Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Assessing Label Frames and Emotional Primes in the Context of Animal Rearing—Response of an Explorative fNIRS Study

Against the background of rising societal interest for sustainable food and nutrition choices, food labels have gained importance in providing important information to consumers. However, little is known about how the differences between quality frames in labels are evaluated and how priming might serve as an anchor for label perception. This study aims to observe the neural reaction of this in the context of differently framed food labels for products of animal origin, claiming the presence or absence of an additional quality aspect and under the impulse of emotional priming. In an explorative setup, we measured the neural prefrontal cortex activity of 26 participants with the neuroimaging technology fNIRS. An idyllic prime and a prime related to a label claiming an additional product quality led to increased neural activity in the OFC and dlPFC. Shedding light on what elements are of importance to identify products that meet consumers’ requirements in terms of quality aspects, this could indicate that the prime stressed the meaning of the label. This strengthens the argument to positively phrase and anchor frames regarding quality attributions as opposed to negative declarations. It further demonstrates the ability of fNIRS to capture processing through labels and primes in the context of consumer behavior.

The Meaning of Mortality in Modern Life

This chapter contextualizes Managing Cancer and Living Meaningfully (CALM) in relation to growing societal interest in the psychological impact of disease and the central role of healthcare providers in managing the threat of mortality and the end of life. There has been increasing recognition that the enormous investment in biotechnology and aggressive medical interventions for advanced disease has not been matched by complementary attention to the human dimensions of these conditions. There is now a growing public voice of patients and their families for more support in managing the psychological, emotional, and spiritual elements of advanced disease. The global palliative care movement emerged to address these unmet needs, but there has been less systematic attention in this field to the relief of psychological than physical suffering. CALM is a psychosocial intervention that is uniquely integrated with oncology and palliative care and focused on the psychological and social dimensions of advanced cancer.

Urban Forest Fragments as a Living Laboratory for Teaching Botany: An Example from Federal University of Rio Grande do Norte, Brazil

Abstract—“Plant blindness” is affecting humans’ relationships with plants, which has negative consequences for both science and conservation. It is, therefore, important to find new ways to promote societal interest in botany and plants. One possibility is encouraging the use of informal settings to promote curiosity and provide education to students. Forest fragments can be regarded as open air labs for teaching botany, especially on university campuses. We aimed to formally document the angiosperm diversity in the Mata dos Saguis (MS), a fragment of Atlantic forest under restoration belonging to the central campus of the Federal University of Rio Grande do Norte (UFRN), Brazil. We recorded 140 species, 113 genera in 52 families, and 24 orders of angiosperms. The MS has nearly 10% of the species and one third of all the families occurring in the entire state of Rio Grande do Norte, representing the main evolutionary groups of angiosperms, and we also recorded two new species occurrences for the state. Here we provide a checklist of the MS, a location that has been used as an open-air laboratory by many UFRN undergraduate courses in biosciences. We also share examples that can be replicated in other institutions and discuss the process of learning systematic botany in floristically rich countries by means of alternative and hands-on experiences.

Epidemics Forecast From SIR-Modeling, Verification and Calculated Effects of Lockdown and Lifting of Interventions

Due to the current COVID-19 epidemic plague hitting the worldwide population it is of utmost medical, economical and societal interest to gain reliable predictions on the temporal evolution of the spreading of the infectious diseases in human populations. Of particular interest are the daily rates and cumulative number of new infections, as they are monitored in infected societies, and the influence of non-pharmaceutical interventions due to different lockdown measures as well as their subsequent lifting on these infections. Estimating quantitatively the influence of a later lifting of the interventions on the resulting increase in the case numbers is important to discriminate this increase from the onset of a second wave. The recently discovered new analytical solutions of Susceptible-Infectious-Recovered (SIR) model allow for such forecast. In particular, it is possible to test lockdown and lifting interventions because the new solutions hold for arbitrary time dependence of the infection rate. Here we present simple analytical approximations for the rate and cumulative number of new infections.

Three-Color Plasmon-Mediated Reduction of Diazonium Salts over Metasurfaces.

Surface plasmon-mediated chemical reactions are of great interest for a variety of applications ranging from micro- and nanoscale device fabrication to chemical reactions of societal interest for hydrogen production or...

Deep Learning Without Neural Networks: Fractal-nets for Rare Event Modeling

Complex phenomena of societal interest such as weather, seismic activity and urban crime, are often punctuated by rare and extreme events, which are difficult to model and predict. Evidence of long-range persistence of such events has underscored the need to learn deep stochastic structures in data for effective forecasts. Recently neural networks (NN) have emerged as a defacto standard for deep learning. However, key problems remain with NN inference, including a high sample complexity, a general lack of transparency, and a limited ability to directly model stochastic phenomena. In this study we suggest that deep learning and the NN paradigm are conceptually distinct -- and that it is possible to learn ``deep' associations without invoking the ubiquitous NN strategy of global optimization via back-propagation. We show that deep learning of stochastic phenomena is related to uncovering the emergent self-similarities in data, which avoids the NN pitfalls offering crucial insights into underlying mechanisms. Using the Fractal Net (FN) architecture introduced here, we actionably forecast various categories of rare weather and seismic events, and property and violent crimes in major US cities. Compared to carefully tuned NNs, we boost recall at 90% precision by 161.9% for extreme weather events, 191.3% for light-to-severe seismic events with magnitudes above the local third quartile, and 50.8% - 404.9% for urban crime, demonstrating applicability in diverse systems of societal interest. This study opens the door to precise prediction of rare events in spatio-temporal phenomena, adding a new tool to the data science revolution.

Organic Farming and Climate Change: The Need for Innovation

Organic agriculture has experienced remarkable growth in recent decades as societal interest in environmental protection and healthy eating has increased. Research has shown that relative to conventional agriculture, organic farming is more efficient in its use of non-renewable energy, maintains or improves soil quality, and has less of a detrimental effect on water quality and biodiversity. Studies have had more mixed findings, however, when examining the impact of organic farming on greenhouse gas (GHG) emissions and climate change. Life cycle assessments (LCAs) in particular have indicated that organic farming can often result in higher GHG emissions per unit product as a result of lower yields. The organic movement has the opportunity to embrace the science of LCA and use this information in developing tools for site-specific assessments that can point toward strategies for improvements. Responding effectively to the climate change crisis should be at the core of the organic movement’s values. Additionally, while societal-level behavioral and policy changes will be required to reduce waste and shift diets to achieve essential reductions in GHG emissions throughout food systems, organic farming should be open to seriously considering emerging technologies and methods to improve its performance and reduce GHG emissions at the production stage.

Epidemics forecast from SIR-modeling, verification and calculated effects of lockdown and lifting of interventions

Due to the current COVID-19 epidemic plague hitting the worldwide population it is of utmost medical, economical and societal interest to gain reliable predictions on the temporal evolution of the spreading of the infectious diseases in human populations. Of particular interest are the daily rates and cumulative number of new infections, as they are monitored in infected societies, and the influence of non-pharmaceutical interventions due to different lockdown measures as well as their subsequent lifting on these infections. Estimating quantitatively the influence of a later lifting of the interventions on the resulting increase in the case numbers is important to discriminate this increase from the onset of a second wave. The recently discovered new analytical solutions of Susceptible-Infectious-Recovered (SIR) model allow for such forecast and the testing of lockdown and lifting interventions as they hold for arbitrary time dependence of the infection rate. Here we present simple analytical approximations for the rate and cumulative number of new infections.