Analogous Model Research Articles

A Model Calibration Approach to U-Value Measurements with Thermography

The thermal properties of a building envelope are key indicators of the energy performance of the building. Therefore, methods are needed to determine quantities like the thermal transmittance (U-value) or heat capacitance in a fast, reliable way and with as little impact on the use of the building as possible. In this paper a technique is proposed that relies on a simplified electrical analogical model of building envelope components which can cover their dynamic thermal behavior. The parameters of this model are optimized to produce the best fit between simulated and measured outside surface temperatures. As the temperatures can be measured remotely with an infrared camera this approach requires significantly less installation effort and intrusion in the building than other methods. At the same time, a single measurement provides data for a large range of locations on a facade or a roof. The paper describes the method and a first experimental implementation of it. The experiment indicates that this method has the potential to produce results which have an accuracy that is comparable to standardized reference methods.

Developing an instrument to examine students' analogical modeling competence: An example of electricity

AbstractScience education reforms advocate modeling as a core practice in which analogy is a significant form and analogical modeling is a creative process for scientific explanation and discovery. This study adopts the self‐generated analogical modeling approach involving electricity, which considers all the modeling subprocesses and includes two subtopics (circuits and energy) with incompatible ontological presuppositions as a conceptual space to develop an analogical modeling competence (AMC) instrument. Two constructs, phenomenon‐related abductive premises (Phenomenon‐AbPr) and analogical modeling processes (AMP), that describe the increasing sophistication of students' AMC (i.e., Level 0 experience to Level 4 high‐order relation) are considered. The procedures utilize a construct‐based approach of developing an assessment to validate the underlying constructs and subtopics to develop the mapping between the target and the analogical models, design items, and determine outcome spaces. Data were collected from 294 Grade 6 students to examine their AMC. The item response theory technique based on the Rasch partial credit model was used to validate the two‐phase model. Student results revealed that the infit and outfit mean square values were between 0.75 and 1.33, indicating acceptable item fit. Results revealed that students scored highest on the open series circuit and lowest on electric energy in the Phenomenon‐AbPr phase. Most students demonstrated relational mapping in the analogical model validation (Level 3) stage, which performed best in the AMP phase. However, they struggled to transfer factors during the analogical model application and deployment (Level 0). Implications for science assessment and teaching are discussed.

Epistemic control without voluntarism

AbstractIt is tempting to think (though many deny) that epistemic agents exercise a distinctive kind of control over their belief‐like attitudes. My aim here is to sketch a “bottom‐up” model of epistemic agency, one that draws on an analogous model of practical agency, according to which an agent's conditional beliefs are reasons‐responsive planning states that initiate and sustain mental behavior so as to render controlled.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

A Computationally Efficient Benders Decomposition for Energy Systems Planning Problems with Detailed Operations and Time-Coupling Constraints

Energy systems planning models identify least-cost strategies for expansion and operation of energy systems and provide decision support for investment, planning, regulation, and policy. Most are formulated as linear programming (LP) or mixed integer linear programming (MILP) problems. Despite the relative efficiency and maturity of LP and MILP solvers, large scale problems are often intractable without abstractions that impact quality of results and generalizability of findings. We consider a macro-energy systems planning problem with detailed operations and policy constraints and formulate a computationally efficient Benders decomposition separating investments from operations and decoupling operational timesteps using budgeting variables in the master model. This novel approach enables parallelization of operational subproblems and permits modeling of relevant constraints coupling decisions across time periods (e.g., policy constraints) within a decomposed framework. Runtime scales linearly with temporal resolution; tests demonstrate substantial runtime improvement for all MILP formulations and for some LP formulations depending on problem size relative to analogous monolithic models solved with state-of-the-art commercial solvers. Our algorithm is applicable to planning problems in other domains (e.g., water, transportation networks, production processes) and can solve large-scale problems otherwise intractable. We show that the increased resolution enabled by this algorithm mitigates structural uncertainty, improving recommendation accuracy. Funding: Funding for this work was provided by the Princeton Carbon Mitigation Initiative (funded by a gift from BP) and the Princeton Zero-carbon Technology Consortium (funded by gifts from GE, Google, ClearPath, and Breakthrough Energy). Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoo.2023.0005 .

Modelling the creep behavior of Gossypium hirsutum cotton fibers based on their physical and mechanical properties

In this work, we investigated the cotton fibers behavior utilizing the analogical modelling method. The aim was to establish the relationships between single and bundle cotton fibers physical and mechanical properties. This study was focused on the creep behavior of cotton fibers. The bundle fibers behaviors to creep tests were modeled considering the correlations between the physical and mechanical results of single ones. Results showed that cotton fibers are viscoelastic and their creep response in single or bundle fibers fitted to a three-element model; the Zener’s model. Furthermore, the tested fibers were classified per length to limit the variability effect. Moreover, Principal Component Analysis (PCA) was employed for data analysis, enabling the identification of correlations between the physical properties and creep behavior of both individual fibers and fiber bundles. This comprehensive study serves as a valuable benchmark for understanding the creep characteristics of cotton fibers.

Religion as Brand: ISIS and Al-Qaeda as Sub-brands of Islam

Theorists emphasize the significance of the conceptualizing phenomena before any quantification in the scientific work process. The role of analogy among all human-beings’ cognitive tools in the process of problem solving and concept creation is undeniable according to experts. Accordingly, this paper defines the analogy of “religion as brand” as an analogical model to shed light on political and religious marketing aspects of two terrorist organizations and religious brands in the Middle East. The concept of “ISIS and Al-Qaeda are sub-brands of Islam” was extracted from this metaphorical structure. The paper illuminates different branding attributes of these two terrorist groups through reviewing approximately fifty first-hand and second-hand materials on the issue. This review reveals how Islam functions as a master-brand and nourishes these two brands ideologically. The analogy entails a variety of attributes among which five aspects of branding including communication, brand mythology, competition, attracting social and symbolic capital, and brand promise are discussed and religious associations which endorse these two groups’ political functions are examined.

Effective Dipole Moment Model for Axially Symmetric C3v Molecules: Application to the Precise Study of Absolute Line Strengths of the ν6 Fundamental of CH335Cl.

The effective dipole moment model for molecules of axial C3v symmetry is derived on the basis of the symmetry properties of a molecule which, on the one hand, is of the same order of efficiency (but much simpler and clearer in applications) as the analogous models derived on the basis of the irreducible tensorial sets theory, and, on the other hand, mathematically more correct in comparison with concepts like the Herman-Walles function used in the models. As an application of the general results obtained, we discuss high-resolution infrared spectra of CH335Cl, recorded with the Zürich prototype ZP2001 (Bruker IFS125 HR) Fourier transform infrared spectrometer at a resolution of 0.001 cm-1 and analyzed in the region of 880-1190 cm-1 (ν6 bending fundamental centered at ν0 = 1018.070790 cm-1). Absolute strengths of more than 2800 transitions (2081 lines) were obtained from the fit of their shapes both with Voigt and Hartmann-Tran profiles, and parameters of the effective dipole moment of the ν6 band were determined by the computer code SYMTOMLIST (SYMmetric TOp Molecules: LIne STrengths), created on the basis of a derived theoretical model. As the first step of the analysis of the experimental data, assignments of the recorded lines were made. A total of 5124 transitions with Jmax = 68, Kmax = 21 were assigned to the ν6 band. The weighted fit of 2077 upper energy values obtained from the experimentally recorded transitions was made with a Hamiltonian which takes into account different types of ro-vibrational effects in doubly degenerate vibrational states of the C3v-symmetric molecule. As the result, a set of 25 fitted parameters was obtained which reproduces the initial 2077 upper "experimental" ro-vibrational energy values with a root mean square deviation drms=4.7×10-5 cm-1. At the second step of the analysis, the computer code SYMTOMLIST was used for determination of the parameters of the derived effective dipole moment model. Six effective dipole moment parameters were obtained from the weighted fit procedure which reproduces absolute experimental strengths of the 2804 initial experimental transitions with a relative drms=3.4%.

Q-neighbor Ising model on multiplex networks with partial overlap of nodes.

The q-neighbor Ising model for the opinion formation on multiplex networks with two layers in the form of random graphs (duplex networks), the partial overlap of nodes, and LOCAL&AND spin update rule was investigated by means of the pair approximation and approximate master equationsas well as Monte Carlo simulations. Both analytic and numerical results show that for different fixed sizes of the q-neighborhood and finite mean degrees of nodes within the layers the model exhibits qualitatively similar critical behavior as the analogous model on multiplex networks with layers in the form of complete graphs. However, as the mean degree of nodes is decreased the discontinuous ferromagnetic transition, the tricritical point separating it from the continuous transition, and the possible coexistence of the paramagnetic and ferromagnetic phases at zero temperature occur for smaller relative sizes of the overlap. Predictions of the simple homogeneous pair approximation concerning the critical behavior of the model under study show good qualitative agreement with numerical results; predictions based on the approximate master equationsare usually quantitatively more accurate but yet not exact. Two versions of the heterogeneous pair approximation are also derived for the model under study, which, surprisingly, yield predictions only marginally different or even identical to those of the simple homogeneous pair approximation. In general, predictions of all approximations show better agreement with the results of Monte Carlo simulations in the case of continuous than discontinuous ferromagnetic transition.

Comparing the diagnostic performance of ordinary, mixed, and lasso logistic regression models at identifying opioid and cannabinoid poisoning in U.S. dogs using pet demographic and clinical data reported to an animal poison control center (2005-2014).

Researchers have begun studying the impact of human opioid and cannabinoid use on dog populations. These studies have used data from an animal poison control center (APCC) and there are concerns that due to the illicit nature and social stigma concerning the use of these drugs, owners may not always be forthcoming with veterinarians or APCC staff regarding pet exposures to these toxicants. As a result, models derived from APCC data that examine the predictability of opioid and cannabinoid dog poisonings using pet demographic and health disorder information may help veterinarians or APCC staff more reliably identify these toxicants when examining or responding to a call concerning a dog poisoned by an unknown toxicant. The fitting of epidemiologically informed statistical models has been useful for identifying factors associated with various health conditions and as predictive tools. However, machine learning, including lasso regression, has many useful features as predictive tools, including the ability to incorporate large numbers of independent variables. Consequently, the objectives of our study were: 1) identify pet demographic and health disorders associated with opioid and cannabinoid dog poisonings using ordinary and mixed logistic regression models; and 2) compare the predictive performance of these models to analogous lasso logistic regression models. Data were obtained from reports of dog poisoning events collected by the American Society for the Prevention of Cruelty to Animals' (ASPCA) Animal Poisoning Control Center, from 2005-2014. We used ordinary and mixed logistic regression models as well as lasso logistic regression models with and without controlling for autocorrelation at the state level to train our models on half the dataset and test their predictive performance on the remainder. Although epidemiologically informed logistic regression models may require substantial knowledge of the disease systems being investigated, they had the same predictive abilities as lasso logistic regression models. All models had relatively high predictive parameters except for positive predictive values, due to the rare nature of calls concerning opioid and cannabinoid poisonings. Ordinary and mixed logistic regression models were also substantially more parsimonious than their lasso equivalents while still allowing for the epidemiological interpretation of model coefficients. Controlling for autocorrelation had little effect on the predictive performance of all models, but it did reduce the number of variables included in lasso models. Several disorder variables were associated with opioid and cannabinoid calls that were consistent with the acute effects of these toxicants. These models may help build diagnostic evidence concerning dog exposure to opioids and cannabinoids, saving time and resources when investigating these cases.

Volcano transition in populations of phase oscillators with random nonreciprocal interactions.

Populations of heterogeneous phase oscillators with frustrated random interactions exhibit a quasiglassy state in which the distribution of local fields is volcanoshaped. In a recent work [Phys. Rev. Lett. 120, 264102 (2018)10.1103/PhysRevLett.120.264102], the volcano transition was replicated in a solvable model using a low-rank, random coupling matrix M. We extend here that model including tunable nonreciprocal interactions, i.e., M^{T}≠M. More specifically, we formulate two different solvable models. In both of them the volcano transition persists if matrix elements M_{jk} and M_{kj} are enough correlated. Our numerical simulations fully confirm the analytical results. To put our work in a wider context, we also investigate numerically the volcano transition in the analogous model with a full-rank random coupling matrix.

Fuzzy Decision-Making Model for Solar Photovoltaic Panel Evaluation

The use of solar photovoltaic (PV) panels is one of the most promising ways to generate electricity. However, the complex technical parameters associated with them make the choice between different PV panels a complicated task. The aim of the article is the analysis and multi-criteria evaluation of PV panels available on the Polish market and to indicate the optimal solar PV panels according to the adopted technical criteria. The practical goal was achieved using a fuzzy approach, taking into account the uncertainty of operational parameters. Based on the applied approach and multi-criteria NEAT F-PROMETHEE method, a fuzzy decision model was built for the evaluation of PV panels. The results of this model were compared with the results of an analogous model that did not take into account the uncertainty of the data. As a result of the research, it was found that the results of the fuzzy model should be considered more reliable, because fuzzy numbers allow for capturing more data than real numbers, which translates into greater reliability of the results of the fuzzy model.

An experimental and simulation study of the impact of emotional information on analogical reasoning

We investigated whether and how emotional information would affect analogical reasoning. We hypothesized that task-irrelevant emotional information would impair performance whereas task-relevant emotional information would enhance it. In Study 1, 233 undergraduates completed a novel version of the People Pieces Task (Emotional Faces People Task), an analogical reasoning task in which the task characters displayed emotional or neutral facial expressions (within-participants). The emotional faces were relevant or irrelevant to the task (between-participants). We simulated the behavioral results using the Learning and Inference with Schemas and Analogies (LISA) model of relational reasoning. LISA is a neurally plausible, symbolic-connectionist computational model of analogical reasoning. In comparison to neutral trials, participants were slower but more accurate on emotion-relevant trials, and were faster but less accurate on emotion-irrelevant trials. Simulations using the LISA model demonstrated that it is possible to account for the effects of emotional information on reasoning in terms of how emotional stimuli attract attention during a reasoning task. In Study 2, 255 undergraduates completed the Emotional Faces People Task at either a high- or low-working memory load. The high working memory load condition of Study 2 replicated the findings of Study 1, showing that participants were more accurate on emotion-relevant trials than on emotion-irrelevant trials; in Study 2, this increased accuracy could not be accounted for by a speed-accuracy tradeoff. The working memory manipulation influenced the manner in which the congruence (with the correct answer) of emotion-irrelevant emotion influenced performance. Simulations using the LISA model showed that manipulating the salience of emotion, the error penalty, as well as vigilance (which determines the likelihood that LISA will notice it has attended to an irrelevant relation), could reasonably reproduce the behavioral results of both low and high working memory load conditions of Study 2.

An Evaluation of Mathematical Models and Stability Analysis of Learning Based on Reaction Kinetics

Human cognition and consciousness are perhaps the most confounding mystery. Somehow it has a linkage to the process of learning and storage of short-term and long-term memory in the form of knowledge. This paper examines a brief background of early models in learning presented by Atkinson and Shriffrin (1965) and related stochastic models utilizing probability functions. Each of these learning models capture certain facets of the learning process but are ineffective in describing the physical basis in which learning occurs. For this reason, this paper explores analogous mathematical models based on reaction kinetics that have been shown to represent chemical reactions found in nature. Six learning models are presented of unitary, binary, reversible binary, reversible binary with mass action, and enzyme learning model reactions with and without decay. Preliminary analysis of time series plots, phase line diagrams, and phase plane plots were conducted to illustrate equilibrium conditions and stability of the models. Each model is examined in terms of its limitations in the philosophy and inability to capture certain elements that are understood about the learning process. Finally, this paper concludes that the feasibility of understanding behavior such as stability through the tools of applied mathematics and thereby illuminating certain layers of human cognition and learning is a useful tool in examining the suitability of a possible deterministic model that could describe the learning process. Further analysis with empirical data would validate the suitability of the presented models.

Combining Near-Infrared (NIR) Analysis and Modelling as a Fast and Reliable Method to Determine the Authenticity of Agarwood (Aquilaria spp.)

The resinous wood produced by the Aquilaria and Gyrinops species—agarwood—is both rare and highly valuable. It is used in products from perfumes to medicines and has an estimated global market value of $32 billion. As a result, the adulteration and illegal purchasing of agarwood is widespread and of specific concern to enforcement agencies globally. Therefore, it is of interest to have a fast, reliable, and user-friendly method to confirm the authenticity of a sample of agarwood. We investigated the use of near infrared (NIR) data to develop a method that rapidly distinguished between authentic and non-authentic agarwood samples, based upon a soft independent model of class analogy (SIMCA), using software specific to the application of infrared data to material authentication. The model showed a clear distinction between the authentic and non-authentic samples. However, the small values involved led to poor automatic validation results.

Bootstrap percolation in random geometric graphs

AbstractFollowing Bradonjić and Saniee, we study a model of bootstrap percolation on the Gilbert random geometric graph on the 2-dimensional torus. In this model, the expected number of vertices of the graph is n, and the expected degree of a vertex is $a\log n$ for some fixed $a>1$ . Each vertex is added with probability p to a set $A_0$ of initially infected vertices. Vertices subsequently become infected if they have at least $ \theta a \log n $ infected neighbours. Here $p, \theta \in [0,1]$ are taken to be fixed constants.We show that if $\theta < (1+p)/2$ , then a sufficiently large local outbreak leads with high probability to the infection spreading globally, with all but o(n) vertices eventually becoming infected. On the other hand, for $ \theta > (1+p)/2$ , even if one adversarially infects every vertex inside a ball of radius $O(\sqrt{\log n} )$ , with high probability the infection will spread to only o(n) vertices beyond those that were initially infected.In addition we give some bounds on the $(a, p, \theta)$ regions ensuring the emergence of large local outbreaks or the existence of islands of vertices that never become infected. We also give a complete picture of the (surprisingly complex) behaviour of the analogous 1-dimensional bootstrap percolation model on the circle. Finally we raise a number of problems, and in particular make a conjecture on an ‘almost no percolation or almost full percolation’ dichotomy which may be of independent interest.

Quenched Kitaev chain: Analogous model of gravitational collapse

We investigate generalized thermalization in an isolated free fermionic chain evolving from an out of equilibrium initial state through a sudden quench. We consider the quench where a fermionic chain is broken into two disjoint chains. We focus on the evolution of the local observables namely, occupation number, nearest neighbor hopping, information sharing and out-of-time-order correlations after the quench and study the relaxation of the observable, leading to generalized Gibbs ensemble for the system in the thermodynamic limit though it has been argued that noninteracting or free fermionic models in general do not relax to the generalized Gibbs ensemble (GGE). We obtain the light cone formed by the evolution of the observables along the fermionic lattice chain due to the sudden quench which abides by the Lieb-Robinson bound in quantum systems. We also analytically study a simpler model which captures the essential features of the system. Our analysis strongly suggest that the internal interactions within the system do not remain of much importance once the quench is sufficiently strong.

Модель оценки функциональной устойчивости элементов информационной инфраструктуры для условий воздействия множества компьютерных атак

Information is given about a new approach to the application of methods of the theory of semi-Markov processes to solve the applied problem of assessing the functional stability of elements that make up the information infrastructure, functioning under the influence of multiple computer attacks. The task of assessing functional stability is reduced to the task of finding the survivability function of the element under study and determining its extreme values. The relevance of the study is substantiated. The rationale is based on the assumption that quantitative methods of studying the stability of technical systems, which operate on the theory of reliability, cannot always be used to assess survivability. The concepts of «stability» and «computer attack» are being clarified. Verbal and formal statements of research tasks are formulated. The novelty of the results obtained lies in the application of well-known methods to solve a practically significant problem in a new formulation, taking into account the limitations on the resource allocated to maintain the survivability of the element under study, provided that arbitrary distribution laws are adopted for the random times of the implementation of computer attacks and the recovery times of the functional element. Recommendations on the formation of initial data, the content of the enlarged stages of modeling and a test case to demonstrate the performance of the model are given. The results of the test simulation are presented in the form of graphs of the survivability function. The resulting application can be used in practice to construct a survivability function when implementing up to three computer attacks, as well as a tool for evaluating the reliability of analogous statistical models. The limitation is explained by a progressive increase in the dimension of the analytical model and a decrease in the possibility of its meaningful interpretation.

Pathophysiological mechanisms of reduced physical activity: Insights from the human step reduction model and animal analogues.

Physical inactivity represents a heavy burden for modern societies and is spreading worldwide, it is a recognised pandemic and is the fourth cause of global mortality. Not surprisingly, there is an increasing interest in longitudinal studies on the impact of reduced physical activity on different physiological systems. This narrative review focuses on the pathophysiological mechanisms of step reduction (SR), an experimental paradigm that involves a sudden decrease in participants' habitual daily steps to a lower level, mimicking the effects of a sedentary lifestyle. Analogous animal models of reduced physical activity, namely, the "wheel-lock" and the "cage reduction" models, which can provide the foundation for human studies, are also discussed. The empirical evidence obtained thus far shows that even brief periods of reduced physical activity can lead to substantial alterations in skeletal muscle health and metabolic function. In particular, decrements in lean/muscle mass, muscle function, muscle protein synthesis, cardiorespiratory fitness, endothelial function and insulin sensitivity, together with an increased fat mass and inflammation, have been observed. Exercise interventions seem particularly effective for counteracting these pathophysiological alterations induced by periods of reduced physical activity. A direct comparison of SR with other human models of unloading, such as bed rest and lower limb suspension/immobilisation, is presented. In addition, we propose a conceptual framework aiming to unravel the mechanisms of muscle atrophy and insulin resistance in the specific context of reduced ambulatory activity. Finally, methodological considerations, knowledge gaps and future directions for both animal and human models are also discussed in the review.

An analogous scale model for laboratory mixing studies

This technical note illustrates application of a model gypsum scale system purposely developed to investigate the effect of flow condition on scale growth in mixing tanks. Photos of non-uniform scale growth distribution in laboratory mixing tanks are presented. The laboratory results are found to match qualitatively to that observed in the full-scale plant. Due to its low cost and simplicity in application, this method can be applied in mixing pilot testing so that scale mitigation can be included in the agitator and tank design optimisation.