Theory Modeling Research Articles

Optimizing Betting Strategies with the Kelly Criterion for Economic

This comprehensive study explores the Kelly Criterion, a strategy devised for betting but with significant implications in economic decision-making and wealth management. The criterion, initially developed to maximize long-term growth in independent repeated bets with positive expected net returns, has evolved to encompass a broad range of applications. Through the use of mathematical modeling and probability theory, this research demonstrates how the Kelly Criterion can be effectively applied to enhance wealth accumulation. The societal benefits of this approach are manifold, extending to the reduction of problem gambling rates and promoting responsible gaming. The research also delves into the broader implications of the Kelly Criterion for society, suggesting that its judicious application can significantly shape the future of gambling as a recreational activity. By ensuring informed decision-making, the criterion benefits both individuals and society as a whole, offering a structured approach to managing financial risks and rewards. The expanded scope of this study highlights the versatility of the Kelly Criterion, showing its potential as a pivotal tool in modern economic scenarios, characterized by uncertainty and fluctuating financial markets.

Innovative modeling of monolayer puckered arsenene: Bridging quantum mechanics and finite element analysis

Current study presents a novel hybrid approach combining finite element modeling and density functional theory calculations to investigate the mechanical properties of monolayer puckered arsenene. The multiscale analysis in this study leverages finite element analysis as a distinctive approach, complementing the nano‐scale capabilities of density functional theory and molecular dynamics by overcoming limitations faced by these two methods in representing complex scenarios. Furthermore, finite element analysis demonstrates computational efficiency for larger structures, making it suitable for systems where atomistic simulations may be impractical. This hybrid methodology offers a unique framework for accurately predicting key properties, including elastic modulus and buckling force, by synergistically integrating the strengths of both computational techniques. In addition to demonstrating the effectiveness of our approach in accurately capturing material behavior, our findings shed light on fundamental aspects of nanoscale mechanics, with implications for various applications in nanotechnology, materials science, and structural engineering. By providing a deeper understanding of the mechanical response of 2D materials, our research contributes to advancing the field of nanoscale materials engineering and informs the design of innovative nanostructures with tailored mechanical properties.

Quantum fluctuations lead to glassy electron dynamics in the good metal regime of electron doped KTaO3

One of the central challenges in condensed matter physics is to comprehend systems that have strong disorder and strong interactions. In the strongly localized regime, their subtle competition leads to glassy electron dynamics which ceases to exist well before the insulator-to-metal transition is approached as a function of doping. Here, we report on the discovery of glassy electron dynamics deep inside the good metal regime of an electron-doped quantum paraelectric system: KTaO3. We reveal that upon excitation of electrons from defect states to the conduction band, the excess injected carriers in the conduction band relax in a stretched exponential manner with a large relaxation time, and the system evinces simple aging phenomena—a telltale sign of glassy dynamics. Most significantly, we observe a critical slowing down of carrier dynamics below 35 K, concomitant with the onset of quantum paraelectricity in the undoped KTaO3. Our combined investigation using second harmonic generation technique, density functional theory and phenomenological modeling demonstrates quantum fluctuation-stabilized soft polar modes as the impetus for the glassy behavior. This study addresses one of the most fundamental questions regarding the potential promotion of glassiness by quantum fluctuations and opens a route for exploring glassy dynamics of electrons in a well-delocalized regime.

Developing a risk framework for assembly construction based on stakeholder theory and structural equation modelling.

Occupational injuries in the construction industry have plagued many countries, and many cases have shown that accidents often occur because of a combination of project participants. Assembled construction (AC) projects have received extensive attention from Chinese scholars as a future trend, but few studies have explored the interrelationships and potential risks of various stakeholders in depth. This study fills this research gap by proposing a multi-stakeholder AC risk framework. The study surveyed 396 stakeholders, then analyzed the collected data and created a risk framework based on Structural Equation Modelling (SEM) and the CRITIC weighting method. The results revealed that factors like "regular supervision is a formality," "blindly approving the wrong safety measures," and "failure to organize effective safety education and training." are vital risks in AC of China. Finally, the study validates the risk factors and the framework with 180 real-life cases, which shows that the proposed framework is theoretically grounded and realistic. The study also suggests multi-level strategies such as introducing AI-based automated risk monitoring, improving the adaptability of normative provisions to technological advances, and advancing the culture of project communities of interest to ensure AC's safe practices.

Thin Film Resistance Gating by Redox Charge Exchange: Evidence for a Quantum Transition State.

Field effect transistors (FETs) and related devices have enabled tremendous advances in electronics, as well as studies of fundamental phenomena. FETs are classically actuated as fields charge/discharge materials, thereby modifying their resistance. Here, we develop charge exchange transistors (CETs) that comprise thin films whose resistance is modified by quantum charge exchange processes, e.g., redox and bonding. We first use CETs to probe the metallocene-thin film interaction during cyclic voltammetry. Remarkably, CETs reveal transient resistance peaks associated with charge transfer during both oxidation and reduction. Our data combined with kinetics and density functional theory modeling are consistent with a multistep redox pathway, including the formation/destruction of a quantum transition state that overlaps molecule + thin film band states. As a further proof-of-principle demonstration, we also use CETs to monitor n-alkanethiol self-assembly on thin Au films in real-time. CETs exhibit monotonic resistance increase consistent with previously reported fast-then-slow kinetics attributed to thiol-thin film bond formation (charge localization) and etching and/or molecule reorganization.

Effects of phantom microstructure on their optical properties.

Developing stable, robust, and affordable tissue-mimicking phantoms is a prerequisite for any new clinical application within biomedical optics. To this end, a thorough understanding of the phantom structure and optical properties is paramount. We characterized the structural and optical properties of PlatSil SiliGlass phantoms using experimental and numerical approaches to examine the effects of phantom microstructure on their overall optical properties. We employed scanning electron microscope (SEM), hyperspectral imaging (HSI), and spectroscopy in combination with Mie theory modeling and inverse Monte Carlo to investigate the relationship between phantom constituent and overall phantom optical properties. SEM revealed that microspheres had a broad range of sizes with average and were also aggregated, which may affect overall optical properties and warrants careful preparation to minimize these effects. Spectroscopy was used to measure pigment and SiliGlass absorption coefficient in the VIS-NIR range. Size distribution was used to calculate scattering coefficients and observe the impact of phantom microstructure on scattering properties. The results were surmised in an inverse problem solution that enabled absolute determination of component volume fractions that agree with values obtained during preparation and explained experimentally observed spectral features. HSI microscopy revealed pronounced single-scattering effects that agree with single-scattering events. We show that knowledge of phantom microstructure enables absolute measurements of phantom constitution without prior calibration. Further, we show a connection across different length scales where knowledge of precise phantom component constitution can help understand macroscopically observable optical properties.

A Novel Explicit Canonical Dynamic Modeling Method for Multi-Rigid-Body Mechanisms Considering Joint Friction

Friction is an inevitable phenomenon in mechanical systems that affects the dynamic characteristics of systems. To reduce the modeling complexity of complex multi-rigid-body mechanisms, a novel explicit canonical dynamic modeling method considering joint friction is proposed. Based on the explicit dynamic modeling theory that we have proposed, the solution of the constraint force required by the joint friction modeling of multi-rigid-body mechanisms is derived and improved, which greatly simplifies the solution of the constraint force. According to the obtained explicit expression of the constraint force equations, two joint friction models of the Coulomb–viscous effect and Stribeck effect are derived in analytical form. Moreover, the Stribeck effect of the joint is experimentally analyzed. A five-axis tree-chain mechanism and a three-loop closed-chain mechanism are chosen to demonstrate the method and compared with ADAMS software. Moreover, the proposed model is analyzed and compared with other methods.

Conformational Dynamics in Corynebacterium glutamicum Diaminopimelate Epimerase: Insights from Ligand Parameterization, Atomistic Simulation, and Markov State Modeling.

The microbial enzyme diaminopimelate epimerase (DapF), a vital enzyme in the lysine biosynthetic pathway, catalyzes the conversion of L, L-diaminopimelate (L, L-DAP) to D, L-diaminopimelate (D, L-DAP) using a catalytic cysteine dyad with one cysteine in thiol state and another in thiolate. Under oxidizing conditions, the catalytic cysteines of apo DapF form a disulfide bond that alters the structure and function of DapF. Given its potential as a target for antimicrobial resistance treatments, understanding DapF's functional dynamics is imperative. In the present work, we employ microsecond-scale all-atom molecular dynamics simulations of product-bound DapF and apo-DapF under oxidized and reduced conditions. We employ a polarized charge model for the ligand and the active site residues, which was necessary to preserve the electrostatic environment in the active site and retain the ligand in the active site. The product-bound DapF and apo-DapF in oxidized and reduced conditions exhibit a closed, semi-open, and open conformation, respectively, as identified using the internal coordinates of the dimeric enzyme and the principal component analysis. The conformational switch is guided by the dynamic catalytic (DC) loop, loop II, and loop III movements in the active site. The time scale of the close-to-open conformational transition is estimated to be 0.8 μs through Markov state modeling (MSM) and transition path theory (TPT). The present study explains the role of various active site residues and loops in ligand binding and protein dynamics in the DapF enzyme under different redox conditions. Such information will be helpful in future inhibitor design studies targeting the DapF enzyme.

A causal trait model for explaining foliar water uptake capacity

AbstractQuestionsPlants largely vary in their capacity for foliar water uptake (FWU), that is, the capacity to increase leaf water content by directly absorbing water from leaf‐wetting events. Climate change will reduce leaf wetting and increase drought events. Therefore, we need a better understanding of the underlying traits and mechanisms that facilitate FWU.LocationSeasonally dry tropical montane grasslands in Brazil (Campos de Altitude).MethodsWe measured FWU and leaf traits related to wettability, surface conductance, water potential and water storage on up to 55 plant species. By using Direct Acyclic Graph theory and Bayesian modelling, we tested how those leaf traits affect FWU.ResultsWe found that stomatal conductance largely explained interspecific variation in FWU, which was also favoured in species with hydrophilic leaves, high cuticular conductance, more negative leaf water potentials, low dry‐matter content, isohydric behaviour, and more elastic cell walls.ConclusionsDue to the existence of trade‐offs, not all species exhibit an optimal combination of traits that favours FWU. Instead, co‐occurring species have achieved a similar capacity for FWU through distinct trait combinations. Consequently, species engaged in FWU may exhibit differential vulnerabilities to climate change as they can cope with drought using other strategies beside FWU.

Compound-choking theory and artificial neural networks-based hybrid modeling for supersonic ejectors

As the core component of ejector refrigeration systems, the research of ejector mechanisms has always been a hot topic. Based on the compound-choking theory, this paper studies the relationship between the entrainment ratio and critical back pressure and designs a neural network-based online error compensation mechanism for supersonic ejectors. Firstly, a thermodynamic model of the ejector is developed by combining the ideal gas model, the compound-choking theory, and the constant-pressure mixing theory. Then, the thermodynamic relationship between the entrainment ratio and critical back pressure is derived by introducing the idea of a hypothetical mixing cross section. Compared with the existing results, our method is able to improve critical back pressure prediction accuracy by virtue of the actual entrainment ratio. Therefore, a new adaptive error compensation algorithm is proposed for supersonic ejectors based on neural networks. Thus, the proposed scheme can be regarded as a hybrid modeling method for supersonic ejectors, which combines thermodynamic laws and data-driven artificial intelligence algorithms. Experimental data from ejector refrigeration systems with R141b, R245fa, water vapor and R134a are used in this paper to verify the validity of the model. The simulation results show that our method can effectively predict the critical back pressure, and all the prediction errors are less than 10%.

Human-inspired similarity control system: Enhancing line-following robot perception

Human-Inspired Control (HIC) holds promise for endowing machines with human-like cognition, decision-making, and adaptability. In this study, we employ a fusion of cognitive modeling, machine learning, and control theory as the foundational architecture and empirically validate its suitability for robot control within the realm of HIC. Fuzzy logic stands out as a viable approach for HIC control, wherein control rules can be devised drawing from human intuitive inspiration. Specifically, this study explores similarity inference control systems in robotics, with the objective of enhancing line-following control as an alternative to fuzzy systems. The experimental optimization results provide insights into the advantages and limitations of the similarity inference control system. Despite achieving performance comparable to that of traditional two-stage fuzzy control systems, careful consideration of noise sensitivity is paramount. While the similarity inference approach streamlines implementation and obviates the necessity for expert-designed fuzzy rules, its susceptibility to noise can compromise performance, particularly in noisy environments. These considerations are pivotal for the development of control systems aimed at mitigating noise sensitivity, enhancing task-specific performance, and ensuring the adaptability and robustness of line-following robots. To tackle this challenge, we both discuss and experimentally evaluate potential solutions and their applicability in this paper.

Quinoline derivatives as corrosion inhibitors for mild steel in acid medium: Deeper insights from experimental, ab initio density functional theory modeling, and in silico ecotoxicity investigations

Two (E)-2-(((2-((7-chloroquinolin-4-yl)amino)ethyl)imino)methyl)-4-R-phenol Schiff bases derivatives CMN (R = NO2) and CMP (R = H) were successfully prepared. Then, CMN and CMP were evaluated as corrosion inhibitors for 1020 mild steel in 1.0molL–1 HCl using different methods. Gravimetric experiments have shown that CMN and CMP reach efficiencies of 80 and 91% at 1.45mmolL–1. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) measurements have indicated the formation of a protective adsorbed film on the mild steel surface. Electrochemical Impedance Spectroscopy (EIS), Linear Polarization Resistance (LPR), Potentiodynamic Polarization (PP), and Electrochemical Frequency Modulation (EFM) have also shed some light on electrochemical behavior of compounds. These data have depicted better polarization resistance and lower corrosion current density in the presence of both organic molecules, also proving that the process is controlled by charge transfer and that CMN and CMP are mixed-type corrosion inhibitors. ab initio Density Functional Theory (DFT) investigated how two quinoline-based molecules (CMN and CMP) prevent corrosion on iron surfaces. Simulations revealed CMP bonds more strongly to iron than CMN, leading to better protection. Simulations also showed that these inhibitor molecules form a protective layer on the iron, slowing down the movement of corrosive substances. Environment risk assessment through QSAR-based models have indicated that the corrosion inhibitors do not have the potential to bioaccumulate in fish. Still, the acute and chronic toxicity values at the different trophic levels investigated call for attention for environmentally friendly structural optimization.

Enhancing Content Validity Assessment With Item Response Theory Modeling.

Ensuring the validity of assessments requires a thorough examination of the test content. Subject matter experts (SMEs) are commonly employed to evaluate the relevance, representativeness, and appropriateness of the items. This article proposes incorporating item response theory (IRT) into model assessments conducted by SMEs. Using IRT allows for the estimation of discrimination and threshold parameters for each SME, providing evidence of their performance in differentiating relevant from irrelevant items, thus facilitating the detection of suboptimal SME performance while improving item relevance scores. Use of IRT was compared to traditional validity indices (content validity index and Aiken's V) in the evaluation of items. The aim was to assess the SMEs' accuracy in identifying whether items were designed to measure conscientiousness or not, and predicting their factor loadings. The IRT-based scores effectively identified conscientiousness items (R2 = 0.57) and accurately predicted their factor loadings (R2 = 0.45). These scores demonstrated incremental validity, explaining 11% more variance than Aiken's V and up to 17% more than the content validity index. Modeling SME assessments with IRT improves item alignment and provides better predictions of factor loadings, enabling improvement of the content validity of measurement instruments.

Gender and race measurement invariance of the Strengths and Difficulties Questionnaire in a U.S. base sample

IntroductionThe Strengths and Difficulties Questionnaire (SDQ) is one the most widely used behavior screening tools for public schools due to its strong psychometric properties, low cost, and brief (25-question) format. However, this screening tool has several limitations including being primarily developed for the purposes of identifying clinical diagnostic conditions and primarily in a European population. To date, there has been minimal comparative research on measurement invariance in relationship to important U.S. socio-demographic metrics such as race and gender.MethodThis study utilized both structural equation modeling (i.e., confirmatory factor analysis) and item response theory (IRT) methods to investigate the measurement invariance of the SDQ across gender (male, female) and race (Black, White). CFA analyses were first conducted for each of the SDQ subscales to identify potential misfit in loadings, thresholds, and residuals. IRT-graded response models were then conducted to identify and quantify the between-group differences at the item and factor levels in terms of Cohen's d styled metrics (d > 0.2 = small, d > 0.5 = medium, d > 8 = large).ResultsThere were 2,821 high school participants (52% Male, 48% Female; 88% Black, 12% White) included in these analyses. CFA analyses suggested that the item-factor relationship for most subscales were invariant, but the Conduct Problems and Hyperactivity subscales were non-invariant for strict measurement invariance. IRT analyses identified several invariant items ranging from small to large. Despite moderate to large effects for item scores on several scales, the test-level effects on scale scores were negligible.DiscussionThese analyses suggest that the SDQ subscale scores display reasonable comparable item-factor relationships across groups. Several subscale item scores displayed substantive item-level misfit, but the test level effects were minimal. Implications for the field are discussed.

Dynamics of an AI-Driven Mathematical Model of Some Learning Theories

Objectives. This paper delves into the intersection of mathematical modelling and learning theory, exploring how mathematical frameworks have been instrumental in advancing our understanding of educational processes and pedagogical practices with the aid of Artificial Intelligence (AI). Material and methods. Advancing learning theories through mathematical modelling offers several significant benefits and opportunities. This is because mathematical models provide a precise and systematic framework for representing complex educational phenomena, allowing researchers to formalize theoretical concepts, relationships, and mechanisms in a clear and rigorous manner. Results and Conclusions. This paper focused on such learning theories as cooperative learning theory, Maslow motivation theory, and social learning theory. By employing mathematical models, researchers have been able to dissect complex educational phenomena, elucidate underlying mechanisms, and predict outcomes with a level of precision that traditional qualitative approaches often struggle to achieve.

Health Motivation in the Influencer Era: Analyzing Entertainment, Personal, and Social Media Role Models

ABSTRACT This research explored how personal and entertainment role models motivate an individual’s health goals, and how following a role model on social media affects this process. A survey of 404 adults revealed that identifying a personal role model, such as a family member, friend, or healthcare professional, had a stronger influence on health motivation in comparison to identifying an entertainment role model. Additionally, our findings indicated that following any role model on social media, whether a personal acquaintance or a celebrity, enhanced motivation. Role model attributes that mediated these relationships included perceived similarity, and in some instances, positive and negative norm deviance. This study highlights the importance of leveraging role model relationships in health communication campaigns and developing authentic influencer-driven health initiatives that feature relatable entertainment figures. Additionally, this research supports the need for further examination of how a role model’s perceived positive or negative norm deviance influences motivational capability. We discuss implications for the motivational theory of role modeling, health promotion, and entertainment education.

Numerical Simulation of Motion Test of Bionic Amphibious Quadruped Robot

Bionic amphibious quadruped robots are a huge leap in robotics, with the ability to navigate various terrains with agility and efficiency. In this study, the researchers utilized numerical simulations of motion tests to assess the locomotion skills of these extraordinary robotic systems. They learned a lot about their functioning and problems by rigorously analyzing performance parameters across various terrains and conditions, such as terrain adaptability, traversal speed, energy efficiency, stability, and manoeuvrability. The results they obtained highlighted the importance of adaptive control algorithms and mechanical designs in maximizing traversal speed and energy economy, especially while negotiating difficult terrains. In addition, the study demonstrated the interdisciplinary character of research in bionic amphibious quadruped robots, drawing on insights from biomechanics, control theory, robotics, and computational modelling. By combining previous research and utilizing computational simulation approaches, they opened the road for the creation of versatile and durable robotic systems capable of navigating difficult terrains and surroundings with precision and efficiency. Future research efforts could focus on improving design and control, investigating novel propulsion mechanisms, adaptive locomotion tactics, and biomimetic control algorithms to improve performance and adaptability. Addressing the challenges identified in the research and seizing opportunities for innovation will drive the development of robotic systems that push the limits of locomotion capabilities and open up new possibilities for applications in exploration, search and rescue, environmental monitoring, and beyond.

Impact of Peripheral Hydrogen Bond on Electronic Properties of the Primary Acceptor Chlorophyll in the Reaction Center of Photosystem I.

Photosystem I (PS I) is a photosynthetic pigment-protein complex that absorbs light and uses the absorbed energy to initiate electron transfer. Electron transfer has been shown to occur concurrently along two (A- and B-) branches of reaction center (RC) cofactors. The electron transfer chain originates from a special pair of chlorophyll a molecules (P700), followed by two chlorophylls and one phylloquinone in each branch (denoted as A-1, A0, A1, respectively), converging in a single iron-sulfur complex Fx. While there is a consensus that the ultimate electron donor-acceptor pair is P700+A0-, the involvement of A-1 in electron transfer, as well as the mechanism of the very first step in the charge separation sequence, has been under debate. To resolve this question, multiple groups have targeted electron transfer cofactors by site-directed mutations. In this work, the peripheral hydrogen bonds to keto groups of A0 chlorophylls have been disrupted by mutagenesis. Four mutants were generated: PsaA-Y692F; PsaB-Y667F; PsaB-Y667A; and a double mutant PsaA-Y692F/PsaB-Y667F. Contrary to expectations, but in agreement with density functional theory modeling, the removal of the hydrogen bond by Tyr → Phe substitution was found to have a negligible effect on redox potentials and optical absorption spectra of respective chlorophylls. In contrast, Tyr → Ala substitution was shown to have a fatal effect on the PS I function. It is thus inferred that PsaA-Y692 and PsaB-Y667 residues have primarily structural significance, and their ability to coordinate respective chlorophylls in electron transfer via hydrogen bond plays a minor role.

Analysis and modeling of two-dimensional piezoelectric semiconductor shell theory

In recent years, there has been a surge in research on piezoelectric semiconductors (PSs). However, studies specifically focused on two-dimensional PS structures remain scarce. In this paper, based on the principle of the virtual work method, the condition of charge continuity and the first-order thickness shear theory, we have established a two-dimensional model to investigate the electromechanical properties of two-dimensional PS shells. The basic field quantities, including the displacement, the electric potential and the carrier concentration perturbations were Fourier expanded along the width direction, and then the governing equations were successfully derived. The differential quadrature method was used to obtain numerical results for the PS shells with different loading modes and structural configurations. It was found that the inhomogeneous shear strain S13 is the key to the potential change in the PS shell structure. The emergence of the potential barriers and wells in the shell structure of PS is well explained. In addition, the position of the potential barriers can be modulated by varying the magnitude of the load, which provides a reference for the design of thin-film devices.

Fenamates and ibuprofen as foundational components in the synthesis of innovative, targeted COX-2 anti-inflammatory drugs, undergoing thorough biopharmacological assessments and in-silico computational studies

Cyclooxygenase-2 plays a vital role in inflammation by catalyzing arachidonic acid conversion toward prostaglandins, making it a prime therapeutic objective. Selective COX-2 inhibitors represent significant progress in anti-inflammatory therapy, offering improved efficacy and fewer side effects. This study describes the synthesis of novel anti-inflammatory compounds from established pharmaceutically marketed agents like fenamates III-V and ibuprofen VI. Through rigorous in vitro testing, compounds 7b-c, and 12a-b demonstrated substantial in vitro selective inhibition, with IC50 values of 0.07 to 0.09 μM, indicating potent pharmacological activity. In vivo assessment, particularly focusing on compound 7c, revealed significant anti-inflammatory effects. Markedly, it demonstrated the highest inhibition of paw thickness (58.62 %) at the 5-hr mark compared to the carrageenan group, indicating its potency in mitigating inflammation. Furthermore, it exhibited a rapid onset of action, with a 54.88 % inhibition observed at the 1-hr mark. Subsequent comprehensive evaluations encompassing analgesic efficacy, histological characteristics, and toxicological properties indicated that compound 7c did not induce gastric ulcers, in contrast to the ulcerogenic tendency associated with mefenamic acid. Moreover, compound 7c underwent additional investigations through in silico methodologies such as molecular modelling, field alignment, and density functional theory. These analyses underscored the therapeutic potential and safety profile of this novel compound, warranting further exploration and development in the realm of pharmaceutical research.