On the peritidal cycles and their diagenetic evolution in the Lower Jurassic carbonates of the Calcare Massiccio Formation (Central Apennines)

This paper shows the environmental changes and high-frequency cyclicity recorded by Lower Jurassic shallow- water carbonates known as the Calcare Massiccio Formation which crop out in the central Apennines of Italy. Three types of sedimentary cycle bounded by subaerial erosion have been recognized: Type I consists of a shallowing upward cycle with oncoidal floatstones to rudstones passing gradationally up into peloidal packstone alternating with cryptoalgal laminites and often bounded by desiccation cracks and pisolitic-peloidal wackestones indicating a period of subaerial exposure. Type II shows a symmetrical trend in terms of facies arrangement with peloidal packstones and cryptoalgal laminites present both at the base and in the upper portion of the cycle, separated by oncoidal floatstones to rudstones. Type III displays a shallowing upward trend with an initial erosion surface overlain by oncoidal floatstones to rudstones that, in turn, are capped by pisolitic-peloidal wackestones and desiccation sheet cracks. Sheet cracks at the top of cycles formed during the initial phase of subaerial exposure were successively enlarged by dissolution during prolonged subaerial exposure. The following sea-level fall produced dissolution cavities in subtidal facies, while the successive sea-level rise resulted in the precipitation of marine cements in dissolution cavities. Spectral analysis revealed six peaks, five of which are consistent with orbital cycles. While a tectonic control cannot be disregarded, the main signal recorded by the sedimentary succession points toward a main control related to orbital forcing. High frequency sea-level fluctuations also controlled diagenetic processes

Governing with urban big data in the smart city environment: an italian perspective

A smart city is more than its mere technological components. From a legal standpoint, smartness means a civic-enabling regulatory environment, access to technological resources, and openness to the political decision-making process. No doubt, the core asset of this socio-technical revolution is the data generated within the urban contest. However, national and EU law does not provide a specific regulation for using this data. Indeed, the next EU data strategy, with the open data and non-personal data legislation and the forthcoming Data Act, aims to promote a more profitable use of urban and local big data. Nonetheless, at present, this latter still misses a consistent approach to this issue. A thorough understanding of the smart city requires, first of all, the reconceptualization of big data in terms of urban data. Existing definitions and studies about this topic converge on the metropolises of East Asia and, sometimes, the USA. Instead, we approach the issues experienced in medium-size cities, focusing on the main Italian ones. Especially in this specific urban environment, data can help provide better services, automatize administrations, and further democratization only if they are understood holistically - as urban data. Cities, moreover, are a comprehensive source of data themselves, both collected from citizens and urban things. Among the various types of data that can be gathered, surveillance recordings play a crucial role. On the one hand, video surveillance is essential for many purposes, such as protecting public property, monitoring traffic, controlling high-security risk areas, and preventing crime and vandalism. From another standpoint, these systems can be invasive towards citizens' rights and freedoms: in this regard, urban data collected from video surveillance systems may be shared with public administrations or other interested entities, only afterward they have been anonymized. Even this process needs to be aligned with the transparency and participation values that inform the city's democracy. Thus, the anonymization process must be fully compliant with data protection legislation, looking for the most appropriate legal basis and assessing all the possible sources of risks to the rights and freedoms of people (DPIA). Urban data, indeed, is a matter of local democracy. The availability of data and the economy of platforms can significantly transform a city's services and geography as well as citizens' lifestyles. However, the participation of citizens to express their views on both the use of urban data for public policy and the regulation of the digital economy is still a challenge. The paper aims to analyze the projects of some Italian cities - including Milan, Rome, and Turin - which have tried to introduce participatory urban data management tools and to highlight the possible challenges of a democratic management of service platforms and data transfer for social and economic development

SINDROME DI DOWN E SPORT: L'attività motoria per il recupero delle funzioni cognitive in età evolutiva

Contesto: Questo lavoro ha esaminato l'efficacia di un programma di allenamento integrato (allenatore e famiglia) in tre bambini con sindrome di Down, per migliorare le loro abilità motorie e cognitive, in particolare il tempo di reazione e la memoria di lavoro. Metodi: Il programma di allenamento integrato è stato utilizzato in tre bambini, due ragazzi (M1, con una età cronologica di 10,3 anni e di età mentale di 4,7 anni; M2, con una età cronologica di 14,6 anni e l'età mentale di meno di 4 anni) e una ragazza (F1, età cronologica 14,0 anni e un'età mentale di meno di 4 anni). Risultati: miglioramenti nei punteggi delle capacità motorie sono stati visti dopo il periodo di formazione. Maggiormente sono stati osservati miglioramenti nel tempo di reazione. Conclusione: Esiste una stretta correlazione tra le abilità motorie e cognitive in individui con sviluppo atipico. Vi è la necessità di pianificare programmi di intervento basati sulla coinvolgimento simultaneo di bambino e genitori e al fine di promuovere uno stile di vita attivo nella le persone con sindrome di Down.Background: This work examined the efficacy of an integrated exercise training program (coach and family) in three children with Down syndrome to improve their motor and cognitive abilities, in particular reaction time and working memory. Methods: The integrated exercise training program was used in three children with Down syndrome, comprising two boys (M1, with a chronological age of 10.3 years and a mental age of 4.7 years; M2, with a chronological age of 14.6 years and a mental age of less than 4 years) and one girl (F1, chronological age 14.0 years and a mental age of less than 4 years). Results: Improvements in gross motor ability scores were seen after the training period. Greater improvements in task reaction time were noted for both evaluation parameters, ie, time and omissions. Conclusion: There is a close interrelationship between motor and cognitive domains in indi- viduals with atypical development. There is a need to plan intervention programs based on the simultaneous involvement of child and parents and aimed at promoting an active lifestyle in individuals with Down syndrome

Stochastic Optimization and Machine Learning Modeling for Wireless Networking

In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data. In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full). In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches

Sensitivity Analysis for a PEM Fuel Cell Model aimed to Optimization

The amount of current density produced by the stack is the key performance parameter for a fuel cell, given a well-defined quantity of reactants flowing through it. A Proton Exchange Membrane fuel cell (PEMFC) distributed parameters model is considered with all the aspects influencing the cell behavior. A sensitivity analysis is performed through a Monte Carlo Simulation to assess the impact on performances of key parameters. The Pareto plot obtained from such analysis allow to operate design variables reduction, aimed to those parameters that show small impact, so to decrease the problem complexity through an increased orthogonality of the input design matrix. The target of the activity is to obtain and validate a method able to reduce the time needed for a complete simulation, so to be able to realize an effective multi-disciplinary design optimization

Stellar photometry with Multi Conjugate Adaptive Optics

We overview the current status of photometric analyses of images collected with Multi Conjugate Adaptive Optics (MCAO) at 8-10m class telescopes that operated, or are operating, on sky. Particular attention will be payed to resolved stellar population studies. Stars in crowded stellar systems, such as globular clusters or in nearby galaxies, are ideal test particles to test AO performance. We will focus the discussion on photometric precision and accuracy reached nowadays. We briefly describe our project on stellar photometry and astrometry of Galactic globular clusters using images taken with GeMS at the Gemini South telescope. We also present the photometry performed with DAOPHOT suite of programs into the crowded regions of these globulars reaching very faint limiting magnitudes Ks ~21.5 mag on moderately large fields of view (~1.5 arcmin squared). We highlight the need for new algorithms to improve the modeling of the complex variation of the Point Spread Function across the field of view. Finally, we outline the role that large samples of stellar standards plays in providing a detailed description of the MCAO performance and in precise and accurate colour{magnitude diagrams.Comment: 17 pages, 12 figures, SPIE 201

Chronic Intestinal Disorders in Humans and Pets: Current Management and the Potential of Nutraceutical Antioxidants as Alternatives

Chronic intestinal disorders (CID) are characterized by persistent, or recurrent gastrointestinal (GI) signs present for at least three weeks. In human medicine, inflammatory bowel disease (IBD) is a group of chronic GI diseases and includes Crohn’s disease (CD) and ulcerative colitis (UC). On the other hand, the general term chronic enteropathies (CE) is preferred in veterinary medicine. Different therapeutic approaches to these diseases are used in both humans and pets. This review is focused on the use of traditional therapies and nutraceuticals with specific antioxidant properties, for the treatment of CID in humans and animal patients. There is strong evidence of the antioxidant properties of the nutraceuticals included in this review, but few studies report their use for treating CID in humans and none in animals. Despite this fact, the majority of the nutraceuticals described in the present article could be considered as promising alternatives for the regular treatment of CID in human and veterinary medicine