Using object-based geomorphometry for hydro-geomorphological analysis in a Mediterranean research catchment

Abstract. The aim of the paper is to apply an object-based geomorphometric procedure to define the runoff contribution areas and support a hydro-geomorphological analysis of a 3 km2 Mediterranean research catchment (southern Italy). Daily and sub-hourly discharge and electrical conductivity data were collected and recorded during a 3-year monitoring activity. Hydro-chemograph analyses carried out on these data revealed a strong seasonal hydrological response in the catchment that differed from the stormflow events that occur in the wet periods and in dry periods. This analysis enabled us to define the hydro-chemograph signatures related to increasing flood magnitude, which progressively involves various runoff components (baseflow, subsurface flow and surficial flow) and an increasing contributing area to discharge. Field surveys and water table/discharge measurements carried out during a selected storm event enabled us to identify and map specific runoff source areas with homogeneous geomorphological units previously defined as hydro-geomorphotypes (spring points, diffuse seepage along the main channel, seepage along the riparian corridors, diffuse outflow from hillslope taluses and concentrate sapping from colluvial hollows). Following the procedures previously proposed and used by authors for object-based geomorphological mapping, a hydro-geomorphologically oriented segmentation and classification was performed with the eCognition (Trimble, Inc.) package. The best agreement with the expert-based geomorphological mapping was obtained with weighted plan curvature at different-sized windows. By combining the hydro-chemical analysis and object-based hydro-geomorphotype map, the variability of the contribution areas was graphically modeled for the selected event, which occurred during the wet season, by using the log values of flow accumulation that better fit the contribution areas. The results allow us to identify the runoff component on hydro-chemographs for each time step and calculate a specific discharge contribution from each hydro-geomorphotype. This kind of approach could be useful when applied to similar, rainfall-dominated, forested and no-karst catchments in the Mediterranean eco-region

Celebrity endorsement and the attitude towards luxury brands for a sustainable consumption

Taking into consideration the increasing role of sustainability in the luxury industry, our study investigates the role of celebrity credibility, celebrity familiarity, luxury brand value, and brand sustainability awareness on attitude towards celebrity, brand, and purchase intention for a sustainable consumption. For this, we explored relationships among these variables to test a conceptual model which is developed using existing knowledge available in academic research on this topic. Data for testing were collected from high-end retail stores in UK about the world top luxury brands by brand value in 2019, also acknowledged for their major engagement in sustainability. Findings from a survey of 514 consumers suggest that celebrity credibility is a very strong key to increase purchase intentions of luxury sustainable goods. The study has important implications for the expansion of current literature, theory development and business practices. Limitations of the study are also outlined and directions for future research are considered too

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Microbiota–Liver Diseases Interactions

: Gut microbiota regulates essential processes of host metabolism and physiology: synthesis of vitamins, digestion of foods non-digestible by the host (such as fibers), and-most important-protects the digestive tract from pathogens. In this study, we focus on the CRISPR/Cas9 technology, which is extensively used to correct multiple diseases, including liver diseases. Then, we discuss the non-alcoholic fatty liver disease (NAFLD), affecting more than 25% of the global population; colorectal cancer (CRC) is second in mortality. We give space to rarely discussed topics, such as pathobionts and multiple mutations. Pathobionts help to understand the origin and complexity of the microbiota. Since several types of cancers have as target the gut, it is vital extending the research of multiple mutations to the type of cancers affecting the gut-liver axis

Digital transformation and tourist experience co-design: big social data for planning cultural tourism

Digital transformation has completely changed the demand/offering interaction in the travel industry, as well as largely affecting the customer journey. In this direction, “big social data” and user-generated content have become key sources of well-timed and rich knowledge supporting data driven decision approaches addressed the managing of complex relationships. Based on this theoretical framework, the paper suggests how to apply “big social data” in the tourist experience co-design, providing an increased value for the visitors and a better decision making approach for managers. In this respect, the field analysis concentrated specifically on user-generated content regarding the Pompeii Archaeological Site (P.A.S.), to trace valuable insights for the tourist experience. Based on double stage of research – netnographic analysis and a supplementary online survey – the study aimed to detect: (a) tourist perception on the P.A.S.; (b) random chat on the part of internet users (tourists and other browsers, not necessarily visitors) on the topic of the P.A.S.; (c) the main characteristics of the P.A.S. that attract internet user attention; (d) the main topics debated by influencers/opinion leaders managing online discussions on the P.A.S. managerial and theoretical implications were investigated highlighting the main limitations of the study as well