Lower Bounds for Heights in Relative Galois Extensions

The goal of this paper is to obtain lower bounds on the height of an algebraic number in a relative setting, extending previous work of Amoroso and Masser. Specifically, in our first theorem we obtain an effective bound for the height of an algebraic number $\alpha$ when the base field $\mathbb{K}$ is a number field and $\mathbb{K}(\alpha)/\mathbb{K}$ is Galois. Our second result establishes an explicit height bound for any non-zero element $\alpha$ which is not a root of unity in a Galois extension $\mathbb{F}/\mathbb{K}$, depending on the degree of $\mathbb{K}/\mathbb{Q}$ and the number of conjugates of $\alpha$ which are multiplicatively independent over $\mathbb{K}$. As a consequence, we obtain a height bound for such $\alpha$ that is independent of the multiplicative independence condition

REMOTELY SENSED IMAGE FAST CLASSIFICATION AND SMART THEMATIC MAP PRODUCTION

Abstract. Apps available for Smartphone, as well as software for GNSS/GIS devices, permit to easily mapping the localization and shape of an area by acquiring the vertices coordinates of its contour. This option is useful for remote sensing classification, supporting the detection of representative sample sites of a known cover type to use for algorithm training or to test classification results. This article aims to analyse the possibility to produce smart maps from remotely sensed image classification in rapid way: the attention is focalized on different methods that are compared to identify fast and accurate procedure for producing up-to-date and reliable maps. Landsat 8 OLI multispectral images of northern Sicily (Italy) are submitted to various classification algorithms to distinguish water, bare soil and vegetation. The resulting map is useful for many purposes: appropriately inserted in a larger database aimed at representing the situation in a space-time evolutionary scenario, it is suitable whenever you want to capture the variation induced in a scene, e.g. burnt areas identification, vegetated areas definition for tourist-recreational purposes, etc. Particularly, pixel-based classification approaches are preferred, and experiments are carried out using unsupervised (k-means), vegetation index (NDVI, Normalized Difference Vegetation Index), supervised (minimum distance, maximum likelihood) methods. Using test sites, confusion matrix is built for each method, and quality indices are calculated to compare the results. Experiments demonstrate that NDVI submitted to k-means algorithm allows the best performance for distinguishing not only vegetation areas but also water bodies and bare soils. The resulting thematic map is converted for web publishing

Remotely sensed image fast classification and smart thematic map production

Apps available for Smartphone, as well as software for GNSS/GIS devices, permit to easily mapping the localization and shape of an area by acquiring the vertices coordinates of its contour. This option is useful for remote sensing classification, supporting the detection of representative sample sites of a known cover type to use for algorithm training or to test classification results. This article aims to analyse the possibility to produce smart maps from remotely sensed image classification in rapid way: The attention is focalized on different methods that are compared to identify fast and accurate procedure for producing up-To-date and reliable maps. Landsat 8 OLI multispectral images of northern Sicily (Italy) are submitted to various classification algorithms to distinguish water, bare soil and vegetation. The resulting map is useful for many purposes: Appropriately inserted in a larger database aimed at representing the situation in a space-Time evolutionary scenario, it is suitable whenever you want to capture the variation induced in a scene, e.g. burnt areas identification, vegetated areas definition for tourist-recreational purposes, etc. Particularly, pixel-based classification approaches are preferred, and experiments are carried out using unsupervised (k-means), vegetation index (NDVI, Normalized Difference Vegetation Index), supervised (minimum distance, maximum likelihood) methods. Using test sites, confusion matrix is built for each method, and quality indices are calculated to compare the results. Experiments demonstrate that NDVI submitted to k-means algorithm allows the best performance for distinguishing not only vegetation areas but also water bodies and bare soils. The resulting thematic map is converted for web publishing

An Integrated Approach to Risk and Impacts of Geo-Resources Exploration and Exploitation

Geo-resources are widely exploited in our society, with huge benefits for both economy and communities. Nevertheless, with benefits come risks and impacts. Understanding how such risks and impacts are intrinsically borne in a given project is of critical importance for both industry and society. In particular, it is crucial to distinguish between the specific impacts related to exploiting a given energy resource and those shared with the exploitation of other energy resources. A variety of different approaches can be used to identify and assess such risks and impacts. In particular, Life Cycle Assessment (LCA) and risk assessments (RAs) are the most commonly adopted. Although both are widely used to support decision making in environmental management, they are rarely used in combination perhaps because they have been developed by largely different groups of specialists. By analyzing the structure and the ratio of the two tools, we have developed an approach for combining and harmonizing LCA and MRA; the resulting protocol envisages building MRA upon LCA both qualitatively and quantitatively. We demonstrate the approach in a case study using a virtual site (based on a real one) for geothermal energy production

Country-level factors dynamics and ABO/Rh blood groups contribution to COVID-19 mortality

The identification of factors associated to COVID-19 mortality is important to design effective containment measures and safeguard at-risk categories. In the last year, several investigations have tried to ascertain key features to predict the COVID-19 mortality tolls in relation to country-specific dynamics and population structure. Most studies focused on the first wave of the COVID-19 pandemic observed in the first half of 2020. Numerous studies have reported significant associations between COVID-19 mortality and relevant variables, for instance obesity, healthcare system indicators such as hospital beds density, and bacillus Calmette-Guerin immunization. In this work, we investigated the role of ABO/Rh blood groups at three different stages of the pandemic while accounting for demographic, economic, and health system related confounding factors. Using a machine learning approach, we found that the “B+” blood group frequency is an important factor at all stages of the pandemic, confirming previous findings that blood groups are linked to COVID-19 severity and fatal outcome

Multi-time-scale features for accurate respiratory sound classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85% ± 3% and an precision of 80% ± 8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

Biological observations of the tope shark, Galeorhinus galeus , in the northern Patagonian gulfs of Argentina

During 1994–96, an experimental longline fishery for tope shark (Galeorhinus galeus) was carried out in the Nuevo Gulf of northern Patagonia and, in the summers of 2000–2001 and 2002, three boats conducted an exploratory commercial fishery for this species, accompanied by a scientific monitoring program. This paper summarizes the results of these fishing trials, and provides information on the biology of tope shark. Catches were highest from February to April, when tope shark represented 36% of the total fish caught, and elephant fish (Callorhynchus callorhynchus) and argentine hake (Merluccius hubbsi) accounted for 33% and 23%, respectively. Tope shark arriving in northern Patagonian waters during the summer are primarily mature males, immature and maturing females in their first and second non-gravid year. No gravid females were caught. These fish are part of the South-western Atlantic stock, which shows signs of over-exploitation, so we suggest that any longline fishery in Patagonia should remain on a small scale. We also recommend that an effective management plan is needed for the whole tope stock, establishing agreements on effort control and co-ordinated research between Brazil, Uruguay and Argentina.Fil: Elias, Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Rodriguez, A. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Hasan, E.. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Reyna, M. V.. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Amoroso, Ricardo Oscar. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentin

Economic Interplay Forecasting Business Success

A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years

Quality improvement activities associated with organisational capacity in general practice

Copyright © 2007 Australian College of General Practitioners Copyright to Australian Family Physician. Reproduced with permission. Permission to reproduce must be sought from the publisher, The Royal Australian College of General Practitioners.BACKGROUND Clinical audit is recognised worldwide as a useful tool for quality improvement. METHODS A feedback report profiling capacity for chronic disease care was sent to 97 general practices. These practices were invited to complete a clinical audit activity based on that feedback. Data were analysed quantitatively and case studies were developed based on the free text responses. RESULTS Eighty-two (33%) of 247 general practitioners participated in the clinical audit process, representing 57 (59%) of 97 general practices. From the data in their feedback report, 37 (65%) of the 57 practices recognised the area most in need of improvement. This was most likely where the need related to clinical practice or teamwork, and least likely where the need related to linkages with other services, and business and finance. Only 25 practices (46%) developed an action plan related to their recognised area for improvement, and 22 (39%) practices implemented their chosen activity. Participating GPs judged that change activity focused on teamwork was most successful. DISCUSSION The clinical audit process offered participating GPs and practices an opportunity to reflect on their performance across a number of key areas and to implement change to enhance the practice’s capacity for quality chronic disease care. The relationship between need and action was weak, suggesting a need for greater support to overcome barriers.Cheryl Amoroso, Judy Proudfoot, Tanya Bubner, Edward Swan, Paola Espinel, Christopher Barton, Justin Beilby and Mark Harri

Health Communications Trial with a Resistant Population to Increase Public Health Compliance during a Pandemic

Background: Georgia has among the worst rates of COVID-19 hospitalization and death rates in the nation. Many identifying as politically conservative resist public health mitigation measures, similar to populations in other politically conservative geographical areas. There are limited peer-reviewed public health communications designed for this population. We aimed to determine if an intervention using a fear appeal approach with efficacy during a pandemic can positively affect knowledge, attitude, perception, and/or behavior (KAP) in Georgia with this population. Methods: We delivered online video stimuli tailored to the geocultural characteristics of the target population. designed to stimulate fear, encourage efficacy, and counter mis- and disinformation. It used three routes to affect participants: narrative, direct messaging, and non-message cues. We measured risk aversion and conspiratorial ideation as moderating psychological factors using psychological batteries. Census and voting data were used to identify a convenience sample of 829 Georgia adults in an outer Atlanta suburb. Results: Exposure to the video, moderated by risk aversion, resulted in increased recommended mitigating behavior to prevent COVID-19 (13.7%, 95% CI: 2.7% to 24.7%,) and increased positive attitude toward the recommendations (7.7%, 95% CI: 5.9% to 9.3%). Exposure to the video, moderated by conspiratorial ideation, resulted in an increase in perception of COVID-19 risk (7.6% 95% CI: 1.8% to 13.5%) among participants. Conclusions: An intervention using a fear appeal approach with efficacy during a pandemic can positively affect attitude and risk perception of a politically conservative population. Scaling similar interventions with resistant geocultural populations has promise of increasing adherence to public health recommendations. The moderating factor of conspiratorial ideation is relevant given conspiracies during pandemics, such as COVID-19. This multidisciplinary study contributes to the extant literature by providing insights of populations influenced by contrary political attitudes