Finite Number Research Articles

Electrochemiluminescence immunoassay system based on PCN-224-Mn and gold-platinum bimetallic nanoflowers for sensitive detection of ochratoxin A

In this work, a novel Electrochemiluminescence Immunosensor was constructed using PCN-224-Mn and gold-platinum nanoflowers (AuPt NFs) for the ultrasensitive detection of ochratoxin A (OTA). PCN-224 modified with Mn (II) was synthesized as a probe material. The interaction efficiency of PCN-224 with S2O82− was also greatly improved. AuPt NFs were used as the substrate material for the electrodes. It has favorable biocompatibility, large specific surface area and can bind more antigen. Also greatly increased the electroactive surface area and conductivity of the electrode. OTA was detected using a competitive immunoassay strategy, in which OTA in the sample competes with the encapsulated antigen for a finite number of antibodies. ECLIA for the detection of OTA was designed to be highly sensitive, with a linear range from 0.0002 ng mL−1 to 1000 ng mL−1 and a LOD as low as 0.067 pg mL−1. In addition, it was evident from the electrochemical analyses that PCN-224-Mn had a stronger and more stable ECL signal compared to the plain PCN-224. The successful preparation of specific, sensitive and reproducible ECL immunosensors confirms the great promise for the detection of OTA or other small molecule mycotoxins.

Output synchronization of a class of complex dynamic networks: A reinforcement learning method

In this paper, to achieve the synchronization control for a class of complex dynamic networks with completely unknown system dynamics, a reinforcement learning output feedback algorithm based on state reconstruction is proposed. Given the high cost and complexity associated with obtaining the full state information, an output-based node state reconstruction method is employed for the first time in complex dynamic networks. The proposed method utilizes a sequence composed of a finite number of output data to reconstruct the current state. At the same time, the overall error system is constructed to handle the coupling relationship between nodes, to facilitate the controller design. Thereafter, considering the system dynamics are unknown, an algorithm based on reinforcement learning is proposed to ensure rapid synchronization of node outputs, and the convergence of proposed method is proven. Finally, the feasibility of proposed algorithm is corroborated through a simulation example and a multi-vehicle system.

Development of Reduced-Order-Discrete-Module method for hydroelastic analysis of floating flexible structures

The present study proposes a novel method for analyzing the hydroelastic response of floating flexible structures based on Reduced-Order-Discrete-Module (RODM) model. In this model, the floating flexible structure is discretized into a finite number of modules. The hydrodynamic problem is simplified as the interaction between waves and multiple modules. The hydroelastic response is approximated by solving the motion equation of the multibody system, in which the mass and stiffness of the structure are obtained from the reduced-order matrices by the finite element method with a system equivalent reduction expansion process. By using the transformation matrix, the detailed floating structure response can be reconstructed from the multibody dynamics. The validity of the proposed method was demonstrated by comparing the results with the experimental data and other existing methods. The results show that this study has developed an accurate hydroelastic model to analyze the hydroelastic response of floating flexible structures. A module number selection formula is proposed to select the appropriate number of modules based on the exciting force frequency. This model is relatively easy to implement for the hydroelastic problem of interconnection modules and take into account the spatial inhomogeneity of wind/wave field. The proposed model can offer a useful tool for analyzing the hydroelastic response of the offshore floating photovoltaic systems.

Pseudo Master Curve Analysis of an Infinite Number of Parallel First-Order Reactions: Improved Distributed Activation Energy Model.

The so-called Distributed Activation Energy Model (DAEM) has been used extensively, mainly to analyze pyrolysis reactions of solid reactants. The model expresses many parallel first-order reactions using the distributions of activation energy f(E) and frequency factor k 0(E). Miura and Maki presented a method to estimate both f(E) and k 0(E) in the DAEM in 1998. This model has been used successfully by many researchers. In this paper more general basic equations are derived for describing an infinite number of parallel first-order reactions by extending the basic equations for the finite number of parallel first-order reactions. Revisiting the Miura-Maki method based on the general basic equations, a graphical analysis method that may be called "Pseudo Master Curve Analysis" is presented. The method not only supplements the Miura-Maki method but gives the underlying concept of the Miura-Maki method clearly. It is also shown that the graphical method can be applicable to analyze single reactions and the experimental data obtained using isothermal reaction techniques. Next, a method that improves the estimation accuracy of k 0(E) is presented. Practical examples analyzing several experimental data are also given to show the usefulness and validity of the Miura-Maki method and the graphical method. Through the examination, it is proposed that the DAEM should be renamed, for example, as the Distributed Rate Constant Model (DRCM).

Psychometric Properties of Telugu Version of Scale of Oral Health Outcomes for 5-year-old Children.

Only a finite number of standard oral health-related quality of life (OHRQoL) measures are available for young children. However, instead of using parents as proxies to report a child's oral health status, children's self-reported oral health measures would be more accurate in providing their own perceptions of oral impacts. The study aimed to evaluate the psychometric properties of the Telugu version of the scale of oral health outcomes (SOHO-5T) for 5-year-old children in Telangana. The forward-backward translated SOHO-5T was pilot-tested among thirty children. The tested questionnaire was administered to 419 children, followed by a clinical oral examination using dentition status to evaluate dental caries (DC). Children (n = 30) were readministered the same questionnaire after a 2-week interval to test reliability. Internal consistency and test-retest reliability were determined by Cronbach's α and intraclass correlation. Correlation with global ratings of oral health questions was done to assess construct validity. Discriminant validity was evaluated based on the presence or absence of DC. A p < 0.05 was considered statistically significant. The mean SOHO-5T score was 4.70, and the mean decayed teeth score was 2.48, with 49.16% of children having DC. Cronbach's α scores and the intraclass correlation (ICC) coefficient for overall SOHO-5T were 0.90 and 0.91, respectively. SOHO-5T also demonstrated good construct validity with a significant positive correlation with global ratings of oral health. SOHO-5T showed good discrimination between the presence (9.43 ± 3.10), or absence (0.14 ± 1.01) of DC. This study shows good internal consistency and test-retest reliability. It also exhibited good construct and discriminant validity. Meghana D, Sukhabogi JR, Doshi D, et al. Psychometric Properties of Telugu Version of Scale of Oral Health Outcomes for 5-year-old Children. Int J Clin Pediatr Dent 2024;17(8):933-937.

A Decision Tree Application for the Development of a Novel Approach to the Traveling Salesman Issue

The Traveling Salesman Problem (TSP) is a combinatorial problem related to computer science and operations research. There are many methods proposed in the literature to solve TSP with profit and losses. In this classical problem, the objective is to visit a finite number of locations exactly once, one by one, while minimizing the total distance traveled by the connecting links (arcs) needed to reach these sites (nodes). This study presents a new procedure that applies TSP and makes use of the decision tree notion. Anywhere in the TSP network, where n is the total number of places (nodes) in the network, this system ends after n-1 iterations. The method's step-by-step computational criteria make it efficient and readily applicable. To demonstrate the process's validity and efficiency, numerical examples are provided.

Uncertainty-informed identification of tie-rod mechanical properties in monumental buildings

Tie-rods are classic structural elements that have been used for centuries to enhance stability in historic masonry buildings and offer valuable insights into structural performance over time through dynamic vibration measurements. This paper tackles the inverse problem, aiming to quantify uncertainties in mechanical properties, tensile axial force and boundary conditions of tie-rods based on experimental vibration frequencies. The approach assumes known probabilistic models for random parameters, such as mass per unit length, bending stiffness, tensile force and boundary conditions, with certain parameters requiring estimation. Finite dimensional models, i.e., deterministic functions in time and/or space, and a finite number of random variables are employed to discretize both physical and probability spaces. The paper concludes with a Monte Carlo simulation, that provides a comprehensive characterization of uncertainties and the best estimates for unknown features, including mass per unit length, stiffness, boundary conditions and tensile force necessary for achieving target free vibration modal parameters, i.e. natural frequencies.

Finite-time contractive stability for fractional-order nonlinear systems with delayed impulses: Applications to neural networks

This paper deals with finite-time stability (FTS) and finite-time contractive stability (FTCS) criteria of fractional-order nonlinear systems (FONSs), where the state of the systems consists of a finite number of delayed impulsive instants. To achieve these results, we have extended the theories on FTS and FTCS criteria from integer-order NSs to fractional-order systems. Employing the fractional-order Lyapunov stability theory, the sufficient conditions of the above-mentioned stability criteria are derived for a class of FONSs with state-dependent delayed impulses. Then, to ensure the applicability of the proposed results, we have analyzed the desired performances for several neural network (NN) models, such as non-autonomous NNs (NANNs), Cohen–Grossberg NNs (CGNNs), switched NNs (SNNs) and bidirectional associative memory NNs (BAMNNs). Finally, four representative numerical examples are illustrated to demonstrate the assurance of the obtained stability conditions of the respective state-dependent impulsive fractional-order NNs (FONNs).

On clusters and exceptional sets

In this paper, we first study clusters in type [Formula: see text] by collecting them into a finite number of infinite families given by Dehn twists of their corresponding triangulations, and show that these families are counted by the Catalan numbers. We also highlight the similarities and differences between the annuli diagrams used to study clusters and those used to study exceptional sets in type [Formula: see text]. We then focus on exceptional collections (sets) of modules over path algebras of quivers by first showing that the notion of relative projectivity in exceptional sets is well defined. We finish by counting the number of exceptional sets of representations of type [Formula: see text] quivers with straight orientation and using this to count the number of families of exceptional sets of type [Formula: see text] with straight orientation.

Sampling Error Analysis in Quantum Krylov Subspace Diagonalization

Quantum Krylov subspace diagonalization (QKSD) is an emerging method used in place of quantum phase estimation in the early fault-tolerant era, where limited quantum circuit depth is available. In contrast to the classical Krylov subspace diagonalization (KSD) or the Lanczos method, QKSD exploits the quantum computer to efficiently estimate the eigenvalues of large-size Hamiltonians through a faster Krylov projection. However, unlike classical KSD, which is solely concerned with machine precision, QKSD is inherently accompanied by errors originating from a finite number of samples. Moreover, due to difficulty establishing an artificial orthogonal basis, ill-conditioning problems are often encountered, rendering the solution vulnerable to noise. In this work, we present a nonasymptotic theoretical framework to assess the relationship between sampling noise and its effects on eigenvalues. We also propose an optimal solution to cope with large condition numbers by eliminating the ill-conditioned bases. Numerical simulations of the one-dimensional Hubbard model demonstrate that the error bound of finite samplings accurately predicts the experimental errors in well-conditioned regions.

Dynamic Responses in a Pipe Surrounded by Compacted Soil Suffering from Water Hammer with Fluid–Structure–Soil Interactions

The current literature analyzing the dynamic response of coupled pipelines neglects the crucial interplay between the pipelines themselves and these constraints. This overlooked interaction has substantial influence on the fluid–structure coupling response, particularly in scenarios involving continuous constraints. We focus on a piping system surrounded by compacted soil, which is regarded as unbounded homogeneous elastic soil that suffers from water hammer. This study established a one-dimensional model for water pipe-embedded compacted soil with fluid–structure–soil interaction. Taking fluid–structure–soil interaction into account, fluid–structure interactions (FSIs) include Poisson coupling, junction coupling emerging at the fluid–structure interface, and pipe–soil coupling (PSC) emerging at the pipe–soil interface. In this study, as soil is assumed to be a homogeneous, isotropic elastic material, the coupling responses are more complex than those of an exposed pipe, and the relevant mechanisms justify further exploration to obtain well-predicted results. To mathematically describe this system considering fluid–structure–soil interaction, the four-equation FSI model was modified to accommodate the piping system surrounded by unbounded homogeneous elastic soil, employing the finite volume method (FVM) as a means to tackle and solve the dynamic problems with FSI and PSC, which partitions the computational domain into a finite number of control volumes and discretizes governing equations within each volume. The results were validated by the experimental and numerical results. Then, dynamic FSI responses to water hammer were studied in a reservoir–pipe–reservoir physical system. The hydraulic pressure, pipe wall stress, and axial motion were discussed with respect to different parameters. With the PSC and FSI taken into account, fluid, soil, and pipe signals were obviously observed. The results revealed the structural and fluid modes. Dynamic responses have been proven to be difficult to understand and predict. Despite this, this study provides a tractable method to capture more accurate systematic characteristics of a water pipe embedded in soil.

Improving QoS Management Using Associative Memory and Event-Driven Transaction History

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm.

On the convergence of phase space distributions to microcanonical equilibrium: dynamical isometry and generalized coarse-graining

Abstract This work explores the manner in which classical phase space distribution functions converge to the microcanonical distribution. We first prove a theorem about the lack of convergence, then define a generalization of the coarse-graining procedure that leads to convergence. We prove that the time evolution of phase space distributions is an isometry for a broad class of statistical distance metrics, implying that ensembles do not get any closer to (or farther from) equilibrium, according to these metrics. This extends the known result that strong convergence of phase space distributions to the microcanonical distribution does not occur. However, it has long been known that weak convergence can occur, such that coarse-grained distributions---defined by partitioning phase space into a finite number of cells---converge pointwise to the microcanonical distribution. We define a generalization of coarse-graining that removes the need for partitioning phase space into cells. We prove that our generalized coarse-grained distribution converges pointwise to the microcanonical distribution if the dynamics are strong mixing. As an example, we study an ensemble of triangular billiard systems.

Development of Synchronization Selection Method in IoT with Secure Channel Bidirectional Communication

Background: The rapid development of wireless communications and mobile computation has given rise to the novel Internet of Things (IoT) systems, which is causing considerable research attention and industrial development. However, the lack of synchronization between the timers of IoT devices compromises the network's security. Aim: The purpose of this patent application is to present a technique for synchronizing the timepieces of IoT gadgets and establishing a secure channel for the transmission of data from source to destination. Objective: This study proposes a Synchronization Selection Method (SSM) for IoT systems to enhance network security and reduce packet loss. Methods: The method utilizes time-lay synchronization and RSA algorithm-based secure channel establishment. Time lay is a technique that was developed for IoT devices to achieve efficient clock synchronization of sensor nodes. Before synchronizing the sensor nodes' timings, the cluster leaders initiate the process. Utilizing a finite number of nodes, the proposed method was executed in MATLAB. Results: Time-lay synchronization involves all network nodes synchronizing their clocks with a third-party clock. In the context of time-lay synchronization, the term “third-party clock” refers to a single specific point that contains the time signal that all nodes in the network use as a reference. This third-party clock is outside of the network nodes and acts as the standard for the precise and synchronized time within the network. Therefore, it can be deduced that each of the techniques possesses its advantages and disadvantages. Each of the synchronization techniques has the potential to significantly benefit the IoT by offering smart clock synchronization that is more secure. Experimental results demonstrate that the proposed method improves throughput and reduces packet loss compared to existing techniques. Conclusion: The potential of this patent is highly significant for solving the synchronization problem of IoT devices and enhancing network security with decreased network packet loss. Other: The SSM would be assessed using the parameters of packet loss and throughput.

Stability of KdV equation on a network with bounded and unbounded lengths

In this work, we studied the exponential stability of the nonlinear KdV equation posed on a finite star shaped network with finite number of branches. On each branch of the network we define a KdV equation posed on a finite domain [[EQUATION]] or the half-line [[EQUATION]]. We start by proving well-posedness and some regularity results. Then, we state the exponential stability of the linear KdV equation by acting with a damping term on some branches. The main idea is to prove a suitable observability inequality. In the nonlinear case, we obtain two kinds of results: The first result holds for small amplitude solutions, and is proved using a perturbation argument from the linear case but without acting on all edges. The second result is a semiglobal stability result, and it is obtained by proving an observability inequality directly for the nonlinear system, but we need to act with damping terms on all the branches. In this case, we are able to prove the stabilization in weighted spaces.

Error-free tracking control of discrete-time systems by combining data-driven control with model-based ILC

For discrete-time linear systems, by applying the advantages of data-driven methods and iterative learning control, this paper proposes three data-driven learning control algorithms based on the linearity of the system. For iterative learning control, if the initial state shift is zero in each iteration and the system matrix is known, the algorithm presented in this paper can ensure that the system achieve complete tracking in the entire interval through a finite number of iterations. If the initial state shift is arbitrary in each iteration and the system matrix is unknown, the algorithm proposed in this paper can ensure that the system achieve complete tracking at all points except the initial point through finite iterations. Even for systems with non-repetitive processes, if the system matrix is unknown, the method in this paper can ensure that the system achieves complete tracking at all points except a finite number of points in the initial stage. Finally, the effectiveness of the three algorithms is verified by three examples, and the superiority of the proposed algorithms is demonstrated by comparison.

Identification of Hamiltonian systems using neural networks and first integrals approaches

This research introduces a class of non-parametric identifiers based on differential neural networks represented by Hamiltonian dynamics. The structure of the identifier corresponds to the form of a canonical Hamiltonian system that uses the evolution of generalized coordinates and momentums. The learning laws of the identifier come from applying the first integrals approach, which justifies the design of an exact identifier considering the time invariance of the Hamiltonian, with a finite number of activation functions in the identifier structure. The first integrals approach derives several learning laws for the proposed class of identifiers. The learning laws design uses the estimated derivative of the generalized momentum assessed via a super-twisting differentiator with multiple inputs and outputs. All proposed laws require the solution of differential continuous-time Riccati equations and nonlinear differential equations for the learning laws, which depend on the identification error and state constraints. The developed identifier was evaluated compared to an identifier that did not consider the Hamiltonian constraint using first integrals. This comparison included a numerical evaluation of the identifier considering its application to a classical Hamiltonian system associated with the Kepler dynamics representing satellite orbital evolution. This evaluation confirmed that the identification results were improved with the proposed learning laws regarding the class of Hamiltonian structures, and the quality indicators based on the mean square error were several times lower.

Replicative Endothelial Cell Senescence May Lead to Endothelial Dysfunction by Increasing the BH2/BH4 Ratio Induced by Oxidative Stress, Reducing BH4 Availability, and Decreasing the Expression of eNOS.

Vascular aging is associated with the development of cardiovascular complications, in which endothelial cell senescence (ES) may play a critical role. Nitric oxide (NO) prevents human ES through inhibition of oxidative stress, and inflammatory signaling by mechanisms yet to be elucidated. Endothelial cells undergo an irreversible growth arrest and alter their functional state after a finite number of divisions, a phenomenon called replicative senescence. We assessed the contribution of NO during replicative senescence of human aortic (HAEC) and coronary (CAEC) endothelial cells, in which accumulation of the senescence marker SA-β-Gal was quantified by β-galactosidase staining on cultured cells. We found a negative correlation in passaged cell cultures from P0 to P12, between a reduction in NO production with increased ES and the formation of reactive oxygen (ROS) and nitrogen (ONOO-) species, indicative of oxidative and nitrosative stress. The effect of ES was evidenced by reduced expression of endothelial Nitric Oxide Synthase (eNOS), Interleukin Linked Kinase (ILK), and Heat shock protein 90 (Hsp90), alongside a significant increase in the BH2/BH4 ratio, inducing the uncoupling of eNOS, favoring the production of superoxide and peroxynitrite species, and fostering an inflammatory environment, as confirmed by the levels of Cyclophilin A (CypA) and its receptor Extracellular Matrix Metalloprotease Inducer (EMMPRIN). NO prevents ES by preventing the uncoupling of eNOS, in which oxidation of BH4, which plays a key role in eNOS producing NO, may play a critical role in launching the release of free radical species, triggering an aging-related inflammatory response.

On generalized concise words

Abstract The study of verbal subgroups within a group is well known for being an effective tool to obtain structural information about a group. Therefore, conditions that allow the classification of words in a free group are of paramount importance. One of the most studied problems is to establish which words are concise, where a word 𝑤 is said to be concise if the verbal subgroup w ⁢ ( G ) w(G) is finite in each group 𝐺 in which 𝑤 takes only a finite number of values. The purpose of this article is to present some results, in which a hierarchy among words is introduced, generalizing the concept of concise word.

Varieties with a Torus Action of Complexity One Having a Finite Number of Automorphism Group Orbits

Varieties with a Torus Action of Complexity One Having a Finite Number of Automorphism Group Orbits