Residual Fluctuations Research Articles

Quantum Coulomb glass on the Bethe lattice

We study the Coulomb glass emerging from the interplay of strong interactions and disorder in a model of spinless fermions on the Bethe lattice. In the infinite coordination number limit, strong interactions induce a metallic Coulomb glass phase with a pseudogap structure at the Fermi energy. Quantum and thermal fluctuations both melt this glass and induce a disordered quantum liquid phase. We combine self-consistent diagrammatic perturbation theory with continuous time quantum Monte-Carlo simulations to obtain the complete phase diagram of the electron glass and to characterize its dynamical properties in the quantum liquid, as well as in the replica symmetry broken glassy phase. Tunneling spectra display an Efros-Shklovskii pseudogap upon decreasing temperatures, but the density of states remains finite at the Fermi energy due to residual quantum fluctuations. Our results bear relevance to the metallic glass phase observed in Si inversion layers.

The Molecular Basis of the Effect of Temperature on the Structure and Function of SARS-CoV-2 Spike Protein

The remarkable rise of the current COVID-19 pandemic to every part of the globe has raised key concerns for the current public healthcare system. The spike (S) protein of SARS-CoV-2 shows an important part in the cell membrane fusion and receptor recognition. It is a key target for vaccine production. Several researchers studied the nature of this protein under various environmental conditions. In this work, we applied molecular modeling and extensive molecular dynamics simulation approaches at 0°C (273.15 K), 20°C (293.15 K), 40°C (313.15 K), and 60°C (333.15 K) to study the detailed conformational alterations in the SARS-CoV-2 S protein. Our aim is to understand the influence of temperatures on the structure, function, and dynamics of the S protein of SARS-CoV-2. The structural deviations, and atomic and residual fluctuations were least at low (0°C) and high (60°C) temperature. Even the internal residues of the SARS-CoV-2 S protein are not accessible to solvent at high temperature. Furthermore, there was no unfolding of SARS-CoV-2 spike S reported at higher temperature. The most stable conformations of the SARS-CoV-2 S protein were reported at 20°C, but the free energy minimum region of the SARS-CoV-2 S protein was sharper at 40°C than other temperatures. Our findings revealed that higher temperatures have little or no influence on the stability and folding of the SARS-CoV-2 S protein.

Anti-Fungal Drug Anidulafungin Inhibits SARS-CoV-2 Spike-Induced Syncytia Formation by Targeting ACE2-Spike Protein Interaction.

Drug repositioning continues to be the most effective, practicable possibility to treat COVID-19 patients. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters target cells by binding to the ACE2 receptor via its spike (S) glycoprotein. We used molecular docking-based virtual screening approaches to categorize potential antagonists, halting ACE2-spike interactions by utilizing 450 FDA-approved chemical compounds. Three drug candidates (i.e., anidulafungin, lopinavir, and indinavir) were selected, which show high binding affinity toward the ACE2 receptor. The conformational stability of selected docked complexes was analyzed through molecular dynamics (MD) simulations. The MD simulation trajectories were assessed and monitored for ACE2 deviation, residue fluctuation, the radius of gyration, solvent accessible surface area, and free energy landscapes. The inhibitory activities of the selected compounds were eventually tested in-vitro using Vero and HEK-ACE2 cells. Interestingly, besides inhibiting SARS-CoV-2 S glycoprotein induced syncytia formation, anidulafungin and lopinavir also blocked S-pseudotyped particle entry into target cells. Altogether, anidulafungin and lopinavir are ranked the most effective among all the tested drugs against ACE2 receptor-S glycoprotein interaction. Based on these findings, we propose that anidulafungin is a novel potential drug targeting ACE2, which warrants further investigation for COVID-19 treatment.

Gaussian network model revisited: effects of mutation and ligand binding on protein behavior

The coarse-grained Gaussian network model (GNM), considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including all atom pairs within the coordination shell of each other into the Kirchoff adjacency matrix. Counting all contacts rather than only alpha carbon contacts diminishes the magnitude of fluctuations in the system. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff adjacency matrix of the protein. This change depends on amino acid type which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modeled in analogy with the phantom network model of rubber elasticity. With this modification, it is now possible to explain the consequences of mutation and ligand binding on residue fluctuations, their pair-correlations and mutual information shared by each pair. We refer to the new model as ‘all-atom GNM’. Using examples from published data we show that the all-atom GNM gives B-factors that are in better agreement with experiment, can explain effects of mutation on long range communication in PDZ domains and can predict effects of GDP and GTP binding on the dimerization of KRAS.

Simulation study of the function of domain swapping in the HSP90 chaperone cycle

HSP90 is one of the molecular chaperones, and it is known as an anti-cancer drug target. In the HSP90 chaperone cycle, when the ATP binds to the HSP90NTD, the HSP90 dimer forms a domain swapping in NTD. The stability of the HSP90 dimer is essential to the HSP90 chaperon cycle process. We have performed several molecular dynamics simulations of the HSP90 dimer to investigate how ATP binding and domain swapping have affected the stability. We analysed the fluctuation of critical residues for ATP binding and ATP hydrolysis in the HSP90NTD dimer conformation. As a result, we found that domain swapping is essential to the stability of the HSP90 dimer, while ATP is not so essential for the stability of the dimer conformation.

Factors That Determine the Variation of Equilibrium and Kinetic Properties of QM/MM Enzyme Simulations: QM Region, Conformation, and Boundary Condition.

To analyze the impact of various technical details on the results of quantum mechanical (QM)/molecular mechanical (MM) enzyme simulations, including the QM region size, catechol-O-methyltransferase (COMT) is studied as a model system using an approximate QM/MM method (DFTB3/CHARMM). The results show that key equilibrium and kinetic properties for methyl transfer in COMT exhibit limited variations with respect to the size of the QM region, which ranges from ∼100 to ∼500 atoms in this study. With extensive sampling, local and global structural characteristics of the enzyme are largely conserved across the studied QM regions, while the nature of the transition state (e.g., secondary kinetic isotope effect) and reaction exergonicity are largely maintained. Deviations in the free energy profile with different QM region sizes are similar in magnitude to those observed with changes in other simulation protocols, such as different initial enzyme conformations and boundary conditions. Electronic structural properties, such as the covariance matrix of residual charge fluctuations, appear to exhibit rather long-range correlations, especially when the peptide backbone is included in the QM region; this observation holds when a range-separated DFT approach is used as the QM region, suggesting that delocalization error is unlikely the origin. Overall, the analyses suggest that multiple simulation details determine the results of QM/MM enzyme simulations with comparable contributions.

Modeling Random Exit Selection in Intercity Expressway Traffic with Quantum Walk

In intercity expressway traffic, the multiplicity of available routes leads to randomness in exit selection. Random exit selection by drivers is hard to observe, and thus it is a challenge to model intercity expressway traffic sufficiently. In this paper, we developed a Random Quantum Traffic Model (RQTM), which modeled the stochastic traffic fluctuation caused by random exit selection and the residual regularity fluctuation with the quantum walk and autoregressive moving average model (ARMA), respectively. The RQTM considered the random exit selection of a driver as a quantum stochastic process with a dynamic probability function. A quantum walk was applied to update the probability function, which simulated when and where a driver will leave the expressway. We validated our model with hourly traffic data from seven exits from the Nanjing–Changzhou expressway in eastern China. For the seven exits, the coefficients of determination of the RQTM ranged from 0.5 to 0.85. Compared with the classical random walk and the ARMA model, the coefficients of determination were increased by 21.28% to 104.98%, and the relative mean square error decreased by 11.61% to 32.92%. We conclude that the RQTM provides new potential for modeling traffic dynamics with consideration of unobservable random driver decision making.

Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

Commercial virtual power plants (CVPP) connect the form of renewable energy resource portfolio to the power market and reduce the risk of the unstable operation of a single renewable energy. Combining different kinds of large-scale renewable energy in CVPP to provide capacity services like base load, peak shaving, and valley-filling, etc., for the system loads is an urgent problem to be solved. Therefore, it is valuable to analyze the capacity allocation ratio of the CVPP to maximize the utilization of all kinds of energy, especially for the large-scale multi-energy base. This paper proposed a multi-energy coordinated operation framework by considering various load demands, including base load and peak shaving for the capacity allocation of CVPP based on the world’s largest renewable energy resource base on the upstream area of the Yellow River. The main procedures of this framework are as follows: (1) A paratactic model satisfying base load and peak shaving is proposed to determine the ability of the CVPP operation model’s capacity services to meet the different demands of the power system load. (2) A hybrid dimension reduction algorithm with a better convergence rate and optimization effect solves the proposed paratactic model based on the ReliefF and the Adaptive Particle Swarm Optimization (APSO). The results show that the large-scale CVPP with different compositions can achieve both of the goals of a stable base load output and stable residual load under different weather conditions. Compared with the operation on sunny days, the base load fluctuation and residual load fluctuation of CVPP on rainy days are reduced by 14.5% and 21.9%, respectively, proving that CVPP can alleviate renewable energy’s dependence on weather and improve energy utilization.

Identification of Halogen-Based Derivatives as Potent Inhibitors of Estrogen Receptor Alpha of Breast Cancer: An In-Silico Investigation

Breast cancer is the most common cancer among women worldwide. Breast cancer is caused by the overexpression of genes that facilitate breast cell proliferation. Estrogen receptor alpha (ER[Formula: see text]) mediation is mostly responsible for the development of malignant tumors by regulating the transcription of various genes as a transcription factor. ER[Formula: see text] is regarded as an important receptor for the proliferation of this disease and its inhibition is necessary for the treatment of breast cancer. In our study, the aim is to find out potential inhibitors for ER[Formula: see text] by docking 100 anticancer constituents of different halogen-based derivatives against ER[Formula: see text]. Among the 100 [Formula: see text]-based derivatives, 20 ligands were selected based on the interaction energy ranging from [Formula: see text]6.7[Formula: see text]kcal/mol to [Formula: see text]8.2[Formula: see text]kcal/mol and lower values of inhibition constant (0.92–11.77[Formula: see text][Formula: see text]). We have performed a comprehensive analysis of five most popular ligands (L17, L57, L61, L67, and L70) among these 20 selected derivatives. The interaction analysis is mainly stabilized by making different interactions including hydrogen bonding, hydrophobic, electrostatic, and halogen bonding with the active site of ER[Formula: see text]. A quantum study based on frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP), and global chemical reactivity descriptor (GCRD) is carried out to explore the structural chemistry of our most popular ligands which indicates that they are quite reactive and kinetically stable. Additionally, we also assess the ADMET profiles to predict the toxicity and drug-likeness of our lead compounds. Our selected ligands have the highest absorption and good clearance rate and have no AMES toxicity, skin sensitization, and hepatotoxicity. Molecular dynamics simulations were run to check the conformational stability of apo form of ER[Formula: see text] and complex state, at 60-ns time scale. The molecular dynamics simulations are based on the plots of RMSD, RMSF, [Formula: see text], SASA, and the number of H-bonds. The results of RMSD and RMSF showed that the lead compounds L17, L57, L61, L67, and L70 are most stable and have no significant residual fluctuations and deviation. The analysis of the plots of [Formula: see text], SASA, and the number of H-bonds revealed that the complexes L17, L57, and L70 are most stable. So, the lead anticancer compounds L17, L57, L61, L67, and L70 are the most promising inhibitors against ER[Formula: see text] of breast cancer. The comprehensive analysis of all studied parameters highlighted that our selected ligands have great potential to inhibit ER[Formula: see text]. So, it can be concluded that the selected halogen-based derivatives can promote rational drug design for the target therapies of ER[Formula: see text] of breast cancer.

Analyses of experiment based ramachandran plots of tri-, tetra- and pentapeptides reveal residue dependent interactions between neighbors

Flory's canonical random coil model is based on the assumption that conformational fluctuations of amino acid residues in unfolded poly(oligo)peptides and proteins are uncorrelated (isolated pair hypothesis, IPH). It implies that conformational energies, entropies, and solvation free energies are all additive. Nearly 25 years ago, analyses of coil libraries casted some doubt on this notion in that they revealed that aromatic residues could change the 3J(HNHCα) coupling of their neighbors. Since then multiple lines of bioinformatical, computational and lately also experimental evidence indicate that the conformational propensities of amino acids in unfolded peptides and proteins depend on their nearest neighbors and possibly even on the nature of the second neighbor.

Thunderstorm-induced mean wind velocities and accelerations through the continuous wavelet transform

By analysing a set of thunderstorm-induced events acquired on the Italian north-west coast, the separation of the relevant wind velocity signals into a time-varying mean and a residual fluctuation is considered. It is assumed that, within a structural design process, the former is used for a quasi-steady evaluation of the aerodynamic loads and the latter to estimate the dynamic response. A procedure based on the continuous wavelet transform is proposed to obtain the time-varying mean velocity and its outcomes are compared with those of different moving average techniques. The wavelet filtering appears to provide a time-varying mean that better describes the main time variations of the original velocity signals; a decrease of the correlation between mean and residual velocities is also found. The derivative of the time-varying mean wind speed leads to a time-varying mean acceleration, whose characteristics and possible role in a design process are discussed. An estimate of the acceleration-induced contribution to the aerodynamic loads on a slender body is also carried out through the Morison equation. The force term directly related to acceleration is shown to generally have a limited influence on the total design loads, except for bodies that are considerably elongated in the flow direction.

Dual-Inhibition of Human N-Myristoyltransferase Subtypes Halts Common Cold Pathogenesis: Atomistic Perspectives from the Case of IMP-1088.

The pharmacological inhibition of human N-myristoyltransferase (HsNMT) has emerged as an efficient strategy to completely prevent the replication process of rhinoviruses, a potential treatment for the common cold. This was corroborated by the recent discovery of compound IMP-1088, a novel inhibitor that demonstrated a dual-inhibitory activity against the two HsNMT subtypes 1 and 2 without inducing cytotoxicity. However, the molecular and structural basis for the dual-inhibitory potential of IMP-1088 has not been investigated. As such, we employ molecular modelling techniques to resolve the structural mechanisms that account for the dual-inhibitory prowess of IMP-1088. Sequence and nanosecond-based analyses identified Tyr296, Phe190, Tyr420, Leu453, Gln496, Val181, Leu474, Glu182, and Asn246 as residues common within the binding pockets of both HsNMT1 and HsNMT2 subtypes whose consistent interactions with IMP-1088 underpin the basis for its dual inhibitory potency. Nano-second-based assessment of interaction dynamics revealed that Tyr296 consistently elicited high-affinity π-π stacked interaction with IMP-1088, thus further highlighting its cruciality corroborating previous report. An exploration of resulting structural changes upon IMP-1088 binding further revealed a characteristic impeding of residue fluctuations, structural compactness, and a consequential burial of crucial hydrophobic residues, features required for HsNMT1/2 functionality. Findings present essential structural perspectives that augment previous experimental efforts and could also advance drug development for treating respiratory tract infections, especially those mediated by rhinoviruses.

The Natural Ligand for Metalloproteinase-A Multifaceted Drug Target.

Metalloproteinase is one of the key components of Russell viper venom and it is the root cause of edema, blood coagulation, local tissue damage, hemorrhage, and inflammation during snakebite envenoming. Hence, finding a suitable metalloproteinase inhibitor from natural source will be of great biological importance in mitigating pathological effects. In this current study, we employed computational analysis to examine the inhibition of metalloproteinase by phytochemicals present in Andrographis paniculata. Molecular docking studies revealed interaction of A. paniculata phytochemicals with the catalytic M domain's active site amino acid residues, namely ASN203, ARG293, PHE203, LEU206, LYS199, and ALA122, similar to that of the reference compound Batimastat. 14-acetylandrographolide, 14-deoxy-11,12 didehydroandrographolide, Andrograpanin, Isoandrographolide, and 14-deoxy-11-oxoandrographolide displayed high binding energy and inhibition against the metalloproteinase. Molecular dynamic simulation analysis revealed less root mean square fluctuation of amino acid residues of metalloproteinase-14-acetylandrographolide complex than metalloproteinase-Batimastat complex indicating the high stability for metalloproteinase with the phytochemical. In silico analysis of parameters like ADME properties and drug-likeness of the phytochemicals exhibited good pharmacokinetic properties. Ligand-based virtual screening of phytochemicals to identify similarity to FDA-approved drugs and identification of their possible targets were also performed. The outcome of the current study strengthens the significance of these phytochemicals as promising lead candidates for the treatment of snakebite envenomation. Moreover, the study also encourages the in vivo and in vitro evaluation of the phytochemicals to validate the computational findings.

Extreme Level Fluctuations in the Seas of the Russian Arctic in the Context of Global Climate Change

The presented article is devoted to the results of detailed studies of the features of extreme sea level fluctuations in the Russian Arctic under the conditions of global climate change. Estimates of the rate of mean sea level rise in the Arctic are obtained from the monthly mean sea level data at coastal tide gauges and satellite altimetry. It was found that for the Arctic Ocean coast from the Kara to the Chukchi Seas, the highest tides take place in the Khatan- ga Bay (Preobrazheniya Island), 131 cm. In the White, Kara, Laptev and Chukchi Seas, the seasonal variability of harmonic tidal constants was studied; it occurs due to changes in hydrometeorological conditions during the year. The ADCIRC hydrodynamic model was adapted for the conditions of the Laptev Sea. Numerical modeling of sea level oscillations from 1980 to 2000 was performed. According to the simulation results, the maximum ranges of residual sea level fluctuations are observed both for the total level and at the mouth of the Khatanga (489 cm) and Oleneksky Bay (444 cm). Based on the data of coastal observations and numerical modeling, the authors obtained estimates of extreme level fluctuations with different recurrence periods.

Biophysical and Structural Characterization of Ribulose-5-phosphate Epimerase from Leishmania donovani.

Pentose phosphate pathway (PPP) plays a crucial role in the maintenance of NADPH/NADP+ homeostasis and provides protection against oxidative stress through detoxification of the reactive oxygen species. Ribulose-5-phosphate epimerase (RPE) participates in catalysis of the interconversion of ribulose-5-phosphate (Ru5P) to xylulose-5-phosphate (Xu5P) during PPP, however the structural attributes of this enzyme are still underexplored in many human pathogens including leishmanial parasites. The present study focuses upon cloning, purification and characterization of RPE of Leishmania donovani (LdRPE) using various biophysical and structural approaches. Sequence analysis has shown the presence of trypanosomatid-specific insertions at the N-terminus that are absent in humans and other eukaryotes. Gel filtration chromatography indicated recombinant LdRPE to exist as a dimer in the solution. Circular dichroism studies revealed a higher alpha helical content at physiological pH and temperature that comparatively varies with changing these parameters. Additionally, intrinsic fluorescence and quenching studies of LdRPE have depicted that tryptophan residues are mainly buried in the hydrophobic regions, and the recombinant enzyme is moderately tolerant to urea. Moreover, homology modeling was employed to generate the three-dimensional structure of LdRPE followed by molecular docking with the substrate, product, and substrate analogues. The modeled structure of LdRPE unravelled the presence of conserved active site residues as well as a single binding pocket for the substrate and product, while an in silico study suggested binding of substrate analogues into a similar pocket with more affinity than the substrate. Additionally, molecular dynamics simulation analysis has deciphered complexes of LdRPE with most of the ligands exhibiting more stability than its apo form and lesser fluctuations in active site residues in the presence of ligands. Altogether, our study presents structural insights into leishmanial RPE that could provide the basis for its implication to develop potent antileishmanials.

Susceptibility of spike glycoprotein and RNA-dependent RNA polymerase of SARS-CoV-2 to mutation: in silico structural dynamics study

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a microorganism that causes coronavirus disease 2019 (COVID-19). Mutations affect evolutionary conservation of microorganisms. The fast pace evolutionary changes are currently affecting pathogenicity of SARS-CoV-2. In this study, the structural fluctuations of the amino acid residues in the spike glycoprotein and RNA-dependent RNA polymerase (nsp12) of SARS-CoV-2 were investigated by in silico approach using structural flexibility dynamics to decipher susceptibility to mutation. The result of this study implicated key amino acid residues (with rmsf) which could be very susceptible to mutation, which include residues 50 (3.79 Å), 119 (4.56 Å), 120 (3.53 Å), 220 (3.84 Å), 265 (4.31 Å) of RNA-dependent RNA polymerase (nsp12), as well as residues 477 (4.21 Å), 478 (4.82 Å), 479 (5.40 Å), 481 (5.94 Å), 560 (4.63 Å), 704 (4.02 Å), 848 (4.58 Å), 1144 (4.56 Å) and 1147 (4.61 Å) of spike glycoprotein. The SARS-CoV-2 mutations destabilized the overall protein structure in multiples of amino acid residues which could interfere with active site leading to insensitivity or resistance to the inhibitors. Mutation T478K of Spike glycoprotein showed the highest deviation in the structure. Overall, spike glycoprotein has the highest number of mutations, and these variants could increase the risk to human health if not mitigated in the population.

Comparison of DOFS Attachment Methods for Time-Dependent Strain Sensing.

Structural health monitoring (SHM) is a challenge for many industries. Over the last decade, novel strain monitoring methods using optical fibers have been implemented for SHM in aerospace, energy storage, marine, and civil engineering structures. However, the practical attachment of optical fibers (OFs) to the component is still problematic. While monitoring, the amount of substrate strain lost by the OF attachment is often unclear, and difficult to predict under long-term loads. This investigation clarifies how different attachment methods perform under time-dependent loading. Optical fibers are attached on metal, thermoset composite, and thermoplastic substrates for distributed strain sensing. Strains along distributed optical fiber sensors (DOFS) are measured by optical backscatter reflectometry (OBR) and compared to contact extensometer strains under tensile creep loading. The quality of the bondline and its influence on the strain transfer is analyzed. Residual strains and strain fluctuations along the sensor fiber are correlated to the fiber attachment method. Results show that a machine-controlled attachment process (such as in situ 3-D printing) holds great promise for the future as it achieves a highly uniform bondline and provides accurate strain measurements.

Secondary Structure and Conformational Stability of the Antigen Residues Making Contact with Antibodies.

Antibodies are crucial biomolecules that bring high therapeutic efficacy in medicine and accurate molecular detection in diagnosis. Many studies have been devoted to analyzing the antigen-antibody interaction from the importance of understanding the antibody recognition mechanism. However, most of the previous studies examined the characteristic of the antibody for interaction. It is also informative to clarify the significant antigen residues contributing to the binding. To characterize the molecular interaction of antigens, we computationally analyzed 350 antigen-antibody complex structures by molecular mechanics (MM) calculations and molecular dynamics (MD) simulations. Based on the MM calculations, the antigen residues contributing to the binding were extracted from all the 350 complexes. The extracted residues are located at the antigen-antibody interface and are responsible for making contact with the antibody. The appearances of the charged polar residues, Asp, Glu, Arg, and Lys, were noticeably large. In contrast, the populations of the hydrophobic residues, Leu, Val, and Ala, were relatively low. The appearance frequencies of the other amino acid residues were almost close to the abundance of general proteins of eukaryotes. The binding score indicated that the hydrophilic interaction was dominant at the antigen-antibody contact instead of the hydrophobic one. The positively charged residues, Arg and Lys, remarkably contributed to the binding compared to the negatively charged ones, Asp and Glu. Considerable contributions were also observed for the noncharged polar residues, Asn and Gln. The analysis of the secondary structures of the extracted antigen residues suggested that there was no marked difference in recognition by antibodies among helix, sheet, turn, and coil. A long helix of the antigen sometimes made contact with antibody complementarity-determining regions, and a large sheet also frequently covered the antibody heavy and light chains. The turn structure was the most popularly observed at the contact with antibody among 350 complexes. Three typical complexes were picked up for each of the four secondary structures. MD simulations were performed to examine the stability of the interfacial structures of the antigens for these 12 complex models. The alterations of secondary structures were monitored through the simulations. The structural fluctuations of the contact residues were low compared with the other domains of antigen molecules. No drastic conversion was observed for every model during the 100 ns simulation. The motions of the interfacial antigen residues were small compared to the other residues on the protein surface. Therefore, diverse molecular conformations are possible for antibody recognition as long as the target areas are polar, nonflexible, and protruding on the protein surface.

Spatiotemporal just noticeable difference modeling with heterogeneous temporal visual features

Developing accurate Just-noticeable difference (JND) models are challenged by complicated HVS characteristics and nonstationary features of video sequence. Great efforts have been devoted to JND modeling, and inspiring performance improvements are witnessed in the literature, especially spatial JND models. However, there are not only urgent requirement but also technical potentiality for improving temporal JND models fully accounting for the temporal perception characteristics. In terms of temporal JND modeling, there are two challenges, one is how to extract perceptual feature parameters of source video, and the other is how to quantitatively characterize the interaction relationship between feature parameters and HVS characteristics? Firstly, this work extracts content-aware temporal feature parameters having predominate impacts on vision perception, including motion (foreground/background), pixel-correspondence duration and inter-frame residue fluctuation intensity along temporal trajectory, and investigates the HVS responses to these four heterogeneous feature parameters. Secondly, this work proposes respective probability density functions (PDF) in the perception sense to quantitatively depict the attention and suppression perception responses of feature parameters, accounting for the temporal perception characteristics. Using these PDF models, we fuse the heterogeneous feature parameters from the viewpoint of uniform dimension,i.e. self-information measured visual attention and information entropy measured masking uncertainty, achieving heterogeneous parameter homogenization. Thirdly, with self-information and entropy results, this work then proposes a temporal weight model, by striking the balance between visual attention and masking suppression, to adjust the spatial JND threshold, and then develops the improved spatiotemporal JND model. Intensive simulation results verity the effectiveness of the proposed spatiotemporal JND profile, with competitive model accuracy compared with the-state-of-the-art candidate models.

Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault

Taking a 1000 MW turbine generator as the research object, the short-circuit fault in electrical disturbance is analyzed. Since it is very difficult to carry out fault analysis experiments and research on actual systems, simulation analysis is one of the more effective means of electrical fault diagnosis; the simulation’s results approach the actual behavior of the system and are ideal tools for power system analysis, and can provide an empirical basis for practical applications. The short-circuit fault model of the SIMULINK power system is built to analyze the two types of faults of generator terminals short-circuit and power grid short-circuit. The impact load spectrum, fault current and speed fluctuation between low-voltage rotors were extracted and analyzed. The conclusion is that the impact value of electromagnetic torque at the generator terminal is greater than that on the power grid side. The impact value of a two-phase short-circuit at the generator terminal is the largest, and that of a three-phase short-circuit on the power grid side is the smallest. The transient impulse current of a three-phase short-circuit at any fault point is greater than that of a two-phase short-circuit; the impulse current of the grid side short-circuit is much greater than that of the generator terminal short-circuit; the speed fluctuation and fluctuation difference caused by the three-phase short-circuit in the grid side are the largest. The alternating frequency of the transient electromagnetic force of the four kinds of faults avoids the natural frequency of the torsional vibration of the shaft system, and the torsional resonance of the shaft system in the time domain of the short-circuit fault will not appear. However, after the fault is removed, the residual small fluctuation torque in the system has a potential impact on the rotor system. This research shows an analysis of the structural integrity and safe operation of turbine generator units after a short-circuit fault, which can not only be applied to engineering practice, but also provide a theoretical basis for subsequent research.