Hausdorff clustering of financial time series

A clustering procedure, based on the Hausdorff distance, is introduced and tested on the financial time series of the Dow Jones Industrial Average (DJIA) index.Comment: 9 pages, 3 figure

Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations

Bike-sharing is adopted as a valid option replacing traditional public transports since they are eco-friendly, prevent traffic congestions, reduce any possible risk of social contacts which happen mostly on public means. However, some problems may occur such as the irregular distribution of bikes on related stations/racks/areas, and the difficulty of knowing in advance what the rack status will be like, or predicting if there will be bikes available in a specific bike-station at a certain time of the day, or if there will be a free slot to leave the rented bike. Thus, providing predictions can be useful to improve the service quality, especially in those cases where bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bike-slots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions, namely of 15, 30, 45, and 60 minutes. The study has demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offers a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. This paper has also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different cluster behaviours. Both solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure

Multi Clustering Recommendation System for Fashion Retail

Fashion retail has a large and ever-increasing popularity and relevance, allowing customers to buy anytime finding the best offers and providing satisfactory experiences in the shops. Consequently, Customer Relationship Management solutions have been enhanced by means of several technologies to better understand the behaviour and requirements of customers, engaging and influencing them to improve their shopping experience, as well as increasing the retailers’ profitability. Current solutions on marketing provide a too general approach, pushing and suggesting on most cases, the popular or most purchased items, losing the focus on the customer centricity and personality. In this paper, a recommendation system for fashion retail shops is proposed, based on a multi clustering approach of items and users’ profiles in online and on physical stores. The proposed solution relies on mining techniques, allowing to predict the purchase behaviour of newly acquired customers, thus solving the cold start problems which is typical of the systems at the state of the art. The presented work has been developed in the context of Feedback project partially founded by Regione Toscana, and it has been conducted on real retail company Tessilform, Patrizia Pepe mark. The recommendation system has been validated in store, as well as online

Ruptured aortic arch aneurysm: transposition of aortic arch branches after insertion of thoracic endovascular stent with extra-anatomic brain perfusion

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Microservices suite for smart city applications

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform

Effects of the combined action of a desensitizing gel and toothpaste on dentin hypersensitivity due to dental bleaching

Objectives: The present study is aimed at evaluating the effectiveness of a fluoride- and potassium nitrate-containing gel and toothpaste in reducing dentinal hypersensitivity due to dental bleaching. Materials and methods: Specific inclusion and exclusion criteria were used to recruit patients for the study. They were randomly allocated to a test or a placebo control group. Patients underwent a treatment of home dental bleaching with 10% carbamide peroxide. Dental shades were evaluated in a standardized environment and dentinal hypersensitivity was valuated by means of evaporation stimuli. A nominal scale was used to score the painful reaction. The patients were recalled 8, 15 and 28 days after the baseline for both shade and sensitivity assessment. Statistical analysis was performed using the Student’s T-test. Results: The patients recall rate was 96.9%. The statistical analysis demonstrated a significant reduction of the painful symptoms in the experimental group (p=0.031) while no statistically significant differences were evidenced in the control group at any follow-up recall (p>0.05). Discussion: The tested agents proved to be safe and effective in the short term. Neither pigmentations nor interferences with the bleaching action of peroxides due to the desensitizing agents were observed. The compliance of the patients to the proposed protocol as well as the motivation to maintain good oral hygiene were paramount in the achievement of the reported results. Conclusions: The use of a desensitizing gel and toothpaste containing fluoride and potassium nitrate was effective in reducing dentinal hypersensitivity due to dental bleaching and did not interfere with the bleaching action of peroxides. Clinical significance: Desensitizing gels and toothpastes containing fluoride and potassium nitrate can be considered safe and effective in the control of tooth sensitivity after dental bleaching

Integrating cogeneration and intermittent waste-heat recovery in food processing: Microturbines vs. ORC systems in the coffee roasting industry

Coffee roasting is a highly energy intensive process wherein a large quantity of heat is discharged from the stack at medium-to-high temperatures. Much of the heat is released from the afterburner, which is required to remove volatile organic compounds and other pollutants from the flue gases. In this work, intermittent waste-heat recovery via thermal energy storage (TES) and organic Rankine cycles (ORCs) is compared to combined heat and power (CHP) based on micro gas-turbines (MGTs) for a coffee roasting plant. With regard to the former, a promising solution is proposed that involves recovering waste heat from the flue gas stream by partial hot-gas recycling at the rotating drum coffee roaster, and coupling this to a thermal store and an ORC engine for power generation. The two solutions (CHP + MGT prime mover vs. waste-heat recovery + ORC engine) are investigated based on mass and energy balances, and a cost assessment methodology is adopted to compare the profitability of three system configurations integrated into the selected roasting process. The case study involves a major Italian roasting plant with a 3,000 kg per hour coffee production capacity. Three options are investigated: (i) intermittent waste-heat recovery from the hot flue-gases with an ORC engine coupled to a TES system; (ii) regenerative topping MGT coupled to the existing modulating gas burner to generate hot air for the roasting process; and (iii) non-regenerative topping MGT with direct recovery of the turbine outlet air for the roasting process. The results show that the profitability of these investments is highly influenced by the natural gas and electricity prices and by the coffee roasting production capacity. The CHP solution via an MGT appears as a more profitable option than waste-heat recovery via an ORC engine primarily due to the intermittency of the heat-source availability and the high electricity cost relative to the cost of natural gas

Molecular determination of epidermal growth factor receptor in normal and neoplastic colorectal mucosa

The epidermal growth factor receptor (EGFr) is considered a major target for treatment of colorectal cancer (CRC). We found a mean EGFr content significantly lower but more activated in colonic neoplastic tissue than in paired normal mucosa. Phosphorylated (pY1068) EGFr detection in CRC may be a better tool than EGFr detection to select patients for targeted therapies