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

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

The immediate impacts of COVID-19 on European electricity systems: a first assessment and lessons learned

The worldwide spread of the COVID-19 pandemic in 2020 forced most countries to intervene with policies and actions—including lockdowns, social-distancing and smart working measures—aimed at mitigating the health system and socio-economic disruption risks. The electricity sector was impacted as well, with performance largely reflecting the changes in the industrial and commercial sectors operations and in the social behavior patterns. The most immediate consequences concerned the power demand profiles, the generation mix composition and the electricity price trends. As a matter of fact, the electricity sectors experienced a foretaste of the future, with higher renewable energy penetration and concerns for security of supply. This paper presents a systemic approach toward assessing the impacts of the COVID-19 pandemic on the power sector. This is aimed at supporting decision making—particularly for policy makers, regulators, and system operators—by quantifying shorter term effects and identifying longer term impacts of the pandemic waves on the power system. Various metrics are defined in different areas—system operation, security, and electricity markets—to quantify those impacts. The methodology is finally applied to the European power system to produce a comparative assessment of the effects of the lockdown in the European context

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

: In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy