Synthesis of Statistical Indicators to Evaluate Quality of Life in the Italian Provinces

This work remarks the need to carefully evaluate the real importance of each variable used in a multivariate analysis context, with particular regard to cases when an overall performance ranking is the main final purpose. In particular, both a preliminary transformation of variables – aimed at reducing asymmetry and variability of their variation ranges – and the evaluation of their intrinsic explicative power – through redundancy analysis and weighting methods – are proposed. Theoretical and empirical considerations are developed in the frame of quality of life evaluation, carried out at the Italian provinces level on the basis of a yearly survey managed by the Italian economic newspaper "Il Sole24ore". A particular emphasis is given to some normalisation criteria and the case when original variables are grouped "a priori" into logical blocks. A final comparison between the actual ranking method and a series of alternatives is proposed as well.multivariate analysis, principal components analysis, ranking, redundant variable, weighing system.

Weed flora and weed management of field peas in Finland

The composition of the weed flora of dry pea (Pisum sativum L.) fields and cropping practices were investigated in southwestern Finland. Surveys were done in 2002–2003 in 119 conventionally cropped fields and 64 fields under organic cropping. Herbicides were applied to 92% of conventionally cropped fields where they provided relatively good control but were costly. Weeds were controlled mechanically only in five fields under organic production. A total of 76 weed species were recorded, of which 29 exceeded the 10% frequency level of occurrence. The average number of weed species per field was 10 under conventional cropping and 18 under organic cropping. The most frequent weed species in both cropping practices were Chenopodium album, Stellaria media and Viola arvensis. Elymus repens was the most frequent grass species. The difference in species composition under conventional and organic cropping was detected with Redundancy Analysis. Under conventional cropping, features of crop stand and weed control explained 38.7% and 37.6% of the variation respectively. Under organic cropping the age of crop stand and field location (y co-ordinate) respectively explained best the variation. Weeds could be efficiently managed with herbicides under conventional cropping, but they represented a significant problem for organic production. Mixed cultivation of pea with cereals is recommended, particularly for organic cropping, as it favours crop competition against weeds

About adaptive coding on countable alphabets

This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring AC code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy % of small source classes by Opper and Haussler (1997) and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate

The Self-Organization of Meaning and the Reflexive Communication of Information

Following a suggestion of Warren Weaver, we extend the Shannon model of communication piecemeal into a complex systems model in which communication is differentiated both vertically and horizontally. This model enables us to bridge the divide between Niklas Luhmann's theory of the self-organization of meaning in communications and empirical research using information theory. First, we distinguish between communication relations and correlations among patterns of relations. The correlations span a vector space in which relations are positioned and can be provided with meaning. Second, positions provide reflexive perspectives. Whereas the different meanings are integrated locally, each instantiation opens global perspectives--"horizons of meaning"--along eigenvectors of the communication matrix. These next-order codifications of meaning can be expected to generate redundancies when interacting in instantiations. Increases in redundancy indicate new options and can be measured as local reduction of prevailing uncertainty (in bits). The systemic generation of new options can be considered as a hallmark of the knowledge-based economy.Comment: accepted for publication in Social Science Information, March 21, 201

"Open Innovation" and "Triple Helix" Models of Innovation: Can Synergy in Innovation Systems Be Measured?

The model of "Open Innovations" (OI) can be compared with the "Triple Helix of University-Industry-Government Relations" (TH) as attempts to find surplus value in bringing industrial innovation closer to public R&D. Whereas the firm is central in the model of OI, the TH adds multi-centeredness: in addition to firms, universities and (e.g., regional) governments can take leading roles in innovation eco-systems. In addition to the (transversal) technology transfer at each moment of time, one can focus on the dynamics in the feedback loops. Under specifiable conditions, feedback loops can be turned into feedforward ones that drive innovation eco-systems towards self-organization and the auto-catalytic generation of new options. The generation of options can be more important than historical realizations ("best practices") for the longer-term viability of knowledge-based innovation systems. A system without sufficient options, for example, is locked-in. The generation of redundancy -- the Triple Helix indicator -- can be used as a measure of unrealized but technologically feasible options given a historical configuration. Different coordination mechanisms (markets, policies, knowledge) provide different perspectives on the same information and thus generate redundancy. Increased redundancy not only stimulates innovation in an eco-system by reducing the prevailing uncertainty; it also enhances the synergy in and innovativeness of an innovation system.Comment: Journal of Open Innovations: Technology, Market and Complexity, 2(1) (2016) 1-12; doi:10.1186/s40852-016-0039-

Fault detection, identification and accommodation techniques for unmanned airborne vehicles

Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures

Biodiversity, community structure and function of biofilms in stream ecosystems

Multi-species, surface-attached biofilms often dominate microbial life in streams and rivers, where they contribute substantially to biogeochemical processes. The microbial diversity of natural biofilms is huge, and may have important implications for the functioning of aquatic environments and the ecosystem services they provide. Yet the causes and consequences of biofilm biodiversity remain insufficiently understood. This review aims to give an overview of current knowledge on the distribution of stream biofilm biodiversity, the mechanisms generating biodiversity patterns and the relationship between biofilm biodiversity and ecosystem functioning