On the Derivation of Optimal Partial Successive Interference Cancellation

The necessity of accurate channel estimation for Successive and Parallel Interference Cancellation is well known. Iterative channel estimation and channel decoding (for instance by means of the Expectation-Maximization algorithm) is particularly important for these multiuser detection schemes in the presence of time varying channels, where a high density of pilots is necessary to track the channel. This paper designs a method to analytically derive a weighting factor $\alpha$, necessary to improve the efficiency of interference cancellation in the presence of poor channel estimates. Moreover, this weighting factor effectively mitigates the presence of incorrect decisions at the output of the channel decoder. The analysis provides insight into the properties of such interference cancellation scheme and the proposed approach significantly increases the effectiveness of Successive Interference Cancellation under the presence of channel estimation errors, which leads to gains of up to 3 dB.Comment: IEEE GLOBECOM 201

Essays on the Italian electricity market

The thesis is a collection of three empirical essays focussing on the Italian electricity market. The first chapter, titled “Assessing market power in the Italian electricity market: a synthetic supply approach”, is a joint work with Prof. Luigi Grossi and Prof. Michael. G. Pollitt and was published among the Energy Policy Research Group Working Papers (no. 1930). This chapter investigates the bidding behaviour of the leading firms in the Italian electricity market, in particular on the Italian day-ahead market. The methodology adopted is synthetic supply, proposed by Ciarreta et al (2010), which consists in a two-step procedure, i.e. 1) power plants association and 2) hourly bidding schedule “translation”. Thanks to synthetic supply it is possible to create hourly counterfactual supply curves and see if there are differences between actual and synthetic equilibria. In other words, the idea is to investigate the difference in mark-up between the bidding schedules of power plants with very similar features. For this reason, power plants were associated with very strict criteria: technology, energy efficiency and many others. Furthermore, an algorithm in R was developed to compute hourly equilibria in the day-ahead market. This way, it is possible to assess if there are any differences between the bidding behaviour of the leading operators and the bidding behaviour of smaller generators. The findings suggest that during the years under examination (2015-2018), the market underwent higher prices and a non-negligible consumer surplus loss, especially during the months when above average heating and cooling were required. The second chapter, titled “Detecting strategic capacity withholding through a synthetic supply approach - Cui Prodest?”, puts forward a new methodology in the field of market monitoring. The methodology of synthetic supply and the R code are employed. However, the way synthetic supply is used in this chapter is completely new. A four-step procedure is proposed to investigate strategic capacity withholding, i.e. 1) hourly supply curves are created and extra capacity is artificially added to the supply schedule, 2) synthetic and actual prices are compared and anomalous price spikes detected, 3) synthetic and actual revenues are compared, to see if any market operator could have obtained more revenues from a scenario where some capacity was withheld, 4) the SSCW index is proposed to better interpret the results. The chapter carries out an empirical analysis of the Italian day-ahead market in 2018. This approach is a significant contribution to the literature because it enables the analysis of manipulative behaviour with a different perspective compared to the methodology currently available. The third chapter, titled “Covid-19 and the Italian electricity market: impacts, developments and implications”, investigates the effects of the restrictive measures employed by the Italian Government in response to Covid-19 on the Italian electricity market. This chapter presents a data description of the main market variables of the electricity market and uses an econometric model to estimate the effects of geographical and production lockdowns on the zonal quantities purchased and on the PUN (nationwide unit price). Data suggests that both quantity purchased, and prices were affected by the lockdowns, especially in the bidding areas of Northern Italy. The bidding zones of Southern Italy seem to be considerably less affected by restrictive measures. This is also confirmed by the econometric model. In addition, fall in demand led to a smaller quantity purchased, compared to the corresponding weeks in previous years, leading to substantial changes in the mix of energy sources. This chapter proposes a complete description of the evolution of the electricity market during the pandemic and provides useful policy recommendations on how financial resources should be allocated to relieve the Italian economy

Impact of Medium Access Control Strategies on the Effectiveness of Advanced Cooperative Hybrid ARQ Techniques

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A topological algorithm for identification of structural domains of proteins

<p>Abstract</p> <p>Background</p> <p>Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure.</p> <p>Results</p> <p>We present a novel graph-theoretical algorithm for the identification of domains in proteins with known three-dimensional structure. We represent the protein structure as an undirected, unweighted and unlabeled graph whose nodes correspond to the secondary structure elements and edges represent physical proximity of at least one pair of alpha carbon atoms from two elements. Domains are identified as constrained partitions of the graph, corresponding to sets of vertices obtained by the maximization of the cycle distributions found in the graph. When a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. The decision to accept or reject a tentative cut position is based on a specific classifier. The algorithm is applied iteratively to each of the resulting subgraphs and terminates automatically if partitions are no longer accepted. The distribution of cycles is the only type of information on which the decision about protein dissection is based. Despite the barebone simplicity of the approach, our algorithm approaches the best heuristic algorithms in accuracy.</p> <p>Conclusion</p> <p>Our graph-theoretical algorithm uses only topological information present in the protein structure itself to find the domains and does not rely on any geometrical or physical information about protein molecule. Perhaps unexpectedly, these drastic constraints on resources, which result in a seemingly approximate description of protein structures and leave only a handful of parameters available for analysis, do not lead to any significant deterioration of algorithm accuracy. It appears that protein structures can be rigorously treated as topological rather than geometrical objects and that the majority of information about protein domains can be inferred from the coarse-grained measure of pairwise proximity between elements of secondary structure elements.</p

Acque Sotterranee 2.0

Not available

From Monoamine Oxidase Inhibition to Antiproliferative Activity: New Biological Perspectives for Polyamine Analogs

: Monoamine oxidases (MAOs) are well-known pharmacological targets in neurological and neurodegenerative diseases. However, recent studies have revealed a new role for MAOs in certain types of cancer such as glioblastoma and prostate cancer, in which they have been found overexpressed. This finding is opening new frontiers for MAO inhibitors as potential antiproliferative agents. In light of our previous studies demonstrating how a polyamine scaffold can act as MAO inhibitor, our aim was to search for novel analogs with greater inhibitory potency for human MAOs and possibly with antiproliferative activity. A small in-house library of polyamine analogs (2-7) was selected to investigate the effect of constrained linkers between the inner amine functions of a polyamine backbone on the inhibitory potency. Compounds 4 and 5, characterized by a dianiline (4) or dianilide (5) moiety, emerged as the most potent, reversible, and mainly competitive MAO inhibitors (Ki < 1 μM). Additionally, they exhibited a high antiproliferative activity in the LN-229 human glioblastoma cell line (GI50 < 1 μM). The scaffold of compound 5 could represent a potential starting point for future development of anticancer agents endowed with MAO inhibitory activity