A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.     

Eugenol,citral, and hexanal,alone or in combination with heat,affect viability,biofilm formation,and swarming on Shiga-toxin-producing Escherichia coli

Shiga-toxin-producing Escherichia coli strains are pathogenic for humans and cause mild to severe illnesses. In this study, the antimicrobial effect of citral, eugenol, and hexanal in combination with heat shock (HS) was evaluated in terms of the growth, biofilm formation, swarming, and expression of virulence genes of STEC serotypes (O157:H7, O103, O111, and O26). Eugenol was the most effective compound against the growth of E. coli strains (MBC = 0.58 to 0.73 mg/mL), followed by citral (MBC = 0.86 to 1.26 mg/mL) and hexanal (MBC = 2.24 to 2.52 mg/mL). Biofilm formation and swarming motility have great variability between STEC strains. Natural compounds—alone or combined with HS—inhibited biofilm formation; however, swarming motility was induced by most treatments. The expression of the studied genes during biofilm formation and swarming under natural antimicrobials was affected but not in a uniform pattern. These treatments could be used to control contamination of STEC and inhibit biofilm formation.     

Investigating measurements of fine particle (PM2.5) emissions from the cooking of meals and mitigating exposure using a cooker hood

There is growing awareness that indoor exposure to particulate matter with diameter ≤ 2.5 μm (PM_2.5) is associated with an increased risk of adverse health effects. Cooking is a key indoor source of PM_2.5 and an activity conducted daily in most homes. Population scale models can predict occupant exposures to PM_2.5, but these predictions are sensitive to the emission rates used. Reported emission rates are highly variable and are typically for the cooking of single ingredients and not full meals. Accordingly, there is a need to assess PM_2.5 emissions from the cooking of complete meals. Mean PM_2.5 emission rates and source strengths were measured for four complete meals. Temporal PM_2.5 concentrations and particle size distributions were recorded using an optical particle counter (OPC), and gravimetric sampling was used to determine calibration factors. Mean emission rates and source strengths varied between 0.54—3.7 mg/min and 15—68 mg, respectively, with 95% confidence. Using a cooker hood (apparent capture efficiency > 90%) and frying in non‐stick pans were found to significantly reduce emissions. OPC calibration factors varied between 1.5 and 5.0 showing that a single value cannot be used for all meals and that gravimetric sampling is necessary when measuring PM_2.5 concentrations in kitchens.     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.     

Improvement in the properties of low carbon MgO-C refractories through the addition of graphite-SiC micro-composite

Graphite–SiC micro-composites have been prepared in–house by carbothermal reduction process. Controlling the process parameters including the weight ratio of SiO₂ to graphite as well as carbothermal reduction temperature during the micro-composite preparation favors the homogeneous formation of SiC with preferred morphologies like ribbons and whiskers/fibers. The micro-composite modified low carbon MgO-C refractories have exhibited significantly improved bulk properties over the standard composition. To understand the beneficial role of SiC reinforcement on hot strength performance under air oxidizing conditions, we propose a scaling parameter known as strength factor (fs) based on the ratio of hot strength (HMOR) to cold strength (CCS). Correlating the strength factor data (fs) with oxidative damage provides new insights into the reinforcing effects of distinct SiC morphologies in this new class of micro-composite fortified refractory systems over the standard compositions.     

Structural Cues for Understanding eEF1A2 Moonlighting

Spontaneous mutations in the EEF1A2 gene cause epilepsy and severe neurological disabilities in children. The crystal structure of eEF1A2 protein purified from rabbit skeletal muscle reveals a post-translationally modified dimer that provides information about the sites of interaction with numerous binding partners, including itself, and maps these mutations onto the dimer and tetramer interfaces. The spatial locations of the side chain carboxylates of Glu301 and Glu374, to which phosphatidylethanolamine is uniquely attached via an amide bond, define the anchoring points of eEF1A2 to cellular membranes and interorganellar membrane contact sites. Additional bioinformatic and molecular modeling results provide novel structural insight into the demonstrated binding of eEF1A2 to SH3 domains, the common MAPK docking groove, filamentous actin, and phosphatidylinositol-4 kinase IIIβ. In this new light, the role of eEF1A2 as an ancient, multifaceted, and articulated G protein at the crossroads of autophagy, oncogenesis and viral replication appears very distant from the “canonical” one of delivering aminoacyl-tRNAs to the ribosome that has dominated the scene and much of the thinking for many decades.     

Synthesis of Diamond@SiO2@Ag composite materials for high SERS effect

Among various carbon materials, diamond stands out due to excellent physical and chemical properties. In this work, we designed Dia@SiO₂@Ag composites combining diamond micropowder and Ag nanoparticles by a simple chemical method and obtained stable substrate for surface enhanced Raman scattering (SERS) owing to its high surface-to-volume ratio, low density, as well as close bond between diamond and Ag. As-prepared Dia@SiO₂@Ag presented high activity to detect crystal violet and rhodamine 6G molecules, which was demonstrated by significantly enhanced SERS spectra and high enhancement factor values (10⁸-10⁹). Moreover, Dia@SiO₂@Ag also showed desired sensitivity, which was investigated by detection limit. Therefore, our study provided more theoretical support and broadened the functional applications of diamond, particularly in Raman detection.     

Chemical Bonding by the Chemical Orthogonal Space of Reactivity

The fashionable Parr–Pearson (PP) atoms-in-molecule/bonding (AIM/AIB) approach for determining the exchanged charge necessary for acquiring an equalized electronegativity within a chemical bond is refined and generalized here by introducing the concepts of chemical power within the chemical orthogonal space (COS) in terms of electronegativity and chemical hardness. Electronegativity and chemical hardness are conceptually orthogonal, since there are opposite tendencies in bonding, i.e., reactivity vs. stability or the HOMO-LUMO middy level vs. the HOMO-LUMO interval (gap). Thus, atoms-in-molecule/bond electronegativity and chemical hardness are provided for in orthogonal space (COS), along with a generalized analytical expression of the exchanged electrons in bonding. Moreover, the present formalism surpasses the earlier Parr–Pearson limitation to the context of hetero-bonding molecules so as to also include the important case of covalent homo-bonding. The connections of the present COS analysis with PP formalism is analytically revealed, while a numerical illustration regarding the patterning and fragmentation of chemical benchmarking bondings is also presented and fundamental open questions are critically discussed.