This technical note addresses an iterative learning control (ILC) design problem for discrete-time linear systems where the trial lengths could be randomly varying in the iteration domain. An ILC ...scheme with an iteration-average operator is introduced for tracking tasks with non-uniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. In addition, the identical initialization condition can be absolutely removed. The learning convergence condition of ILC in mathematical expectation is derived through rigorous analysis. As a result, the proposed ILC scheme is applicable to more practical systems. In the end, two illustrative examples are presented to demonstrate the performance and the effectiveness of the averaging ILC scheme for both time-invariant and time-varying linear systems.
When a homogeneous system is placed under a temperature gradient for a sufficient time, both its chemical and isotopic compositions will differentiate between the hot and the cold ends. ...Molecular-level knowledge of this process is of critical importance to understanding concentration and isotopic distributions in many geologic systems. Recently, different theoretical models have been proposed to explain isotopic fractionations observed in laboratory experiments under high temperatures, but there is still a lot of debating. Here we provide a unified theory based on local thermodynamic equilibrium approach to evaluating thermal isotope fractionations under a wide range of temperatures. For high temperature silicate melts, our theory offers a simple equation for calculating isotopic fractionations of all isotope systems: ΔXM=−(3/2)ln(m∗/m)ln(T/T0). The results from this equation agree with observed data for the most of network modifiers and resolve existing discrepancies among different interpretations. It can also explain O and Si isotope results if consider their diffusing species not as a single ion but a larger unit (e.g., SiO3 or SiO4). The simplicity of the equation support a classical mechanical collision model for high-temperature diffusing particles in silica melts.
Caching state data of real-world entities just in the cloud without any distinction will cause search performance degrading, due to the characteristics of uncountable number of entities and ...time-varying state of entities in cyber-physical systems (CPSs). Considering the diverse time-varying features of CPS entities, an edge-cloud collaborative entity state data caching strategy toward networking search application in CPSs is proposed in this article. Specifically, an entity state feature extraction method is presented to mine underlying changing rules of CPS entities via raw entity state observation sequence. Then, an edge and cloud collaborative entity state data caching strategy is devised to improve the search accuracy of CPSs search service and reduce the search delay and energy consumption, in which entities are clustered first according to the time-varying degree of their state and then these state information are discriminately cached based on their belonging clusters. Simulation results validate the effectiveness of the proposed strategy in terms of real-time and accuracy performances.
In this paper, we present a novel work in which an iterative learning control (ILC) method is applied to a two-link Carangiform robotic fish in real time and achieves precise speed tracking ...performance. By virtue of the Lagrangian mechanics method, we establish a mathematical model for the robotic fish. The robotic fish model is highly nonlinear and nonaffine in control input, which hinders the applicability of most control methods that require affine-in-input. ILC is suitable because it works for such circumstances. A P-type ILC algorithm is adopted for speed tracking tasks of the robotic fish. The rigorous convergence analysis is derived based on composite energy function (CEF). In practice, the precise model of robotic fish is difficult to be obtained due to many uncertain factors. By employing ILC, the speed tracking control performance can be improved significantly without using the perfect model. Both simulations and experiments are conducted to illustrate the effectiveness of ILC, and excellent speed tracking is achieved for the robotic fish.
In this paper, a D-type anticipatory iterative learning control (ILC) scheme is applied to the boundary control of a class of inhomogeneous heat equations, where the heat flux at one side is the ...control input while the temperature measurement at the other side is the control output. By transforming the inhomogeneous heat equation into its integral form and exploiting the properties of the embedded Jacobi Theta functions, the learning convergence of ILC is guaranteed through rigorous analysis, without any simplification or discretization of the 3D dynamics in the time, space as well as iteration domains. The adopted ILC scheme makes full use of the process repetition and deals with state-independent or state-dependent uncertainties. Meanwhile, due to the feedforward characteristic of ILC, the proposed scheme not only makes anticipatory compensation possible to overcome the heat conduction delay in boundary output tracking, but also eliminates the gain margin limitation encountered in feedback control. In the end, an illustrative example is presented to demonstrate the performance of the proposed ILC scheme.
Pneumocystis jirovecii pneumonia (PJP) can be a life-threatening opportunistic infection. We aimed to evaluate the diagnostic accuracy of metagenomic next-generation sequencing (mNGS) for PJP.
A ...comprehensive electronic literature search of Web of Knowledge, PubMed, Cochrane Library, CNKI and Wanfang data was performed. Bivariate analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), the area under the summary receiver operator characteristic (SROC) curve and the Q-point value (Q*).
The literature search resulted in 9 studies with a total of 1343 patients, including 418 cases diagnosed with PJP and 925 controls. The pooled sensitivity of mNGS for diagnosis of PJP was 0.974 95% confidence interval (CI), 0.953-0.987. The pooled specificity was 0.943 (95% CI, 0.926-0.957), the DOR was 431.58 (95% CI, 186.77-997.27), the area under the SROC curve was 0.987, and the Q* was 0.951. The I
test indicated no heterogeneity between studies. The Deek funnel test suggested no potential publication bias. Subgroup analyses showed that the area under the SROC curve of mNGS for diagnosis of PJP in immunocompromised and non-HIV patients was 0.9852 and 0.979, respectively.
Current evidence indicates that mNGS exhibits excellent accuracy for the diagnosis of PJP. The mNGS is a promising tool for assessment of PJP in both immunocompromised and non-HIV patients.
Understanding the strengths and limitations of the modeling capacity of surface flooding in urbanized floodplains is of utmost importance as such events are becoming increasingly frequent and ...extreme. In this study, we assess two computational models against laboratory observations of surface urban flooding in a reduced‐scale physical model of idealized urban districts. Four urban layouts were considered, involving each three inlets and three outlets as well as a combination of three‐ and four‐branch crossroads together with open spaces. The first model (2D) solves the shallow‐water equations while the second one (3D) solves the Reynolds‐averaged Navier‐Stokes equations. Both models accurately predict the flow depths in the inlet branches. For the discharge partition between the outlets, deviations between the computations and laboratory observations remain close to the experimental uncertainties (maximum 2.5 percent‐points). The velocity fields computed in 3D generally match the measured surface velocity fields. In urban layouts involving mostly a network of streets, the depth‐averaged velocity fields computed by the 2D model agree remarkably well with those of the 3D model, with differences not exceeding 10%, despite the presence of helicoidal flow (revealed by the 3D computations). In configurations with large open areas, the 3D model captures generally well the trajectory and velocity distribution of main surface flow jet and recirculations; but the 2D model does not perform as well as it does in relatively channelized flow regions. Visual inspection of the jet trajectories computed by the 2D model in large open areas reveals that they substantially deviate from the observations.
Plain Language Summary
Advancing our modeling capacity of urban flooding is of utmost importance for improving the design of risk reduction measures. During extreme urban flooding, complex flow patterns develop in urban environments, involving three‐dimensional flow structures. Though, urban floods are commonly simulated with two‐dimensional computational models. So far, no detailed comparison between flow fields predicted by two‐ and three‐dimensional computational models were conducted and assessed against reference data such as experimental observations for representative configurations of urban flooding. In this study, we assess two computational models against laboratory observations of urban flooding in a reduced‐scale physical model of an idealized district.
Key Points
Predictions of 2D and 3D computational models were compared against laboratory experiments representing urban flooding in a steady‐state
Both models perform equally well to predict upstream flow depth, outlet discharge partition, and velocity field in street networks
In urban layouts with large open spaces, only the 3D model accurately predicts the velocity field
Several important equilibrium Se isotope fractionation parameters are investigated by first-principles calculations, involving dominant inorganic and organic Se-bearing species in gaseous, aqueous ...and condensed phases. Because anharmonic effects are found to be negligible for Se isotope fractionation calculation, the Bigeleisen–Mayer equation method is used without corrections beyond harmonic approximation. All calculations are made at B3LYP/6-311
+
G(d,p) level, with a frequency scaling factor of 1.05. Solvation effects are carefully evaluated by the explicit solvent model (i.e. the “water droplet” method). A number of conformers are used for aqueous complexes in order to reduce the possible error coming from different configurations.
Redox state is found to be an important factor controlling equilibrium Se isotope fractionations. Our results suggest a trend of heavy Se isotopes enrichment as SeO
4
2−
>
SeO
3
2−
>
HSeO
3
−
>
SeO
2
>
selenoamino acids
>
alkylselenides
>
Se(0) or H
2Se
>
HSe
−. The Se(−
II) species regardless of organic and inorganic forms can enrich extremely light Se isotopes comparing with other species. Equilibrium Se isotope fractionation factors provided in this study suggest Se isotopes can be used as a tracer of redox conditions and also useful to study Se cycling.
► Several equilibrium Se isotope fractionation parameters are provided. ► Redox state is found to be an important factor controlling equilibrium Se isotope fractionations. ► Se isotopes can be used as a tracer of redox conditions.
Hereditary tyrosinemia type 1 (HT1; OMIM# 276700) is a genetic metabolism disorder caused by disease-causing variants in the fumarylacetoacetate hydrolase (FAH) gene encoding the last enzyme of the ...tyrosine catabolic pathway. Herein, we describe the clinical features and genetic characteristics of HT1 in a five years and seven months old Chinese patient.
After clinical diagnosis of the proband with HT1, genetic testing was performed by Sanger sequencing of the FAH gene in all family members. Functional analysis of the disease-causing variant was performed by cDNA sequencing to understand the effect of the variant on FAH transcript. To further predict the variant effect, we used Human Splicing Finder (HSF) and PyMol in silico analysis.
We identified a novel previously undescribed intronic variant in the FAH gene (c.914-1G>A). It was detected in a child who was homozygous for the variant and had the clinical presentation of HT1. cDNA sequencing showed that this splice-junction variant affected the transcription of FAH by formation of two different transcripts. Our observations and laboratory experiments were in line with in silico methods.
Our study provides new insight into the HT1 variant spectrum and a better understanding of this disease in the Chinese population. This will be useful for molecular diagnosis in our country in cases where premarital screening, prenatal diagnosis and preimplantation genetic diagnosis are planned.
There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation ...technologies that can actively provide instant and precise entity state information come into being. Existing IoT data recommendation methods ignore the characteristics of IoT data and user search behavior; thus the recommendation performances are relatively limited. Considering the time-varying characteristics of the IoT entity state and the characteristics of user search behavior, an edge-cloud collaborative entity recommendation method is proposed via combining the advantages of edge computing and cloud computing. First, an entity recommendation system architecture based on the collaboration between edge and cloud is designed. Then, an entity identification method suitable for edge is presented, which takes into account the feature information of entities and carries out effective entity identification based on the deep clustering model, so as to improve the real-time and accuracy of entity state information search. Furthermore, an interest group division method applied in cloud is devised, which fully considers user's potential search needs and divides user interest groups based on clustering model for enhancing the quality of recommendation system. Simulation results demonstrate that the proposed recommendation method can effectively improve the real-time and accuracy performance of entity recommendation in comparison with traditional methods.
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