Linear Form Research Articles

Selection of Peptide-Bismuth Bicycles Using Phage Display.

Cysteine conjugation is widely used to constrain phage displayed peptides for the selection of cyclic peptides against specific targets. In this study, the nontoxic Bi3+ ion was used as a cysteine conjugation reagent to cross-link peptide libraries without compromising phage infectivity. We constructed a randomized 3-cysteine peptide library and cyclized it with Bi3+, followed by a selection against the maltose-binding protein as a model target. Next-generation sequencing of selection samples revealed the enrichment of peptides containing clear consensus sequences. Chemically synthesized linear and Bi3+ cyclized peptides were used for affinity validation. The cyclized peptide showed a hundred-fold better affinity (0.31 ± 0.04 μM) than the linear form (39 ± 6 μM). Overall, our study proved the feasibility of developing Bi3+ constrained bicyclic peptides against a specific target using phage display, which would potentially accelerate the development of new peptide-bismuth bicycles for therapeutic or diagnostic applications.

Group invariant variational principles

In this paper we introduce a group invariant version of the well-known Ekeland variational principle. To achieve this, we define the concept of convexity with respect to a group and establish a version of the theorem within this framework. Additionally, we present several consequences of the group invariant Ekeland variational principle, including Palais-Smale minimizing sequences, the Brønsted-Rockafellar theorem, and a characterization of the linear and continuous group invariant functionals space. Moreover, we provide an alternative proof of the Bishop-Phelps theorem and proofs for the group-invariant Hahn-Banach separating theorems. Finally, we discuss some implications and applications of these results.

A Semi‐parametric Panel Data Model with Common Factors and Spatial Dependence

AbstactIn the analysis of the Griliches' knowledge capital production function, previous works pointed out the relevance of incorporating slope heterogeneity in the technological parameters, cross‐sectional dependence arising simultaneously from common factors and spillovers, and possible nonlinear effects of relevant common observed variables. In order to solve the above problems, in this article we introduce a semi‐parametric model in a partially linear form that copes simultaneously with all the previous specification issues. The asymptotic properties of the resulting estimators are obtained and the theoretical findings are further supported for small samples via several Monte Carlo experiments and an empirical application.

Research on two stage obstacle-avoidance trajectory planning and trajectory tracking control in curves

The existing obstacle-avoidance trajectory planning and trajectory tracking control algorithms have limitations such as long-time consumption, high failure rate in dynamic traffic environments, and insufficient trajectory tracking accuracy in curved roads. Based on the above problems, this paper designs a two stage obstacle-avoidance trajectory planner based on nonlinear optimization theory. In first stage Part-NLP, only considering the safety obstacle avoidance, a point mass model and linearization constraints are established to quickly solve the initial trajectory. In the second stage Full-NLP, considering smooth soft constraints comprehensively, the initial trajectory is optimized by establishing driving corridors and a lightweight iterative framework. In control module, this paper selects a linear quadratic form lateral trajectory tracking controller, and the parameters were optimized through the carnivorous plant algorithm. The joint simulation results show that in dynamic traffic environment of curved roads, the two stage planner proposed can accurately plan safe and smooth obstacle avoidance trajectories, and there is a significant reduction in time consumption compared to traditional NLP algorithms. The control strategy can accurately track the planned trajectories, with lateral error controlled within plus or minus 0.1 m, heading error controlled within plus or minus 0.15 rad, speed tracking error controlled within plus or minus 0.15 m/s, and vehicle yaw angle error controlled within plus or minus 0.04 rad; the hardware-in-loop test results indicate that the controller can achieve real-time and accurate trajectory tracking.

Positivity and Positivity-Definiteness for Cauchy Powers of Linear Functionals on the Linear Space of Polynomials

Positivity and Positivity-Definiteness for Cauchy Powers of Linear Functionals on the Linear Space of Polynomials

Implementation and Testing of Generalized Perturbation Theory Capabilities in TRIPOLI-4®

In this paper, we describe the implementation, verification, and numerical validation of Generalized Perturbation Theory (GPT) capabilities in the TRIPOLI4® Monte Carlo code. The GPT is applied to reaction rate ratios (linear forms) and effective kinetics parameters (bilinear forms). For these latter, we focus in particular on the effective neutron generation time Λ eff , the effective prompt lifetime ℓ eff , and the effective delayed neutron fraction β eff . Verification is achieved comparing the results obtained using TRIPOLI-4® to benchmarks where analytical solutions are available. Numerical validation is performed with respect to independent Monte Carlo calculations using the SCALE 6.2.4 and SERPENT 2.2 codes, to published results, and to direct perturbations. Finally, a comparison with respect to the APOLLO3® deterministic code is performed for the EPICURE experimental configuration of the water-moderated EOLE reactor core, formerly operated at CEA.

Poincaré’s method of normal forms in the study of factors of Production

As the main factors of production, the paper considers physical capital and labor resources. The study of the behavior of the main factors of production is based on the analysis of the volume of investment in them, the depreciation of physical capital and changes in the volume of labor resources. Most of the studies on this topic do not address issues related to distortions in financing and their possible consequences for the main factors of production. To assess these consequences, formulas were derived that include partial derivatives of investment volumes for each of the main factors. The use of these formulas helps to develop an investment algorithm that contributes to the sustainable growth of the main factors of production. The method of Poincaré normal forms was chosen as the main research method, which allows us to simplify the initial problem and reduce it to an analysis in a linear form. A classification of possible variants of eigenvalues, the resulting linear form was carried out. The behavior of the main factors of production depending on the found eigenvalues is determined. This method allows you to get more understandable results and draw more accurate conclusions. The results obtained were tested in a number of federal districts of Russia and a number of EU countries. Testing showed a qualitative match with the real state of the main factors of production. This suggests that the developed investment algorithm can be applied in practical conditions and contribute to the achievement of stable growth in various regions of Russia. This study is important for understanding and optimizing production processes and economic development. It provides a basis for developing effective investment and decision-making strategies that promote both a balanced and directed development of the main factors of production.

The Cluster Model of Energy Transduction in Biological Systems

The central problem in transduction is to explain how the energy caught from sunlight by chloroplasts becomes biological work. Or to express it in different terms: how does the energy remain trapped in the biological network and not get lost through thermalization into the environment? The pathway consists of an immensely large number of steps crossing hierarchical levels – some upwards, to larger assemblies, others downwards into energy rich molecules – before fuelling an action potential or a contracting cell. Accepting the assumption that steps are executed by protein domains, we expect that transduction mechanisms are the result of conformational changes, which in turn involve rearrangements of the bonds responsible for the protein fold. But why are these essential changes so difficult to detect? In this presentation, the metabolic pathway is viewed as equivalent to an energy conduit composed of equally sized units – the protein domains – rather than a row of catalysts. The flow of energy through them occurs by the same mechanism as through the cytoplasmic medium (water). This mechanism is based on the cluster-wave model of water structure, which successfully explains the transfer of energy through the liquid medium responsible for the build up of osmotic pressure. The analogy to the line of balls called “Newton’s cradle” provides a useful comparison, since there the transfer is also invisible to us because the intermediate balls are motionless. It is further proposed that the spatial arrangements of the H-bonds of the α and β secondary structures support wave motion, with the linear and lateral forms of the groups of bonds belonging to the helices and sheets executing the longitudinal and transverse modes, respectively.

The analytical solutions of long waves over geometries with linear and nonlinear variations in the form of power-law nonlinearities with solid inclined wall

In this paper, we derive the exact analytical solutions for the long-wave equation in both linear and nonlinear power-law form depth and breadth geometries containing a solid inclined wall. Firstly, we give general information about the concept of partial reflection and its components, and formulate the solid inclined wall boundary condition. For these specific power-law forms of depth and breadth geometries, we show that in the presence of the solid inclined wall, the long-wave equation admits solutions in terms of Bessel-Z functions and the Cauchy–Euler series. Since the presence of solid vertical wall removes the singular point from the domain, the solution admits both the first and the second kind of the Bessel functions and Cauchy–Euler series terms. We derive results for the general case and also discuss their significance using six different geometries with solid inclined wall.

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult to predict without sufficient historical data. The purpose of this paper is to develop a framework for near-optimal scheduling of IC packaging considering processing time variations and factory practices. Using an orthogonal greedy algorithm and recurrent neural network, this framework extracts key features and predicts the processing times of trial run lots and production lots for each operation using these algorithms. After formulating the scheduling problem in an integer linear programming form, an ordinal optimization method is embedded within the decomposition and coordination framework to obtain dynamic and near-optimal schedules in a computationally efficient manner. This approach can improve the efficiency of IC packaging and potentially other operations by optimizing lot scheduling, reducing production tardiness, and increasing productivity in the factory, paving the way for self-optimizing factories in the future.

Stability of charged anisotropic thin shell gravastars admitting conformal motion in [formula omitted] gravity

This paper investigates the interesting features of gravastar configuration under charged anisotropic fluid distribution considering geometry-matter coupling theory. In the context of f(R,T) gravity, we derive exact solutions of the Einstein field equations using the linear form of the function f(R,T)=R+2γT, where R is the Ricci scalar and T is the trace of the energy–momentum tensor with conformal motion. We consider three different regions of gravastar, the interior region having a definite radius r, the intermediate layer of matter with the thickness ϵ, and the outer region with the radius r+ϵ. For the viability of our developed solution, we examine the metric potentials, energy density, and pressure components inside the thin shell for different values of theory parameter γ, and charge Q. Using the well-known matching conditions, we interpret the dynamical properties of gravastar such as surface charge density, surface pressure, proper length, equation of state parameter, entropy, and evolution of shell energy under the allowed range of parameters. Moreover, we analyze the presence of realistic matter inside the thin shell through energy conditions. The dynamical stability of the gravastar is checked through the equilibrium profile and speed of sound parameter, which shows the stable configuration of the gravastar in our theoretical studies.

Homography-Based Visual Servoing of Eye-in-Hand Robots With Exact Depth Estimation

Visual servoing can effectively control robots using visual feedback to improve their intelligence and reliability. For a feature point detected by a monocular camera, the time-varying depth appearing nonlinearly in the Jacobian matrix is difficult to be measured without the prior geometry knowledge of the observed object. Hence, the depth of the feature point is one of the major uncertain parameters in visual servoing. Considering unknown Cartesian feature positions, this study presents a dynamics-based homography-based visual servoing (HBVS) controller for the three-dimensional (3-D) pose regulation of eye-in-hand robot arms with monocular cameras. The uncertain depth is represented as a linear form of its Cartesian feature position, and a composite learning law is applied to estimate position parameters accurately, resulting in exact depth estimation. Compared to existing adaptive HBVS methods, the distinctive feature of the proposed method is that it is a dynamics-based design and guarantees exact depth estimation under a much weaker condition termed interval excitation than persistent excitation. Simulations and experiments on a collaborative robot with 7 degrees of freedom named Franka Emika Panda have verified the effectiveness of the proposed method.

Nonparametric Smoothing Spline Approach in Examining Investor Interest Factors

The nonparametric approach is an appropriate approach for patterns of relationships between predictor variables and response variables that are not or have not been known in form. In other words, there is no complete information about the pattern of relationships between variables. Curve estimation is determined based on relationship patterns in existing data. The nonparametric approach has great flexibility for estimating regression curves. This study aims to form a model on investor interest factors in improving tourism investment decisions with a nonparametric approach. The nonparametric method used is the smoothing spline regression method. The smoothing spline method is used because the modeling results from the smoothing spline approach can follow the relationship model between variables contained in the data. Thus, this method really helps researchers to model relationships between variables that are not linear and whose linear form is unknown. The results of the analysis showed that the nonparametric smoothing spline regression analysis method could model data by 94.63%, indicates that data variance can be explained by 94.63% with models, while other variance outside the study explain the remaining 5.37%. That is, investment motivation is one of the most important factors to improve investment decisions. 

Kinetic Study of Sodium Dodecylsulfate Degradation by Solar Heterogeneous Photocatalysis Based on Tungsten Oxide

Sodium Dodecyl Sulfate or SDS is a chemical compound widely used in detergent and cosmetic formulations. This work describes the application of solar heterogeneous photocatalysis based on tungsten oxide (WO3) to the kinetic study of the degradation of sodium dodecylsulfate. The experiments were performed at room temperature to study the effect of the initial catalyst mass. The results showed that a small amount of tungsten oxide is sufficient to degrade sodium dodecyl sulfate. The initial concentrations of SDS varied between 5 and 15 mg.L-1. According to the results, more than 87% reduction is obtained after 120 min of solar irradiation of the SDS solution for a WO3/SDS mass ratio of 1/5. The detailed kinetic analysis of the photodegradation of sodium dodecylsulfate showed that the disappearance of the anionic surfactant follows a pseudo-prime model. According to the Langmuir-Hinshelwood model, the kinetics constant calculated from the linear form of this model are : k = 0.042 mg.L.min-1 and K = 0.060 L.mg-1.

Elasto‐thermodiffusive interaction under void due to nonlocal stress theory

AbstractThe current investigation address a novel generalized elasto‐thermodiffusion model for a thermoelastic porous half‐space incorporating the nonlocal stress theory proposed by Eringen. Modeling of the problem is performed by adopting Moore‐Gibson‐Thompson (MGT) thermoelasticity theory defined in an integral form of a common derivative on a slipping interval, well known as the memory‐dependent derivative. The bounding plane of the medium is subjected to time‐dependent thermal and chemical shocks and there is no change in the volume fraction field. Laplace transform and the Fourier transform techniques have been adopted to represent the analytical solutions in the transformed domain. The distributions of the physical fields such as the temperature, stress, chemical potential, mass concentration and the volume fraction field were found in the real space‐time domain adopting suitable numerical scheme based on the Fourier series expansion. According to the discussion of the computational results and the respective graphical representations, the prominent role of different parameters such as the effect of nonlocality, effect of void and thermodiffusion is analyzed. Moreover, the superiority of a nonlinear kernel function compared to a linear form is also reported.

Phase change in a one-dimensional functionally graded material

Functionally graded materials (FGMs) are a class of materials with engineered microstructure to achieve spatial variation in properties so as to serve a desired function. While FGMs have been well-researched for their structural properties, there is a lack of investigation of FGMs in solid-liquid phase change heat transfer processes. A well-designed functionally graded phase change material (FG-PCM) may overcome some of the well-known limitations of the phase change process. In order to investigate this possibility, this work presents a theoretical model to predict phase change propagation in a one-dimensional FG-PCM containing an arbitrary spatial variation in thermal conductivity, thermal diffusivity and latent heat. The problem is solved using the method of eigenfunction expansion to determine the temperature distribution, wherein, spatial variation in thermophysical properties listed above is accounted for by deriving a first-order matrix ordinary differential equation in the coefficients of the series solution. Results are found to correctly reduce to the well-known Stefan solution under special conditions. The impact of variation in thermophysical properties is investigated in detail. It is found that while spatial variation in thermal conductivity and latent heat has a significant effect on the rate of phase change, the impact of the thermal diffusivity function is negligible for realistic values of the Stefan number. The effect of the Stefan number governing this problem on the rate of melting is investigated. The nature of phase change under linear (1+aξ), polynomial (1+aξn) and exponential (eaξ) functional forms of thermophysical properties is investigated. It is shown that an appropriate choice of these spatial functions may help overcome the well-known self-limiting nature of phase change and produce a nearly-linear phase change propagation curve. For example, results show 33% increase in phase change propagation for a linear (1+5ξ) variation in thermal conductivity compared to baseline. The theoretical tools developed here may help design and optimize functionally graded PCMs towards improved phase change heat transfer in applications of practical interest.

Body‐fitted topology optimization via integer linear programming using surface capturing techniques

AbstractRecent advancements in computational tools and additive manufacturing have expanded design possibilities on fluid devices and structures to also include aesthetics. However, traditional discrete density‐based topology optimization methods usually use square and cubic regular meshes, resulting in jagged contour patterns that require mesh refinement and post‐processing to numerical solution steps of complex physics problems. In this article, we propose a new isosurface boundary capture strategy for topology optimization in structural and fluid flow problems. The capture of smoothed boundaries is done via a simple strategy through element splitting and analysis domain remeshing. The smoothing procedure novelty lies on the optimizer regular mesh decomposition into smaller triangular or tetrahedral elements with a pseudo‐density nodal function that produces implicit geometry boundaries. We employ the TOBS‐GT (topology optimization of binary structures with geometry trimming) method to solve optimization problems for mean compliance and fluid flow energy dissipation, subject to a volume fraction constraint. Since, we perform discrete body‐fitted optimization, the material interpolation models are expressed in linear form without penalty factors. Our method produces a lower computational cost topology evolution with high resolution and mitigated sharp details, achieved via smooth edges and surfaces, as demonstrated through benchmark numerical examples in two‐ and three‐dimensional space. In addition, the proposed strategy facilitates the optimization of benchmark fluid flow examples for moderate Reynolds numbers.

A comprehensive study of massive compact star admitting conformal motion under bardeen geometry

This article primarily investigates the existence of the charged compact star under the conformal motion treatment within the context of f(Q) gravity. We have developed two models by implementing the power-law and linear form of conformal factor, enabling an in-depth comparison in our study. We have selected the MIT Bag model equation of state to describe the connection between pressure and energy density and matched the interior spherically symmetric space-time with the Bardeen space-time. In addition, the present research examines various physically valid characteristics of realistic stars, such as PSR J1614−2230, PSR J1903+327, Vela X-1, Cen X-3, and SMC X-1. We compare two constructed models by attributing the behavior of density, pressure, equilibrium conditions, and the adiabatic index. We have additionally included a brief analysis of the scenario involving Reissner-Nordstrom spacetime as an external geometry for the matching condition. In contrast to the Reissner-Nordstrom instance, the Bardeen model with the extra term in the asymptotic representations yields a more intriguing and viable result. The current analysis reveals that the resulting compact star solutions are physically acceptable and authentic when considering the presence of charge with conformal motion in f(Q) gravity.

Spectral transformations and second kind polynomials associated with a hermitian linear functional

The aim of this paper is to analyze the relation between the Christoffel transformations in the framework of hermitian linear functionals and the associated polynomials of the second kind through of their associated Carathéodory formal power series. In particular, some results are given in the way of characterization of linear spectral transformations using Christoffel and Geronimus transformations. This gives a new example of rational spectral transformation that has not yet been studied in the literature.

Detection of the phosphorothioate oligonucleotide fomivirsen using a ligase detection reaction with polymerase chain reaction.

This study aimed to develop a simple and sensitive detection method for fomivirsen, a 21-nucleotide phosphorothioate oligonucleotide used as a nucleic acid medicine, using a ligase detection reaction. A ligation probe was designed to hybridize with fomivirsen and polymerase chain reaction (PCR) primers, with a deoxyuridine part between the primer binding sites. The probe was ligated to a circular product by Taq DNA ligase, and the resulting product was converted to a linear form through the removal of the uracil base using uracil DNA glycosylase. The linear product was then quantified using real-time PCR. The developed method could detect 0.025-6.4nM of fomivirsen in water and HeLa genomic DNA solutions and 0.6-160nM of fomivirsen in mouse serum in combination with an extraction method based on alkalinization and neutralization. This method could be useful for not only detecting fomivirsen but also other functional oligonucleotides composed of phosphorothioate oligonucleotides. In summary, this study presents a practical and effective approach to the detection of the nucleic acid medicine fomivirsen.