Classical Framework Research Articles

Alkoxycarbonyl Groups in Metalloesters Showing Oxocarbenium‐like Structure and Alkylating Reactivity

AbstractIn contrast to the well‐documented acylating reactivity, the alkylating reactivity of the alkoxycarbonyl group, as signified by its oxocarbenium‐like resonance structure, remains almost unexplored. Herein, the first series of Co/Ni dinuclear metalloesters exhibiting the novel oxocarbenium‐like alkoxycarbonyl groups were synthesized and characterized. In these deformed alkoxycarbonyl groups, the Ccarbonyl−Oalkoxyl bonds were contracted to 1.177(11)~1.191(9) Å with the elongations of the Ccarbonyl=Ocarbonyl bonds to 1.368(13)~1.441(9) Å. Meanwhile, the O−Calkyl bonds were also elongated to 1.522(11) ~1.607(15) Å, and were by far the longest O−Calkyl bonds reported for alkoxycarbonyl groups. As triggered by the long O−Calkyl distances, the alkylating reactivity of the oxocarbenium‐like methoxycarbonyl group towards a series of C/N/O‐nucleophiles via the rare BAL2 mechanism at ambient conditions was examined. Furthermore, the homo‐etherifications of alcohols mediated by the Co/Ni dinuclear metalloesters were investigated. The yields followed the trend ethanol≫n‐propanol≫n‐butanol ≈n‐pentanol, that closely related to the structure features of the alkoxycarbonyl groups in corresponding metalloesters: while the ethoxycarbonyl group showed the reactive oxocarbenium‐like framework, the n‐propoxycarbonyl group displayed the dioxocarbenium‐like skeleton with a shorter O−Calkyl bond; In comparison, the classical frameworks with unactivated alkyl moieties were observed for n‐butoxycarbonyl and n‐pentoxycarbonyl groups.

Alkoxycarbonyl Groups in Metalloesters Showing Oxocarbenium-like Structure and Alkylating Reactivity.

In contrast to the well-documented acylating reactivity, the alkylating reactivity of the alkoxycarbonyl group, as signified by its oxocarbenium-like resonance structure, remains almost unexplored. Herein, the first series of Co/Ni dinuclear metalloesters exhibiting the novel oxocarbenium-like alkoxycarbonyl groups were synthesized and characterized. In these deformed alkoxycarbonyl groups, the Ccarbonyl-Oalkoxyl bonds were contracted to 1.177(11)~1.191(9) Å with the elongations of the Ccarbonyl=Ocarbonyl bonds to 1.368(13)~1.441(9) Å. Meanwhile, the O-Calkyl bonds were also elongated to 1.522(11) ~1.607(15) Å, and were by far the longest O-Calkyl bonds reported for alkoxycarbonyl groups. As triggered by the long O-Calkyl distances, the alkylating reactivity of the oxocarbenium-like methoxycarbonyl group towards a series of C/N/O-nucleophiles via the rare BAL2 mechanism at ambient conditions was examined. Furthermore, the homo-etherifications of alcohols mediated by the Co/Ni dinuclear metalloesters were investigated. The yields followed the trend ethanol≫n-propanol≫n-butanol ≈n-pentanol, that closely related to the structure features of the alkoxycarbonyl groups in corresponding metalloesters: while the ethoxycarbonyl group showed the reactive oxocarbenium-like framework, the n-propoxycarbonyl group displayed the dioxocarbenium-like skeleton with a shorter O-Calkyl bond; In comparison, the classical frameworks with unactivated alkyl moieties were observed for n-butoxycarbonyl and n-pentoxycarbonyl groups.

Bayesian Forecasting of Mortality Rates for Small Areas Using Spatiotemporal Models

Estimation and prediction of subnational mortality rates for small areas are essential planning tools for studying health inequalities. Standard methods do not perform well when data are noisy, a typical behavior of subnational datasets. Thus, reliable estimates are difficult to obtain. I present a Bayesian hierarchical model framework for prediction of mortality rates at a small or subnational level. By combining ideas from demography and epidemiology, the classical mortality modeling framework is extended to include an additional spatial component capturing regional heterogeneity. Information is pooled across neighboring regions and smoothed over time and age. To make predictions more robust and address the issue of model selection, a Bayesian version of stacking is considered using leave-future-out validation. I apply this method to forecast mortality rates for 96 regions in Bavaria, Germany, disaggregated by age and sex. Uncertainty surrounding the forecasts is provided in terms of prediction intervals. Using posterior predictive checks, I show that the models capture the essential features and are suitable to forecast the data at hand. On held-out data, my predictions outperform those of standard models lacking a regional component.

A multidimensional examination of phase separation in single-component fluids

A thermodynamic instability in a homogeneous fluid can lead to spontaneous formation of distinct domains within the fluid. This process involves not only the spatial redistribution of fluid density but also transient exchanges of pressure, temperature, and energy. However, classical theoretical frameworks, such as the Ginzburg–Landau and Cahn–Hilliard models, lack incorporation of these essential thermodynamic aspects. To investigate the dynamics of multiple physical fields during phase separation, we numerically solve a two-dimensional van der Waals fluid model. Thermodynamic consistency is demonstrated by verifying the coexistence curve. While the equilibrium pressure remains similar across the unstable region of the isotherm, we demonstrate that the energy in the system depends on the initial density. Although the majority of energy is stored as heat at typical values of the heat capacity, high-density domains contain less specific energy compared to their low-density counterparts due to interparticle attraction. Consequently, the transition of low-density domains into high-density through the process of coalescence releases excess energy, which redistributes in the form of longitudinal waves and heat. We also highlight the role of parameters, such as heat capacity and thermal conductivity, in less intuitive phenomena, including elevated temperature fluctuations and memory preservation.

The Application of Arch Structural Thinking in Walter Piston’s Three Pieces for Flute, Clarinet, and Bassoon

Arch-like structures are common in both the natural world and human society, representing a classical mechanical framework. Many twentieth-century composers utilized this structure to compose music, aiming to achieve a certain level of control over the music. Walter Piston, a prolific composer, used arch structural thinking in many works, taking it as an integral part of both the overall structure and structural force.

Approximating Option Greeks in a Classical and Multi-Curve Framework Using Artificial Neural Networks

In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated option price data given the illiquid nature of the South African market, and the out-of-sample performance of the optimized ANNs was evaluated using an implied volatility surface constructed from published volatility skews. The results from this paper show that ANNs trained on artificially generated input data are able to accurately approximate the explicit solutions to the respective option price sensitivities of both a classical and a modern multi-curve framework in a real-world out-of-sample application to the South African market.

Contemporary Diagnostic Reporting for Prostatic Adenocarcinoma: Morphologic Aspects, Molecular Correlates, and Management Perspectives.

The diagnosis and reporting of prostatic adenocarcinoma have evolved from the classic framework promulgated by Dr Donald Gleason in the 1960s into a complex and nuanced system of grading and reporting that nonetheless retains the essence of his remarkable observations. The criteria for the "Gleason patterns" originally proposed have been continually refined by consensuses in the field, and Gleason scores have been stratified into a patient-friendly set of prognostically validated and widely adopted Grade Groups. One product of this successful grading approach has been the opportunity for pathologists to report diagnoses that signal carefully personalized management, placing the surgical pathologist's interpretation at the center of patient care. At one end of the continuum of disease aggressiveness, personalized diagnostic care means to sub-stratify patients with more indolent disease for active surveillance, while at the other end of the continuum, reporting histologic markers signaling aggression allows sub-stratification of clinically significant disease. Whether contemporary reporting parameters represent deeper nuances of more established ones (eg, new criteria and/or quantitation of Gleason patterns 4 and 5) or represent additional features reported alongside grade (intraductal carcinoma, cribriform patterns of carcinoma), assessment and grading have become more complex and demanding. Herein, we explore these newer reporting parameters, highlighting the state of knowledge regarding morphologic, molecular, and management aspects. Emphasis is made on the increasing value and stakes of histopathologists' interpretations and reporting into current clinical risk stratification and treatment guidelines.

Belief-consistent information is most shared despite being the least surprising

In the classical information theoretic framework, information “value” is proportional to how novel/surprising the information is. Recent work building on such notions claimed that false news spreads faster than truth online because false news is more novel and therefore surprising. However, another determinant of surprise, semantic meaning (e.g., information’s consistency or inconsistency with prior beliefs), should also influence value and sharing. Examining sharing behavior on Twitter, we observed separate relations of novelty and belief consistency with sharing. Though surprise could not be assessed in those studies, belief consistency should relate to less surprise, suggesting the relevance of semantic meaning beyond novelty. In two controlled experiments, belief-consistent (vs. belief-inconsistent) information was shared more despite consistent information being the least surprising. Manipulated novelty did not predict sharing or surprise. Thus, classical information theoretic predictions regarding perceived value and sharing would benefit from considering semantic meaning in contexts where people hold pre-existing beliefs.

Norm retrieval algorithms: A new frame theory approach

In this paper, we introduce a new approach based on the excess components of a frame for recognizing norm retrieval frames (NRFs) in finite‐dimensional Hilbert spaces. This method leads to a complete characterization of NRFs in and and then to 1‐excess NRFs in . This approach is not only more effective in higher dimensions for detecting NRF than the previous approaches but is also applicable to constructing NRF from a given Riesz basis. In addition, we establish a relationship between the norm retrievability of a frame and its duals. We also show that, unlike the phase retrieval property, the set of all norm retrievable dual frames is generally not dense in the set of all dual frames. Then, we present numerical results to illustrate the potentiality of the NRF approach in signal recovery with respect to the classical framework in frame theory, and finally, we raise some perspectives and problems concerning NRF.

Neoclassical Theory of Atoms

The "Neoclassical Theory of Atoms" challenges the dominance of quantum mechanics in explaining certain atomic phenomena. This work argues that a classical approach, utilizing electromagnetic Coulomb forces and Newtonian mechanics, can potentially account for discrete energy levels and spectral lines observed in hydrogen and helium atoms. It questions the necessity of invoking the seemingly counterintuitive aspects of quantum mechanics for these specific phenomena. By demonstrating the potential of a classical framework, this research aims to stimulate debate and exploration of alternative explanations within physics. This could potentially lead to a deeper understanding of the nature of reality and the limitations (or potential for expansion) of current physical theories.

A crystal plasticity model for multiaxial cyclic deformation of U75V rail steel

The theoretical investigations of cyclic deformation behavior and deformation mechanism of the rail steels under multiaxial loadings are of great significance to the study of rolling contact fatigue. Based on the classical crystal plasticity framework, a new nonproportional parameter at the mesoscale is proposed, and the isotropic and kinematic hardening models are modified. A series of finite element simulations of U75V rail steel under uniaxial and multiaxial cyclic loading are carried out using the proposed model. The simulation results show that the proposed model can reasonably reflect the cyclic softening under strain-controlled uniaxial cyclic loading, cyclic hardening under strain-controlled multiaxial cyclic loadings, and ratcheting behavior under stress-controlled uniaxial and multiaxial cyclic loadings of U75V rail steel. Moreover, the influence of multiaxial loading paths on the plastic strain is explained from a mesoscopic perspective. The values of nonproportional parameters of different slip systems under uniaxial and multiaxial paths are compared, and the difference of nonproportional parameters at the macroscale and mesoscale are discussed. Finally, the simulation results of the proposed model and the existing model with modified kinematic hardening rules are compared. The results show that the proposed model has better predictions on the evolution of ratcheting strain and nonproportionally additional hardening effect caused by nonproportionally multiaxial loadings.

A new xG model for football analytics

The aim of this exploratory study is to refine and improve, in terms of prediction performance, the expected goal (xG) model, one of the emerging tools in the field of football analytics. With this final goal, we merged data from different sources: tracking data, match event data and some players’ performance composite indicators. Using an original sample of match data relying on the 2019/2020 season of the Italian Serie A, composed of 660 shots, one outcome (i.e. the GOAL) and 22 regressors, a supervised machine learning approach (logistic regression model with imbalanced training sample adjustment) was applied to different scenarios for sample balanced techniques. Results are interesting in terms of sensitivity and F1 metrics, compared with a benchmark (Understat). Other results concerning the classic imbalance framework significantly outperform the benchmark in terms of the AUC metric. In addition, some performance composite indicators and one original tracking variable are significant for the classification model, contributing to increasing the goal prediction probability compared with the benchmark.

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

This paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from O(N2.37286) classically to O(N2logN×plogp) in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

Mathematical modeling and stability analysis of the novel fractional model in the Caputo derivative operator: A case study

The fundamental goal of this research is to suggest a novel mathematical operator for modeling visceral leishmaniasis, specifically the Caputo fractional-order derivative. By utilizing the Fractional Euler Method, we were able to simulate the dynamics of the fractional visceral leishmaniasis model, evaluate the stability of the equilibrium point, and devise a treatment strategy for the disease. The endemic and disease-free equilibrium points are studied as symmetrical components of the proposed dynamical model, together with their stabilities. It was shown that the fractional calculus model was more accurate in representing the situation under investigation than the classical framework at α = 0.99 and α = 0.98. We provide justification for the usage of fractional models in mathematical modeling by comparing results to real-world data and finding that the new fractional formalism more accurately mimics reality than did the classical framework. Additional research in the future into the fractional model and the impact of vaccinations and medications is necessary to discover the most effective methods of disease control.

Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region

BackgroundMore than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005.MethodsBased on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model.ResultsDuring the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI (ADID) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) ADID and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1.ConclusionsThe present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system.Graphical

Reactive transport in open-channel flows with bed adsorption and desorption

Researches on the solute dispersion with boundary adsorption and desorption are common in various fields, such as chemistry, biology, and hydraulics. However, the Laplace transform, as a method available for the transient transport with coupled processes of adsorption–desorption at boundaries, is complicated to apply. Recently, Jiang et al. (2022) proposed a much simpler analytical method by extending the classic framework of separation of variables to derive solutions of concentration moments for tube flows, which is valid for the entire range of the reactive transport process. Here we apply this simple approach to solute transport in open-channel flows with bed adsorption and desorption. We obtain analytical solutions capturing exactly the transient statistics for the solute cloud as compared to the numerical simulations, regarding the variation of the cloud mass, the motions of the centroid of the cloud, and the scattering of the cloud, respectively. We further investigate the effect of adsorption–desorption on the dispersion process and the influence of initial conditions on the non-uniformity of dispersion characteristics over the cross-section before the Taylor dispersion regime. Through comparisons with that of tube flows, we find that the distribution of solute mass, the drift, and the dispersivity for open channel flows change more slowly with time and the response time is longer.

On the Generalized Lemaitre Tolman Bondi Metric: Classical Sensitivities and Quantum Einstein‐Vaz Shells

AbstractIn this paper, in the classical framework, the lower bounds for the sensitivities of the generalized Lemaitre Tolman Bondi metric are evaluated. The calculated lower bounds via the linear dynamical systems , , and are , and respectively. The sensitivities and the lower sensitivities via are zero are also shown. In the quantum framework, the properties of the Einstein‐Vaz shells which are the final result of the quantum gravitational collapse arising from the Lemaitre Tolman Bondi discussed by Vaz in 2014 are analyzed. In fact, Vaz showed that continued collapse to a singularity can only be obtained if one combines two independent and entire solutions of the Wheeler‐DeWitt equation. Forbidding such a combination leads naturally to matter condensing on the Schwarzschild surface during quantum collapse. In that way, an entirely new framework for black holes (BHs) has emerged. The approach of Vaz was also consistent with Einstein's idea in 1939 of the localization of the collapsing particles within a thin spherical shell. Here, following an approach of oned of us (CC), we derive the BH mass and energy spectra via a Schrodinger‐like approach, by further supporting Vaz's conclusions that instead of a spacetime singularity covered by an event horizon, the final result of the gravitational collapse is an essentially quantum object, an extremely compact “dark star”. This “gravitational atom” is held up not by any degeneracy pressure but by quantum gravity in the same way that ordinary atoms are sustained by quantum mechanics. Finally, the time evolution of the Einstein‐Vaz shells is discussed.

A strain-hardening macro-element model for pile groups under vertical–horizontal-moment loading

Pile foundations supporting wind turbines, silos, elevated water tanks or bridge piers are frequently subjected to multi-component loads, which may lead to significant rotations and settlements compromising the safe operation of the structure. The prediction of these displacements is of major concern and can be easily carried out using force-resultants plasticity models, also referred to as ‘macro-elements’, stemming from the idea of describing the foundation behaviour by a single upscaled constitutive relationship between generalized forces and displacements. When properly formulated, they can reproduce key aspects of the mechanical response of the foundation at low computational cost as compared to numerical analysis. To this end, a new macro-element for pile groups formulated in the classical framework of strain-hardening elasto-plasticity is presented and discussed. The required model parameters can be calibrated by closed-form equations and additional few data of numerical and/or experimental nature. The proposed mathematical framework is finally validated against the results of centrifuge tests and 3D finite element analyses.

On the Network Index of MAS with Layered Lattice-like Structures of Multiple Vertex-Related Parameters

In this article, a robust index named first-order network coherence (FONC) for the multi-agent systems (MASs) with layered lattice-like structure is studied via the angle of the graph spectra theory. The union operation of graphs is utilized to construct two pairs of non-isomorphic layered lattice-like structures, and the expression of the index is acquired by the approach of Laplacian spectra, then the corresponding asymptotic results are obtained. It is found that when the cardinality of the node sets of coronary substructures with better connectedness tends to infinity, the FONC of the whole network will have the same asymptotic behavior with the central lattice-like structure in the considered classic graph frameworks. The indices of the networks were simulated to illustrate the the asymptotic results, as described in the last section.

A homogenized two-phase computational framework for meso- and macroscale blood flow simulations

Background and ObjectiveOwing to the complexity of physics linked with blood flow and its associated phenomena, appropriate modeling of the multi-constituent rheology of blood is of primary importance. To this effect, various kinds of computational fluid dynamic models have been developed, each with merits and limitations. However, when additional physics like thrombosis and embolization is included within the framework of these models, computationally efficient scalable translation becomes very difficult. Therefore, this paper presents a homogenized two-phase blood flow framework with similar characteristics to a single fluid model but retains the flow resolution of a classical two-fluid model. The presented framework is validated against four different sets of experiments. MethodsThe two-phase model of blood presented here is based on the classical diffusion-flux framework. Diffusion flux models are known to be less computationally expensive than two-fluid multiphase models since the numerical implementation resembles single-phase flow models. Diffusion flux models typically use empirical slip velocity correlations to resolve the motion between phases. However, such correlations do not exist for blood. Therefore, a modified slip velocity equation is proposed, derived rigorously from the two-fluid governing equations. An additional drag law for red blood cells (RBCs) as a function of volume fraction is evaluated using a previously published cell-resolved solver. A new hematocrit-dependent expression for lift force on RBCs is proposed. The final governing equations are discretized and solved using the open-source software OpenFOAM. ResultsThe framework is validated against four sets of experiments: (i) flow through a rectangular microchannel to validate RBC velocity profiles against experimental measurements and compare computed hematocrit distributions against previously reported simulation results (ii) flow through a sudden expansion microchannel for comparing experimentally obtained contours of hematocrit distributions and normalized cell-free region length obtained at different flowrates and inlet hematocrits, (iii) flow through two hyperbolic channels to evaluate model predictions of cell-free layer thickness, and (iv) flow through a microchannel that mimics crevices of a left ventricular assist device to predict hematocrit distributions observed experimentally. The simulation results exhibit good agreement with the results of all four experiments. ConclusionThe computational framework presented in this paper has the advantage of resolving the multiscale physics of blood flow while still leveraging numerical techniques used for solving single-phase flows. Therefore, it becomes an excellent candidate for addressing more complicated problems related to blood flow, such as modeling mechanical entrapment of RBCs within blood clots, predicting thrombus composition, and visualizing clot embolization.