Quantitative Behavior Research Articles

Exploring the Link between Transformational Leadership and Teacher Innovation

Transformational leadership is a shared belief between academic leaders and their followers. Studies support the significant and progressive influence of transformational leadership behavior on academic followers, especially teachers. The close relationship between transformational leadership in the university and teachers’ organizational commitment paved the way for a proposed theoretical analytical model to understand how university leadership affected teachers’ organizational commitment. The technological demand, the innovation of teachers, and transformative leadership in the educational system are also faced in China. In the university context, Chinese university professors’ innovative work behavior is found to be directly linked to their transformational leadership and trust. For this reason, the study on transformational leadership and innovation was conducted for its influence on the university teachers’ enhancement plan in China. Through quantitative descriptive comparative design, the evaluation, experiences, and behaviors of university teachers across different areas of specialization were compared between transformational leadership and teacher innovation. This study was anchored on the elements of transformational leadership theory specifically the intellectual stimulation element of transformational leadership theory which was focused on innovation. Results revealed that teachers’ assessments regarding perceptions of transformational leadership “expects excellence” was more evident than “respect others.” While teachers’ innovative work behavior was generally average, teaching content was viewed as most favorable, and challenges/issues were the least. Since transformational leadership was positively correlated with the assessment of the innovative work behavior of teachers, recommendations were noted in designing the enhancement plan for university teachers to strengthen the link.

Hirshfeld surface, fukui function, molecular docking, molecular dynamics investigation on human immunodeficiency virus-1 (HIV) organism with 2,6-dibromo-4-chloroaniline

2,6-Dibromo-4-chloroaniline is a planar molecule. Functional analysis based on vibrational modes is made clearer by spectral analysis. Quantum chemical calculation are characterized, its electronegativity in addition to local characteristics such as electrophilicity and Fukui function. ELF study aims to understand the quantitative behavior of electrons within a system. Docking simulation has been used to assess the energy of binding and coupling between the enhanced protein and ligand combination molecules. The research and development of new drugs heavily relies on the chemical properties of toxicity, metabolism, excretion, distribution, and absorption. The Hirshfeld surface was a helpful tool for illustrating characteristics, such as the electrostatic potential of a molecule inside a crystal structure, that could be quickly and easily learned.

Chemical and Structural Transformations of M-Al-CO3 Layered Double Hydroxides (M = Mg, Zn, or Co, M/Al = 2) at Elevated Temperatures: Quantitative Descriptions and Effect of Divalent Cations.

Layered double hydroxides (LDHs) exhibit diverse chemical compositions and are being designed for promising applications such as CO2 adsorbents. Although many researchers have analyzed CO2 gas evolution and structural transformation behavior at elevated temperatures, there are still inconsistencies in results on the effect of different metal ions in LDHs. In this study, on the basis of atomic/molecular-level findings from our previous study on multistep structural/chemical transformation of Mg-Al LDHs, we analyzed the quantitative gas evolution behavior and structural transformations of M-Al-CO3 LDHs with different divalent metal ions (M = Mg, Zn, or Co, M/Al = 2) at elevated temperatures. Our quantitative analysis revealed that all three LDH samples undergo the three-step chemical transformations: release of interlayer water, partial dehydroxylation of the hydroxyl layers, and complete dehydroxylation of layers and decomposition of interlayer CO32-. However, the temperature range for each step differs, as do the structural transformations for each sample: the layered structure collapses in the first step for Zn-Al LDH and Co-Al LDH, and the third step for Mg-Al LDH. Our results provide for quantitative and concrete understanding of the effect of divalent metal ions in LDHs on thermal decomposition.

Interpreting SNP heritability in admixed populations.

SNP heritability is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability ( ), being equal to it if all causal variants are known. Despite the simple intuition behind , its interpretation and equivalence to is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in estimates because of confounding due to linkage disequilibrium (LD) or shared environment. Here we use analytical theory and simulations to demonstrate that estimates can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates LD, which contributes to the genetic variance, and therefore to heritability. Genome-wide restricted maximum likelihood (GREML) does not capture this contribution leading to under- or over-estimates of relative to , depending on the genetic architecture. In contrast, Haseman-Elston (HE) regression exaggerates the LD contribution leading to biases in the opposite direction. For the same reason, GREML and HE estimates of local ancestry heritability are also biased. We describe this bias in and as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We clarify the interpretation of in admixed populations and discuss its implication for genome-wide association studies and polygenic prediction.

Precisely identifying and mitigating shallow reading behaviors through online reading data: exploring quantitative reading behavior methods and precise behavioral intervention

ABSTRACT Online learning has become a part of daily life. However, it has fostered a tendency to shallow reading. Notably, little attention has been paid to precisely identifying shallow reading behaviors using quantified education big data in favor of methods such as conceptual descriptions, opinions, and questionnaires, resulting in an imprecise analysis of its impact on learning and a lack of strategies to mitigate its causative factors. This study addresses the issue by introducing a behavior-identifying model grounded in measurable data to precisely identify shallow reading behaviors. Initially, three key dimensions of shallow reading behaviors were proposed, along with corresponding mathematical indicators. Subsequently, the k-means algorithm and Pearson correlation coefficient were employed to evaluate the relationship between shallow reading and learning performance, underscoring its significance. The analysis showed that shallow reading behaviors are highly associated with poor learning performance, thus hindering critical reading. These findings further validate the predominant influence of reading speed on learning performance. Consequently, a strategy was developed to intentionally decrease reading speed. Following that, an experiment was conducted to assess the effectiveness of this strategy in mitigating shallow reading. The results revealed improved learning performance and a significant reduction in shallow reading behaviors without increasing cognitive load.

Two wrongs do not make a right: the assumption that an inhibitor acts as an inverse activator

Models of biochemical networks are often large intractable sets of differential equations. To make sense of the complexity, relationships between genes/proteins are presented as connected graphs, the edges of which are drawn to indicate activation or inhibition relationships. These diagrams are useful for drawing qualitative conclusions in many cases by the identifying recurring of topological motifs, for example positive and negative feedback loops. These topological features are usually classified under the presumption that activation and inhibition are inverse relationships. For example, inhibition of an inhibitor is often classified the same as activation of an activator within a motif classification, effectively treating them as equivalent. Whilst in many contexts this may not lead to catastrophic errors, drawing conclusions about the behavior of motifs, pathways or networks from these broad classes of topological feature without adequate mathematical descriptions can lead to obverse outcomes. We investigate the extent to which a biochemical pathway/network will behave quantitatively dissimilar to pathway/ networks with similar typologies formed by swapping inhibitors as the inverse of activators. The purpose of the study is to determine under what circumstances rudimentary qualitative assessment of network structure can provide reliable conclusions as to the quantitative behaviour of the network. Whilst there are others, We focus on two main mathematical qualities which may cause a divergence in the behaviour of two pathways/networks which would otherwise be classified as similar; (i) a modelling feature we label ‘bias’ and (ii) the precise positioning of activators and inhibitors within simple pathways/motifs.

Quantitative imaging reveals the role of MpARF proteasomal degradation during gemma germination

The auxin signaling molecule controls a variety of growth and developmental processes in land plants. Auxin regulates gene expression through a nuclear auxin signaling pathway (NAP) consisting of the ubiquitin ligase auxin receptor TIR1/AFB, its Aux/IAA degradation substrate, and DNA-binding ARF transcription factors. Although extensive qualitative understanding of the pathway and its interactions has been obtained, mostly by studying the flowering plant Arabidopsis thaliana, it remains unknown how these translate to quantitative system behavior in vivo, a problem that is confounded by the large NAP gene families in most species. Here, we used the minimal NAP of the liverwort Marchantia polymorpha to quantitatively map NAP protein accumulation and dynamics in vivo through the use of knockin fluorescent fusion proteins. Beyond revealing the dynamic native accumulation profile of the entire NAP protein network, we discovered that the two central ARFs, MpARF1 and MpARF2, are proteasomally degraded. This auxin-independent degradation tunes ARF protein stoichiometry to favor gene activation, thereby reprogramming auxin response during the developmental progression. Thus, quantitative analysis of the entire NAP has enabled us to identify ARF degradation and the stoichiometries of activator and repressor ARFs as a potential mechanism for controlling gemma germination.

The evolution of olfactory sensitivity, preferences, and behavioral responses in Mexican cavefish is influenced by fish personality.

Animals are adapted to their natural habitats and lifestyles. Their brains perceive the external world via their sensory systems, compute information together with that of internal states and autonomous activity, and generate appropriate behavioral outputs. However, how do these processes evolve across evolution? Here, focusing on the sense of olfaction, we have studied the evolution in olfactory sensitivity, preferences, and behavioral responses to six different food-related amino acid odors in the two eco-morphs of the fish Astyanax mexicanus. To this end, we have developed a high-throughput behavioral setup and pipeline of quantitative and qualitative behavior analysis, and we have tested 489 six-week-old Astyanax larvae. The blind, dark-adapted morphs of the species showed markedly distinct basal swimming patterns and behavioral responses to odors, higher olfactory sensitivity, and a strong preference for alanine, as compared to their river-dwelling eyed conspecifics. In addition, we discovered that fish have an individual 'swimming personality', and that this personality influences their capability to respond efficiently to odors and find the source. Importantly, the personality traits that favored significant responses to odors were different in surface fish and cavefish. Moreover, the responses displayed by second-generation cave × surface F2 hybrids suggested that olfactory-driven behavior and olfactory sensitivity is a quantitative genetic trait. Our findings show that olfactory processing has rapidly evolved in cavefish at several levels: detection threshold, odor preference, and foraging behavior strategy. Cavefish is therefore an outstanding model to understand the genetic, molecular, and neurophysiological basis of sensory specialization in response to environmental change.

The evolution of olfactory sensitivity, preferences, and behavioral responses in Mexican cavefish is influenced by fish personality

Animals are adapted to their natural habitats and lifestyles. Their brains perceive the external world via their sensory systems, compute information together with that of internal states and autonomous activity, and generate appropriate behavioral outputs. However, how do these processes evolve across evolution? Here, focusing on the sense of olfaction, we have studied the evolution in olfactory sensitivity, preferences, and behavioral responses to six different food-related amino acid odors in the two eco-morphs of the fish Astyanax mexicanus. To this end, we have developed a high-throughput behavioral setup and pipeline of quantitative and qualitative behavior analysis, and we have tested 489 six-week-old Astyanax larvae. The blind, dark-adapted morphs of the species showed markedly distinct basal swimming patterns and behavioral responses to odors, higher olfactory sensitivity, and a strong preference for alanine, as compared to their river-dwelling eyed conspecifics. In addition, we discovered that fish have an individual ‘swimming personality’, and that this personality influences their capability to respond efficiently to odors and find the source. Importantly, the personality traits that favored significant responses to odors were different in surface fish and cavefish. Moreover, the responses displayed by second-generation cave × surface F2 hybrids suggested that olfactory-driven behavior and olfactory sensitivity is a quantitative genetic trait. Our findings show that olfactory processing has rapidly evolved in cavefish at several levels: detection threshold, odor preference, and foraging behavior strategy. Cavefish is therefore an outstanding model to understand the genetic, molecular, and neurophysiological basis of sensory specialization in response to environmental change.

Mechanisms of whole body, respiratory, acid-base buffering: a first computer-model test of three physicochemical, acid-base theories.

Acid-base disorders are currently analyzed and treated using a bicarbonate-centered approach derived from blood studies prior to the advent of digital computers, which could solve computer models capable of quantifying the complex physicochemical nature governing distribution of water and ions between fluid compartments. An alternative is the Stewart approach, which can predict the pH of a simple mixture of ions and electrically charged proteins; hence, the role of extravascular fluids has been largely ignored. The present study uses a new, comprehensive computer model of four major fluid compartments, based on a recent blood model, which included ion binding to proteins, electroneutrality constraints, and other essential physicochemical laws. The present model predicts quantitative respiratory acid-base buffering behavior in the whole body, as well as determining roles of each compartment and their species, particularly compartmental electrically charged proteins, largely responsible for buffering. The model tested an early theory that H+ was conserved in the body fluids; hence, when changing Pco2 states, intracellular buffering could be predicted by net changes in bicarbonate and protein electrical charge in the remaining fluids. Even though H+ is not conserved in the model, the theory held in simulated respiratory disorders. Model results also agreed with a second part of the theory, that ion movements between cells and interstitial fluid were linked with H+ buffering, but by electroneutrality constraints, not necessarily by some membrane-related mechanisms, and that the strong ion difference (SID), an amalgamation of ionic electrical charges, was approximately conserved when going between equilibrium states caused by Pco2 changes in the body-fluid system.NEW & NOTEWORTHY For the first time, a physicochemically based, whole body, four-compartment, computer model was used to study respiratory whole body acid-base buffering. An improved approach to quantify acid-base buffering, previously used by this author, was able to determine contributions of the various compartmental fluids to whole body buffering. The model was used to test, for the first time, three fundamental theories of whole body acid-base homeostasis, namely, H+-conservation, its linkage to ion transport, and strong ion difference conservation.

Various optimized machine learning techniques to predict agricultural commodity prices

AbstractRecent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

Revisiting the scattering regime map based on transport scattering coefficient for dielectric particulate medium

The independent scattering assumption is frequently employed in radiative transfer, as it is applicable to numerous scientific and practical scenarios involving sparsely distributed scatterers. However, dependent scattering effects must be considered if the particle distribution is not sparse, which requires a relatively more rigorous analysis. A scattering regime map that summarizes the conditions where the independent scattering assumption can be used safely was established in the 1980s. The validity of this regime map and corresponding transition criteria has been questioned due to revealed inaccuracies in certain portions of the map based on the recent experimental studies. This study aims to reevaluate the regime map for dielectric particulate medium through numerical simulations utilizing dense medium radiative transfer theory relying on static structure factor for incorporating the effects of dependent scattering. The transport scattering coefficient, encompassing both the scattering coefficient and the asymmetry parameter, is employed as the metric of evaluation as it accounts for both the quantitative and directional behavior of the scattering. Besides, it also reflects the behavior of reflectance/transmittance closely, which is presented by comparing the regime maps based on transport scattering coefficient and reflectance. While the resulting modified regime map agrees mostly with the established map, it corrects the parts where discrepancies were observed so that the modified map is in agreement with all available data. Moreover, the effects of particle size distribution and material properties are also investigated and it was found that a regime map for lower relative refractive index miss-match between particle and medium produces a demarcation line that is more conservative.

Multi-body dynamical modeling and prediction of flexible origami/kirigami structures by affine transformation

A generalized multi-body dynamical modeling method of Origami/Kirigami structures considering the elastic–flexible coupling of surfaces and nonlinear creases is proposed. For different structures with complex spatial configuration and constraints, an Affine Transformation (AT) technique based on Absolute Nodal Coordinate Formulation (ANCF) is proposed to make the modeling more convenient and efficient. The surfaces are discretized by triangle reduced elements, and the constitutive relations of creases are described by nonlinear state-dependent torque constraints. The proof of the topological invariance of configurations is also given. The simulation of the folding capacity and dynamic behaviors on two Origami structures, Origami solar array mechanism, and enclosed Yoshimura Origami, are given, which verifies the effectiveness of the modeling method. Additionally, the quantitative dynamic behaviors contributes to the design principle of a Kirigami structure for multi-stability. Comparisons between theoretical results from modeling and experiments demonstrate the effectiveness of the modeling method and crease design. The AT-ANCF lays a theoretical foundation for dynamic prediction and design for large deformation Origami/ Kirigami structures, which has significant potential applications in dynamic properties design in the fields of aerospace, robotic etc.

Ultra-sensitive fluorescent immunoassay based on bright AIE nanoparticle for biomarkers detection and quantification

Fluorescence immunoassay (FIA) is one of the most immunoassay diagnosis methods in clinic for biomarker quantification in recent years. The development of reliable FIA strategy with high-sensitivity, large-rang and outstanding efficiency for biomarkers rapid diagnosis, especially in complicated samples, is still a great challenge. Herein, an ultra-sensitive FIA combined with magnetic separation was established to achieve efficient point-of-care diagnosis in quantitative behavior based on bright aggregation-induced emission nanoparticles (AIE NPs). By utilizing swelling method, AIE NPs with controllable size, relatively high luminescence efficiency and large Stokes shift were prepared, moreover, the obtained NPs also exhibit narrow size distribution and well-defined spherical morphology. Stability studies prove that AIE NPs not only possess quite high photostability, but also present good resistance towards harsh acid-alkaline steadily. By using AIE NPs as the reporter, a reliable and efficient FIA sensor was successfully constructed. The precise detection of C-reaction protein (CRP) antigen was acquired with the linear range of 0.5 ng·mL−1 ∼ 1000 ng·mL−1 and the limit of detection (LOD) of 0.35 ng·mL−1, which is comparative with the conventional chemiluminescence immunoassays. Moreover, the whole detection operation could be accomplished within 10 min, which is relatively rapid and desirable for point-of-care diagnosis. In addition, this strategy also presented excellent specificity towards CRP antigens among various inflammatory antigens. These results confirm that AIE NPs-based FIA strategy provides an ideal and reliable approach for precise and quantitive biosensing of in vitro diagnostics.

A synoptic literature review of animal models for investigating the biomechanics of knee osteoarthritis.

Osteoarthritis (OA) is a common chronic disease largely driven by mechanical factors, causing significant health and economic burdens worldwide. Early detection is challenging, making animal models a key tool for studying its onset and mechanically-relevant pathogenesis. This review evaluate current use of preclinical in vivo models and progressive measurement techniques for analysing biomechanical factors in the specific context of the clinical OA phenotypes. It categorizes preclinical in vivo models into naturally occurring, genetically modified, chemically-induced, surgically-induced, and non-invasive types, linking each to clinical phenotypes like chronic pain, inflammation, and mechanical overload. Specifically, we discriminate between mechanical and biological factors, give a new explanation of the mechanical overload OA phenotype and propose that it should be further subcategorized into two subtypes, post-traumatic and chronic overloading OA. This review then summarises the representative models and tools in biomechanical studies of OA. We highlight and identify how to develop a mechanical model without inflammatory sequelae and how to induce OA without significant experimental trauma and so enable the detection of changes indicative of early-stage OA in the absence of such sequelae. We propose that the most popular post-traumatic OA biomechanical models are not representative of all types of mechanical overloading OA and, in particular, identify a deficiency of current rodent models to represent the chronic overloading OA phenotype without requiring intraarticular surgery. We therefore pinpoint well standardized and reproducible chronic overloading models that are being developed to enable the study of early OA changes in non-trauma related, slowly-progressive OA. In particular, non-invasive models (repetitive small compression loading model and exercise model) and an extra-articular surgical model (osteotomy) are attractive ways to present the chronic natural course of primary OA. Use of these models and quantitative mechanical behaviour tools such as gait analysis and non-invasive imaging techniques show great promise in understanding the mechanical aspects of the onset and progression of OA in the context of chronic knee joint overloading. Further development of these models and the advanced characterisation tools will enable better replication of the human chronic overloading OA phenotype and thus facilitate mechanically-driven clinical questions to be answered.

Distributed Saturation-Tolerant Fuzzy Control for Constrained Stochastic Multi-agent Systems with Resilient Quantitative Behaviors

Distributed Saturation-Tolerant Fuzzy Control for Constrained Stochastic Multi-agent Systems with Resilient Quantitative Behaviors

Percolation on irregular high-dimensional product graphs

AbstractWe consider bond percolation on high-dimensional product graphs $G=\square _{i=1}^tG^{(i)}$ , where $\square$ denotes the Cartesian product. We call the $G^{(i)}$ the base graphs and the product graph $G$ the host graph. Very recently, Lichev (J. Graph Theory, 99(4):651–670, 2022) showed that, under a mild requirement on the isoperimetric properties of the base graphs, the component structure of the percolated graph $G_p$ undergoes a phase transition when $p$ is around $\frac{1}{d}$ , where $d$ is the average degree of the host graph.In the supercritical regime, we strengthen Lichev’s result by showing that the giant component is in fact unique, with all other components of order $o(|G|)$ , and determining the sharp asymptotic order of the giant. Furthermore, we answer two questions posed by Lichev (J. Graph Theory, 99(4):651–670, 2022): firstly, we provide a construction showing that the requirement of bounded degree is necessary for the likely emergence of a linear order component; secondly, we show that the isoperimetric requirement on the base graphs can be, in fact, super-exponentially small in the dimension. Finally, in the subcritical regime, we give an example showing that in the case of irregular high-dimensional product graphs, there can be a polynomially large component with high probability, very much unlike the quantitative behaviour seen in the Erdős-Rényi random graph and in the percolated hypercube, and in fact in any regular high-dimensional product graphs, as shown by the authors in a companion paper (Percolation on high-dimensional product graphs. arXiv:2209.03722, 2022).

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products.

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.

Quantitative behavior of unipotent flows and an effective avoidance principle

Quantitative behavior of unipotent flows and an effective avoidance principle

Exclusion processes on a roundabout traffic model with constrained resources.

Motivated by the vehicular traffic phenomenon at roundabouts, we examine how the limited availability of resources affects the movement of two distinct types of particles on bidirectional lanes connected by two bridges, with each bridge specifically designated for the transportation of one species. To provide a theoretical ground for our findings, we employ a mean-field framework and successfully validate them through dynamic Monte Carlo simulations. Based on the theoretical analysis, we analytically derive various stationary properties, such as the particle densities, phase boundaries, and particle currents, for all the possible symmetric as well as asymmetric phases. The qualitative as well as quantitative behavior of the system is significantly affected by the constraint on the number of resources. The complexity of the phase diagram shows a nonmonotonic behavior with an increasing number of particles in the system. Analytical arguments enable the identification of several critical values for the total number of particles, leading to a qualitative change in the phase diagrams. The interplay of the finite resources and the bidirectional transport yields unanticipated and unusual features such as back-and-forth transition, the presence of two congested phases where particle movement is halted, as well as shock phases induced by boundaries and the bulk of the system. Also, it is found that spontaneous symmetry-breaking phenomena are induced even for very few particles in the system. Moreover, we thoroughly examine the location of shocks by varying the parameters controlling the system's boundaries, providing insights into possible phase transitions.