Analytical Iteration Research Articles

Unveiling promising immunogenic targets in Coxiella burnetii through in silico analysis: paving the way for novel vaccine strategies

BackgroundCoxiella burnetii, an intracellular pathogen, serves as the causative agent of zoonotic Q fever. This pathogen presents a significant threat due to its potential for airborne transmission, environmental persistence, and pathogenicity. The current whole-cell vaccine (WCV) utilized in Australia to combat Q fever exhibits notable limitations, including severe adverse reactions and limited regulatory approval for human use. This research employed the reverse vaccinology (RV) approach to uncover antigenic proteins and epitopes of C. burnetii, facilitating the development of more potent vaccine candidates.MethodsThe potential immunogenic proteins derived from C. burnetii RSA493/Nine Mile phase I (NMI) were extracted through manual, automated RV, and virulence factor database (VFDB) methods. Web tools and bioinformatics were used to evaluate physiochemical attributes, subcellular localization, antigenicity, allergenicity, human homology, B-cell epitopes, MHC I and II binding ratios, functional class scores, adhesion probabilities, protein-protein interactions, and molecular docking.ResultsOut of the 1850 proteins encoded by RSA493/NMI, a subset of 178 demonstrated the potential for surface or membrane localization. Following a series of analytical iterations, 14 putative immunogenic proteins emerged. This collection included nine proteins (57.1%) intricately involved in cell wall/membrane/envelope biogenesis processes (CBU_0197 (Q83EW1), CBU_0311 (Q83EK8), CBU_0489 (Q83E43), CBU_0939 (Q83D08), CBU_1190 (P39917), CBU_1829 (Q83AQ2), CBU_1412 (Q83BU0), CBU_1414 (Q83BT8), and CBU_1600 (Q83BB2)). The CBU_1627 (Q83B86 ) (7.1%) implicated in intracellular trafficking, secretion, and vesicular transport, and CBU_0092 (Q83F57) (7.1%) contributing to cell division. Additionally, three proteins (21.4%) displayed uncharacterized functions (CBU_0736 (Q83DJ4), CBU_1095 (Q83CL9), and CBU_2079 (Q83A32)). The congruent results obtained from molecular docking and immune response stimulation lend support to the inclusion of all 14 putative proteins as potential vaccine candidates. Notably, seven proteins with well-defined functions stand out among these candidates.ConclusionsThe outcomes of this study introduce promising proteins and epitopes for the forthcoming formulation of subunit vaccines against Q fever, with a primary emphasis on cellular processes and the virulence factors of C. burnetii.

An analytical solution of fuel-near-optimal trajectory for three-dimensional planetary landings

The existing solutions for three-dimensional fuel-optimal trajectory generation are inadequate in terms of real-time performance and convergence property, which are vital to successful and reliable powered planetary landings. In this study, the fast generation of three-dimensional fuel-near-optimal landing trajectories is achieved based on problem decomposition and analytical iterative techniques. Specifically, the following three contributions are emphasized. First, the original three-dimensional (3D) thrust-constrained powered planetary landing problem is decoupled into three analytically solvable one-dimensional (1D) sub-problems: one along the vertical direction, and two along the horizon direction. These three 1D problems are re-coupled together by six to-be-determined coordination coefficients. Second, an analytical iteration algorithm is developed to determine these six coordination coefficients and to ensure the satisfaction of thrust magnitude and direction constraints. Third, three necessary proofs are provided to validate the guaranteed convergence of the proposed analytical iteration algorithm. Simulation results of various planetary landing missions on Earth demonstrate that the proposed analytical algorithm can achieve a significant fast generation of 3D fuel-near-optimal landing trajectories with acceptable optimality (2.6% more fuel on average) and guaranteed convergence rate (100%) for planetary landings.

Reframing fundraising research: The challenges and opportunities of interpretivist research practices and practitioner researchers in fundraising studies

AbstractThis paper examines interpretivism as an approach for researching fundraising practice as context and practice specific. In doing so, it highlights the agency of fundraisers and extends critical analysis of fundraising practice and its broader implications in ways that the dominant positivistic, functionalist literature does not. Building on previous comparisons of positivist and interpretivist approaches, we consider two fundraising studies, and benefits and challenges of using an interpretivist approach. These observations highlight both the complexity and messiness of interpretivist studies, and of the fundraising process. The paper concludes by discussing how these challenges can be managed through analytical iteration, adjustment, and pragmatism throughout the research process; while advocating for including more critical and dialectic approaches to research fundraising and fundraisers' broader practice.

Masses of Single, Double, and Triple Heavy Baryons in the Hyper-Central Quark Model by Using GF-AEIM

By using the generalized fractional analytical iteration method (GF-AEIM), the single, the double, and the triple heavy baryons masses are calculated in the hyper-central model in the two cases. In the first case, the potential is a combination of Coulombic potential, the linear confining potential, and the harmonic oscillator potential. In the second case, we add the hyperfine interaction. The energy eigenvalues and the baryonic wave function are obtained in the fractional forms. The present results are a good agreement with experimental data and are improved with other recent works.

Recurrent neural network-induced Gaussian process

In this study, we develop a recurrent neural network-induced Gaussian process (RNNGP) to model sequence data. We derive the equivalence between infinitely wide neural networks and Gaussian processes (GPs) for a relaxed recurrent neural network (RNN) with untied weights. We compute the covariance function of the RNNGP using an analytical iteration formula derived through the RNN procedure with an error-function-based activation function. To simplify our discussion, we use the RNNGP to perform Bayesian inference on vanilla RNNs for various problems, such as Modified National Institute of Standards and Technology digit identification, Mackey–Glass time-series forecasting, and lithium-ion battery state-of-health estimation. The results demonstrate the flexibility of the RNNGP in modeling sequence data. Furthermore, the RNNGP predictions typically outperform those of the original RNNs and GPs, demonstrating the efficiency of the RNNGP as a data-driven model. Moreover, the RNNGP can quantify the uncertainty in the predictions, which implies the significant potential of the RNNGP in uncertainty quantification analyses.

Abstract 3920: Precise solid tumor classification through unbiased quantification of proteoforms deep into tissue leakage

Abstract Blood is the most frequently used sample in diagnostic processes, and its liquid component is known as plasma. Plasma potentially contains molecular clues coming from all the tissues that make up an organism, thus possibly enable the holistic analysis of the health state of an individual. The major challenge in plasma protein analysis is the 1013- fold dynamic range and the strong prevalence of few, very abundant proteins. Depletion of the most abundant proteins is used to increase coverage of the plasma proteome. To be able to profile human plasma at large scale and at maximal depth, we developed and optimized two main aspects. Firstly, we automated the depletion of the top 14 most abundant proteins in human plasma to a throughput of 40 depletions per day per setup. A comparison of native and depletion workflows with a controlled quantitative experiment demonstrated that depletion led to a significant increase in the number of identifications (&gt200%) and number of true hits (&gt300% at controlled actual FDR &lt1%), while conserving quantitative accuracy. Secondly, we further optimized FAIMS-DIA methods for deep plasma proteome profiling and applied the optimized workflow to a cohort coming from the deadliest five solid tumor types. To research large cohorts’ feasibility, we analyzed a cohort comprising 30 each of healthy, lung, colorectal, pancreatic, breast and prostate cancer. Altogether, we processed 180 samples (plus 24 quality controls) and quantified 2,732 proteins, of which 1,804 in at least 50% of the runs. Based on quality control samples, we could characterize variance introduced on each level, all much lower than the inter-individual variability. We reached 8 orders of magnitude of dynamic range, within this range we covered extensively tissue leakage proteome, interleukins and signaling proteins such as Egf, Klk3 (PSA), Akt1, Cd86, Met, Erbb2 and Cd33. Using machine learning we reduced to an average of 129 (5% of the quantified proteins) biomarker candidates per cancer type, making biological interpretation more feasible while enabling classification of health and disease. The model performance was 86-100% on the 20% hold-out validation set when healthy and overall disease status were considered. Importantly, the biomarker candidates were predominantly (70%) coming from low abundance regions clearly demonstrating the need to measure deeply because they would be missed by shallow plasma profiling. Hence, we showed that our automated plasma depletion workflow has the potential to enable the unbiased and reproducible quantification of more than 2,700 proteins across very large cohorts and unlock biomarker candidate panels in different cancer types. Furthermore, with the latest analytical method iteration applied to a subset of the samples, we could quantify more than 3,500 proteins, demonstrating the full potential of the workflow. Citation Format: Marco Tognetti, Kamil Sklodowski, Sebastian Mueller, Dominique Kamber, Jan Muntel, Yuehan Feng, Roland Bruderer, Lukas Reiter. Precise solid tumor classification through unbiased quantification of proteoforms deep into tissue leakage [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3920.

The BLUES function method for second-order partial differential equations: Application to a nonlinear telegrapher equation

An analytic iteration sequence based on the extension of the BLUES (Beyond Linear Use of Equation Superposition) function method to partial differential equations (PDEs) with second-order time derivatives is studied. The original formulation of the BLUES method is modified by introducing a matrix formalism that takes into account the initial conditions for higher-order time derivatives. The initial conditions of both the solution and its derivatives now play the role of a source vector. The method is tested on a nonlinear telegrapher equation, which can be reduced to a nonlinear wave equation by a suitable choice of parameters. In addition, a comparison is made with three other methods: the Adomian decomposition method, the variational iteration method (with Green function) and the homotopy perturbation method. The matrix BLUES function method is shown to be a worthwhile alternative for the other methods.

Inside the multi-tier supply firm: exploring responses to institutional pressures and challenges for sustainable supply management

PurposeThe purpose of this study is to examine how multi-tier suppliers respond to the institutional pressures for the implementation of sustainable supply management (SSM) practices in supply chains, and what institutional logics allow them to do so.Design/methodology/approachThis study employs a qualitative research design, drawing on data from semi-structured interviews with 46 owners and managers of multi-tier suppliers and 18 key informants of diverse stakeholders. Following an abductive approach, institutional theory conceptually guides the analytical iteration processes between theory and interview data.FindingsThe findings demonstrate two kinds of thematic responses to institutional pressures – coupling (good side) and decoupling (dark side) of the supply chain – used by the factory management of multi-tier suppliers. This paper also identifies multiple institutional logics – market-led logic, values-led logic and holistic sustainability logic – that are perceived to conflict (trade-offs) and complement (synergies) the SSM implementation.Research limitations/implicationsBy investigating the perspectives of the factory management of upstream apparel suppliers, this study enhances the understanding of the connection between (de)coupling responses and institutional logics inside the multi-tier supplier firms. Further research would be required to include more downstream tiers including the ultimate users.Practical implicationsThe findings may be of particular attention to brand-owning apparel retailers, industry leaders and policymakers who are seeking to understand multi-tier suppliers' challenges, conflicts and (de)coupling responses, and become aware of how they can be dealt with.Originality/valueThis study contributes to and expands the embryonic research stream of sustainable multi-tier supply chain management by connecting it to the wider application of institutional theory.

Triads in sustainable supply-chain perspective: why is a collaboration mechanism needed?

This research explores focal actors and their dyads, addressing their sustainable collaboration in triads and its relevance in the agri-triads that share information along the supply chains. We employ a multiple-case approach and presents two triads through 42 interviews, observation and documentaries. An abductive approach, Transaction Cost Economics (TCE), conceptually guides the analytical iteration stages between theory and data. The findings describe the collaboration mechanisms of focal actors at triad levels and the development of their dyads capabilities for sustainable supply chains. Eight triad approaches emerge in the present research; these are grouped into three aggregated levels according to the intensity of information-sharing between focal and associate actors in each triad. The research has identified six contextual factors that are linked to the TCE dimensions of asset specificity, uncertainty and frequency, and offers key propositions. The research gained insights into how focal actors in dyadic triads choose between the available approaches based on different intensity levels of information-sharing. This research presents a conceptual framework informing the choice of triad approaches reflecting collaboration mechanism strategies, and thus the intensity of information-sharing and sustainability performance. The study elaborates on the application of TCE and extends it empirically to the literature.

Comparing a Three-Term Perturbation Solution of the Nonlinear ODE of the Jacobi Elliptic SN Function to Its Approximation into Circular Functions

In this paper, the nonlinear differential equation of the elliptic sn function is solved analytically using the Lindstedt-Poincare perturbation method. This differential equation has a cubic nonlinearity and a con- stant known as the modulus of elliptic integral. This constant takes any value from zero to one and the square of its value is used as a small parameter to expand the dependent variable in series and start analyti- cal iterations. Fortunately, there is an exact solution to this differential equation known as the Jacobi sn elliptic function. When the modulus approaches zero the elliptic differential equation becomes linear with the circular sine function as exact solution. Thus, the sine function is con- sidered as the unperturbed solution and is used as the basis to add more correction terms through analytical iterations. The Lindstedt-Poincare technique is used to render the perturbation solution uniformly valid at larger values of the independent variable. A three-term perturbation so- lution is obtained and shows good convergence and boundedness. This solution is compared with the exact, numerically calculated, sn elliptic function. It is also compared analytically with the approximate expan- sion of the elliptic function into circular functions in case of a small modulus. The relative percentage error between the perturbation solution and the exact one is calculated at certain values of the modulus and for all values of the independent variable. The relative error is reasonably small but increases at larger values of the modulus. In addition, the ap- proximation of the exact solution gives smaller relative error than that of the perturbation solution including the same order of the modulus.

Asymptotic beam theory for non-classical elastic materials

This paper is devoted to the study of the plane-stress deformation of a beam composed of non-classical elastic materials that are suitable for modeling certain inter-metallic alloys with a nonlinear constitutive relation between the linearized strain and stress. The aim is to derive a consistent asymptotic beam theory without the ad hoc assumptions usually made in the development of beam theories. The methodology involves expanding the displacement, the in-plane strain tensor and stress tensor in a Taylor series, leading to a system of nonlinear equations that is solved. An analytical iteration procedure is developed to solve the system of equations leading to an analytical solution. The beam theory and the approximate general analytical solutions are used to study four examples. For the purpose of validation of the approximate analytical solution, we use a spectral collocation method to carry out numerical simulations for the full 2D problem, which confirms the validity of the approximate analytical solution. The study also reveals that the Euler-Bernoulli type of hypotheses is not suitable for a certain class of problems.

The effect of spin and tensor interactions on heavy–light mesons in the nonrelativistic quark model

By using analytical exact iteration method (AEIM), the N-radial Schrodinger equation is analytically solved. The present potential is extended to include the spin hyperfine interaction, spin–orbit interaction, and tensor interaction. The eigenvalues of energy and corresponding wave functions are obtained in the N-dimensional space. The present results are applied for calculating the spectra masses of the heavy–light mesons (HLM) such as D, $$D_\mathrm{s}$$ , B, and $$B_\mathrm{s}$$ meson in the three-dimensional space. The effect of dimensional number is studied on the HLM masses. A comparison is studied with other recent works and experimental data. The present potential provides satisfying results in comparison with recent other works and experimental data.

Generative Noise Reduction in Dental Cone-Beam CT by a Selective Anatomy Analytic Iteration Reconstruction Algorithm

Dental cone-beam computed tomography (CBCT) is a powerful tool in clinical treatment planning, especially in a digital dentistry platform. Currently, the “as low as diagnostically acceptable” (ALADA) principle and diagnostic ability are a trade-off in most of the 3D integrated applications, especially in the low radio-opaque densified tissue structure. The CBCT benefits in comprehensive diagnosis and its treatment prognosis for post-operation predictability are clinically known in modern dentistry. In this paper, we propose a new algorithm called the selective anatomy analytic iteration reconstruction (SA2IR) algorithm for the sparse-projection set. The algorithm was simulated on a phantom structure analogous to a patient’s head for geometric similarity. The proposed algorithm is projection-based. Interpolated set enrichment and trio-subset enhancement were used to reduce the generative noise and maintain the scan’s clinical diagnostic ability. The results show that proposed method was highly applicable in medico-dental imaging diagnostics fusion for the computer-aided treatment planning, because it had significant generative noise reduction and lowered computational cost when compared to the other common contemporary algorithms for sparse projection, which generate a low-dosed CBCT reconstruction.

Combination of the variational iteration method and numerical algorithms for nonlinear problems

A very simple and efficient local variational iteration method (LVIM), or variational iteration method with local property, for solving problems of nonlinear science is proposed in this paper. The analytical iteration formula of this method is derived first using a general form of first order nonlinear differential equations, followed by straightforward discretization using Chebyshev polynomials and collocation method. The resulting numerical algorithm is very concise and easy to use, only involving highly sparse matrix operations of addition and multiplication, and no inversion of the Jacobian in nonlinear problems. Apart from the simple yet efficient iteration formula, another extraordinary feature of LVIM is that in each local domain, all the collocation nodes participate in the calculation simultaneously, thus each local domain can be regarded as one “node” in calculation through GPU acceleration and parallel processing. For illustration, the proposed algorithm of LVIM is applied to various nonlinear problems including Blasius equations in fluid mechanics, buckled bar equations in solid mechanics, the Chandrasekhar equation in astrophysics, the low-Earth-orbit equation in orbital mechanics, etc. Using the built-in highly optimized ode45 function of MATLAB as a comparison, it is found that the LVIM is not only very accurate, but also much faster by an order of magnitude than ode45 in all the numerical examples, especially when the nonlinear terms are very complicated and difficult to evaluate.

Trigonometric Rosen–Morse Potential as a Quark–Antiquark Interaction Potential for Meson Properties in the Non-relativistic Quark Model Using EAIM

Trigonometric Rosen-Morse potential is suggested as the quark-antiquark interaction potential for studying the thermodynamic properties and the masses of heavy and heavy-light mesons. For this purpose, N-radial Schrodinger equation is analytically solved using an exact analytical iteration method (EAIM). The eigenvalues of energy and corresponding wave functions are obtained in the N-dimensional space. The present results are applied for calculating the mass of heavy mesons such as charmonium, bottomonium, bc, and cs mesons and thermodynamic properties such as the mean-internal energy, the specific heat, the free energy, and the entropy. The effect of dimensional number is studied on the meson masses. A comparison is studied with other works and experimental data. The present potential provides satisfying results in comparison with other works and experimental data.

Analytic iteration procedure for solitons and traveling wavefronts with sources

A method is presented for calculating solutions to differential equations analytically for a variety of problems in physics. An iteration procedure based on the recently proposed beyond-linear-use-of-equation-superposition function method is shown to converge for nonlinear ordinary differential equations. Case studies are presented for solitary wave solutions of the Camassa–Holm equation and for traveling wavefront solutions of the Burgers equation, with source terms. The convergence of the analytical approximations towards the numerically exact solution is exponentially rapid. In practice, the zeroth-order approximation (a simple convolution) is already useful and the first-order approximation is already accurate while still easy to calculate. The type of nonlinearity can be chosen rather freely, which makes the method generally applicable.

Neural-net-induced Gaussian process regression for function approximation and PDE solution

Neural-net-induced Gaussian process (NNGP) regression inherits both the high expressivity of deep neural networks (deep NNs) as well as the uncertainty quantification property of Gaussian processes (GPs). We generalize the current NNGP to first include a larger number of hyperparameters and subsequently train the model by maximum likelihood estimation. Unlike previous works on NNGP that targeted classification, here we apply the generalized NNGP to function approximation and to solving partial differential equations (PDEs). Specifically, we develop an analytical iteration formula to compute the covariance function of GP induced by deep NN with an error-function nonlinearity. We compare the performance of the generalized NNGP for function approximations and PDE solutions with those of GPs and fully-connected NNs. We observe that for smooth functions the generalized NNGP can yield the same order of accuracy with GP, while both NNGP and GP outperform deep NN. For non-smooth functions, the generalized NNGP is superior to GP and comparable or superior to deep NN.

Progressive failure behavior of composite flywheels stacked from annular plain profiling woven fabric for energy storage

Annular disk flywheels made of plain profiling woven carbon-fiber-reinforced composites possess favorable biaxial strength along radial and circumferential directions to get higher rotation speed and higher energy density in flywheel energy storage system. Each preform disk layer of fabric stacked for the new composite flywheel in our case study was profiling woven with a continuous fill yarn and radial arranged warp yarns in plain woven style. The progressive failure numerical algorithm was applied to reveal the failure phenomenon in fan-shaped representative volume unit of the plain woven fabric composite flywheel under centrifugal load in high speed running. In the numerical simulation of progressive failure, the analytical iterations were compiled with ANSYS-APDL based on 3D Tsai-Wu failure criterion and Reddy’s sudden material property degradation model. The analysis results indicated that the orthogonal plain profiling fabrics enhanced flywheel’s damage tolerance while the radial fiber yarns delayed transvers cracks. Spin experiments of the test flywheels were carried out on the one-point supporting shaft system driven by high speed motor in a vacuum chamber. All eight flywheel samples reached the tangential speed beyond 850 m/s in spin test, exhibiting a good consistency in material performance. The maximum speed was 898 m/s and the corresponding energy storage density was 64.5 W h/kg in the damage limitation spin test of the plain woven composite flywheel. The spin test verified the validity of the biaxial reinforcement using plain fabric by profiling woven method for composite flywheels with thick radial size. Progressive failure analysis simulation was useful in predicting the burst speed of woven fabric composite flywheels.

Tackling the sustainability iceberg

PurposeThe purpose of this paper is to investigate how buying firms manage their lower tier sustainability management (LTSM) in their supply networks and what contextual factors influence the choice of approaches. As most of the environmental and social burden is caused in lower tiers, the authors use the iceberg analogy.Design/methodology/approachFindings from 12 case studies and 53 interviews, publicly available and internal firm data are presented. In an abductive research approach, transaction cost economics (TCE) conceptually guides the analytical iteration processes between theory and data.FindingsThis study provides eight LTSM approaches grouped into three categories: direct (holistic, product-, region-, and event-specific) indirect (multiplier-, alliance- and compliance-based) and neglect (tier-1-based). Focal firms choose between these approaches depending on the strength of observed contextual factors (stakeholder salience, structural supply network complexity, product and industry salience, past supply network incidents, socio-economic and cultural distance and lower tier supplier dependency), leading to perceived sustainability risk (PSR).Research limitations/implicationsBy depicting TCE’s theoretical boundaries in predicting LTSM governance modes, the theory is elevated to the supply network level of analysis. Future research should investigate LTSM at the purchasing category level of analysis to compare and contrast PSR profiles for different purchase tasks and to validate and extend the framework.Practical implicationsThis study serves as a blueprint for the development of firms’ LTSM capabilities that suit their unique PSR profiles. It offers knowledge regarding what factors influence these profiles and presents a model that links the effectiveness of different LTSM approaches to resource intensity.Originality/valueThis study extends the application of TCE and adds empirically to the literature on multi-tier and sustainable supply chain management.

Analytical entry guidance for no-fly-zone avoidance

This paper is devoted to designing an analytical entry guidance for steering a hypersonic glide vehicle of high Lift-to-Drag ratio (L/D) in the presence of numerous No-Fly Zones (NFZs). The NFZ constraints pose serious challenges to the guidance design: (1) because there is no engine to provide thrust, the vehicle needs to manage the energy of motion carefully and cannot perform avoidance maneuvers arbitrarily; (2) due to disorder distribution of NFZs, there are multitudinous complicated cases to consider. As the basis of the guidance, the adopted 3-D analytical glide formulae are improved by posing a more accurate Earth-rotation compensation model. Then, the guidance uses the new downrange formula to plan longitudinal reference profile according to the energy-management requirement, and mainly employs the new crossrange formula to schedule proper bank reversals under the NFZ and terminal-position constraints. Here, a complex but rigorous scheme is proposed for fast planning bank-reversal sequence based on six analytical iteration algorithms, which are designed to search the nearest trajectory point to NFZ, to adjust the existing bank reversals or add a new one for NFZ avoidance, and to regulate the last bank reversals for eliminating terminal crossrange error, respectively. The scheme begins with two bank reversals and then gradually adds more bank reversals according to the needs of avoiding the NFZs. As the scheme prefers to adjust the existing bank reversals to address the NFZ constraints, it commonly requires only a few bank reversals. The superior performance of the guidance is verified by sufficient trajectory simulations in disturbed environments.