I doveri e le responsabilità contabili nella corporate governance delle società di capitali

La costruzione di un modello efficiente di corporate governance deve offrire una disciplina adeguata dei doveri contabili. Ciò nonostante, gli ordinamenti giuridici configurano i doveri di contabilità in modo incompleto, giacché l’inadempimento di questi non comporta una sanzione diretta per il soggetto inadempiente. Come informazione sulla situazione economica e finanziaria della società, esiste un interesse pubblico nella contabilità, e questa può servire come base di giudizio a soggetti interni ed esterni all’impresa, nell’adozione delle sue scelte. Disporre di un’informazione falsa o inesatta al riguardo può comportare un danno ingiustificato alla società stessa, ai soci o ai terzi, che potranno esercitare le azioni precise per il risarcimento del danno cagionato. Per evitare la produzione di questi danni, da una prospettiva preventiva, la corporate governance delle società di capitali può prevedere dei meccanismi di controllo che riducano il rischio di offrire un’informazione sbagliata. Questi controlli potranno essere esercitati da soggetti interni o esterni (revisori legali) alla struttura della società, ed avranno una configurazione diversa a seconda che le società adottino una struttura monistica o dualistica di governance. Questo ci colloca di fronte ad una eventuale situazione di concorrenza delle colpe, giacché i diversi soggetti che intervengono nel processo d’elaborazione dell’informazione contabile versano la sua attuazione sullo stesso documento: il bilancio. Risulta dunque cruciale determinare il contributo effettivo di ciascuno per analizzare il suo grado di responsabilità nella produzione del danno.

Use of shared memory in the context of embedded multi-core processors: exploration of the technology and its limits

Modern embedded systems embrace many-core shared-memory designs. Due to constrained power and area budgets, most of them feature software-managed scratchpad memories instead of data caches to increase the data locality. It is therefore programmers’ responsibility to explicitly manage the memory transfers, and this make programming these platform cumbersome. Moreover, complex modern applications must be adequately parallelized before they can the parallel potential of the platform into actual performance. To support this, programming languages were proposed, which work at a high level of abstraction, and rely on a runtime whose cost hinders performance, especially in embedded systems, where resources and power budget are constrained. This dissertation explores the applicability of the shared-memory paradigm on modern many-core systems, focusing on the ease-of-programming. It focuses on OpenMP, the de-facto standard for shared memory programming. In a first part, the cost of algorithms for synchronization and data partitioning are analyzed, and they are adapted to modern embedded many-cores. Then, the original design of an OpenMP runtime library is presented, which supports complex forms of parallelism such as multi-level and irregular parallelism. In the second part of the thesis, the focus is on heterogeneous systems, where hardware accelerators are coupled to (many-)cores to implement key functional kernels with orders-of-magnitude of speedup and energy efficiency compared to the “pure software” version. However, three main issues rise, namely i) platform design complexity, ii) architectural scalability and iii) programmability. To tackle them, a template for a generic hardware processing unit (HWPU) is proposed, which share the memory banks with cores, and the template for a scalable architecture is shown, which integrates them through the shared-memory system. Then, a full software stack and toolchain are developed to support platform design and to let programmers exploiting the accelerators of the platform. The OpenMP frontend is extended to interact with it.

Using national FADN database to describe Italian farms and arable fields. A comparison of sustainability level between organic versus conventional regimes.

The study defines a new farm classification and identifies the arable land management. These aspects and several indicators are taken into account to estimate the sustainability level of farms, for organic and conventional regimes. The data source is Italian Farm Account Data Network (RICA) for years 2007-2011, which samples structural and economical information. An environmental data has been added to the previous one to better describe the farm context. The new farm classification describes holding by general informations and farm structure. The general information are: adopted regime and farm location in terms of administrative region, slope and phyto-climatic zone. The farm structures describe the presence of main productive processes and land covers, which are recorded by FADN database. The farms, grouped by homogeneous farm structure or farm typology, are evaluated in terms of sustainability. The farm model MAD has been used to estimate a list of indicators. They describe especially environmental and economical areas of sustainability. Finally arable lands are taken into account to identify arable land managements and crop rotations. Each arable land has been classified by crop pattern. Then crop rotation management has been analysed by spatial and temporal approaches. The analysis reports a high variability inside regimes. The farm structure influences indicators level more than regimes, and it is not always possible to compare the two regimes. However some differences between organic and conventional agriculture have been found. Organic farm structures report different frequency and geographical location than conventional ones. Also different connections among arable lands and farm structures have been identified.