellite technology for identifying suitable areas of wind power plants could be a powerful tool that is constantly increasing in its applications but requires good planning to apply it in various projects. Proper planning is only possible with a better understanding of satellite capabilities and different methods for measuring available wind resources. To this end, a better understanding in interdisciplinary fields with the exchange of updated information between different sectors of development, such as universities and companies, will be most effective. In this context, by reviewing the available satellite technologies, the ability of this tool to measure the marine renewable energies (MREs) sector in large and small areas is considered. Secondly, an attempt is made to identify the strengths and weaknesses of using these types of tools and techniques that can help in various projects. Lastly, specific scenarios related to the application of such systems in existing and new developments are reviewed and discussed

PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring

Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However, existing machine learning-based health monitoring applications are mostly supervised learning algorithms, trained on labels and they cannot make adaptive decisions in an uncertain complex environment. This study proposes a novel and generic system, predictive deep reinforcement learning (PDRL) with multiple RL agents in a time series forecasting environment. The proposed generic framework accommodates virtual Deep Q Network (DQN) agents to monitor predicted future states of a complex environment with a well-defined reward policy so that the agent learns existing knowledge while maximizing their rewards. In the evaluation process of the proposed framework, three DRL agents were deployed to monitor a subject's future heart rate, respiration, and temperature predicted using a BiLSTM model. With each iteration, the three agents were able to learn the associated patterns and their cumulative rewards gradually increased. It outperformed the baseline models for all three monitoring agents. The proposed PDRL framework is able to achieve state-of-the-art performance in the time series forecasting process. The proposed DRL agents and deep learning model in the PDRL framework are customized to implement the transfer learning in other forecasting applications like traffic and weather and monitor their states. The PDRL framework is able to learn the future states of the traffic and weather forecasting and the cumulative rewards are gradually increasing over each episode.Comment: This work has been submitted to the Springer for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Does Compound I Vary Significantly between Isoforms of Cytochrome P450?

status: publishe

Immune response of macrophages from young and aged mice to the oral pathogenic bacterium Porphyromonas gingivalis

Periodontal disease is a chronic inflammatory gum disease that in severe cases leads to tooth loss. Porphyromonas gingivalis (Pg) is a bacterium closely associated with generalized forms of periodontal disease. Clinical onset of generalized periodontal disease commonly presents in individuals over the age of 40. Little is known regarding the effect of aging on inflammation associated with periodontal disease. In the present study we examined the immune response of bone marrow derived macrophages (BMM) from young (2-months) and aged (1-year and 2-years) mice to Pg strain 381. Pg induced robust expression of cytokines; tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-10, chemokines; neutrophil chemoattractant protein (KC), macrophage colony stimulating factor (MCP)-1, macrophage inflammatory protein (MIP)-1α and regulated upon activation normal T cell expressed and secreted (RANTES), as well as nitric oxide (NO, measured as nitrite), and prostaglandin E2 (PGE2) from BMM of young mice. BMM from the 2-year age group produced significantly less TNF-α, IL-6 and NO in response to Pg as compared with BMM from 2-months and 1-year of age. We did not observe any difference in the levels of IL-1β, IL-10 and PGE2 produced by BMM in response to Pg. BMM from 2-months and 1-year of age produced similar levels of all chemokines measured with the exception of MCP-1, which was reduced in BMM from 1-year of age. BMM from the 2-year group produced significantly less MCP-1 and MIP-1α compared with 2-months and 1-year age groups. No difference in RANTES production was observed between age groups. Employing a Pg attenuated mutant, deficient in major fimbriae (Pg DPG3), we observed reduced ability of the mutant to stimulate inflammatory mediator expression from BMMs as compared to Pg 381, irrespective of age. Taken together these results support senescence as an important facet of the reduced immunological response observed by BMM of aged host to the periodontal pathogen Pg

Animal keeping in Chalcolithic North-Central Anatolia:What can stable isotope analysis add?

Stable isotope analysis is an essential investigative technique, complementary to more traditional zooarchaeological approaches to elucidating animal keeping practices. Carbon (δ13C) and nitrogen (δ15N) stable isotope values of 132 domesticates (cattle, caprines and pigs) were evaluated to investigate one aspect of animal keeping, animal forage, at the Late Chalcolithic (mid-fourth millennium BC) site of Çamlıbel Tarlası, which is located in north-central Anatolia. The analyses indicated that all of the domesticates had diets based predominantly on C3 plants. Pig and caprine δ13C and δ15N values were found to be statistically indistinguishable. However, cattle exhibited distinctive stable isotope values and, therefore, differences in diet from both pigs and caprines at Çamlıbel Tarlası. This difference may relate to the distinct patterns of foraging behaviour exhibited by the domesticates. Alternatively, this diversity may result from the use of different grazing areas or from the foddering practices of the Çamlıbel Tarlası inhabitants