Steady-state Generation Research Articles

An individual-level probabilistic model and solution for control of infectious diseases

<p>We present an individual-level probabilistic model to evaluate the effectiveness of two traditional control measures for infectious diseases: the isolation of symptomatic individuals and contact tracing (plus subsequent quarantine). The model allows us to calculate the reproduction number and the generation-time distribution under the two control measures. The model is related to the work of Fraser et al. on the same topic <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup>, which provides a population-level model using a combination of differential equations and probabilistic arguments. We show that our individual-level model has certain advantages. In particular, we are able to provide more precise results for a disease that has two classes of infected individuals – the individuals who will remain asymptomatic throughout and the individuals who will eventually become symptomatic. Using the properties of integral operators with positive kernels, we also resolve the important theoretical issue as to why the density function of the steady-state generation time is the eigenfunction associated with the largest eigenvalue of the underlying integral operator. Moreover, the same theoretical result shows why the simple algorithm of repeated integration can find numerical solutions for virtually all initial conditions. We discuss the model's implications, especially how it enhances our understanding about the impact of asymptomatic individuals. For instance, in the special case where the infectiousness of the two classes is proportional to each other, the effects of the asymptomatic individuals can be understood by supposing that all individuals will be symptomatic but with modified infectiousness and modified efficacy of the isolation measure. The numerical results show that, out of the two measures, isolation is the more decisive one, at least for the COVID-19 parameters used in the numerical experiments.</p>

SECURITY CONSTRAINED OPTIMAL POWER FLOW BASED ON AN ARTIFICIAL INTELLIGENCE TECHNIQUE

In the past, artificial intelligence techniques were successfully adopted for obtaining optimal power flow in a power system. However, this optimality is limited to the economic aspects of the system's operating conditions. The other aspects of the operation, like security conditions, have been given limited attention. Hence, this paper presents an attempt to dispatch the power generation in electrical power systems optimally by taking into consideration both economic and secure operations, so that modern power systems can operate reliably and effectively. Security-constrained optimal power flow is addressed in this paper as a multi-objective optimization problem, consisting of four objective functions: minimizing power generation costs; minimizing voltage deviation; minimizing power losses; and alleviating the overloading on transmission lines. A detailed steady-state generator model is adopted in the present formulation. A metaheuristic optimization technique, namely, differential evolution, is used to obtain the security constraint optimal power dispatch. Additionally, the operating states of a power system have been addressed in this paper. The identification of the operating states is vital to the assessment of the security of the EPS. Improvements and appropriate security assessments have been made in some cases. The proposed algorithm is applied to a typical power system with different operating strategies. The obtained results are compared to those obtained from previous studies in the literature to demonstrate the suggested method's validity and effectiveness.

Thermoelectric Nanofluidics Probing Thermal Heterogeneity inside Single Cells.

Accurate temperature measurement in one living cell is of great significance for understanding biological functions and regulation. Here, a nanopipet electric thermometer (NET) is established for real-time intracellular temperature measurement. Based on the temperature-controlled ion migration, the temperature change in solution results in altered ion mobilities and ion distributions, which can be converted to the thermoelectric responses of NET in a galvanostatic configuration. The exponential relationship between the voltage and the temperature promises highly sensitive thermoelectric responses up to 11.1 mV K-1, which is over an order of magnitude higher than previous thermoelectric thermometry. Moreover, the NET exhibits superior thermal resolution of 25 mK and spatiotemporal resolution of 100 nm and 0.9 ms as well as excellent stability and reproducibility. Benefiting from these unique features, both thermal fluctuations in steady-state cells and heat generation and dissipation upon drug administration can be successfully monitored, which are hardly achieved by current methods. By using NET, thermal heterogeneities of single cancer cells during immunotherapy were reported first in this work, in which the increased intracellular temperature was demonstrated to be associated with the survival benefit and resistance of cancer cells in immunotherapy. This work not only provides a reliable method for microscopic temperature monitoring but also gains new insights to elucidate the mechanism of immune evasion and therapeutic resistance.

Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization

A computationally efficient dehomogenization technique was developed based on a bioinspired diffusion-based pattern generation algorithm to convert an orientation field into explicit large-scale fluid flow channel structures. Due to the transient nature of diffusion and reaction, most diffusion-based pattern generation models were solved in both time and space. In this work, we remove the temporal dependency and directly solve a steady-state equation. The steady-state Swift-Hohenberg model was selected due to its simplistic form as a single variable equation and intuitive parameter setting for pattern geometry control. Through comparison studies, we demonstrated that the steady-state model can produce statistically equivalent solutions to the transient model with potential computational speedup. This work marks an early foray into the use of steady-state pattern generation models for rapid dehomogenization in multiphysics engineering design applications. To highlight the benefits of this approach, the steady-state model was used to dehomogenize optimized orientation fields for the design of microreactor flow structures involving hundreds of microchannels in combination with a porous gas diffusion layer. A homogenization-based multi-objective optimization routine was used to produce a multi-objective Pareto set that explored the trade-offs between flow resistance and reactant distribution variability. In total, the diffusion-based dehomogenization method enabled the generation of 200 unique and distinctly different microreactor flow channel designs. The proposed dehomogenization approach permits comprehensive exploration of numerous bioinspired solutions capturing the full complexity of the optimization and Swift-Hohenberg design space.

Finite-Time Distributed Average Tracking for a Class of Nonlinear Multi-Agent Systems With External Disturbances

In this article, a finite-time distributed average tracking (DAT) problem is considered for unity relative degree nonlinear multi-agent systems (MASs) subject to external disturbances. The objective of this article is to design a distributed controller such that the output of each agent converges to the desired trajectory (the average of multiple nonlinear signals) within a finite time. First, we introduce a steady-state generator, which can reproduce the desired trajectory. Based on the steady-state generator, we design a distributed observer to estimate the desired trajectory, which is robustness to initialization errors. Then, an observer-based output-feedback controller is proposed for each agent such that the output of each agent can converge to its own generated signal within a finite time. Finally, it is shown that the output of each agent can converge to the desired trajectory within a finite time using the proposed control strategy, and hence the finite-time DAT problem is solved. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.

Collagenase treatment decreases muscle stiffness in cerebral palsy: A preclinical ex vivo biomechanical analysis of hip adductor muscle fiber bundles.

To determine the dose-response relationship of collagenase Clostridium histolyticum (CCH) on collagen content and the change in muscle fiber bundle stiffness after ex vivo treatment of adductor longus biopsies with CCH in children with cerebral palsy (CP). Biopsy samples of adductor longus from children with CP (classified in Gross Motor Function Classification System levels IV and V) were treated with 0 U/mL, 200 U/mL, 350 U/mL, or 500 U/mL CCH; percentage collagen reduction was measured to determine the dose-response. Peak and steady-state stresses were determined at 1%, 2.5%, 5%, and 7.5% strain increments; Young's modulus was calculated. Eleven patients were enrolled (nine males, two females, mean age at surgery 6 years 5 months; range: 2-16 years). A linear CCH dose-response relationship was determined. Peak and steady-state stress generation increased linearly at 5.9/2.3mN/mm2 , 12.4/5.3mN/mm2 , 22.2/9.7mN/mm2 , and 33.3/15.5mN/mm2 at each percentage strain increment respectively. After CCH treatment, peak and steady-state stress generation decreased to 3.2/1.2mN/mm2 , 6.5/2.9mN/mm2 , 12.2/5.7mN/mm2 , and 15.4/7.7mN/mm2 respectively (p < 0.004). Young's modulus decreased from 205 kPa to 100 kPa after CCH (p = 0.003). Steady-state Young's elastic modulus decreased from median (IQR) 205.4 (118.5) to 100.3 (64.0) after CCH treatment (p = 0.003). This preclinical ex vivo study provides proof of concept for the use of collagenase to decrease muscle stiffness in individuals with CP.

Pulse Power Generation Chronoamperometry as an Advanced Readout for (Bio)sensors: Application for Noninvasive Diabetes Monitoring.

We propose pulse power generation (PPG) amperometry as an advanced readout realized for Prussian blue (PB)-based (bio)sensors. In contrast to the conventional power generation mode, when the current response is generated upon continuous short-circuiting, the suggested pulse regime is fulfilled by periodic opening and shorting of the circuit. Despite PB being electroactive, the pulse readout is advantageous over conventional steady-state power generation, providing up to a 15-fold increased signal-to-background ratio as well as dramatically improved sensitivity exceeding 10 A·M-1·cm-2 for H2O2 sensors and 3.9 A·M-1·cm-2 for glucose biosensors. Such analytical performance characteristics are, most probably, achieved due to the enrichment of the diffusion layer by analyte mass transfer from the bulk upon opening of the circuit. Due to an improved sensitivity-to-background ratio, reduced flow-rate dependence, and enhanced operational stability, the regime allows reliable monitoring of blood glucose variations through sweat analysis with the on-skin device.

Neutral Beam Coupling with Plasma in a Compact Fusion Neutron Source

FNS-ST is a fusion neutron source project based on a spherical tokamak (R/a = 0.5 m/0.3 m) with a steady-state neutron generation of ~1018 n/s. Neutral beam injection (NBI) is supposed to maintain steady-state operation, non-inductive current drive and neutron production in FNS-ST plasma. In a low aspect ratio device, the toroidal magnetic field shape is not optimal for fast ions confinement in plasma, and the toroidal effects are more pronounced compared to the conventional tokamak design (with R/a &gt; 2.5). The neutral beam production and the tokamak plasma response to NBI were efficiently modeled by a specialized beam-plasma software package BTR-BTOR, which allowed fast optimization of the neutral beam transport and evolution within the injector unit, as well as the parametric study of NBI induced effects in plasma. The “Lite neutral beam model” (LNB) implements a statistical beam description in 6-dimensional phase space (106–1010 particles), and the beam particle conversions are organized as a data flow pipeline. This parametric study of FNS-ST tokamak is focused on the beam-plasma coupling issue. The main result of the study is a method to achieve steady-state current drive and fusion controllability in beam-driven toroidal plasmas. LNB methods can be also applied to NBI design for conventional tokamaks.

Rapid Solar Heating of Antimicrobial Ag and Cu2O Nanostructured Plasmonic Textile for Clean Water Production.

Solar steam generation is an attractive method to produce clean water, and high steam generation rates have been achieved using nanostructured light absorbers. However, since it usually takes minutes to reach the temperature for steady-state steam generation under solar illumination, a material that responds quickly to intermittent sunlight is strongly desired. Here, we report an unprecedented heating rate in an ultralight freestanding textile consisting of interconnected Ag and Cu2O nanoparticles. The textile demonstrated high solar absorption with low reflectance and transmittance, which were rationalized using our multiphysics simulations. A commercial polystyrene foam wrapped with this broadband light-absorbing textile showed the fastest response to sunlight together with a good steam generation rate compared to reported inorganic nanostructured steam generators. Furthermore, the textile exhibited antibacterial property, which might lower the risk of the vapor-induced transfer of bacteria during long-term intermittent use and the cost of subsequent water sanitization.

Characterization, stability, and feasibility of long-term use of light-absorbing components of aqueous spinach extract-based photogalvanic electrolyte

In the present work, the photogalvanic cells have been studied with respect to the photo-stability and the long-term use of the electrolyte based on crude aqueous spinach extract sensitizer for solar energy harvesting. Further, the nature of chemical components present in the old and photo-decayed electrolyte and their current generation capacity has also not been investigated so far otherwise it is of much significance for durable use of the same electrolyte in cells. In earlier studies, the steady-state photo-generation of current for about two hours from crude spinach extract-based cell has been shown during illumination. But, the data for only two hours of the steady-state current generation is not sufficient to show the feasibility of working with photogalvanic cells. Therefore, to fill this research gap of lack of characterization of sensitizers’ molecules of crude spinach extract and lack of study on long-term use of this electrolyte (crude spinach extract-surfactant-reductant-alkali-water), the present extensive study has been done. The observed spectrum of crude spinach extract resembles that of chlorophyll–protein complex showing it is the main chemical component in extract absorbing light. A strong acid adversely affects the extract’s photogalvanics and high pH is friendly to the physiological and photogalvanic activity of the extract. The spectra of illuminated and very old crude spinach extract-NaOH-Sodium lauryl sulfate (NaLS)-Fructose photogalvanic electrolyte solution show negligible absorbance (540–700 nm) and zero absorbance (at 700 nm) suggesting the absence of chlorophyll due to its photo-degradation. When this photo-degraded electrolyte is again illuminated, the power output obtained is nearly equal to that for the first time illuminated fresh electrolyte. The observed current at zero time and after 2641 h from the same electrolyte used in long term is 50 mA cm−2 and 40 mA cm−2, respectively. It means that the fresh crude spinach extract, as well as the photo-degraded extract at high pH, are almost equally capable of power generation.

Analysis of contact conditions and microstructure evolution in shear assisted processing and extrusion using smoothed particle hydrodynamics method

Shear assisted processing and extrusion (ShAPE) is a solid-phase processing technique that adds an additional shear force as compared with a conventional extrusion approach. Recently, ShAPE has demonstrated the capability of extruding high-performance aluminum alloy 7075 (AA7075) tubes at speeds up to 12.2 m/min without surface tearing. However, the relationship among the ShAPE processing parameters, thermomechanical conditions, contact conditions, heat generation, and microstructure evolution remains primarily empirical because an insightful understanding of the associated physics is still lacking. To help elucidate these relationships, this work proposes a thermomechanical meshfree model for the first time for ShAPE processing of AA7075 using the smoothed particle hydrodynamics (SPH) method. The meshfree model is first validated thoroughly by experimental data in terms of material flow, die face temperature, and extrusion force with various processing parameters. The validated model is then used to analyze the steady-state contact conditions and heat generation rates during ShAPE processing. Distributions of the average grain size of AA7075 being extruded are calculated using the SPH model output. The meshfree model results reveal that extrusions conducted at lower temperatures and higher strain rates yield more refined grains and possibly higher material strength, which is also consistent with the experimental observations.

Alterations in motor modules and their contribution to limitations in force control in the upper extremity after stroke.

The generation of isometric force at the hand can be mediated by activating a few motor modules. Stroke induces alterations in motor modules underlying steady-state isometric force generation in the human upper extremity (UE). However, how the altered motor modules impact task performance (force production) remains unclear as stroke survivors develop and converge to the three-dimensional (3D) target force. Thus, we tested whether stroke-specific motor modules would be activated from the onset of force generation and also examined how alterations in motor modules would induce changes in force representation. During 3D isometric force development, electromyographic (EMG) signals were recorded from eight major elbow and shoulder muscles in the paretic arm of 10 chronic hemispheric stroke survivors and both arms of six age-matched control participants. A non-negative matrix factorization algorithm identified motor modules in four different time windows: three “exploratory” force ramping phases (Ramps 1–3; 0–33%, 33–67%, and 67–100% of target force magnitude, respectively) and the stable force match phase (Hold). Motor module similarity and between-force coupling were examined by calculating the scalar product and Pearson correlation across the phases. To investigate the association between the end-point force representation and the activation of the motor modules, principal component analysis (PCA) and multivariate multiple linear regression analyses were applied. In addition, the force components regressed on the activation profiles of motor modules were utilized to model the feasible force direction. Both stroke and control groups developed exploratory isometric forces with a non-linear relationship between EMG and force. During the force matching, only the stroke group showed abnormal between-force coupling in medial-lateral and backward-forward and medial-lateral and downward-upward directions. In each group, the same motor modules, including the abnormal deltoid module in stroke survivors, were expressed from the beginning of force development instead of emerging during the force exploration. The PCA and the multivariate multiple linear regression analyses showed that alterations in motor modules were associated with abnormal between-force coupling and limited feasible force direction after stroke. Overall, these results suggest that alterations in intermuscular coordination contribute to the abnormal end-point force control under isometric conditions in the UE after stroke.

Examining the Economic Optimality of Automatic Generation Control

The automatic generation control (AGC) system is temporally situated between economic dispatch and synchronous generator dynamics, and its primary role is to regulate frequency within and tie-line flows between control areas. Given appropriate choice of participation factors (feed-forward controller gains that govern the disaggregation of the area-level power requirement to individual generators), the AGC can be engineered to nudge system dynamics toward a steady-state operating point corresponding to economic optimality. This paper establishes necessary and sufficient conditions under which a widely accepted choice of participation factors guarantees the alignment of steady-state synchronous generator outputs with a global minimum of a prototypical economic dispatch problem. In so doing, it resolves several ambiguities and formalizes technical assumptions governing the role of the standard AGC architecture in the context of economic dispatch and steady-state operation. Numerical case studies tailored to a modified version of the New England 39-bus 10-machine test system validate the theoretical results.

A sophisticated modeling approach for photovoltaic systems in load frequency control

This paper proposes a new model to mimics accurately the real dynamic and steady-state power generation profiles of a photovoltaic (PV) power plant connected to the power system. The model is targeted at load frequency control (LFC) studies, which takes into account the effect of the dynamic behavior of the system frequency on the PV output power. The artificial neural network using the radial bias function (ANN-RBF) is adopted to model the nonlinear behavior of the PV power plant only considering the frequency deviation as one of the inputs at different operating conditions. Also, the ANN prevents going into complex mathematical analysis to get the dynamic behavior of the output power from the PV plant. The input layer of the ANN-RBF receives the temperature, humidity, irradiation level, and frequency deviation, while the output layer represents the output power from the PV power plant. The main idea to include the frequency deviation in the PV modeling is to represent the dynamic behavior of the PV power that is injected by the PV inverter that is synchronized with the power system using the utility grid angle, i.e. system frequency. The ANN-RBF has been trained using actual data collected from a PV power plant in Aswan, Egypt. The ANN dataset contains significant variations of operating parameters to generalize the output modeling of the PV array. Moreover, the shortages in the previous models for the PV system used in the LFC studies, which are the first-order model, second-order model, and noise-based model, are discussed in detail. The simulation results show that the proposed model of the PV system based on the ANN-RBF is more suitable for the LFC studies than the well-known models based on the first-order representation as it takes into account the dynamic behavior of the system frequency during the load disturbance.

Impact of Combustion Variance on Sustainability of Free-Piston Linear Generator during Steady-State Generation

A free-piston linear generator (FPLG) has a number of advantages compared to a traditional crank-slider internal combustion engine, including better thermal and mechanical efficiencies, different fuel compatibility, and a higher power-to-weight ratio. For electric vehicle propulsion and generation of portable power, an FPLG is a very attractive alternative source of energy. This paper presents the development of an FPLG simulation model using MATLAB-Simulink and investigates the impact of combustion variance on its operation. Results provided insight into various characteristics of system behavior through variation of structural dimension and operational parameters. In steady-state operation with fixed electrical load and fixed ignition for combustion, it was found that consecutively low combustion pressures can easily lead to engine stoppage, pointing to the significance of control for continuous operation. Due to the absence of the moment of inertia and flywheel character of the rotating engine, a linear engine-generator is subject to ceased operation even after two consecutively low combustions under 10% variance. This will not be a fundamental problem in an ordinary crank-slider engine-generator, but in a linear engine-generator, control measure will be necessary to ensure sustained operation.

Propagating Wigner-Negative States Generated from the Steady-State Emission of a Superconducting Qubit.

We experimentally demonstrate the steady-state generation of propagating Wigner-negative states from a continuously driven superconducting qubit. We reconstruct the Wigner function of the radiation emitted into propagating modes defined by their temporal envelopes, using digital filtering. For an optimized temporal filter, we observe a large Wigner logarithmic negativity, in excess of 0.08, in agreement with theory. The fidelity between the theoretical predictions and the states generated experimentally is up to 99%, reaching state-of-the-art realizations in the microwave frequency domain. Our results provide a new way to generate and control nonclassical states, and may enable promising applications such as quantum networks and quantum computation based on waveguide quantum electrodynamics.

Modelling human liver microphysiology on a chip through a finite element based design approach.

Organ-on-a-chip (OoaC) are microfluidic devices capable of growing living tissue and replicate the intricate microenvironments of human organs in vitro, being heralded as having the potential to revolutionize biological research and healthcare by providing unprecedented control over fluid flow, relevant tissue to volume ratio, compatibility with high-resolution content screening and a reduced footprint. Finite element modelling is proven to be an efficient approach to simulate the microenvironments of OoaC devices, and may be used to study the existing correlations between geometry and hydrodynamics, towards developing devices of greater accuracy. The present work aims to refine a steady-state gradient generator for the development of a more relevant human liver model. For this purpose, the finite element method was used to simulate the device and predict which design settings, expressed by individual parameters, would better replicate in vitro the oxygen gradients found in vivo within the human liver acinus. To verify the model's predictive capabilities, two distinct examples were replicated from literature. Finite element analysis enabled obtaining an ideal solution, designated as liver gradient-on-a-chip, characterised by a novel way to control gradient generation, from which it was possible to determine concentration values ranging between 3% and 12%, thus providing a precise correlation with in vivo oxygen zonation, comprised between 3%-5% and 10%-12% within respectively the perivenous and periportal zones of the human liver acinus. Shear stress was also determined to average the value of 0.037 Pa, and therefore meet the interval determined from literature to enhance liver tissue culture, comprised between 0.01 - 0.05 Pa.

TANGRA multidetector systems for investigation of neutron-nuclear reactions at the JINR Frank Laboratory of Neutron Physics

In the framework of TANGRA-project at the Frank Laboratory of Neutron Physics of the Joint Institute for Nuclear research in Dubna (Russia), two experimental setups (Fig. 1) have been designed and tested for investigation of 14-MeV neutron-induced nuclear reactions on a number of important for nuclear science and engineering isotopes. As a source of 14-MeV “tagged” neutrons we are using the VNIIA ING-27 steady-state portable neutron generator with embedded in its vacuum tube 64-pixel charge-particle detector. The “Romashka” system is an array of up-to 24 hexagonal NaI(Tl)-crystal scintillation probes, while the “Romasha” array consists of 18 cylindrical BGO-crystal detectors of neutrons and gamma-rays. In addition to these detectors there is a HPGe gamma-ray spectrometer and a number of Stilbene detectors that can be added for high-resolution gamma-ray spectrometry and neutron-gamma detection. The main characteristics of the neutron-induced nuclear reaction products can be investigated by commissioning the detectors in suitable for these experiments’ geometries. Both setups can be used for doing basic and applied scientific research, because they permit simultaneously to measure the energy, angle and multiplicity distributions of gamma-rays and neutrons, produced in the competitive neutron-induced nuclear reactions (n, n’γ), (n,2n), (n, xnγ) and (n, f) in pure or complex substances.

A Steady-State Thermal Gradient Generator for Material Characterization Using an Induction Heating System

Mobility/diffusion of ions can occur mainly in two situations. The first one when a potential difference is applied to electrodes of an electrochemical device as in batteries for instance (i.e., charge/discharge). In the second one, the diffusion process is driven by the thermal gradient existing within the material. This article deals with the design of a controlled thermal gradient generator (TGG), which opens the way to thoroughly study the effect of temperature difference on the chemical diffusion and mechanical resistance of shaped samples (e.g., a pellet and a rod). This TGG uses a combination of two susceptors involving the control of their respective temperatures. One is controlled by adjusting the power of a high-frequency generator that supplies an induction heating coil. The other is controlled, mainly, by the displacement of the sample/susceptors set through this same coil, with a motorized stage. The operation and control parameters are defined within a dedicated user-friendly software application. During the whole experiment, the two temperatures can be varied, while the inner thermal gradient of the sample is kept constant, as long as these temperatures are high enough.

Build-up dynamics in bidirectional soliton fiber lasers

Bidirectional ultrafast fiber lasers present an attractive solution, enabling the generation of two mutually coherent ultrashort pulse trains in a simple and turnkey system. Still, the lack of a comprehensive numerical model describing steady-state bidirectional generation, and even less ultrafast soliton breakdowns and collisions, is obstructing the achievement of the performance compared with unidirectional lasers. In this paper, we have experimentally investigated real-time build-up dynamics of counter-propagating solitons in an ultrafast ring Er-doped fiber laser via the dispersive Fourier transform methodology. We parade that counter-propagating pulses experience independent build-up dynamics from modulation instability, undergoing breathing dynamics and diverging subordinate pulse structure formation and annihilation to a stable bidirectional pulse train. Yet, the interaction of pulses in the cavity presents the key underlying phenomenon driving formation evolution distinct from unidirectional pulse build-up. Our findings will provide physical foundations for bidirectional ultrafast fiber laser design to carry forward their application.