Material recognition by feature classification using time-of-flight camera

We propose a method for solving one of the significant open issues in computer vision: material recognition. A time-of-flight range camera has been employed to analyze the characteristics of different materials. Starting from the information returned by the depth sensor, different features of interest have been extracted using transforms such as Fourier, discrete cosine, Hilbert, chirp-z, and Karhunen-Loève. Such features have been used to build a training and a validation set useful to feed a classifier (J48) able to accomplish the material recognition step. The effectiveness of the proposed methodology has been experimentally tested. Good predictive accuracies of materials have been obtained. Moreover, experiments have shown that the combination of multiple transforms increases the robustness and reliability of the computed features, although the shutter value can heavily affect the prediction rates

Frequency Models and Control in Normal Operation: the Sardinia Case Study

Frequency signal is an indicator of the unbalance between the power generation and the load demand. Frequency power reserves in different timeframes are commonly deployed to keep this signal inside strict ranges around the nominal value. Reserves must be carefully dimensioned, and their dynamic performance correctly evaluated to enhance system security. This paper proposes a novel methodology to reproduce frequency fluctuations of entire days and to compute the power reserves activation dynamics by using a two-step process. Firstly, given a real power system frequency signal, a reverse aggregate model provides the unbalance in the system. Secondly, this unbalance is used to recreate and validate the original frequency signal by a forward aggregate model. After this procedure, Battery Energy Storage Systems (BESSs) are added and their impact on the frequency signal is quantified, in terms of different control schemes. The proposed method is tested in the real case of the Sardinian power system. Results show that this methodology can provide accurate estimation of the unbalance, frequency and reserves in the system, giving an understanding of the BESS impact on the frequency control

gene relevance based on multiple evidences in complex networks

Abstract Motivation Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers). Results We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios. Availability and implementation The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd. Supplementary information Supplementary data are available at Bioinformatics online

A multilevel data integration resource for breast cancer study

BACKGROUND: Breast cancer is one of the most common cancer types. Due to the complexity of this disease, it is important to face its study with an integrated and multilevel approach, from genes, transcripts and proteins to molecular networks, cell populations and tissues. According to the systems biology perspective, the biological functions arise from complex networks: in this context, concepts like molecular pathways, protein-protein interactions (PPIs), mathematical models and ontologies play an important role for dissecting such complexity. RESULTS: In this work we present the Genes-to-Systems Breast Cancer (G2SBC) Database, a resource which integrates data about genes, transcripts and proteins reported in literature as altered in breast cancer cells. Beside the data integration, we provide an ontology based query system and analysis tools related to intracellular pathways, PPIs, protein structure and systems modelling, in order to facilitate the study of breast cancer using a multilevel perspective. The resource is available at the URL http://www.itb.cnr.it/breastcancer. CONCLUSIONS: The G2SBC Database represents a systems biology oriented data integration approach devoted to breast cancer. By means of the analysis capabilities provided by the web interface, it is possible to overcome the limits of reductionist resources, enabling predictions that can lead to new experiments

Simultaneous sampling of vapor and particle-phase carcinogenic polycyclic aromatic hydrocarbons on functionalized glass fiber filters

The sampling of polycyclic aromatic hydrocarbons (PAHs) in the atmosphere is often performed on filters, which retain only aerosol particles, disregarding the vapor fraction; after the filter, an adsorbent (e.g., polyurethane foam, PUF, or styrene/divinylbenzene, XAD) is sometimes used for sampling vapors not retained from the filter. However, the use of an adsorbent may lead to many disadvantages: contaminations, analysis time and costs, and size problems when developing automated or personal samplers. In this work, a functionalized glass fiber filter for the simultaneous sampling of aerosol particles and vapor fraction is presented for the sampling of PAHs in air. A low sampling efficiency was observed for 3 ring PAHs, but all carcinogenic PAHs (according to IARC) were totally retained on functionalized filters. On the other hand, a comparison with normal filter sampling was performed, and results obtained confirm that > 10% of benzo(a)pyrene can be lost from normal filters. Together with size reduction, another advantage of the functionalized filter is an enhancement in the extraction and purification recovery. © Taiwan Association for Aerosol Research

Frequency Stability of the European Interconnected Power System Under Grid Splitting in Market Zones

This paper proposes a graph theory-based approach to define the possible separation of the market zones in large power systems. The market zone partitioning is used to assess the frequency stability based on a set of parameters, including the inertia, the running capacity of the separated areas, and the power exchanged on the interconnection lines. A system split indicator is finally used to rank the worst split lines. The methodology has been tested on real scenarios of the interconnected Continental Europe power system

Technical and Economic Impact of the Inertia Constraints on Power Plant Unit Commitment

The whole interconnected European network is involved in the energy transition towards power systems based on renewable power electronics interfaced generation. In this context, the major concerns for both network planning and operation are the inertia reduction and the frequency control due to the progressive decommissioning of thermal power plants with synchronous generators. This paper investigates the impact of different frequency control constraints on the unit commitment of power plants resulting from market simulations. The market outputs are compared in terms of system costs, and of frequency stability performance evaluated on the basis of the rate of change of frequency and the maximum frequency excursion. The best compromise solution is found using a multiple-criteria decision analysis method, depending on the choice of the decision maker’s weighting factors. The proposed approach is tested on a real case taken from one of the most relevant future scenarios of the Italian transmission system operator. The results show how the best compromise solution that can be found depends on the decision maker preference towards cost-based or frequency stability-based criteria

Profiling the Course of Resolving vs. Persistent Inflammation in Human Monocytes: The Role of IL-1 Family Molecules

Monocytes and macrophages have a central role in all phases of an inflammatory reaction. To understanding the regulation of monocyte activation during a physiological or pathological inflammation, we propose two in vitro models that recapitulate the different phases of the reaction (recruitment, initiation, development, and resolution vs. persistence of inflammation), based on human primary blood monocytes exposed to sequential modifications of microenvironmental conditions. These models exclusively describe the functional development of blood-derived monocytes that first enter an inflammatory site. All reaction phases were profiled by RNA-Seq, and the two models were validated by studying the modulation of IL-1 family members. Genes were differentially modulated, and distinct clusters were identified during the various phases of inflammation. Pathway analysis revealed that both models were enriched in pathways involved in innate immune activation. We observe that monocytes acquire an M1-like profile during early inflammation, and switch to a deactivated M2-like profile during both the resolving and persistent phases. However, during persistent inflammation they partially maintain an M1 profile, although they lose the ability to produce inflammatory cytokines compared to M1 cells. The production of IL-1 family molecules by ELISA reflected the transcriptomic profiles in the distinct phases of the two inflammatory reactions. Based on the results, we hypothesize that persistence of inflammatory stimuli cannot maintain the M1 activated phenotype of incoming monocytes for long, suggesting that the persistent presence of M1 cells and effects in a chronically inflamed tissue is mainly due to activation of newly incoming cells. Moreover, being IL-1 family molecules mainly expressed and secreted by monocytes during the early stages of the inflammatory response (within 4-14 h), and the rate of their production decreasing during the late phase of both resolving and persistent inflammation, we suppose that IL-1 factors are key regulators of the acute defensive innate inflammatory reaction that precedes establishment of longer-term adaptive immunity, and are mainly related to the presence of recently recruited blood monocytes. The well-described role of IL-1 family cytokines and receptors in chronic inflammation is therefore most likely dependent on the continuous influx of blood monocytes into a chronically inflamed site

A technology platform for automatic high-level tennis game analysis

Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours