Set Of Variables Research Articles

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study.

Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake. This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction. We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction. Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake. The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

Principal Component Analysis of Yield and Its Attributing Traits in Recombinant Inbred Lines of Rice Under Submerged Condition (Oryza sativa L.)

Rice, Oryza sativa L., is the world's most important staple crop, feeding more than half of the world's population. The phenotype of plant is the result of interaction of many factors and final yield is the sum of total effect of several component factors. Therefore evaluation of genetic variability forms the basis for any crop improvement programme, the success of which depends on sufficient genetic variability among genotypes so as to permit effective selection. Hence Evaluation of Principle Component Analysis (PCA)of recombinant inbred lines (RILs) was done at phenotypic level under submerged conditions to reduce a large series of data into smaller number of components by looking for groups that have very strong inter-correlation in a set of variables and each component explained per cent variation to the total variability. The RIL population was derived from an inter-specific cross between BPT5204 and HPR14 parents. A study was conducted using 1256 Recombinant Inbreed Lines submerged condition in the two seasons at College of Agriculture V.C. Farm, Mandya with nine agro-morphological traits and a principle component analysis was carried out. Out of nine principle components, four exhibited Eigenvalue more than one governing 77.74% variance and 69.86% variance in the summer and kharif seasons respectively. The highest positive Eigenvalue was observed for total number of tillers, productive tillers, non-productive tillers and fallowed by single plant yield in PC1 in the summer and kharif season respectively. The highest positive Eigenvalue was observed for five panicle weight, single panicle length, single plant yield and plant height in PC2 of summer and kharif season respectively. Indicating their pronounced effect on the overall variation in the Recombinant Inbreed Lines of Rice.

The Impact of Economic Integration on Income Inequality in the EU: A Panel Data Analysis of the EU Members from 2002-2020

This paper investigates the relation between economic integration and income inequality for the 27 current EU members from 2002 to 2020. The Gini coefficient is the dependent variable in a panel data regression analysis, and is expressed as a function of indicators related to economic integration (total trade, intra-EU trade, foreign direct investment (FDI) inflow and outflow) and a set of control variables. Although much research points to a significant relationship between inequality and integration, there is no consensus on the sign and magnitude of the effect. In this paper, four random effects panel models are estimated with robust standard errors to uncover this relationship. The results show that in the context of the EU, intra-EU trade is associated with decreased income inequality, while overall trade and FDI in- or outflow seem to have no direct effect on the income gap. In addition, the level of economic development seems to moderate the effect of intra-EU trade: for countries with a below-average gross domestic product (GDP) per capita, the reducing effect of integration on Gini is weaker than for richer countries. Additionally, market capitalisation, the presence of natural resources and government spending on social benefits are associated with reduced inequality, while unemployment and population size seem to drive income disparities. These results are consistent with a major part of the existing literature and lead to interesting conclusions for policy makers. The originality of this work differentiating it from prior research is twofold: (1) the region of examination is the EU, which is not often the subject of similar analyses, and (2) an interaction effect is examined that differs from the conventional measures for less developed economies.

The role of longevity-related genetic variant interactions as predictors of survival after 85 years of age

Genome-wide association studies and candidate gene studies have identified several genetic variants that might play a role in achieving longevity. This study investigates interactions between pairs of those single nucleotide polymorphisms (SNPs) and their effect on survival above the age of 85 in a sample of 327 Croatian individuals. Although none of the SNPs individually showed a significant effect on survival in this sample, 14 of the 359 interactions tested (between SNPs not in LD) reached the level of nominal significance (p<0.05), showing a potential effect on late-life survival. Notably, SH2B3 rs3184504 interacted with different SNPs near TERC, TP53 rs1042522 with different SNPs located near the CDKN2B gene, and CDKN2B rs1333049 with different SNPs in FOXO3, as well as with LINC02227 rs2149954. The other interaction pairs with a possible effect on survival were FOXO3 rs2802292 and ERCC2 rs50871, IL6 rs1800795 and GHRHR rs2267723, LINC02227 rs2149954 and PARK7 rs225119, as well as PARK7 rs225119 and PTPN1 rs6067484. These interactions remained significant when tested together with a set of health-related variables that also had a significant effect on survival above 85 years. In conclusion, our results confirm the central role of genetic regulation of insulin signalling and cell cycle control in longevity.

South African Media and Politics: Is the Three Models Approach Still Valid After Two Decades?

When Hallin and Mancini (2004) produced their watershed three models theory, South Africa was a new democracy barely a decade old. Even then, along with other countries of the Global South, the experience of a young democracy posed certain critical challenges to Hallin and Mancini’s understanding of the way that media and politics interrelate. Two decades later, South Africa has continued to change. There has been increased diversity in media ownership, rapid growth in community and social media, digital disruption, and significant challenges to media freedom. How does the three models theory stack up now? This article reviews scholarly critiques of Hallin and Mancini’s model, including their follow-up work, &lt;em&gt;Comparing Media Systems Beyond the Western World&lt;/em&gt; (2012), and assesses to what extent the three models is still a valid approach to understanding the connection between media and politics in the Global South. The article concludes by evaluating Hadland’s (2012) Africanisation of the model in light of the complex postcolonial trajectories of South Africa, suggesting that this, along with Hallin et al.’s (2021) expanded hybridisation model, still offers a better set of variables with which to understand how the media and political systems intertwine in the postcolony.

Nature's Clean-Up Crew: Unlocking the Potential of Water Hyacinth as a Phytoremediation Wonder for Eutrophication Reduction in Aquatic Environments

This study aims to determine whether water hyacinth can be a phytoremediation tool to mitigate eutrophication in water bodies. Specifically, it seeks to observe the morphological changes in the leaves and stems of water hyacinth in terms of length and width, to describe the similarities and differences of water hyacinth from eutrophic and normal water aquariums, and to determine the efficacy of water hyacinth in mitigating eutrophication. The research employed an experimental research design, which constitutes a structured set of protocols and procedures tailored for conducting experimental investigations scientifically, employing two distinct sets of variables. In conclusion, the study found significant changes in water hyacinth's morphology when exposed to eutrophic water, with signs of distress observed after ten days due to high nutrient levels. Despite this, the findings support the potential of water hyacinth to mitigate eutrophication in water bodies, offering promise for enhanced water quality management.

Niche position accounts for the positive occupancy–abundance relationship of lake fishes

AbstractAimIf the positive occupancy–abundance relationship prevails, locally abundant species are widely distributed. Although broadly supported, studies on lake fishes have thus far contributed little to this topic, especially at intermediate and small spatial scales. Here, the main objective was to investigate the relationship between occupancy and abundance of lake fish species within a large lake, as well as examine the relative importance of ecological niches, functional traits and phylogenetic relatedness in forming the relationship.LocationPoyang Lake, China.TaxonFishes.MethodsWe calculated occupancy and mean abundance for 78 fish species and estimated niche position and niche breadth for each species based on a set of environmental variables. Additionally, we generated four functional trait vectors and one phylogenetic vector, describing trait similarity and phylogenetic relatedness, respectively. Linear models, hierarchical partitioning analyses and boosted regression trees were used in combination to explore the relative role of niche position, niche breadth, trait vectors and phylogenetic relatedness in determining fish occupancy, abundance and their relationship.ResultsOccupancy and mean abundance of fish species in Poyang Lake showed a significantly positive and rather strong interspecific relationship. Interspecific variations in fish occupancy and abundance were well accounted for by niche position, followed by niche breadth. Nonetheless, trait vectors and phylogenetic relatedness were of minor importance in affecting fish occupancy and abundance. Additionally, occupancy was better explained than abundance by the predictor variables used, which was shown by both linear models and boosted regression trees.Main ConclusionsThis study suggests that the positive correlation between occupancy and abundance also occurs in lake fish species. Furthermore, ecological niche features, especially niche position, were more important than functional traits and phylogenetic relatedness in accounting for occupancy and abundance of fish species in Poyang Lake.

Macroeconomic effects of a declining wage share: A meta‐analysis of the functional income distribution and aggregate demand

AbstractThis paper reviews the theoretical and empirical literature on the relationship between the functional distribution of income and aggregate demand, which investigates whether declining wage shares increase (“profit‐led”) or decrease (“wage‐led”) demand. It conducts a meta‐regression analysis of 33 studies with 578 estimates for total and domestic demand, covering up to 163 years and 59 countries and regions. Our results suggest that, on average and across all countries, total demand is predominantly profit‐led and domestic demand mainly wage‐led. The effects in the literature range between 0.8 and −0.3 within one standard deviation for domestic demand and between 0.4 and −0.7 for total demand, which are economically significant at the outer bounds. We find mixed evidence for publication selectivity, which may affect the size but not the direction of the results in the literature. If one was to nonetheless correct for this, then total demand would be less profit‐led or statistically insignificant. A set of moderator variables, including publication characteristics, estimation strategies, the covariates included in the studies’ estimation functions, and, in particular, controls for time and space, help explain the variation in the empirical estimates.

Special function form solutions of multi-parameter generalized Mittag-Leffler kernel based bio-heat fractional order model subject to thermal memory shocks.

The primary objective of this research is to develop a mathematical model, analyze the dynamic occurrence of thermal shock and exploration of how thermal memory with moving line impact of heat transfer within biological tissues. An extended version of the Pennes equation as its foundational framework, a new fractional modelling approach called the Prabhakar fractional operator to investigate and a novel time-fractional interpretation of Fourier's law that incorporates its historical behaviour. This fractional operator has multi parameter generalized Mittag-Leffler kernel. The fractional formulation of heat flow, achieved through a generalized fractional operator with a non-singular type kernel, enables the representation of the finite propagation speed of heat waves. Furthermore, the dynamics of thermal source continually generates a linear thermal shock at predefined locations within the tissue. Introduced the appropriate set of variables to transform the governing equations into dimensionless form. Laplace transform (LT) is operated on the fractional system of equations and results are presented in series form and also expressed the solution in the form of special functions. The article derives analytical solutions for the heat transfer phenomena of both the generalized model, in the Laplace domain, and the ordinary model in the real domain, employing Laplace inverse transformation. The pertinent parameter's influence, such as α, β, γ, a0, b0, to gain insights into the impact of the thermal memory parameter on heat transfer, is brought under consideration to reveal the interesting results with graphical representations of the findings.

Scalar-potential mapping of the steady-state magnetosheath model

Abstract. The steady-state magnetosheath model has various applications for studying the plasma and magnetic field profile around the planetary magnetospheres. In particular, the magnetosheath model is analytically obtained by solving the Laplace equation for parabolic boundaries (bow shock and magnetopause). We address the question, how can we utilize the magnetosheath model by transforming into a more general, empirical, non-parabolic magnetosheath geometry? To achieve the goal, we develop the scalar-potential mapping method which provides a semi-analytic estimate of steady-state flow velocity and magnetic field in the empirical magnetosheath domain. The method makes use of a coordinate transformation from the empirical magnetosheath domain into the parabolic magnetosheath domain and evaluates a set of variables (shell variable and connector variable) to utilize the solutions of the Laplace equation obtained for the parabolic magnetosheath domain. Our model uses two invariants of transformation: the zenith angle within the magnetosheath with respect to the direction to the Sun and the ratio of the distance to the magnetopause and the thickness of magnetosheath along the magnetopause-normal direction. The use of magnetopause-normal direction makes a marked difference from the earlier model construction using the radial direction as reference. The plasma flow and magnetic field can be determined as a function of the upstream condition (flow velocity or magnetic field) in a wide range of zenith angles. The scalar-potential mapping method is computationally inexpensive, using analytic expressions as much as possible, and is applicable to various planetary magnetosheath domains.

Modified Structure of Deep Neural Network for Training Multi-Fidelity Data With Non-Common Input Variables

Abstract Multi-fidelity surrogate (MFS) modeling technology, which efficiently constructs surrogate models using low-fidelity (LF) and high-fidelity (HF) data, has been studied to enhance the predictive capability of engineering performances. In addition, several neural network (NN) structures for MFS modeling have been introduced, benefiting from recent developments in deep learning research. However, existing multi-fidelity (MF) NNs have been developed assuming identical sets of input variables for LF and HF data, a condition that is often not met in practical engineering systems. Therefore, this study proposes a new structure of composite NN designed for MF data with different input variables. The proposed network structure includes an input mapping network that connects the LF and HF data's input variables. Even when the physical relationship between these variables is unknown, the input mapping network can be concurrently trained during the process of training the whole network model. Customized loss functions and activation variables are suggested in this study to facilitate forward and backward propagation for the proposed NN structures when training MF data with different inputs. The effectiveness of the proposed method, in terms of prediction accuracy, is demonstrated through mathematical examples and practical engineering problems related to tire performances. The results confirm that the proposed method offers better accuracy than existing surrogate models in most problems. Moreover, the proposed method proves advantageous for surrogate modeling of nonlinear or discrete functions, a characteristic feature of NN-based methods.

Determinants of Change in Physical Activity in Children During the Transition from Elementary to High School.

Effective public health interventions targeting factors that influence physical activity are urgently needed to reduce the age-related decline in physical activity in youth. The purpose of this study was to identify associations between physical activity and a set of potential influences on physical activity in children as they transition from elementary to high school. Participants were 951 children from South Carolina school districts who completed outcome and independent variable measures on at least two time points from the 5th through 11th grades in 2010 - 2017. The primary outcome variable was physical activity, measured by accelerometry. Independent variables included a comprehensive set of variables in the child, parent/home, school, and community domains. Children, parents and school administrators, and staff completed questionnaires to assess psychosocial and home, school, and neighborhood environmental influences. Growth curve analyses identified independent variables associated with physical activity over time, either as a main effect or as an interaction with age. As main effects, self-efficacy, self-schema, sport participation, weekday outdoor hours, importance of child participating in sports and physical activity, safe to play outside, and Physical Activity Resource Assessment weighted score were positively associated with physical activity. The associations between physical activity and enjoyment motivation, appearance motivation, weekend outdoor time, and home equipment exhibited significant interactions with age. Enjoyment motivation influenced physical activity during the earlier years, whereas the remaining three variables influenced physical activity in the later years. Interventions should target multiple domains of influences that may vary by age.

Land Price Variability Assessment of a Transport Infrastructure Project in Pabna District, Bangladesh

Transport infrastructure development projects play a vital role in improving accessibility, which has dynamic effects on the regional economy as well as increases property values. This research aims to assess the land price variability in control and catchment areas before and after the Dasuria-to-Dhalarchar railway development project in Pabna district, Bangladesh. A set of independent variables was considered to measure the correlation between development project and land price through a questionnaire survey and focus group discussion. The impact of a railway project on land price was determined by the ordinary least squares method, after which the method was justified through Cook’s distance, P-P plot and scatter plot. The data analysis reveals a strong association between land price and distance to a central business district. The data shows that the land price increased significantly after implementation of the railway project with different intensities, as functional characteristics vary among selected stations.

Low-Thrust Trajectory Optimization with Averaged Dynamics Using Analytical Switching Detection

This paper presents an efficient indirect optimization method for the time- and fuel-optimal low-thrust many-revolution transfers with averaged dynamics. Compared with the previous indirect method, the proposed method involves a new set of variables and several near-optimal control laws to formulate the two-point boundary value problem based on three practical assumptions. Then, the costate transformations between two reference frames are first derived with respect to the the corresponding Eulerian angles, such that the switching function can be expressed with a six-degree polynomial function and the number of parameters used to determine the energy-optimal solutions is reduced to 6. Finally, a neural network is trained to generated the initial guesses, and the solution algorithms for solving the time- and fuel-optimal problem are formulated. Numerical simulations demonstrate that the convergence ratio of initial guesses is higher than 98%, the proposed assumptions are reasonable for guaranteeing the near optimality, and the presented method can obtain convergent solutions within tenths of a second for the Earth- and moon-centered external/interior transfers considering the shadow J2 and third-body perturbations.

Deciding where to put them: Sensitivity tests and independent evaluation are critical when using species distribution models to inform conservation translocations

Abstract Conservation translocations are an important tool for combating species declines and population losses. Species distribution models (SDMs) can facilitate the selection of suitable release sites for translocation programs. However, these models can be sensitive to several modelling decisions. In this study, we explore the impacts of three key modelling decisions on Maxent models developed to inform reintroductions of the long‐toed salamander (Ambystoma macrodactylum) in southwestern Alberta. We specifically test the sensitivity of model predictions to (1) the type of environmental variables used to generate models, (2) whether the background points used to calibrate the models reflects the potential bias in the input locality records and (3) the choice of geographic study extent. We use independent presence‐absence data from an extensive field survey to test the accuracy of models based on different decisions. Both model predictions and performance were sensitive to these modelling decisions. Models developed using local study extents were more accurate than those based on range‐wide extents. Both study extent and type of background points impacted model predictions and performance more than the set of environmental variables included in the models for this species. We further demonstrate the impacts of these decisions on the ranking of potential release sites and present a final set of recommendations that accounts for this uncertainty under both current and future climatic conditions. We specifically identify three sites that are expected to be suitable in both present and future time periods as potential release sites for salamander reintroductions in southwestern Alberta. Synthesis and applications: Our study adds to our understanding of how different modelling decisions impact SDMs and the downstream conclusions from these models while simultaneously demonstrating a rigorous approach for the use of SDMs in conservation translocation planning.

Adaptive Safe Reinforcement Learning With Full-State Constraints and Constrained Adaptation for Autonomous Vehicles.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Young Consumers’ Preferences for Natural Wine: An Italian Exploratory Study

Although much has been written about sustainable wine consumption, the topic of young consumers’ (age 18–39 years) preferences for natural wine in Italy has been poorly explored. Through an online survey of 640 Italian respondents, we investigate the characteristics that affect consumption of natural wine in Italy. First, a factor analysis was applied to reduce data to a smaller set of summary variables. A probit regression model was then used to explore the effects and evaluate the relative importance of identified factors on participants’ willingness to consume natural wine. The results reveal that being female, in the older age cohort (25–39 years), and being consumer of certified wine and/or biodynamic wine significantly increase the probability of drinking natural wine. The findings also showed that health benefits, natural product interest, previous experience with natural wine, and positive perception of natural wine also drive young consumers of natural wine.

Predictive mapping of tree species assemblages in an African montane rainforest

AbstractConservation of mountain ecosystems can benefit from knowledge of habitats and their distribution patterns. This benefit is particularly true for diverse ecosystems with high conservation values such as the “Afromontane” rainforests. We mapped the vegetation of one such forest: the rugged Bwindi Impenetrable Forest, Uganda—a World Heritage Site known for its many restricted‐range plants and animal taxa including several iconic species. Given variation in elevation, terrain and human impacts across Bwindi, we hypothesized that these factors influence the composition and distribution of tree species. To test this, detailed surveys were carried out using stratified random sampling. We established 289 georeferenced sample sites (each with 15 trees ≥20 cm dbh) ranging from 1320 to 2467 m a.s.l. and measured 4335 trees comprising 89 species that occurred in four or more sample sites. These data were analyzed against 21 digitally mapped biophysical variables using various analytical techniques including nonmetric multidimensional scaling (NMDS) and random forests. We identified six tree species assemblages with distinct compositions. Among the biophysical variables, elevation had the strongest correlation with the ordination (r2 = 0.5; p &lt; 0.001). The “out‐of‐bag” (OOB) estimate of the error rate for the best final model was 50.7% meaning that nearly half of the variation was accounted for using a limited set of variables. We demonstrate that it is possible to predict the spatial pattern of such a forest based on sampling across a highly complex landscape. Such methods offer accurate mapping of composition that can guide conservation.

Networking trait resilience: Unifying fragmented trait resilience systems from an ecological systems theory perspective.

This study reconceptualized trait resilience, defining it as a network of systems; utilizing direct resilience assessments-engineering, ecological, adaptive capacity, social cohesion-and proxy resilience assessments-personality, cognitive, emotional, eudaimonia, and health. The background of the study addresses the fragmented conceptualization of trait resilience by proposing a unifying network model based on ecological systems theory, illustrating the dynamic interplay of resilience factors across varying levels of disturbance. In Study One, four USA or UK samples (total n = 2396) were used to depict the trait resilience network. Study Two (n = 1091) examined the relationship between the network and disturbance at two time-points, using mental health levels as a disturbance metric. Study One found that adaptive capacity, and sometimes positive emotional processes, were central variables to the network. Study Two found that in lower disturbance groups, adaptive capacity remained important, while in higher disturbance groups, a broader set of variables became central to the network. Study One suggests a Broaden-and-Build approach, where adaptive capacity is a foundational resilience capability, reciprocally associated with positive emotional mechanisms. Study Two suggests a new "Dynamic Resilience Spectrum Theory," proposing that increased disturbances necessitate the use of a more diverse set of resilience traits.

العوامل المؤثرة في استخدام المرأة اليمنية لتكنولوجيا الأموال عبر الهاتف المحمول

The study aimed to investigate the factors affecting Yemeni women's use of mobile money technology, through a set of variables derived from the unified theory of acceptance and use of technology. A descriptive analytical approach was used, and a questionnaire was designed to collect data from a sample of (401) of Yemeni women users and non-users of mobile money technology. The data was analyzed using simple, multiple, stepwise regression and structural modeling to test the study model. The results showed that the factors (innovation adoption, perceived enjoyment of Try and use, social influence, financial and technological knowledge, ease of use, trust and privacy, compatibility, perceived usefulness) collectively explained 78% of the variance in the dependent variable represented by (Yemeni women's use of mobile money technology). It also showed a positive statistically significant impact of the independent variables (compatibility, perceived enjoyment of Try and use, perceived usefulness, trust and privacy) on Yemeni women's use of mobile money technology. While the impact of the factors (innovation adoption, social influence, financial and technological knowledge, ease of use) was statistically insignificant. The study recommended the need for financial and technological awareness for Yemeni women and highlighting the importance of their use of mobile money technology and spreading awareness of its benefits to increase women's usage rates.