HOW DO PUBLIC INSTITUTIONS SELECT COMPETITIVE AGRICULTURAL R&D PROJECTS? - THE CASE OF AN ITALIAN REGION

This paper analyses, through a Random Utility Model (RUM), how a public institution selects among competitive agricultural R&D projects on the basis of observable distinctive features. In particular, we aim at verifying if, which and how other criteria, beyond the pure scientific value, are decisive for selection. From such information, like cost, duration, etc., the institution must infer about the unobservable actual ability, effort and reliability of the scientists themselves. Such analytical framework is empirically applied to a real case, the agricultural R&D activity funded by the Emilia-Romagna Region (Italy) between 2001 and 2006.Public Agricultural R&D Funding, Random Utility Model, Logit Model, Agribusiness, Community/Rural/Urban Development, Public Economics,

The Agricultural Knowledge and Innovation System in Italy: dynamics, incentives, monitoring and evaluation experiences

The new challenges facing the European agricultural and rural sectors call for a review of the links between knowledge production and its use to foster innovation, and for a deeper analysis of the potential of the current Agricultural Knowledge and Innovation Systems (AKIS) to react to the evolving context. This paper highlights how the Italian AKIS places itself in the new emerging framework, with a particular emphasis on the incentives guiding the system and the experiences of monitoring and evaluating the national AKIS policy. It shows that important changes are needed to approach the new efforts Europe is adopting to match innovation demand and supply

Does cognitive control affect successful sandbagging of concussion symptoms?

The value of concussion baseline assessments is dependent upon athletes giving their best effort. If an athlete fakes poor performance or “sandbags”, a future injury may go undetected. Therefore, the purpose of this study was to determine if the SportGait concussion baseline assessment detects differences between participants instructed to sandbag and those who are not. Furthermore, I examined whether participants’ cognitive control is related to their ability to fake poor performance on SportGait. Forty-four participants completed two cognitive control tasks, were randomly assigned to “sandbag” or do their best and completed the SportGait baseline concussion assessment. Results revealed that “sandbagging” participants endorsed more concussion symptoms, made more errors on the CPT-3, and demonstrated lower stride power in their gait. However, cognitive control did not predict sandbagging performance. Together these results indicate that SportGait detects sandbagging, but additional investigation of factors including the impact of coaching on faking behaviors is needed

Figuraciones posthumanas de la naturaleza

Dossie

Exekutivfunktionen bei obstruktiver Schlafapnoe und Insomnie

EINFÜHRUNG: Beeinträchtigungen der Exekutivfunktionen bei Personen mit obstruktivem Schlafapnoesyndrom (OSA) sind wiederholt nachgewiesen worden. Die Rolle der Ursachen und Wirkfaktoren, die zu den Beeinträchtigungen beitragen sind jedoch weitgehend unbekannt. Kognitive Beeinträchtigungen bei Insomnie konnten nur selten nachgewiesen werden, gerade im Bereich exekutiver Funktionen gibt es kaum einschlägige Studien. Personen mit OSA dürften im Bereich exekutiver Funktionen größere Beeinträchtigungen zeigen als Insomniebetroffene. Die Wirkfaktoren dieser Beeinträchtigungen sollen ermittelt werden. METHODEN: 37 Männer und Frauen unterzogen sich einer nächtlichen Polysomnografie (PSG) im Schlaflabor und bearbeiteten zuvor die Tests Wahrnehmungs- und Aufmerksamkeitsfunktionen: Vigilanz (WAFV), N-back verbal (NBV), Stroop-Test (STROOP), Turm von London-Test (ToL-F) und Wortschatztest (WST). Es wurde eine Schlafapnoe- und eine Insomniegruppe aus den Versuchspersonen gebildet und auf Unterschiede hinsichtlich der Testleistungen untersucht. Zwischen den Indices der PSG und den Testwerten wurden Korrelationen errechnet. ERGEBNISSE: Es zeigten sich keine kognitiven Leistungsunterschiede zwischen OSA- und Insomniegruppe, außer bei der Lese-Interferenzneigung – Insomniepatienten zeigten sich störungsanfälliger. Mit niedrigerer Schlafeffizienz sank, unabhängig von der Schlafstörung, auch die Testleistung im NBV. DISKUSSION: Zwischen Personen mit leichtem OSA und Insomnie zeigen sich keine Leistungsunterschiede exekutiver Funktionen. Niedrige Schlafeffizienz trägt zu schlechterer Leistung im Bereich des Arbeitsgedächtnisses bei.INTRODUCTION: Impairment of executive functions in persons with sleep apnea has been reported frequently, but contributing factors still remain unclear. Contrary in persons with insomnia, executive function deficits mostly could not be found. Persons with sleep apnea should therefore show stronger executive function impairment compared to insomniacs. Contributing factors should be examined. METHODS: 37 males and females undertook full polysomnography (PSG) at night in a sleep laboratory and participated in the tests Wahrnehmungs- und Aufmerksamkeitsfunktionen: Vigilanz (WAFV), N-back verbal (NBV), Stroop-Test (STROOP), Tower of London-Test (ToL-F) and Wortschatztest (WST). Sleep apnea- and insomnia group have been compared in test performance. Indices of PSG have been correlated to test parameters. RESULTS: No differences between sleep apnea- and insomnia group were found except in the domain of reading interference – insomniacs were more prone to inferference. Lower sleep efficiency was generally related to lower test performance in NBV. DISCUSSION: There is no difference in executive function performance level between persons with slight sleep apnea and insomniacs. Low sleep efficiency contributes to deficits in working memory

Teknika Experimentalak I : Fisikako laborategiko praktikak

Fisikako Gradua Ingeniaritza Elektronikoko Gradua Fisikan eta Ingeniaritza Elektronikoan Gradu Bikoitza 1. mailaLiburu honek lehen mailako Teknika Experimentalak-I irakasgaiko praktikak egiteko gidoiak biltzen ditu. Halaber, erroreen teoriari eta laborategian erabiliko den treseneriari buruzko kapituluak ditu

Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region