Predictions Of Theory Research Articles

A harmonic property of right invariant priors

AbstractIt is shown that, on any Lie group, the density ratio of the right invariant measure to the left invariant measure is harmonic with respect to the left invariant Riemannian metric. This result is applied to the Bayesian prediction theory on group invariant statistical models. A method of constructing Bayesian prior distributions that asymptotically dominate the right invariant priors is provided.

Supplemental Material for Dual-Systems and Fuzzy-Trace Theory Predictions of COVID-19 Risk Taking in Young Adults

Supplemental Material for Dual-Systems and Fuzzy-Trace Theory Predictions of COVID-19 Risk Taking in Young Adults

Local gate control of Mott metal-insulator transition in a 2D metal-organic framework

Electron-electron interactions in materials lead to exotic many-body quantum phenomena, including Mott metal-insulator transitions (MITs), magnetism, quantum spin liquids, and superconductivity. These phases depend on electronic band occupation and can be controlled via the chemical potential. Flat bands in two-dimensional (2D) and layered materials with a kagome lattice enhance electronic correlations. Although theoretically predicted, correlated-electron Mott insulating phases in monolayer 2D metal-organic frameworks (MOFs) with a kagome structure have not yet been realised experimentally. Here, we synthesise a 2D kagome MOF on a 2D insulator. Scanning tunnelling microscopy (STM) and spectroscopy reveal a MOF electronic energy gap of ∼200 meV, consistent with dynamical mean-field theory predictions of a Mott insulator. Combining template-induced (via work function variations of the substrate) and STM probe-induced gating, we locally tune the electron population of the MOF kagome bands and induce Mott MITs. These findings enable technologies based on electrostatic control of many-body quantum phases in 2D MOFs.

First-Principles and Experimental Investigation of ABO4 Zircons as Calcium Intercalation Cathodes.

Identifying next-generation batteries with multivalent ions, such as Ca2+ is an active area of research to meet the increasing demand for large-scale, renewable energy storage solutions. Despite the promise of higher energy densities with multivalent batteries, one of their main challenges is addressing the sluggish kinetics in cathodes that arise from stronger electrostatic interactions between the multivalent ion and host lattice. In this paper, zircons are theoretically and experimentally evaluated as Ca cathodes. A migration barrier as low as 113 meV is computationally found in YVO4, which is the lowest Ca2+ barrier reported to date. Low barriers are confirmed across 18 zircon compositions, which are related to the low coordination change and reduced interstitial site preference of Ca2+ along the diffusion pathway. Among the four materials (BiVO4, YVO4, EuCrO4, and YCrO4) that were synthesized, characterized, and electrochemically cycled, the highest initial capacity of 81 mA h/g and the most reversible capacity of 65 mA h/g were achieved in YVO4 and BiVO4, respectively. Despite the facile migration of multivalent ions in zircons, density functional theory predictions of the unstable, discharged structures at higher Ca2+ concentrations (Cax>0.25ABO4), the low dimensionality of the migration pathway, and the defect analysis of the B site atom can rationalize the limited intercalation observed upon electrochemical cycling.

Causal chain event graphs for remedial maintenance.

The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application, we need to define a new class of formal intervention we call remedial to model the causal effects of remedial maintenance. This fixes the root causes of a failure and returns the status of the system to as good as new. We demonstrate that the semantics of the CEG are rich enough to express this novel type of intervention. Furthermore, through the bespoke causal algebras, the CEG provides a transparent framework with which to guide and express the rationale behind predictive inferences about the effects of various types of remedial intervention. A backdoor theorem is adapted to apply to these interventions to help discover when a system is only partiallyobserved.

Investigation of cathinone analogs targeting human dopamine transporter using molecular modeling

In a step towards understanding the structure–property relationship among Synthetic Cathinones (SCs), a combined methodology based on Density Functional Theory (DFT), Administration, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, docking and molecular dynamics simulations have been applied to correlate physicochemical descriptors of various SCs to their biological activity. The results from DFT and molecular docking studies correlate well with each other explaining the biological activity trends of the studied SCs. Quantum mechanical descriptors viz. polarizability, electron affinity, ionization potential, chemical hardness, electronegativity, molecular electrostatic potential, and ion interaction studies unravel the distinguishingly reactive nature of Group D (pyrrolidine substituted) and Group E (methylenedioxy and pyrrolidine substituted) compounds. According to ADMET analysis, Group D and Group E molecules have a higher probability of permeating through the blood–brain barrier. Molecular docking results indicate that Phe76, Ala77, Asp79, Val152, Tyr156, Phe320, and Phe326 constitute the binding pocket residues of hDAT in which the most active ligands MDPV, MDPBP, and MDPPP are bound. Finally, to validate the derived quantum chemical descriptors and docking results, Molecular Dynamics (MD) simulations are performed with homology-modelled hDAT (human dopamine transporter). The MD simulation results revealed that the majority of SCs remain stable within the hDAT protein’s active sites via non-bonded interactions after 100 ns long simulations. The findings from DFT, ADMET analysis, molecular docking, and molecular dynamics simulation studies complement each other suggesting that pyrrolidine-substituted SCs (Group D and E), specifically, MPBP and PVN are proven potent SCs along with MDPV, validating various experimental observations. Communicated by Ramaswamy H. Sarma

Research on Intelligent Recognition and Verification System of Football Offside Penalty Based on Artificial Intelligence

An automatic recognition method for ball trajectory and penalty is extensively studied, leveraging computer vision technology. The core hardware components of this system are designed to effectively filter out noise and extract crucial information from the moving track. This enables the system to accurately identify the speed and position of the ball in real-time. To enhance the precision of image segmentation, a novel approach is introduced that utilizes the threshold vector in high-resolution color spaces. This method determines the color of each pixel through bitwise "and" operations between pixel points and subsets. Additionally, a moving window image filtering algorithm is proposed, incorporating trajectory prediction theory. Experimental results demonstrate that this approach significantly reduces recognition errors, resulting in a notably higher accuracy level, making it a promising solution for enhancing the performance of soccer robot systems.

Prime Surprisal as a Tool for Assessing Error-Based Learning Theories: A Systematic Review

Error-based learning theories of language acquisition are highly influential in language development research, yet the predictive learning mechanism they propose has proven difficult to test experimentally. Prime surprisal—the observation that structural priming is stronger following more surprising primes—has emerged as a promising methodology for resolving this issue as it tests a key prediction of error-based learning theories: surprising input leads to increased structure repetition as well as learning. However, as prime surprisal is a relatively new paradigm, it is worth evaluating how far this promise has been fulfilled. We have conducted a systemic review of PS studies to assess the strengths and limitations of existing approaches, with 13 contributions selected out of 66 search results. We found that alongside inconsistency in statistical power and how the level of surprisal is measured, the limited scope of current results cast doubt on whether PS can be used as a general tool to assess error-based learning. We suggest two key directions for future research: firstly, targeting the scope of the prime surprisal effect itself with reliable statistical power and appropriate surprisal measurements across a greater variety of languages and grammatical structures; and secondly, using the prime surprisal method as a tool to assess the scope of an error-based learning mechanism utilising conditions in which prime surprisal has been reliably established.

Intimate Partner Violence, Femicide, and General Theories: Issues for Research and Policy From the View of Modern Control Theory

Intimate partner violence (IPV), including intimate partner homicide (IPH) and femicide, raises issues for general theories of crime, such as control and opportunity theories, that see close relationships among friends and family as barriers to interpersonal crimes. Crime-specific studies of both correlates and trends in IPV, including recent interrupted trend studies that examine the effects of COVID restrictions, often test opportunity theories absent considerations of theoretically driven images of actors. Review of empirical research on IPV and IPH reveals strong compatibility between the predictions of modern control theory and consistent findings from trend data. Barriers to understanding of the explanatory power of general theories of crime (including, for example, control theories and feminist perspectives) in contemporary research include use of poor definitions of intimacy, misspecification of age effects, failure to consider the versatility of offending behavior, neglecting the importance of trends in analogous behaviors, neglecting the role of situational factors in violence, and the limitations in the measurement of repetitive victimization. Theories such as routine activity and situational crime prevention that fail to explicitly include characteristics of actors can go only a limited way in providing meaningful policy. Research supports the potential policy effects of investments in early childhood and attention to situational barriers (including limitations on alcohol use and firearm availability) to reduce IPV. Although modern control theory is used to illustrate these issues, other general theories, like feminist theories, can make similar arguments.

Violation of emergent rotational symmetry in the hexagonal Kagome superconductor CsV3Sb5

Superconductivity is caused by electron pairs that are canonically isotropic, whereas some exotic superconductors are known to exhibit non-trivial anisotropy stemming from unconventional pairings. However, superconductors with hexagonal symmetry, the highest rotational symmetry allowed in crystals, exceptionally have strong constraint that is called emergent rotational symmetry (ERS): anisotropic properties should be very weak especially near the critical temperature Tc even for unconventional pairings such as d-wave states. Here, we investigate superconducting anisotropy of the recently-found hexagonal Kagome superconductor CsV3Sb5, which is known to exhibit various intriguing phenomena originating from its undistorted Kagome lattice formed by vanadium atoms. Based on calorimetry performed under accurate two-axis field-direction control, we discover a combination of six- and two-fold anisotropies in the in-plane upper critical field. Both anisotropies, robust up to very close to Tc, are beyond predictions of standard theories. We infer that this clear ERS violation with nematicity is best explained by multi-component nematic superconducting order parameter in CsV3Sb5 intertwined with symmetry breakings caused by the underlying charge-density-wave order.

CEO Type and Firm Performance: Evidence from Nasdaq Baltic

Despite the well-established characteristics of shareholder-CEOs and professional-CEOs, previous research has yielded inconclusive findings on the actual impact of the separation of shareholding and management on firm financial performance. This study aimed to address this gap by going off the beaten path of research centered on the U.S.-listed firms and investigating 55 firms listed on the Nasdaq Baltic market in the post-Soviet countries of Estonia, Latvia, and Lithuania from 2017 to 2021. While professional-CEO-led firms displayed higher Tobin’s Q (M = 1.37) compared to shareholder-CEO-led firms (M = 1.17), and shareholder-CEO-led firms had a higher average ROE (M = 7.76%) compared to professional-CEO-led firms (M = -1.74%), independent samples t-test analysis revealed that these differences in either stock market performance (p = .250 > .05) or shareholder return (p = .193 > .05) were statistically insignificant. These findings challenge organizational life cycle theory and agency theory predictions, aligning instead with stewardship theory and upper echelons theory, suggesting that CEO characteristics, motivation, and actions, while clearly distinct for shareholder-CEOs and professional-CEOs, are not the sole determinant of financial performance in mature firms. Accordingly, shareholder-CEOs, other stockholders, and boards of directors should draw support from these findings in their considerations regarding firm leadership.

Inverse Differential Quadrature Based Model for Static Behaviour of Variable Stiffness Curved Composite Beams

Recent advancements in fibre placement technologies have expanded the potential applications of variable stiffness curved composite beams in industries such as aerospace, automotive, and naval engineering. Accurate solution techniques for examining these beams, especially those composed of advanced composite materials, are indispensable. In view of this demand, this study proposes a new high-order computational tool that combines the higher-order accuracy of the emerging inverse differential quadrature method (iDQM) and the simple kinematics of the Timoshenko beam theory for efficient and accurate prediction of the static behaviour of both constant stiffness and variable stiffness curved beam structures. This novel application of iDQM to curved beam analysis is leveraged upon its excellent potential to mitigate differentiation-induced errors by using the so-called indirect approximation strategy. Simple procedures for implementing different orders of iDQM models are presented to analyse curved beam problems, and are independently benchmarked against closed-form Navier's solutions, as well as numerical solutions obtained through the differential quadrature method (DQM) and finite element method (FEM), demonstrating excellent spectral accuracy. Furthermore, the iDQM scheme offers outstanding potential in recovering transverse shear stress, achieving superior accuracy over lower-order FEM in approximating higher-order derivatives. Remarkably, iDQM predictions for variable stiffness curved beams exhibit satisfactory agreement with the Strong Unified Formulation, achieving over 98% computational efficiency. Finally, convergence analysis of iDQM solutions reveal up to three orders of improved accuracy and faster convergence rates compared to the DQM, constituting a new benchmark for curved beam analysis.

Hybrid model predictive control for vehicle trajectory tracking with integrated attitude adjustment

This paper presents a vehicle trajectory tracking method that integrates attitude adjustment. The method utilizes the asymmetric adjustment of the damping of the adjustable damping shock absorber to lift the left and right vehicle body and carry out the attitude adjustment of the vehicle. Additionally, it actively tilts toward the inside of the curve during turning. In order to address the nonlinear constraints of damping asymmetry regulation and the mixed logic relationship between suspension asymmetry damping switching and left and right body lifting during body attitude regulation, a mixed logic dynamic system (MLD) for vehicle attitude adjustment is established. Subsequently, the vehicle trajectory tracking controller is designed using the hybrid model predictive control theory (HMPC), which integrates active steering control and attitude adjustment. The optimal control problem of hybrid systems is transformed into a hybrid integer quadratic programming problem and solved through finite time domain rolling optimization. The effectiveness of the proposed integrated controller is confirmed through Simulink simulation. Compared with pure tracking control and roll tracking control with zero roll angle as the control target, this controller demonstrates its ability to effectively track the reference trajectory and enhance the vehicle’s dynamic performance.

Design, synthesis, and bioevaluation of novel unsaturated cyanoacetamide derivatives: In vitro and in silico exploration

In this study, we synthesized novel α,β-unsaturated 2-cyanoacetamide derivatives (1–5) using microwave-assisted Knoevenagel condensation. Characterization of these compounds was carried out using FTIR and 1H NMR spectroscopy. We then evaluated their in vitro antibacterial activity against both gram-positive and gram-negative pathogenic bacteria. Additionally, we employed in silico methods, including ADMET prediction and density functional theory (DFT) calculations of molecular orbital properties, to investigate these cyanoacetamide derivatives (1–5). Molecular docking was used to assess the binding interactions of these derivatives (1–5) with seven target proteins (5MM8, 4NZZ, 7FEQ, 5NIJ, ITM2, 6SE1, and 5GVZ) and compared them to the reference standard tyrphostin AG99. Notably, derivative 5 exhibited the most favorable binding affinity, with a binding energy of -7.7 kcal mol−1 when interacting with the staphylococcus aureus (PDB:5MM8), while also meeting all drug-likeness criteria. Additionally, molecular dynamics simulations were carried out to evaluate the stability of the interaction between the protein and ligand, utilizing parameters such as Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (Rg), and Principal Component Analysis (PCA). A 50 nanosecond molecular dynamics (MD) simulation was performed to investigate stability further, incorporating RMSD and RMSF analyses on compound 5 within the active binding site of the modeled protein across different temperatures (300, 305, 310, and 320 K). Among these temperatures, compound 5 exhibited an RMSD value ranging from approximately 0.2 to 0.3 nm at 310 K (body temperature) with the 5MM8 target, which differed from the other temperature conditions. The in silico results suggest that compound 5 maintained significant conformational stability throughout the 50 ns simulation period. It is consistent with its low docking energy and in vitro findings concerning α,β-unsaturated cyanoacetamides.Key insights from this study include:•The creation of innovative α,β-unsaturated 2-cyanoacetamide derivatives (1–5) employing cost-effective, licensed, versatile, and efficient software for both in silico and in vitro assessment of antibacterial activity.•Utilization of FTIR and NMR techniques for characterizing compounds 1–5.

Pregnancy is linked to faster epigenetic aging in young women

A central prediction of evolutionary theory is that energy invested into reproduction comes at the expense of somatic maintenance and repair, accelerating biological aging. Supporting this prediction are findings that high fertility among women predicts shorter lifespan and poorer health later in life. However, biological aging is thought to begin before age-related health declines, limiting the applicability of morbidity and mortality for studying the aging process earlier in life. Here, we examine the relationship between reproductive history and biological aging in a sample of young (20 to 22yo) men and women from the Cebu Longitudinal Health and Nutrition Survey, located in the Philippines (n = 1,735). We quantify biological aging using six measures, collectively known as epigenetic clocks, reflecting various facets of cellular aging, health, and mortality risk. In a subset of women, we test whether longitudinal changes in gravidity between young and early-middle adulthood (25 to 31yo) are associated with changes in epigenetic aging during that time. Cross-sectionally, gravidity was associated with all six measures of accelerated epigenetic aging in women (n = 825). Furthermore, longitudinal increases in gravidity were linked to accelerated epigenetic aging in two epigenetic clocks (n = 331). In contrast, the number of pregnancies a man reported fathering was not associated with epigenetic aging among same-aged cohort men (n = 910). These effects were robust to socioecological, environmental, and immunological factors, consistent with the hypothesis that pregnancy accelerates biological aging and that these effects can be detected in young women in a high-fertility context.

Improving the density functional theory prediction accuracy for elastic moduli and thermal expansion of MoAlB, Fe2AlB2, and Mn2AlB2

AbstractThe elastic moduli and thermal expansion of MoAlB, Fe2AlB2, and Mn2AlB2 are investigated by first‐principles calculations, in particular to improve the density functional theory (DFT) prediction accuracy after clarifying the critical role of their lattice constants. By calculating the lattice constants and elastic moduli using the typical exchange‐correlation functionals, the observed positive correlation between the calculation errors of elastic moduli and those of cell volumes suggests a method to significantly improve the calculation accuracy for elastic moduli using the experimental cell volume to correct the input model (volume correction method), resulting in satisfactory results. Furthermore, a two‐step strategy is proposed for the precise high‐temperature elastic moduli with the help of quasi‐harmonic approximation for the most accurate high‐temperature cell volume using an exchange‐correlation functional in the first step, and another one used in the volume correction method in the second step. Similarly, the calculation accuracy of thermal expansion also heavily depends on that of cell volumes and configurations, where their thermal expansion is estimated with great accuracy by using the exchange‐correlation functional that well reproduces the lattice constants.

Investigating change in network structure of eating disorder symptoms after delivery of a smartphone app-based intervention.

Eating disorder (ED) research has embraced a network perspective of psychopathology, which proposes that psychiatric disorders can be conceptualized as a complex system of interacting symptoms. However, existing intervention studies using the network perspective have failed to find that symptom reductions coincide with reductions in strength of associations among these symptoms. We propose that this may reflect failure of alignment between network theory and study design and analysis. We offer hypotheses for specific symptom associations expected to be disrupted by an app-based intervention, and test sensitivity of a range of statistical metrics for identifying this intervention-induced disruption. Data were analyzed from individuals with recurrent binge eating who participated in a randomized controlled trial of a cognitive-behavioral smartphone application. Participants were categorized into one of three groups: waitlist (n = 155), intervention responder (n = 49), and intervention non-responder (n = 77). Several statistical tests (bivariate associations, network-derived strength statistics, network invariance tests) were compared in ability to identify change in network structure. Hypothesized disruption to specific symptom associations was observed through change in bivariate correlations from baseline to post-intervention among the responder group but were not evident from symptom and whole-of-network based network analysis statistics. Effects were masked when the intervention group was assessed together, ignoring heterogeneity in treatment responsiveness. Findings are consistent with our contention that study design and analytic approach influence the ability to test network theory predictions with fidelity. We conclude by offering key recommendations for future network theory-driven interventional studies.

Molecular simulation-guided and physics-informed constitutive modeling of highly stretchable hydrogels with dynamic ionic bonds

Adaptive polymers are being designed with dynamic molecular bonds or chain interactions to respond with external stimuli with unparalleled mechanical properties and multifunctionality. An elegant example is to substantially enhance the stretchability and toughness of hydrogels through the use of ionic bond interactions. To assist the materials design and applications, a predictive theory is in high demand. However, existing multi-scale mechanics models often rely on empirical assumptions and relationships derived from polymer chemistry or physics to describe the evolution of microscale details under external stimuli, which are challenging to be validated experimentally. This study introduces a new methodology to develop constitutive theories for stretchable hydrogels based on insights garnered from molecular dynamics (MD) simulations. The continuum-level viscoelastic theory establishes the thermodynamics framework for stress-strain relationships, while MD simulations inform the evolution mechanisms of microscale bond interactions and network rearrangements, such as the bond distance and network relaxation time. These insights are then properly formulated based on polymer physics principles and fed into the continuum-level model. The resulting constitutive theory closely captures the stress responses at various loading conditions observed in experiments, as well as the microscale system volume and bond distance uncovered in MD simulations. Parametric studies are conducted to investigate the influences of various loading and material parameters on the mechanical properties of the materials, including loading rates, network crosslinking density, maximum strain, and bonding strength. Overall, the study establishes the connection between microscale network structure and mechanical responses of stretchable hydrogels with dynamic ionic bonds. It also offers practical guidance for optimizing material structures and loading conditions to enhance energy absorption and dissipation capabilities. The modeling approach can be extended to the study of other adaptive polymers with different dynamic bonds to create more precise and physically meaningful constitutive models.

Theories of Uncertainty Communication: An Interdisciplinary Literature Review

This systematic literature review presents an interdisciplinary overview of theories tested in experiments on the effects of communicating uncertainty. Using a machine learning-aided pipeline, we selected and manually coded 413 experimental studies. We discuss core assumptions and predictions of the main theories of uncertainty communication. Most normative theorizing (e.g., Bayesianism, Expected Utility Theory) is rooted in Probability Theory, which is only suitable for addressing shallow and medium uncertainty. This explains the underrepresentation of experimental research into deep uncertainty communication. To foster a more comprehensive understanding of uncertainty communication effects, we identify research questions and theories deserving greater attention.

Evaluation of the helicoidal surface theory for the noise prediction of small UAS propellers

Evaluation of the helicoidal surface theory for the noise prediction of small UAS propellers