Model Theory Research Articles

How to Do Things with Normative Political Theories: The Performative Nature of Political Philosophy

Recently, there has been much debate about the role and nature of political theories. Jeremy Waldron has argued that we misconstrue a theory’s purpose if we summarize it in terms of ‘What Plato Would Allow’, because a normative theory is a conceptual exploration and should not be reduced to a policy wish list. To make sense of such phrases beyond Waldron’s critique, this paper provides some conceptual tools to clarify the practical purpose of normative political theories. First, it draws on Bernard Williams’ distinction between the enactive and structural models in political theory. Second, it seeks to reconstruct normative political theories as sets of speech acts having an illocutionary point that expresses the relation between theory and practice. Third, the paper outlines five types of illocutionary points (Intervention, Justification, Reconciliation, Critique, and Imagination of alternatives). The idea of an illocutionary point can make sense of the practical import of a theory in a multifaceted manner and without subscribing to a specific conception of practicality.

Angle-dependent Hall resistivity and longitudinal resistivity of type-II superconductor

We use time-dependent Ginzburg–Landau theory in a three-dimensional model, including thermal noise, to analyze angle-dependent Hall resistivity and longitudinal resistivity of type-II superconductor. The Hall resistivity and longitudinal resistivity are calculated as functions of temperature, magnetic field and the angle θ between the magnetic field and the ab-plane in the vortex-liquid regime. Our theoretical calculations within a self-consistent fluctuation approximation for MgB2 and HgBa2CaCu2O6 materials are in good agreements with the experimental findings for both below and above the critical temperature Tc. We observe that when the field angle decreases, the transition temperature increases and the magnitude of longitudinal resistivity decreases, which is qualitatively comparable to decrease of the perpendicular field component. However, when the magnetic field direction approaches the layer surface, it shows a clear different effect from that of a perpendicular field with the same normal component.

Nanoindentation-induced serrated flow behavior and deformation mechanism in metallic glass: A combined experiments and molecular dynamics simulation

In this study, the deformation behaviors of bulk metallic glass (BMG) during the nanoindentation are presented via the experiments and molecular dynamics (MD). The relationship between shear transformation zone (STZ) formation and serrated flow dynamics are developed to explain the deformation characterization evolution of BMG. It is found that as the peak load increases, the hardness and elastic modulus significantly decrease. Creep behavior at room temperature was also revealed by analyzing the creep displacement curve and the stress index under different peak loads.The accumulation of free volume during the deformation process promotes more uniform creep deformation. Furthermore, the serrated flow behaviors were analyzed innovatively using the shear stress drops further statistically. At lower loads, the occurrence of serrated retention is due to energy exceeding the potential barrier required for flow unit activation, ultimately released in the form of kinetic energy. At higher loads, the transition from intermittent curve to smooth curve exhibits a typical self-organized critical (SOC) state dynamic characteristics, and ISE phenomenon is also observed. The results also suggested that the shear deformation of the spherical indenter is more pronounced, with the shear band forming at a 45° angle to the direction of the downward pressure. And based on the Cooperative shear model (CSM) theory, the STZ size was estimated and verify that the increment of STZ makes it easier to generate stronger plastic strain to dissipate STZ, ultimately leading to complete plastic deformation. Combining five fold symmetry and gradient atoms, the majority of atoms with five fold symmetry (LFFS>0.5) have stronger resistance to plastic deformation when the load rate is low.

Social Work Practice During Times of Disaster: A Transformative Green Social Work Model for Theory, Education and Practice in Disaster Interventions

Social Work Practice During Times of Disaster: A Transformative Green Social Work Model for Theory, Education and Practice in Disaster Interventions

A comprehensive analysis of charged pulsars and cracking condition

The main aim of this manuscript is to analyze the viability and stability of pulsars filled with charged anisotropic matter configuration in extended symmetric teleparallel theory. A particular model of this gravitational theory is considered to reduce the complexity of the system and formulate the explicit field equations which govern the interaction between matter and geometry. The configuration of static spherical symmetric structures is examined through the non-singular viable solutions. The undetermined constants in the metric coefficients are determined by the Darmois junction conditions. Further, we explore different physical characteristics in the interior of charged pulsars to check their viability. The equilibrium state of the charged stellar objects is discussed using the Tolman–Oppenheimer–Volkoff equation and the stability is examined by the causality condition, Herrera cracking approach and adiabatic index, respectively. Our findings indicate that the proposed charged stars in this modified gravity are physically viable and stable.

Relationship between teacher's ability model and students' behavior based on emotion-behavior relevance theory and artificial intelligence technology under the background of curriculum ideological and political education

PurposeThis paper aims to fully understand the relationship between students' classroom emotions and teachers' behaviors in the teaching environment, provide personalized teaching support, stimulate teachers' motivation, and enhance the emotional connection between teachers and students. This paper designs a teacher's ability model based on emotion-behavior relevance theory and artificial intelligence (AI) technology. MethodsFirstly, this paper expounds on the integration of emotion-behavior relevance theory, AI, and teacher's ability model under the background of ideological and political education. Subsequently, the artificial neural network (ANN) model is deeply analyzed in relation to the development of psychology and AI technology. Guided by the theory of emotion-behavior relevance, an ANN model is used to optimize the classroom emotion recognition module in the teacher's ability model. The accuracy of the optimized teacher's ability model in recognizing attention and resistance is verified by experiments. The relationship between the teacher's classroom emotions, students' classroom behavior, and the teacher's teaching ability is analyzed. ResultsThe experimental results show that the optimized model can effectively identify students' classroom emotions, and the accuracy of identifying attention and resistance reaches 93.6 % and 92 %, respectively, significantly exceeding the traditional model. For other emotions, the accuracy of the experimental group ranges from 80.6 % to 87.9 %, while that of the control group is only 61.1 %. The optimized model shows a better effect on the emotional recognition of multiple students. This proves the effectiveness of the proposed optimization model. In addition, by analyzing teachers' emotions in real classroom videos, people can observe that teachers' psychological emotions and behaviors change with the changes in students' classroom emotions. Under the students' positive emotions, teachers scored high in psychological emotions, behaviors, and teaching achievements, with the lowest scores of 87, 80, and 85, respectively. Under students' negative emotions, teachers' related scores are low, with the highest scores of 40, 40, and 42. This highlights the critical influence of the emotional attitude and values of the main characters on teachers' teaching ability in the classroom environment from the psychological perspective. ConclusionStudents' positive emotions, such as concentration and devotion, will arouse teachers' satisfaction and happiness, motivate them to teach more diligently, and improve the teaching effect. On the contrary, students' negative emotions, such as resistance and doubt, may cause teachers to feel depressed and discouraged. This proves the importance and effectiveness of integrating the emotion-behavior relevance theory into teaching. Students' emotions impact teachers' psychological emotions and behaviors and affect the teaching effect. In this case, teachers need to use their emotional adjustment ability to adjust their feelings in time to better guide students and improve teaching efficiency. This paper provides valuable insights for optimizing the teacher's ability model.

A Theoretical Study of the Effects of Co-Doping Ions at K and Nb Sites on the Properties of KNbO3 Nanoparticles.

Using a microscopic model and Green's function theory, we have investigated the co-doping effect on ferroelectric KNbO3 nanoparticles. Let us emphasize that while the doping with transition metal ions at the Nb site leads an increase in the ferromagnetism and a reduction the band gap, it also decreases the ferroelectricity. On the other hand, doping with La or Ba at the K site leads to enhanced polarization, but does not lead to the appearance of ferromagnetism and reduction in the band gap. Therefore, we have studied co-doping with La/Cr and La/Co ions, which leads to increasing the magnetization and polarization as well as to strongly decreasing the band gap energy. Thus, we observe a multiferroic material with room-temperature ferromagnetism and ferroelectricity as well as small band gap energy which can be tuned using various co-doping ions. There is a good agreement with the existing experimental data.

A TCCM-Based Review of Consumer Behaviour Towards Digital Voice Assistants and Future Research Agenda

This Systematic Literature Review explores the evolving landscape of consumer behaviour towards DVAs using the TCCM (Theory-Context-Characteristics-Methodology) framework. Through a comprehensive analysis of 64 research papers sourced from the Scopus database, indexed in the ABDC (Australian Business Deans Council) category list, and published until December 2023, we uncover pivotal themes and significant knowledge gaps in the current body of research. The findings show that most studies on consumer-DVA interactions are empirical in nature, predominantly focused on the shopping and hospitality sectors, and grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks. Applying the TCCM framework, this paper uncovers fertile ground for future research, highlighting opportunities for further exploration and development. Building on the identified research gaps and our insights, we present integrated frameworks on consumer behaviour towards DVAs. Additionally, we provide actionable recommendations for DVA researchers, developers, and marketers, highlighting critical areas for future research, such as integrating sentiment analysis and emotional recognition capabilities. We also propose tailored strategies, including customizable DVA personalities and engaging voice experiences, to enhance consumer engagement and brand loyalty. The paper concludes with a summary of limitations and key findings.

The Impact of Prospect Theory on Asset Forecasting: A Comparative Study Based on Random Forest and Linear Regression

This study focuses on the application of predictive models and prospect theory in the field of machine learning to predict financial asset prices. Here, the linear regression and random forest algorithms were employed to forecast the price values and fluctuations of two different financial assets: the S&P 500 index and Bitcoin prices. Meanwhile, this study compared the performance of different financial asset datasets before and after the introduction of prospect theory models. Through calculation and comparative analysis, this article provides an in-depth discussion on the predictive performance of regression models and classification models. The research results indicate that in most cases, simple linear regression models have high prediction accuracy. Meanwhile, this paper also found that introducing prospect theory can effectively improve the accuracy of prediction models for specific financial assets. This result has a positive impact on the financial industry, helping to optimize risk management and investment decision-making, improving efficiency at the market level and promoting information transparency, while also promoting the development of emerging financial formats in financial innovation

Molecular kinetic modelling of non-equilibrium evaporative flows

Recent years have seen the emergence of new technologies that exploit nanoscale evaporation, ranging from nanoporous membranes for distillation to evaporative cooling in electronics. Despite the increasing depth of fundamental knowledge, there is still a lack of simulation tools capable of capturing the underlying non-equilibrium liquid–vapour phase changes that are critical to these and other such technologies. This work presents a molecular kinetic theory model capable of describing the entire flow field, i.e. the liquid and vapour phases and their interface, while striking a balance between accuracy and computational efficiency. In particular, unlike previous kinetic models based on the isothermal assumption, the proposed model can capture the temperature variations that occur during the evaporation process, yet does not require the computational resources of more complicated mean-field kinetic approaches. We assess the present kinetic model in three test cases: liquid–vapour equilibrium, evaporation into near-vacuum condition, and evaporation into vapour. The results agree well with benchmark solutions, while reducing the simulation time by almost two orders of magnitude on average in the cases studied. The results therefore suggest that this work is a stepping stone towards the development of an accurate and efficient computational approach to optimising the next generation of nanotechnologies based on nanoscale evaporation.

Quantum cosmological models in the Einstein-aether theory with radiation fluid

Quantum cosmological models in the Einstein-aether theory with radiation fluid

The Multiple Resource Theory and Model. Some Misconceptions in Data Interpretations

Multiple Resource Theory, and one particular instantiation of the theory in the 4-Dimensional Multiple Resource Model have often been invoked as mechanisms to account for dual task interference. Here we present the data and analyses requirements necessary to conclude that the multiple resource model either does or does not predict an increase in the dual task decrement; and in particular we describe the need to consider and control both task difficulty and task priority in comparing conditions that are argued to contrast shared versus separate resources. We then consider one common misconception regarding what the model predicts regarding perfect time sharing, and address the importance of considering alternative theory-based mechanisms of task switching and of confusion and crosstalk, to account for patterns of multi-task interference.

Entanglement membrane in exactly solvable lattice models

Entanglement membrane theory is an effective coarse-grained description of entanglement dynamics and operator growth in chaotic quantum many-body systems. The fundamental quantity characterizing the membrane is the entanglement line tension. However, determining the entanglement line tension for microscopic models is in general exponentially difficult. We compute the entanglement line tension in a recently introduced class of exactly solvable yet chaotic unitary circuits, so-called generalized dual-unitary circuits, obtaining a nontrivial form that gives rise to a hierarchy of velocity scales with vE<vB. For the lowest level of the hierarchy, L¯2 circuits, the entanglement line tension can be computed entirely, while for the higher levels the solvability is reduced to certain regions in spacetime. This partial solvability enables us to place bounds on the entanglement velocity. We find that L¯2 circuits saturate certain bounds on entanglement growth that are also saturated in holographic models. Furthermore, we relate the entanglement line tension to temporal entanglement and correlation functions. We also develop methods of constructing generalized dual-unitary gates, including constructions based on complex Hadamard matrices that exhibit additional solvability properties and constructions that display behavior unique to local dimension greater than or equal to three. Our results shed light on entanglement membrane theory in microscopic Floquet lattice models and enable us to perform nontrivial checks on the validity of its predictions by comparison to exact and numerical calculations. Moreover, they demonstrate that generalized dual-unitary circuits display a more generic form of information dynamics than dual-unitary circuits. Published by the American Physical Society 2024

Role of Point-of-Purchase on Consumers’ Perception of Coca-Cola Beverage in Nigeria

Consumers have needs, values, and preferences, which is important to them while making buying decisions. This preference determines the choice made by individuals when they see the product(s) or hear sounds that attract them to an environment in which they want to make a purchase decision. Having the opportunity to see product(s), does not only instigate a consumer to buy but reduce the uncertainties the customer experience while making buying decisions. The need to instigate consumers to buy a brand’s product among several competing brands makes brands provide point-of-purchase display tools to help push products to consumers through retail outlets whilst influencing consumers' buying decisions. Using the Servqual model and conditioning theory, this research work was carried out to examine the role of point-of-purchase display on consumers’ perception of brands. Survey research was conducted for this purpose. A total of two hundred valid responses were collected from respondents who are consumers of the Coca-Cola brand products. The data collected from questionnaires were analyzed using ordinal logistic regression on SPSS version 25. The research findings show that there is a significant positive relationship between the independent variable- point of purchase display (atmospherics, product display) and the dependent variable- perception (value, reputation). Hence, the study recommends that brands should always motivate retail outlets to keep their products on display and their display items in strategic places in the store to thrive in the competitive market. Lastly, because the atmospherics attract customers before the display in indoor settings, it is recommended that the atmosphere should create a remarkable experience for customers by leveraging on enticing the human sensory.

A Theory of Inaccessible Information

What would be the consequences if there were fundamental limits to our ability to experimentally explore the world? In this work we seriously consider this question. We assume the existence of statements whose truth value is not experimentally accessible. That is, there is no way, not even in theory, to directly test if these statements are true or false. We further develop a theory in which experimentally accessible statements are a union of a fixed minimum number of inaccessible statements. For example, the value of truth of the statements "a" and "b" is not accessible, but the value of truth of the statement "a or b" is accessible. We do not directly assume probability theory, we exclusively define experimentally accessible and inaccessible statements and build on these notions using the rules of classical logic. We find that an interesting structure emerges. Developing this theory, we relax the logical structure to a probabilistic one, obtaining a theory rich in structure that we call "theory of inaccessible information". Surprisingly, the simplest model of theory of inaccessible information is the qubit in quantum mechanics. Along the path for the construction of this theory, we characterise and study a family of multiplicative information measures that we call "inaccessibility measures".

Primordial black holes and induced gravitational waves from a smooth crossover beyond standard model theories

Primordial black holes and induced gravitational waves from a smooth crossover beyond standard model theories

Habitat status and feasibility of constructing corridors for a vulnerable population of giant pandas

Habitat corridors provide connectivity to decrease the negative effects of fragmentation. However, numerous current studies on habitat corridors remain in the design stage, and there is a lack of research addressing the challenges and feasibility of actual construction. Therefore, fine-scale analyses of habitat corridors are needed to provide deeper insights. We proposed a fine-scale habitat corridor design using the giant panda (Ailuropoda melanoleuca) population in the Mabian Dafengding National Nature Reserve as a case study. The research combined field quadrats, camera traps, and remote sensing data to complete analyses using the species distribution model, landscape pattern index, circuit theory model, and least-cost path (LCP) model. The results show that 53 km2 of suitable habitat for giant pandas exists in the reserve, and anthropogenic disturbance and bamboo cover were key environmental factors affecting giant pandas’ habitat suitability. Fragmentation has caused suitable habitat isolation, and the core habitat is non-contiguously scattered between the north and south. In the design of ten habitat corridors, two primary corridors connect the core habitat from north to south. The proposed primary corridors, roughly 1.4 km wide, are limited by natural impediments such as rivers, food resources, and anthropogenic disturbance. Our research used the latest methods and data to design the habitat corridor and reveals many difficulties for actual construction, despite employing approaches such as the LCP. Our findings enhance the understanding of habitat corridor feasibility and support the development of giant panda conservation efforts.

Artificial Intelligence (AI) Challenges and Opportunities in Translation : An African Experience

The Artificial Intelligence (AI) revolution has become a reality in today’s world and its importance for linguistics was recognized very early. Despite its unprecedent surge and integration into various academic fields including language teaching and translation, surprisingly, little work has been done by scholars in advancing discussions on the profound impact of the AI on the diversity of widely available languages in both developed and developing world. Africa is linguistically diverse continent with about one third of the world’s languages that are vastly underrepresented in the global digital data pool. AI translation machine is supported in only 25 languages out of over 2000 languages in the continent. The paper deploys homomorphism model of AI theory to interrogate the natural language data drawn the African languages to present the current and future challenges, opportunities and potential for developing AI algorithms that could fit neatly into the translation of the African languages. Most of the discussions in the paper focuses on the seven patterns of the AI, the usage and implementation of AI algorithms in the translation science. The research findings show some of the complexities of the African languages in which their syntactic categories have multiple corresponding semantic objects. Unlike English, the findings also reveal that syntactic operation in the African languages do not always have one corresponding semantic operation as postulated by the homomorphism model of AI theory. the study contributes to scholarly literature by stressing the limits and opportunities that relate to using AI in translation science and supplying input from NLP algorithms practitioners to expand the AI applicability operation in the translation science.

Multi-strain phage induced clearance of bacterial infections.

Bacteriophage (or 'phage' - viruses that infect and kill bacteria) are increasingly considered as a therapeutic alternative to treat antibiotic-resistant bacterial infections. However, bacteria can evolve resistance to phage, presenting a significant challenge to the near- and long-term success of phage therapeutics. Application of mixtures of multiple phage (i.e., 'cocktails') have been proposed to limit the emergence of phage-resistant bacterial mutants that could lead to therapeutic failure. Here, we combine theory and computational models of in vivo phage therapy to study the efficacy of a phage cocktail, composed of two complementary phages motivated by the example of Pseudomonas aeruginosa facing two phages that exploit different surface receptors, LUZ19v and PAK_P1. As confirmed in a Luria-Delbrück fluctuation test, this motivating example serves as a model for instances where bacteria are extremely unlikely to develop simultaneous resistance mutations against both phages. We then quantify therapeutic outcomes given single- or double-phage treatment models, as a function of phage traits and host immune strength. Building upon prior work showing monophage therapy efficacy in immunocompetent hosts, here we show that phage cocktails comprised of phage targeting independent bacterial receptors can improve treatment outcome in immunocompromised hosts and reduce the chance that pathogens simultaneously evolve resistance against phage combinations. The finding of phage cocktail efficacy is qualitatively robust to differences in virus-bacteria interactions and host immune dynamics. Altogether, the combined use of theory and computational analysis highlights the influence of viral life history traits and receptor complementarity when designing and deploying phage cocktails in immunocompetent and immunocompromised hosts.

Fully coupled finite strain consolidation analysis for soft clay treated by prefabricated horizontal drain with vacuum and heat preloading

This paper established a one-dimensional fully coupled finite strain consolidation analysis for soft clay treated by thermal prefabricated horizontal drain under vacuum and heat preloading with the consideration of thermal elastic viscoplastic (TEVP) behaviors of soft clay. Firstly, the governing equations for finite strain consolidation, heat conduction, and heat convention process have been carefully derived based on the well-established Gibson’s large strain consolidation theory and TEVP constitutive model. Secondly, the accuracy and reliability of the numerical method have been verified with benchmark cases and three physical model tests. Then, improvement of consolidation efficiency rate (IER) was defined and used to study the effect of three variables on consolidation efficiency. Last but not least, a graphical user interface (GUI) was provided to support researchers and engineers in utilizing the proposed numerical method for conducting consolidation analysis of soft soil treated in the same or similar fashion.