Dynamical Manner Research Articles

A Versatile Visual Molecular Imprinting-Driven Switchable Nanozyme Activity-Based Trimodal Assay and Logic Gate Circuits of Ethyl Carbamate.

Concerns regarding the hazard of the carcinogenic ethyl carbamate (EC) have driven attempts to exploit efficient, timely, straightforward, and economic assays for warning early food safety. Here, we proposed a novel molecularly imprinted polymer Co@MOF-MIP, with a high peroxidase (POD)-like activity and a bright blue fluorescence emission, to develop a versatile visual assay for colorimetric, fluorescent, and photothermal trimodal detection and logic gate outputting of EC. Briefly, the POD-like activity of Co@MOF-MIP made it to decompose H2O2 into ·OH for oxidizing colorless 3,3',5,5'-tetramethylbenzidine (TMB) into a blue oxTMB, resulting in a 660 nm irradiated photothermal effect and bursting the blue fluorescence of Co@MOF-MIP via inner filter effect, observing a decreased fluorescence signal together with an increased colorimetric and 660 nm irradiated photothermal signals. However, EC could specifically fill the imprinted cavities of Co@MOF-MIP to block the catalytic substrates TMB and H2O2 out of Co@MOF-MIP for further reacting with the inside catalytic center of Co2+, resulting in the transformation suppressing of TMB into oxTMB, yielding an EC concentration-dependent trimodal responses in fluorescence signal enhancement, colorimetric, and 660 nm irradiated photothermal signal decreases. Assisted by the portable devices such as smartphones and hand-held thermal imagers, a visual onsite portable trimodal analytical platform was proposed for EC fast and accurate detection with the low detection limits of 1.64, 1.24, and 1.78 μg/L in colorimetric, fluorescent, and photothermal modes, respectively. Interestingly, these reactive events could be programmed by the classical Boolean logic gate analysis to offer a novel promising avenue for the big data Internet of Things monitoring and warning early residual EC in a more intelligent, dynamical, fast, and accurate manner, safeguarding food safety.

Coexistence of stable and unstable population dynamics in a nonlinear non-Hermitian mechanical dimer.

Non-Hermitian two-site dimers serve as minimal models in which to explore the interplay of gain and loss in dynamical systems. In this paper, we experimentally and theoretically investigate the dynamics of non-Hermitian dimer models with nonreciprocal hoppings between the two sites. We investigate two types of non-Hermitian couplings; one is when asymmetric hoppings are externally introduced, and the other is when the nonreciprocal hoppings depend on the population imbalance between the two sites, thus introducing the non-Hermiticity in a dynamical manner. We engineer the models in our synthetic mechanical setup comprised of two classical harmonic oscillators coupled by measurement-based feedback. For fixed nonreciprocal hoppings, we observe that, when the strength of these hoppings is increased, there is an expected transition from a PT-symmetric regime, where oscillations in the population are stable and bounded, to a PT-broken regime, where the oscillations are unstable and the population grows/decays exponentially. However, when the non-Hermiticity is dynamically introduced, we also find a third intermediate regime in which these two behaviors coexist, meaning that we can tune from stable to unstable population dynamics by simply changing the initial phase difference between the two sites. As we explain, this behavior can be understood by theoretically exploring the emergent fixed points of a related dimer model in which the nonreciprocal hoppings depend on the normalized population imbalance. Our study opens the way for the future exploration of non-Hermitian dynamics and exotic lattice models in synthetic mechanical networks.

Spatiotemporal information conversion machine for time-series forecasting

Making time-series forecasting in a robust way is a difficult task only based on the observed data of a nonlinear system. In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a forecasting of a time series by employing a spatial-temporal information (STI) transformation. STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the forecasting of the target variable. From the observed variables, the STICM also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the robustness of time-series forecasting. The STICM was successfully applied to both benchmark systems and real-world datasets, all of which show superior and robust performance in time-series forecasting, even when the data were perturbed by noise. From both theoretical and computational viewpoints, the STICM has great potential in practical applications in artificial intelligence (AI) or as a model-free method based only on the observed data, and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.

Investigation of vacancy defects and temperature effects on the GaN bombarding with argon atoms: Molecular dynamics simulation

In this work, molecular dynamics (MD) approach was performed in order to study the vacancy defect and temperature effects of GaN under Ar atomic bombardment (AB). In our computational study, the interatomic forces of nanostructures are based on Lennard-Jones and Tersoff force-fields. The results show that the vacancy defect of atomic structures is an important factor in AB procedure and the ideal matrix shows the maximum mechanical strength after Ar AB. In this structure, the final number of Ga and N atoms missing is smaller than defected matrix at T = 300 K. Furthermore, the effects of matrix temperature on dynamical manner of samples were investigated. MD calculations show that by temperature increasing from T = 300 K to T = 350 K, the GaN structure atom missing increases and so the atomic mechanical stability decreases in this procedure. Numerically, by a 50 K increase in the temperature of GaN atoms, the number of atoms missing in this structure increases by 54.

The atomic interactions between Histone and 3LPT protein using an equilibrium molecular dynamics simulation

The atomic interactions between histone molecule and 3LPT protein are reported using the molecular dynamics (MD) method in this computational work. This computational study is done for the first time. The results of the MD simulations on the dynamical manner of these structures have been reported by calculating various physical parameters. Physically, our results show that the attraction forces between the histone molecule and 3LPT protein destructed the 3LPT protein after 2 ns. Further, the temperature and pressure of these structures are important parameters in their atomic interactions. Numerically, by increasing these two parameters from 300 K to 350 K and 0 to 1 bar, respectively, the interatomic distance between these structures decreases from 7 Å to 2.54 Å and 7 Å to 2.79 Å (respectively). Further, bonding energy in these simulations varies from 23.51 eV to 31.78 eV. The diffusion coefficient of histone structure into 3LPT protein is another important parameter. Numerically, this physical coefficient changes from 0.58μm2/s to 0.89 μm2/s.

Evaluation in 4D printing – A review

A 4D printing is created by keeping the base of 3D Printing. 3D printing has major advances in the field of engineering, from the time of beginning of the concept, the applications of this field is also extended in very huge scale. The applications are like biomaterials, miniature modeling and various engineering applications. It makes over 3D files into physical items very simply but the produced items are only inactive and not applicable when comes into dynamic use. The 4D printed components can be modified to the required shape based on demand, over the period of time the components are fabricated in controlled dynamical manner based on the shapes. The 4D printed items will have the capacity to change the dimensions over the time and it has well prospective for the biomedical field. In the medical field the customized products can produced easily for bone, ear tissue and muscle construction and unnatural products can be made-up using 4D printing.

Investigation of additives nanoparticles and sphere barriers effects on the fluid flow inside a nanochannel impressed by an extrinsic electric field: A molecular dynamics simulation

In this paper, MD technique was employed to predict the atomic barrier impact on H2O/Copper nanofluid dynamic manner in existence of outer electric subject. To examine the H2O/Copper nanofluid flow, the Copper nanochannel with sphere barriers was simulated. In these MD simulations, the interactions between atoms of atomic structures were explained via Lenard-Jones force fields and Embedded Atom Model. To examine the atomic manner of these structures, physical specification like density, velocity and temperature profiles of H2O/Copper nanofluid reported. MD simulation outcomes display that, these physical specification of H2O/Copper nanofluid within non-optimal nanochannel effected by atomic barriers size transformations. Further, the effects of external electric field magnitude and frequencies variation on dynamical manner of H2O/Cu calculated. Numerically, it is calculated that, highest rate of temp., velocity and density profiles reach to 0.123 atom/Å3, 0.986 Å/ps, and 513 K rates which these theatrical reports can be significant for actual application of nanofluid structures.

The Electric Field and Microchannel Type Effects on H2O/Fe3O4 Nanofluid Boiling Process: Molecular Dynamics Study

In this computational research, we study the electric field and microchannel type effects on H2O/Fe3O4 nanofluid dynamical manner. The dynamical characteristics of this atomic structure are calculated using molecular dynamics method and we reported physical parameters such as total temperature, total energy, radial distribution function, velocity, density and temperature profiles of H2O/Fe3O4 nanofluid with 2 spherical nanoparticles. Physically, inserting external electric field causes phase transition (boiling process) in H2O/Fe3O4 nanofluid in shorter simulation time. Further, our results show that magnitude of external electric field is a significant parameter in nanofluid dynamical manner in microchannels. By external electric field magnitude increasing, the highest rate of velocity, temperature and density of nanofluid enhance to 0.0071 A·ps−1, 714 K and 0.038 atom·A−3 rates, numerically. Further, by microchannel atomic type variation from Pt to Au one, the rate of temperature and velocity of nanofluids reach to maximum rate at Au microchannel. So we conclude that phase transition of nanofluid in Au microchannel occurs in shorter MD simulation time.

Molecular dynamics study of barrier effects on Ferro- nanofluid flow in the presence of constant and time-dependent external magnetic fields

Abstract In this work, the computational method is implemented to study the atomic barrier effects on water/Fe3O4 nanofluid manner in the presence external magnetic field. The Copper nanochannel by 3 atomic barriers is simulated to flow of water/Fe3O4 nanofluids. Results show that the density, velocity, and temperature profiles of water/Fe3O4 nanofluid inside Cu nanochannel changed by adding atomic barriers to the interior surface of the nanochannel. Furthermore, the effects of nanoparticle number variation on the dynamical manner of nanofluids calculated. From a numerical perspective, the aggregation time of Fe3O4 nanoparticles in simulated systems varies from 1 ns to 0.75 ns by adding a magnetic field to simulated nanofluid. This mechanical manner arises from external force powering by adding constant and time-dependent magnetic field to the simulation box. Finally, we reported that the constant and time-dependent external magnetic field cause nanofluids agglomerates in a short time rather than magnetic field-free models.

Mechanism of non-steady Petschek-type reconnection with uniform resistivity

The Sweet-Parker and Petschek models are well-established magnetohydrodynamics (MHD) models of steady magnetic reconnection. Recent findings on magnetic reconnection in high-Lundquist-number plasmas indicate that Sweet-Parker-type reconnection in marginally stable thin current sheets connecting plasmoids can produce fast reconnection. By contrast, it has proven difficult to achieve Petschek-type reconnection in plasmas with uniform resistivity because sustaining it requires localization of the diffusion region. However, Shibayama et al. [Phys. Plasmas 22, 100706 (2015)] recently noted that Petschek-type reconnection can be achieved spontaneously in a dynamical manner even under uniform resistivity through what they called dynamical Petschek reconnection. In this new type of reconnection, Petschek-type diffusion regions can be formed in connection with plasmoids. In this paper, we report the results of two-dimensional resistive MHD simulation with uniform resistivity, undertaken to determine the diffusion region localization mechanism under dynamical Petschek reconnection. Through this modeling, we found that the separation of the X-point from the flow stagnation point (S-point) plays a crucial role in the localization of the diffusion region because the motion of the X-point is restricted by the strong flow emanating from the flow stagnation point. This mechanism suggests that dynamical Petschek reconnection is possible even in large systems such as the solar corona.

Dynamically adjustable spin component scaled second order Møller-Plesset perturbation theory for strongly correlated molecular systems.

We present a novel spin-component scaled Møller-Plesset second order (MP2) perturbation theory [S. Grimme, J. Chem. Phys. 118, 9095 (2003)] in which the singlet and triplet correlation channels are dressed in a dynamical manner over the entire molecular potential energy surface. In order to strike the right balance between the short and long range correlation, the different correlation channels are scaled by two complementary functions without introducing any external parameter: while the singlet channel contribution to correlation energy is attenuated with increasing strong correlation of the system, the triplet channel contribution is amplified. We have justified our approach from physical reasoning as well as a few numerical examples with some difficult systems, like symmetric stretching of water and nitrogen molecules, which clearly demonstrate the efficacy of this method in describing the molecular potential energy surface, even in the strongly correlated regions where the conventional MP2 and its other variants disastrously fail.

Game theory based complex analysis for nuclear security using non-zero sum algorithm

The non-linear complex algorithm is applied to the nuclear security where the non-zero sum quantifications of the game theory is applied for simulations. In the results, the values are described as the integers, because the Boolean values are calculated in the modeling. There are several interested factors as Nuclear Fuel, Safety System, Security System, Operator, and Government. In the simulations, the positive values show the possibility of terror incidents which are obtained as dynamical manner in the Boolean numbers of 1.0 or −1.0. The cumulative values show the five nuclear security factors are summed up. For the aging effect, the graph gives the highest value as 18 at 715th month. The analysis of payoff graph shows the result of the payoff of each factor where there are meeting points around 360th month and 450th month. Therefore, the possibility of the terrorism increases as time goes on.

Ultrafast dynamics-driven biomolecular recognition where fast activities dictate slow events

In general, biological macromolecules require significant dynamical freedom to carry out their different functions, including signal transduction, metabolism, catalysis and gene regulation. Effectors (ligands, DNA and external milieu, etc) are considered to function in a purely dynamical manner by selectively stabilizing a specific dynamical state, thereby regulating biological function. In particular, proteins in presence of these effectors can exist in several dynamical states with distinct binding or enzymatic activity. Here, we have reviewed the efficacy of ultrafast fluorescence spectroscopy to monitor the dynamical flexibility of various proteins in presence of different effectors leading to their biological activity. Recent studies demonstrate the potency of a combined approach involving picosecond-resolved Forster resonance energy transfer, polarisation-gated fluorescence and time-dependent stokes shift for the exploration of ultrafast dynamics in biomolecular recognition of various protein molecules. The allosteric protein-protein recognition following differential protein-DNA interaction is shown to be a consequence of some ultrafast segmental motions at the C-terminal of Gal repressor protein dimer with DNA operator sequences OE and OI. Differential ultrafast dynamics at the C-terminal of λ-repressor protein with two different operator DNA sequences for the protein-protein interaction with different strengths is also reviewed. We have also systemically briefed the study on the role of ultrafast dynamics of water molecules on the functionality of enzyme proteins alpha-chymotrypsin and deoxyribonuclease I. The studies on the essential ultrafast dynamics at the active site of the enzyme alpha-chymotrypsin by using an anthraniloyl fluorescent extrinsic probe covalently attached to the serine-195 residue for the enzymatic activity at homeothermic condition has also been reviewed. Finally, we have highlighted the evidence that a photoinduced dynamical event dictates the molecular recognition of a photochromic ligand, dihydroindolizine with the serine protease alpha-chymotrypsin and with a liposome (L-a-phosphatidylcholine).

Observing quantum control of up-conversion luminescence in Dy3+ ion doped glass from weak to intermediate shaped femtosecond laser fields

Controlling the up-conversion luminescence of rare-earth ions in real-time, in a dynamical and reversible manner, is very important for their application in laser sources, fiber-optic communications, light-emitting diodes, color displays and biological systems. In previous studies, the up-conversion luminescence control mainly focused on the weak femtosecond laser field. Here, we further extend this control behavior from weak to intermediate femtosecond laser fields. In this work, we experimentally and theoretically demonstrate that the up-conversion luminescence in Dy3+ ion doped glass can be artificially controlled by a π phase step modulation, but the up-conversion luminescence control behavior will be affected by the femtosecond laser intensity, and the up-conversion luminescence is suppressed by lower laser intensity while enhanced by higher laser intensity. We establish a new theoretical model (i.e. the fourth-order perturbation theory) to explain the physical control mechanism by considering the two- and four-photon absorption processes, and the theoretical results show that the relative weight of four-photon absorption in the whole excitation process will increase with the increase in laser intensity, and the interference between two- and four-photon absorptions results in up-conversion luminescence control modulation under different laser intensities. These theoretical and experimental works can provide a new method to control and understand up-conversion luminescence in rare-earth ions, and also may open a new opportunity to the related application areas of rare-earth ions.

Measure of invulnerability for command and control network based on mission link

In Command and Control (C2) network, the measure of invulnerability mainly focuses on structural characteristics of the network, where the operational mission has not been adequately considered. As a result, it becomes difficult to assess the invulnerability of C2 network in a dynamical manner. In this paper, the operational entities and heterogeneous relationships among combat entities are analyzed, where the operational C2 network model is constructed based on the combat theory of OODA and the super network. Subsequently, the mission link is defined, which can be used to characterize the combat network. Finally, a new measure of invulnerability for C2 networks is proposed based on the efficiency and entropy of the mission link. In particular, this measure can desirably represent the efficiency of information transmission and robustness of network structures, respectively. The simulation results have demonstrated that the proposed invulnerability measure is highly sensitive and accurate. More specifically, the proposed measure could more accurately reveal the invulnerability of C2 network, where theoretical basis for designing and optimizing the structure of C2 networks can be also provided.

An input-output linear time invariant model captures neuronal firing responses to external and behavioral events.

Investigating how neurons in different motor regions respond to external stimuli and behavioral events provides insight into motor control. A recent approach to studying neuronal activity is to construct a zero-input linear time invariant (ZI-LTI) state-space model, wherein the state vector consists of firing rate signals for different populations of neurons across motor regions. This approach allows for the populations to influence each other in a dynamical manner given an initial firing rate condition, and the model can accurately reconstruct firing rates within a limited epoch in the motor task during which no event occurs. Here, we generalize this LTI modeling approach to characterize firing responses of neurons to two events (a go cue and movement onset) in a movement task with a non-zero input LTI state-space model, herein referred to as input-output LTI (IO-LTI). Specifically, responses from 196 neurons in the primary motor (M1), ventral premotor (PMv), and dorsal premotor cortex (PMd) were recorded and modeled in two nonhuman primates executing a reach-to-grasp task. We found that a single IO-LTI model can reconstruct neuronal firing rate patterns of six populations of these neurons across the three areas in the presence of multiple events (go cue, movement onset). This is the first step towards constructing generative models of neuronal firing rates in the presence of multiple events, which then can be used to construct better decoders for brain machine interactive control.

Influence of chemistry, interfacial width, and non-isothermal conditions on spatially heterogeneous activated relaxation and elasticity in glass-forming free standing films.

We employ the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory of activated relaxation to study several questions in free standing thin films of glass-forming molecular and polymer liquids. The influence of non-universal chemical aspects on dynamical confinement effects is found to be relatively weak, but with the caveat that for the systems examined, the bulk ECNLE polymer theory does not predict widely varying fragilities. Allowing the film model to have a realistic vapor interfacial width significantly enhances the reduction of the film-averaged glass transition temperature, Tg, in a manner that depends on whether a dynamic or pseudo-thermodynamic averaging of the spatial mobility gradient is adopted. The nature of film thickness effects on the spatial profiles of the alpha relaxation time and elastic modulus is studied under non-isothermal conditions and contrasted with the corresponding isothermal behavior. Modest differences are found if a film-thickness dependent Tg is defined in a dynamical manner. However, adopting a pseudo-thermodynamic measure of Tg leads to a qualitatively new form of the alpha relaxation time gradient where highly mobile layers near the film surface coexist with strongly vitrified regions in the film interior. As a consequence, the film-averaged shear modulus can increase with decreasing film thickness, despite the Tg reduction and presence of a mobile surface layer. Such a behavior stands in qualitative contrast to the predicted mechanical softening under isothermal conditions. Spatial gradients of the elastic modulus are studied as a function of temperature, film thickness, probing frequency, and experimental protocol, and a rich behavior is found.

Modulating the phases of iron carbide nanoparticles: from a perspective of interfering with the carbon penetration of Fe@Fe3O4 by selectively adsorbed halide ions.

Iron carbide nanoparticles (ICNPs) are considered to have great potential in new energy conversion, nanomagnets and biomedical applications due to their intrinsically peculiar magnetic and catalytic properties. However, the synthetic routes were greatly limited in morphology and phase controlled synthesis. In this article, we present a versatile solution chemistry route towards colloidal ICNPs (Fe2C-hexagonal and monoclinic syngony, Fe5C2-monoclinic syngony and Fe3C-orthorhombic syngony) derived from body centered cubic Fe@Fe3O4 by introducing heteroatoms to restrain their phase transformation. We found that the phases of Fe2C NPs could be controlled by direct phase transformation in the drastic thermally driven procedure (defined as thermodynamical manner). Meanwhile, the selective adsorption of Cl ions weakened the bonding between Fe and C atoms, thus interfering with the penetration of C atoms to form lower carbon content Fe5C2 and Fe3C NPs. The kinetic mechanisms were evaluated using density functional theory (DFT) simulations focusing on the bonding energy between Fe-C and Fe-Cl atoms. All the obtained ICNPs exhibited typically soft ferromagnetic properties with the highest saturation magnetization value of 101.2 emu g-1 and the highest Curie temperature of 497.8 K.

Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals.

Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression data from a malaria vaccine trial by big-data-based edge biomarkers from module network rewiring-analysis. The illustrative results show that the identified module biomarkers can accurately distinguish vaccines with or without protection and outperformed previous reported gene signatures in terms of effectiveness and efficiency.

Exploring the quantum critical behaviour in a driven Tavis-Cummings circuit.

Quantum phase transitions play an important role in many-body systems and have been a research focus in conventional condensed-matter physics over the past few decades. Artificial atoms, such as superconducting qubits that can be individually manipulated, provide a new paradigm of realising and exploring quantum phase transitions by engineering an on-chip quantum simulator. Here we demonstrate experimentally the quantum critical behaviour in a highly controllable superconducting circuit, consisting of four qubits coupled to a common resonator mode. By off-resonantly driving the system to renormalize the critical spin-field coupling strength, we have observed a four-qubit nonequilibrium quantum phase transition in a dynamical manner; that is, we sweep the critical coupling strength over time and monitor the four-qubit scaled moments for a signature of a structural change of the system's eigenstates. Our observation of the nonequilibrium quantum phase transition, which is in good agreement with the driven Tavis–Cummings theory under decoherence, offers new experimental approaches towards exploring quantum phase transition-related science, such as scaling behaviours, parity breaking and long-range quantum correlations.