Exact and heuristic algorithms for the integrated planning of multi-energy systems

The aim of this thesis is to present exact and heuristic algorithms for the integrated planning of multi-energy systems. The idea is to disaggregate the energy system, starting first with its core the Central Energy System, and then to proceed towards the Decentral part. Therefore, a mathematical model for the generation expansion operations to optimize the performance of a Central Energy System system is first proposed. To ensure that the proposed generation operations are compatible with the network, some extensions of the existing network are considered as well. All these decisions are evaluated both from an economic viewpoint and from an environmental perspective, as specific constraints related to greenhouse gases emissions are imposed in the formulation. Then, the thesis presents an optimization model for solar organic Rankine cycle in the context of transactive energy trading. In this study, the impact that this technology can have on the peer-to-peer trading application in renewable based community microgrids is inspected. Here the consumer becomes a prosumer and engages actively in virtual trading with other prosumers at the distribution system level. Moreover, there is an investigation of how different technological parameters of the solar Organic Rankine Cycle may affect the final solution. Finally, the thesis introduces a tactical optimization model for the maintenance operations’ scheduling phase of a Combined Heat and Power plant. Specifically, two types of cleaning operations are considered, i.e., online cleaning and offline cleaning. Furthermore, a piecewise linear representation of the electric efficiency variation curve is included. Given the challenge of solving the tactical management model, a heuristic algorithm is proposed. The heuristic works by solving the daily operational production scheduling problem, based on the final consumer’s demand and on the electricity prices. The aggregate information from the operational problem is used to derive maintenance decisions at a tactical level.

A high energy perspective of the co-evolution of black holes and their host galaxies

Thanks to the Chandra and XMM–Newton surveys, the hard X-ray sky is now probed down to a flux limit where the bulk of the X-ray background is almost completely resolved into discrete sources, at least in the 2–8 keV band. Extensive programs of multiwavelength follow-up observations showed that the large majority of hard X–ray selected sources are identified with Active Galactic Nuclei (AGN) spanning a broad range of redshifts, luminosities and optical properties. A sizable fraction of relatively luminous X-ray sources hosting an active, presumably obscured, nucleus would not have been easily recognized as such on the basis of optical observations because characterized by “peculiar” optical properties. In my PhD thesis, I will focus the attention on the nature of two classes of hard X-ray selected “elusive” sources: those characterized by high X-ray-to-optical flux ratios and red optical-to-near-infrared colors, a fraction of which associated with Type 2 quasars, and the X-ray bright optically normal galaxies, also known as XBONGs. In order to characterize the properties of these classes of elusive AGN, the datasets of several deep and large-area surveys have been fully exploited. The first class of “elusive” sources is characterized by X-ray-to-optical flux ratios (X/O) significantly higher than what is generally observed from unobscured quasars and Seyfert galaxies. The properties of well defined samples of high X/O sources detected at bright X–ray fluxes suggest that X/O selection is highly efficient in sampling high–redshift obscured quasars. At the limits of deep Chandra surveys (∼10−16 erg cm−2 s−1), high X/O sources are generally characterized by extremely faint optical magnitudes, hence their spectroscopic identification is hardly feasible even with the largest telescopes. In this framework, a detailed investigation of their X-ray properties may provide useful information on the nature of this important component of the X-ray source population. The X-ray data of the deepest X-ray observations ever performed, the Chandra deep fields, allows us to characterize the average X-ray properties of the high X/O population. The results of spectral analysis clearly indicate that the high X/O sources represent the most obscured component of the X–ray background. Their spectra are harder (G ∼ 1) than any other class of sources in the deep fields and also of the XRB spectrum (G ≈ 1.4). In order to better understand the AGN physics and evolution, a much better knowledge of the redshift, luminosity and spectral energy distributions (SEDs) of elusive AGN is of paramount importance. The recent COSMOS survey provides the necessary multiwavelength database to characterize the SEDs of a statistically robust sample of obscured sources. The combination of high X/O and red-colors offers a powerful tool to select obscured luminous objects at high redshift. A large sample of X-ray emitting extremely red objects (R−K >5) has been collected and their optical-infrared properties have been studied. In particular, using an appropriate SED fitting procedure, the nuclear and the host galaxy components have been deconvolved over a large range of wavelengths and ptical nuclear extinctions, black hole masses and Eddington ratios have been estimated. It is important to remark that the combination of hard X-ray selection and extreme red colors is highly efficient in picking up highly obscured, luminous sources at high redshift. Although the XBONGs do not present a new source population, the interest on the nature of these sources has gained a renewed attention after the discovery of several examples from recent Chandra and XMM–Newton surveys. Even though several possibilities were proposed in recent literature to explain why a relatively luminous (LX = 1042 − 1043erg s−1) hard X-ray source does not leave any significant signature of its presence in terms of optical emission lines, the very nature of XBONGs is still subject of debate. Good-quality photometric near-infrared data (ISAAC/VLT) of 4 low-redshift XBONGs from the HELLAS2XMMsurvey have been used to search for the presence of the putative nucleus, applying the surface-brightness decomposition technique. In two out of the four sources, the presence of a nuclear weak component hosted by a bright galaxy has been revealed. The results indicate that moderate amounts of gas and dust, covering a large solid angle (possibly 4p) at the nuclear source, may explain the lack of optical emission lines. A weak nucleus not able to produce suffcient UV photons may provide an alternative or additional explanation. On the basis of an admittedly small sample, we conclude that XBONGs constitute a mixed bag rather than a new source population. When the presence of a nucleus is revealed, it turns out to be mildly absorbed and hosted by a bright galaxy.

Light energy management in peach: utilization, photoprotection , photodamage and recovery. Maximizing light absorption in orchard is not always the best solution

The relation between the intercepted light and orchard productivity was considered linear, although this dependence seems to be more subordinate to planting system rather than light intensity. At whole plant level not always the increase of irradiance determines productivity improvement. One of the reasons can be the plant intrinsic un-efficiency in using energy. Generally in full light only the 5 – 10% of the total incoming energy is allocated to net photosynthesis. Therefore preserving or improving this efficiency becomes pivotal for scientist and fruit growers. Even tough a conspicuous energy amount is reflected or transmitted, plants can not avoid to absorb photons in excess. The chlorophyll over-excitation promotes the reactive species production increasing the photoinhibition risks. The dangerous consequences of photoinhibition forced plants to evolve a complex and multilevel machine able to dissipate the energy excess quenching heat (Non Photochemical Quenching), moving electrons (water-water cycle , cyclic transport around PSI, glutathione-ascorbate cycle and photorespiration) and scavenging the generated reactive species. The price plants must pay for this equipment is the use of CO2 and reducing power with a consequent decrease of the photosynthetic efficiency, both because some photons are not used for carboxylation and an effective CO2 and reducing power loss occurs. Net photosynthesis increases with light until the saturation point, additional PPFD doesn’t improve carboxylation but it rises the efficiency of the alternative pathways in energy dissipation but also ROS production and photoinhibition risks. The wide photo-protective apparatus, although is not able to cope with the excessive incoming energy, therefore photodamage occurs. Each event increasing the photon pressure and/or decreasing the efficiency of the described photo-protective mechanisms (i.e. thermal stress, water and nutritional deficiency) can emphasize the photoinhibition. Likely in nature a small amount of not damaged photosystems is found because of the effective, efficient and energy consuming recovery system. Since the damaged PSII is quickly repaired with energy expense, it would be interesting to investigate how much PSII recovery costs to plant productivity. This PhD. dissertation purposes to improve the knowledge about the several strategies accomplished for managing the incoming energy and the light excess implication on photo-damage in peach. The thesis is organized in three scientific units. In the first section a new rapid, non-intrusive, whole tissue and universal technique for functional PSII determination was implemented and validated on different kinds of plants as C3 and C4 species, woody and herbaceous plants, wild type and Chlorophyll b-less mutant and monocot and dicot plants. In the second unit, using a “singular” experimental orchard named “Asymmetric orchard”, the relation between light environment and photosynthetic performance, water use and photoinhibition was investigated in peach at whole plant level, furthermore the effect of photon pressure variation on energy management was considered on single leaf. In the third section the quenching analysis method suggested by Kornyeyev and Hendrickson (2007) was validate on peach. Afterwards it was applied in the field where the influence of moderate light and water reduction on peach photosynthetic performances, water requirements, energy management and photoinhibition was studied. Using solar energy as fuel for life plant is intrinsically suicidal since the high constant photodamage risk. This dissertation would try to highlight the complex relation existing between plant, in particular peach, and light analysing the principal strategies plants developed to manage the incoming light for deriving the maximal benefits as possible minimizing the risks. In the first instance the new method proposed for functional PSII determination based on P700 redox kinetics seems to be a valid, non intrusive, universal and field-applicable technique, even because it is able to measure in deep the whole leaf tissue rather than the first leaf layers as fluorescence. Fluorescence Fv/Fm parameter gives a good estimate of functional PSII but only when data obtained by ad-axial and ab-axial leaf surface are averaged. In addition to this method the energy quenching analysis proposed by Kornyeyev and Hendrickson (2007), combined with the photosynthesis model proposed by von Caemmerer (2000) is a forceful tool to analyse and study, even in the field, the relation between plant and environmental factors such as water, temperature but first of all light. “Asymmetric” training system is a good way to study light energy, photosynthetic performance and water use relations in the field. At whole plant level net carboxylation increases with PPFD reaching a saturating point. Light excess rather than improve photosynthesis may emphasize water and thermal stress leading to stomatal limitation. Furthermore too much light does not promote net carboxylation improvement but PSII damage, in fact in the most light exposed plants about 50-60% of the total PSII is inactivated. At single leaf level, net carboxylation increases till saturation point (1000 – 1200 μmolm-2s-1) and light excess is dissipated by non photochemical quenching and non net carboxylative transports. The latter follows a quite similar pattern of Pn/PPFD curve reaching the saturation point at almost the same photon flux density. At middle-low irradiance NPQ seems to be lumen pH limited because the incoming photon pressure is not enough to generate the optimum lumen pH for violaxanthin de-epoxidase (VDE) full activation. Peach leaves try to cope with the light excess increasing the non net carboxylative transports. While PPFD rises the xanthophyll cycle is more and more activated and the rate of non net carboxylative transports is reduced. Some of these alternative transports, such as the water-water cycle, the cyclic transport around the PSI and the glutathione-ascorbate cycle are able to generate additional H+ in lumen in order to support the VDE activation when light can be limiting. Moreover the alternative transports seems to be involved as an important dissipative way when high temperature and sub-optimal conductance emphasize the photoinhibition risks. In peach, a moderate water and light reduction does not determine net carboxylation decrease but, diminishing the incoming light and the environmental evapo-transpiration request, stomatal conductance decreases, improving water use efficiency. Therefore lowering light intensity till not limiting levels, water could be saved not compromising net photosynthesis. The quenching analysis is able to partition absorbed energy in the several utilization, photoprotection and photo-oxidation pathways. When recovery is permitted only few PSII remained un-repaired, although more net PSII damage is recorded in plants placed in full light. Even in this experiment, in over saturating light the main dissipation pathway is the non photochemical quenching; at middle-low irradiance it seems to be pH limited and other transports, such as photorespiration and alternative transports, are used to support photoprotection and to contribute for creating the optimal trans-thylakoidal ΔpH for violaxanthin de-epoxidase. These alternative pathways become the main quenching mechanisms at very low light environment. Another aspect pointed out by this study is the role of NPQ as dissipative pathway when conductance becomes severely limiting. The evidence that in nature a small amount of damaged PSII is seen indicates the presence of an effective and efficient recovery mechanism that masks the real photodamage occurring during the day. At single leaf level, when repair is not allowed leaves in full light are two fold more photoinhibited than the shaded ones. Therefore light in excess of the photosynthetic optima does not promote net carboxylation but increases water loss and PSII damage. The more is photoinhibition the more must be the photosystems to be repaired and consequently the energy and dry matter to allocate in this essential activity. Since above the saturation point net photosynthesis is constant while photoinhibition increases it would be interesting to investigate how photodamage costs in terms of tree productivity. An other aspect of pivotal importance to be further widened is the combined influence of light and other environmental parameters, like water status, temperature and nutrition on peach light, water and phtosyntate management.

Management and routing algorithms for ad-hoc and sensor networks

Large scale wireless adhoc networks of computers, sensors, PDAs etc. (i.e. nodes) are revolutionizing connectivity and leading to a paradigm shift from centralized systems to highly distributed and dynamic environments. An example of adhoc networks are sensor networks, which are usually composed by small units able to sense and transmit to a sink elementary data which are successively processed by an external machine. Recent improvements in the memory and computational power of sensors, together with the reduction of energy consumptions, are rapidly changing the potential of such systems, moving the attention towards datacentric sensor networks. A plethora of routing and data management algorithms have been proposed for the network path discovery ranging from broadcasting/floodingbased approaches to those using global positioning systems (GPS). We studied WGrid, a novel decentralized infrastructure that organizes wireless devices in an adhoc manner, where each node has one or more virtual coordinates through which both message routing and data management occur without reliance on either flooding/broadcasting operations or GPS. The resulting adhoc network does not suffer from the deadend problem, which happens in geographicbased routing when a node is unable to locate a neighbor closer to the destination than itself. WGrid allow multidimensional data management capability since nodes' virtual coordinates can act as a distributed database without needing neither special implementation or reorganization. Any kind of data (both single and multidimensional) can be distributed, stored and managed. We will show how a location service can be easily implemented so that any search is reduced to a simple query, like for any other data type. WGrid has then been extended by adopting a replication methodology. We called the resulting algorithm WRGrid. Just like WGrid, WRGrid acts as a distributed database without needing neither special implementation nor reorganization and any kind of data can be distributed, stored and managed. We have evaluated the benefits of replication on data management, finding out, from experimental results, that it can halve the average number of hops in the network. The direct consequence of this fact are a significant improvement on energy consumption and a workload balancing among sensors (number of messages routed by each node). Finally, thanks to the replications, whose number can be arbitrarily chosen, the resulting sensor network can face sensors disconnections/connections, due to failures of sensors, without data loss. Another extension to {WGrid} is {W*Grid} which extends it by strongly improving network recovery performance from link and/or device failures that may happen due to crashes or battery exhaustion of devices or to temporary obstacles. W*Grid guarantees, by construction, at least two disjoint paths between each couple of nodes. This implies that the recovery in W*Grid occurs without broadcasting transmissions and guaranteeing robustness while drastically reducing the energy consumption. An extensive number of simulations shows the efficiency, robustness and traffic road of resulting networks under several scenarios of device density and of number of coordinates. Performance analysis have been compared to existent algorithms in order to validate the results.

Probabilistic approaches in civil engineering: generation of random fields and structural identification with genetic algorithms

The inherent stochastic character of most of the physical quantities involved in engineering models has led to an always increasing interest for probabilistic analysis. Many approaches to stochastic analysis have been proposed. However, it is widely acknowledged that the only universal method available to solve accurately any kind of stochastic mechanics problem is Monte Carlo Simulation. One of the key parts in the implementation of this technique is the accurate and efficient generation of samples of the random processes and fields involved in the problem at hand. In the present thesis an original method for the simulation of homogeneous, multi-dimensional, multi-variate, non-Gaussian random fields is proposed. The algorithm has proved to be very accurate in matching both the target spectrum and the marginal probability. The computational efficiency and robustness are very good too, even when dealing with strongly non-Gaussian distributions. What is more, the resulting samples posses all the relevant, welldefined and desired properties of “translation fields”, including crossing rates and distributions of extremes. The topic of the second part of the thesis lies in the field of non-destructive parametric structural identification. Its objective is to evaluate the mechanical characteristics of constituent bars in existing truss structures, using static loads and strain measurements. In the cases of missing data and of damages that interest only a small portion of the bar, Genetic Algorithm have proved to be an effective tool to solve the problem.

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities. The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule. On this regard, since embedded systems tend to run the same set of applications for their entire lifetime, off-line, exact optimization approaches are particularly appealing. Quite surprisingly, the use of exact algorithms has not been well investigated so far; this is in part motivated by the complexity of integrated allocation and scheduling, setting tough challenges for ``pure'' combinatorial methods. The use of hybrid CP/OR approaches presents the opportunity to exploit mutual advantages of different methods, while compensating for their weaknesses. In this work, we consider in first instance an Allocation and Scheduling problem over the Cell BE processor by Sony, IBM and Toshiba; we propose three different solution methods, leveraging decomposition, cut generation and heuristic guided search. Next, we face Allocation and Scheduling of so-called Conditional Task Graphs, explicitly accounting for branches with outcome not known at design time; we extend the CP scheduling framework to effectively deal with the introduced stochastic elements. Finally, we address Allocation and Scheduling with uncertain, bounded execution times, via conflict based tree search; we introduce a simple and flexible time model to take into account duration variability and provide an efficient conflict detection method. The proposed approaches achieve good results on practical size problem, thus demonstrating the use of exact approaches for system design is feasible. Furthermore, the developed techniques bring significant contributions to combinatorial optimization methods.

Spectrum Sensing Algorithms for Cognitive Radio Applications

Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission. In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies.

Nanocrystalline Silicon Based Films for Renewable Energy Applications

The present thesis is focused on the study of innovative Si-based materials for third generation photovoltaics. In particular, silicon oxi-nitride (SiOxNy) thin films and multilayer of Silicon Rich Carbide (SRC)/Si have been characterized in view of their application in photovoltaics. SiOxNy is a promising material for applications in thin-film solar cells as well as for wafer based silicon solar cells, like silicon heterojunction solar cells. However, many issues relevant to the material properties have not been studied yet, such as the role of the deposition condition and precursor gas concentrations on the optical and electronic properties of the films, the composition and structure of the nanocrystals. The results presented in the thesis aim to clarify the effects of annealing and oxygen incorporation within nc-SiOxNy films on its properties in view of the photovoltaic applications. Silicon nano-crystals (Si NCs) embedded in a dielectric matrix were proposed as absorbers in all-Si multi-junction solar cells due to the quantum confinement capability of Si NCs, that allows a better match to the solar spectrum thanks to the size induced tunability of the band gap. Despite the efficient solar radiation absorption capability of this structure, its charge collection and transport properties has still to be fully demonstrated. The results presented in the thesis aim to the understanding of the transport mechanisms at macroscopic and microscopic scale. Experimental results on SiOxNy thin films and SRC/Si multilayers have been obtained at macroscopical and microscopical level using different characterizations techniques, such as Atomic Force Microscopy, Reflection and Transmission measurements, High Resolution Transmission Electron Microscopy, Energy-Dispersive X-ray spectroscopy and Fourier Transform Infrared Spectroscopy. The deep knowledge and improved understanding of the basic physical properties of these quite complex, multi-phase and multi-component systems, made by nanocrystals and amorphous phases, will contribute to improve the efficiency of Si based solar cells.

Development of a microgrid with renewable energy sources and electrochemical storage system integration

Beside the traditional paradigm of "centralized" power generation, a new concept of "distributed" generation is emerging, in which the same user becomes pro-sumer. During this transition, the Energy Storage Systems (ESS) can provide multiple services and features, which are necessary for a higher quality of the electrical system and for the optimization of non-programmable Renewable Energy Source (RES) power plants. A ESS prototype was designed, developed and integrated into a renewable energy production system in order to create a smart microgrid and consequently manage in an efficient and intelligent way the energy flow as a function of the power demand. The produced energy can be introduced into the grid, supplied to the load directly or stored in batteries. The microgrid is composed by a 7 kW wind turbine (WT) and a 17 kW photovoltaic (PV) plant are part of. The load is given by electrical utilities of a cheese factory. The ESS is composed by the following two subsystems, a Battery Energy Storage System (BESS) and a Power Control System (PCS). With the aim of sizing the ESS, a Remote Grid Analyzer (RGA) was designed, realized and connected to the wind turbine, photovoltaic plant and the switchboard. Afterwards, different electrochemical storage technologies were studied, and taking into account the load requirements present in the cheese factory, the most suitable solution was identified in the high temperatures salt Na-NiCl2 battery technology. The data acquisition from all electrical utilities provided a detailed load analysis, indicating the optimal storage size equal to a 30 kW battery system. Moreover a container was designed and realized to locate the BESS and PCS, meeting all the requirements and safety conditions. Furthermore, a smart control system was implemented in order to handle the different applications of the ESS, such as peak shaving or load levelling.

Nano-Power Integrated Circuits for Energy Harvesting

The energy harvesting research field has grown considerably in the last decade due to increasing interests in energy autonomous sensing systems, which require smart and efficient interfaces for extracting power from energy source and power management (PM) circuits. This thesis investigates the design trade-offs for minimizing the intrinsic power of PM circuits, in order to allow operation with very weak energy sources. For validation purposes, three different integrated power converter and PM circuits for energy harvesting applications are presented. They have been designed for nano-power operations and single-source converters can operate with input power lower than 1 μW. The first IC is a buck-boost converter for piezoelectric transducers (PZ) implementing Synchronous Electrical Charge Extraction (SECE), a non-linear energy extraction technique. Moreover, Residual Charge Inversion technique is exploited for extracting energy from PZ with weak and irregular excitations (i.e. lower voltage), and the implemented PM policy, named Two-Way Energy Storage, considerably reduces the start-up time of the converter, improving the overall conversion efficiency. The second proposed IC is a general-purpose buck-boost converter for low-voltage DC energy sources, up to 2.5 V. An ultra-low-power MPPT circuit has been designed in order to track variations of source power. Furthermore, a capacitive boost circuit has been included, allowing the converter start-up from a source voltage VDC0 = 223 mV. A nano-power programmable linear regulator is also included in order to provide a stable voltage to the load. The third IC implements an heterogeneous multisource buck-boost converter. It provides up to 9 independent input channels, of which 5 are specific for PZ (with SECE) and 4 for DC energy sources with MPPT. The inductor is shared among channels and an arbiter, designed with asynchronous logic to reduce the energy consumption, avoids simultaneous access to the buck-boost core, with a dynamic schedule based on source priority.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.