Set Of Variables Research Articles

Perpetrators’ Characteristics and Intimate Partner Violence in Informal Settlements at Iringa Municipality Tanzania

Intimate partner violence (IPV) is the most common form of violence faced by women globally. The prevalence of lifetime experiences of physical and sexual IPV is estimated to range from 15% to 71%. IPV where a perpetrator is a male current or ex-partner is an existing gender problem in many countries. Millions of women in Africa especially those living in informal settlements are affected by IPV. The most common form of IPV is physical, psychological and sexual abuse. Several studies conducted in Tanzania on IPV have not yet examined the characteristics of perpetrators of such violence acts in informal settlements. This cross-sectional study aimed to examine IPV perpetrators’ characteristics and their association with IPV perpetration in informal settlements. Multi-stage sampling was applied. Random sampling was used to obtain a sample of 300 women living in informal settlements of Iringa Municipality. A survey was used to collect data and SPSS version 20 was used for analysis. A χ2 test was used to assess the association between the outcome variable and a set of exposure variables. Ethical clearance was obtained from Muhimbili University Institutional Review Board. Written consent was sought before participation in the study. IPV perpetrators’ socio-demographic characteristics were age, marital status, marriage type, occupation and level of education. Alcohol intake characteristics were alcohol intake status and intake frequency, where 62.7% were reported to take alcohol many times. Marital status, level of education, occupation, alcohol intake status and frequency of alcohol intake were linked to physical IPV ( p < 0.005). Perpetrators’ characteristics associated with both sexual and emotional IPV were marital status, alcohol intake and the frequency of alcohol intake ( p < 0.005). IPV perpetrators’ socio-demographic characteristics mainly marital status, level of education, alcohol abuse and alcohol intake frequency have a major contribution to the burden of IPV in Iringa municipality. Low level of education and occupation status as business persons are at higher risk of being violent physically, sexually and emotionally. Men aged between 31 and 50 years are more violent in all forms than others. Community IPV prevention interventions focusing on both men and women will be helpful since IPV perpetration may not only be initiated by men but also by women who may lack life and relationship coping skills. More research is important focusing on IPV perpetration attitude and perceptions from men and women perspectives to inform IPV prevention and management.

Carbon Storage Patterns and Landscape Sustainability in Northeast Portugal: A Digital Mapping Approach

This study investigated the impact of regional land abandonment in northeast Portugal. It specifically focused on carbon sequestration opportunities in the Upper Sabor River Watershed, situated in the northeast of Portugal, amidst agricultural land abandonment. The study involved mapping the distribution of soil organic carbon (SOC) across four soil layers (0–5 cm, 5–10 cm, 10–20 cm, and 20–30 cm) at 120 sampling points. The quantification of SOC storage (measured in Mg C ha−1) allowed for an analysis of its relationship with various landscape characteristics, including elevation, land use and land cover (LULC), normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), topographic wetness index (TWI), and erosion risk (ER). Six statistical tests were employed, including multivariate approaches like Cubist and Random Forest, within different scenarios to assess carbon distribution within the watershed’s soils. These modeling results were then utilized to propose strategies aimed at enhancing soil carbon storage. Notably, a significant discrepancy was observed in the carbon content between areas at higher elevations (>1000 m) and those at lower elevations (<800 m). Additionally, the study found that the amount of carbon stored in agricultural soils was often significantly lower than in other land use categories, including forests, mountain herbaceous vegetation, pasture, and shrub communities. Analyzing bi- and multivariate scenarios, it was determined that the scenario with the greatest number of independent variables (set 6) yielded the lowest RMSE (root mean squared error), serving as a key indicator for evaluating predicted values against observed values. However, it is important to note that the independent variables used in set 4 (elevation, LULC, and NDVI) had reasonably similar values. Ultimately, the spatialization of the model from scenario 6 provided actionable insights for soil carbon conservation and enhancement across three distinct elevation levels.

Estimating gravity coefficients with multiple layers of heterogeneity

AbstractWe estimate a gravity model for 205 countries, with data from 1954 to 2014, allowing for multiple layers of heterogeneity. The first layer arises from the interactions between a set of traditional gravity variables. The second one comes from country pairs that differ in values of the binary time‐independent gravity variables. Further layers come from different income groups and regions. Our results show the importance of heterogeneities at various levels. For instance, landlockedness is less restrictive when trading partners are contiguous or colonially linked. We also find, for example, that the positive interaction between landlockedness and contiguity is more (less) pronounced when non‐high‐income countries import (export) or when Middle East and North Africa (MENA) countries import, and the negative interaction between landlockedness and language similarities is stronger when MENA countries export.

Do STEM Students Vote?12

Pundits, politicians, and academics have long worried about potentially low rates of civic participation among STEM-oriented students. Does studying STEM actually decrease the odds that young people will be actively involved in democracy? To answer this question, we created a dataset of over 23 million students in the United States, matched to national validated voting records. This novel dataset is the largest known individual-level dataset in the United States, connecting high school and college students to voting outcomes. It also contains a rich set of demographic and academic variables, to account for many of the common issues related to students’ selection into STEM coursework. We consider two measures of STEM participation: Advanced Placement (AP) exam taking in high school and college major. Using both measures, we find that, across model specifications, the estimated relationships between STEM and voting are small in magnitude—about the same effect size as a single get-out-the-vote mailer. Our analyses demonstrate that, on average, marginally more STEM coursework in high school and college does not contribute noticeably to the low voting rates among young people in the United States.

The effects of class attendance on academic performance: Evidence from synchronous courses during Covid-19 at a Chinese research university

Using 13,584 observations of 4772 undergraduate students enrolled in 523 online classes offered synchronously at a research university in China, and using online-learning platform data and administrative data, this paper examines the impacts of class attendance on academic performance. Based on student-level and course-level fixed effects models with a unique set of student-course level control variables, this paper finds class attendance to have a significantly positive impact on academic performance. This effect is larger for low-performing students and smaller for students in courses with larger class sizes. The above findings survive a number of robustness checks, including a bounding technique, restricting sample to compulsory courses and limiting variation in attendance caused by whole day absence.

Modeling Corporate CDS Spreads Using Markov Switching Regressions

Abstract This paper investigates the determinants of the European iTraxx corporate CDS index considering a large set of explanatory variables within a Markov switching model framework. The influence of financial and economic variables on CDS spreads are compared using linear, two, three, and four-regime models in a sample post-subprime financial crisis up to the COVID-19 pandemic. Results indicate that four regimes are necessary to model the CDS spreads. The fourth regime was activated during the COVID-19 pandemic and in high volatility periods. Further, the effect of the covariates differs significantly across regimes. Brent and term structure factors became relevant after the outbreak of the COVID-19 pandemic.

What does the MAIHDA method explain?

Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a new approach to quantitative intersectional modelling. Along with an outcome of interest, MAIHDA entails the use of two sets of independent variables. These include group demographics such as race, gender, and poverty status as well as strata which are constructs such as Black female poor, Black female wealthy, and White female poor. These constructs represent the combination of the demographic variables. To operationalize the approach, an initial random intercepts model with strata as a level 2 context is specified. Then, another model is specified that includes the strata as well as the demographic variables as level 1 fixed effects. As such, it is argued that MAIHDA uniquely identifies the additive and intersectional effects for any given outcome. In this paper we show that MAIHDA falls short of this promise: the strata are an individual-level composite variable not a level 2 context. Rather than being analogous to neighborhoods as contexts, strata are analogous to socio-economic status which is a combination of individual-level demographic variables, albeit often presented as a group-level characteristic. The result is that the demographic variables are inserted in both level 2 and 1. This duplication across the levels in MAIHDA means that there is a built-in collinearity across the levels and that the models are mis-specified and, therefore, redundant. We conclude that single-level models with the demographic variables and interactions or with the strata as fixed effects are still the more accurate models for quantitative intersectional analyses.

Incorporating novel input variable selection method for in the different water basins of Thailand

Selecting appropriate input variables for developing a rainfall prediction model is significantly difficult. The present study proposed an innovative framework for input variable selection (IVS) model bootstrapped long short-term recurrent neural network (BTSP-LSTM-RNN) to identify relevant variables for monthly rainfall forecasting. Monthly meteorological and large-scale climatic variables (LCVs) from 1993 to 2022 at two selected river basins in the northern region of Thailand were used for model development. The proposed BTSP-LSTM-RNN model results were compared with the support vector regression with recursive feature elimination (SVR-RFE) and Gradient boosting (GB) by statistical metrics such as coefficient of determination (R2), mean absolute error (MAE), relative root mean squared error (RRMSE), Pearson’s correlation coefficient (r) and mean absolute percentage error (MAPE). BTSP-LSTM-RNN demonstrated exceptional performance, boasting a higher R2 (0.84), MAE (92.28), RRMSE (10.36) in the Wang basin, and R2 (0.83), MAE (242.60), RRMSE (9.93) in the Nan basin. BTSP-LSTM-RNN also achieved the lowest MAPE of 29.82%. Based on this IVS model results, two input variable combinations (IVCs) were designed. IVC-1 is based on BTSP-LSTM-RNN selection, and IVC-2 is an original set of variables. LSTM-RNN, multi-layer perceptron artificial neural network (MLP-ANN), and ensemble model with bootstrapping on random forest (RF) were employed for monthly prediction. When BTSP-LSTM-RNN's selected input variables from IVC-1 are utilized, the BSTP-RF model demonstrates robust performance. It achieves a high R2 (0.82), a low RRMSE (10.13%) suggests accurate predictions, and the r of 0.91 further supports the model's strong linear relationship with observed rainfall data. Based on prediction model results, the BTSP-LSTM-RNN (IVS) model plays a pivotal role in the selection of input variables for rainfall forecasting and its impact on the performance of prediction models (BSTP-RF, MLP-ANN, and LSTM-RNN). These results consistently underscored the pivotal role of the BTSP-LSTM-RNN IVS model in enhancing the precision and reliability of rainfall predictions.

A Memristive Oscillator

AbstractMemristors, or more generally “memristive systems,” are nonlinear electric components that change their resistance depending on a set of state variables. Meanwhile, negative differential resistance (NDR) is an uncommon electrical property that occurs in a few nonlinear electric components. Such a nonlinear property is now widely used in amplifiers and oscillators. It finds that one of the bilayer‐type nickel‐dithiolene complexes shows nonlinear transport with the NDR property and self‐oscillates under a bias current by simply adding a capacitor in parallel. Surprisingly, the Lissajous figure of this molecular material shows a “pinched hysteresis loop” typical of a memristor, and an inductive reactance emerges when a bias current or voltage is applied to the sample. Herein, a new type of oscillator that uses the NDR and hidden inductive properties of a memristive system is reported. The findings suggest that a memristive system can mimic other passive elements, such as an inductor, and can oscillate itself without having them in the circuit. The oscillation mechanism is straightforward and applicable to a wide variety of memristive materials, including resistors with large negative temperature coefficients, discharge tubes, and even nerve cells in the living body.

Towards an automatic early screening system for autism spectrum disorder in toddlers based on eye-tracking

AbstractAccording to official estimations, autism spectrum disorder (ASD) affects around 1% of European newborns. The high level of dependency of ASD-affected subjects entails an extremely high social and economic cost. However, early intervention can drastically improve children’s development and thus reduce their dependency. One of the main common characteristics of subjects with ASD is difficulties with social interaction, which determines how they react to certain stimuli. This behavior can be automatically detected by analyzing their gaze. This study explores and evaluates the feasibility of automatic screening for ASD in toddlers under 24 months of age based on this specific behavior. We applied a matched pairs experimental design and a set of test videos, using a set of variables extracted from gaze analysis from toddlers using eye-tracking devices. The different videos try to capture social engagement, social information gathering gaze exchanges, and gaze following. We used the data to make a thorough comparison of machine learning algorithms (nine learning schemes), including some that were used in related prior research, and others that are popular in classification problems. The results show that several of the tested algorithms provided notable performance.

Capturing the well-being exposome in poly-environmental scores.

In this study we use aggregated weighted scores of environmental effects to study environmental influences on well-being and happiness. To this end, we split a sample of Netherlands Twin Register (NTR) participants into a training (N = 4857) and test (N = 2077) sample. In the training sample, we use elastic net regression to estimate effect sizes for associations between life satisfaction and two sets of environmental variables: one based on self-report socioenvironmental data, and one based on objective physical environmental data. Based on these effect sizes, we create two poly-environmental scores (PES-S and PES-O, for self-reports and objective data respectively). In the test sample, we perform association analyses between different measures of well-being and the two PESs. We find that the PES-S explains ∼36% of the variance in well-being, while the PES-O does not significantly contribute to the model. Variance in other well-being measures (i.e., different life satisfaction domains, subjective happiness, quality of life, flourishing, psychological well-being, self-rated health, depressive problems, and loneliness) are explained to varying extents by the PESs, ranging from 6.36% (self-rated health) to 36.66% (loneliness). These predictive values did not change during the COVID-19 pandemic (N = 3214). Validating the PES-S in the UK biobank (N = 40,614), we find that the UK biobank PES-S explains about ∼12% of the variance in happiness. Lastly, we examine if there is any indication for gene-environment correlation (rGE), the phenomenon where one's genetic predisposition influences exposure to the environment, by associating the PESs with polygenic scores (PGS) in a sample of Netherlands Twin Register (NTR) and UK Biobank participants. While the PES and PGS were not correlated in the NTR sample, they were correlated in the larger UK biobank sample, indicating the potential presence of rGE. We discuss several limitations pertaining to our dataset, such as a potential influence of common method bias, and reflect on how PESs might be used in future research.

Data-driven meal events detection using blood glucose response patterns

BackgroundIn the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present.MethodsThe method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as Meal or Non-Meal. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual.ResultsThe results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results.ConclusionsThe approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events.

Continuous microfluidic platform combining cell lysis and protein extraction for screening overall process conditions

AbstractBACKGROUNDMicrofluidic devices have been increasingly used as tools to accelerate the development of new biomanufacturing methods, given their ability to test a large set of variables in a short amount of time with minimal reagent consumption. However, while being able to expedite a bioprocess design, the modular aspect of these devices has not been systematically explored, with most applications focusing on single unitary operations. This paper explores the modular design of these devices through the development of an integrated microfluidic chip.RESULTSTwo separate unitary operations were included into a single device for continuous operation: product release by chemical cell lysis and product concentration by aqueous two‐phase system. In this way, it is possible to screen multiple conditions for each operation and evaluate their combined effect on the final product, making this a great tool in the development and optimization of a process. A recombinant Escherichia coli (E. coli) strain producing green fluorescent protein (GFP) was used as model system, which allows for lysis efficiency and partition coefficient to be evaluated by fluorescence microscopy. A chemical lysis solutions was used in the lysis step, presenting lysis efficiencies of about 100%, while a polyethylene glycol (PEG)/phosphate system was screened for the separation stage, presenting partition coefficients of about 4.CONCLUSIONSThe integrated device allowed for the continuous screening of multiple combinations of operation conditions, highlighting, for example, conditions where improvements on the lysis efficiency subsequently impaired the separation, decreasing the partition of the target product, thus demonstrating the need for the evaluation of a process in its entirety. In this way, the microfluidic device delivered a rapid process screening and optimization, with very low reagent consumption, and using a simple fluorescence microscope for data collection. © 2023 Society of Chemical Industry (SCI).

Analysis of the Third Digital Divide in Relation to Digital Socialization Itineraries among University Students in Uruguay

The social transformations generated by digitization and the increasing prominence of Information and Communication Technology (ICT) in social relations have underscored the need to delve deeper into the analysis of digital divides to enhance our understanding of social inequalities in the digital age. Thus, sociology has delved into identifying the variables that underpin differential capacities to make productive use of digital technologies for improving living conditions, a phenomenon referred to as the third digital divide. This study delves into this issue by analyzing the digital socialization itineraries of young people. To achieve this, the technique of techno-biographical interviews was employed with 30 university students in Uruguay. Through the classification of interviews into clusters based on coding similarity, five digital socialization itineraries were identified: contextual disengagement, aspirational advancement, productive channeling, controlled development, and abusive development. By closely examining these groups, a set of variables with significant biographical impacts was identified, affecting both the capacity to harness digital technologies and the potential risks associated with their use. The findings hold relevance for guiding research in the field and for policymakers in addressing ICT education challenges during childhood and adolescence.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Relativistic hydrodynamics from the single-generator bracket formalism of nonequilibrium thermodynamics

Abstract We employ the generalized bracket formalism of nonequilibrium thermodynamics by Beris and Edwards to derive Lorentz-covariant time-evolution equations for an imperfect fluid with viscosity, dilatational viscosity, and thermal conductivity. Following closely the analysis presented by Öttinger (Physica A, 259, 1998, 24–42; Physica A, 254, 1998, 433–450) to the same problem but for the GENERIC formalism, we include in the set of hydrodynamic variables a covariant vector playing the role of a generalized thermal force and a covariant tensor closely related to the velocity gradient tensor. In our work here, we derive first the nonrelativistic equations and then we proceed to obtain the relativistic ones by elevating the thermal variable to a four-vector, the mechanical force variable to a four-by-four tensor, and by representing the Hamiltonian of the system with the time component of the energy-momentum tensor. For the Poisson and dissipation brackets we assume the same general structure as in the nonrelativistic case, but with the phenomenological coefficients in the dissipation bracket describing friction to heat and viscous effects being properly constrained for the resulting dynamic equations to be manifest Lorentz-covariant. The final relativistic equations are identical to those derived by Öttinger but the present approach seems to be more general in the sense that one could think of alternative forms of the phenomenological coefficients describing friction that could ensure Lorentz-covariance.

Large eddy simulation-bivariate sectional model for non-spherical TiO2 nanoparticle synthesis in flame

Numerical simulation of nanoparticle synthesis in flame necessitates a high level of fidelity for turbulent flame, as well as delivers more detail insight into the evolution of structure and size distribution of particles undergoing various dynamic events. In the present work, a new simulation approach coupling large-eddy simulation (LES) and bivariate sectional model is proposed to explore the synthesis of nanoparticles in turbulent flame. More specifically, the nonlinear LES-partially stirred reactor model (NLES-PaSR) is utilized to simulate the turbulent flame, wherein the gradient-type structural subgrid-scale (SGS) model for turbulent flow and partially stirred reactor (PaSR) model for turbulent combustion are considered. In addition, the volume-based bivariate sectional model is thoroughly coupled with the LES model for the first time to describe the spatiotemporally resolved formation and growth of nanoparticles using particle number concentration and surface area concentration as two sets of variables. Compared directly with the LES-monodisperse (LES-Mono) and LES-univariate sectional (LES-UniSe) models, the LES-bivariate sectional (LES-BiSe) model has significant advantages in its ability to analyze and predict the size, morphology, as well as polydispersed particle size distribution (PSD) evolution of nanoparticles at a moderate computational cost simultaneously. In the LES framework, the proposed model successfully simulates the spatiotemporally resolved formation and growth of non-spherical TiO2 nanoparticle in turbulent diffusion burner. The predicted results derived from the LES-BiSe model are in good agreement with the experimental measurements. Also, the aggregate and primary particle size distribution shows highly temporal fluctuation, which mainly results from the continuous variations of particle path induced by the unsteady turbulent combustion. Furthermore, the present work demonstrates the profound dependence of particle morphological on both spatiotemporal evolution and their intrinsic size distribution. In particular, the large aggregates in the higher sections are commonly found as irregular structure with fractal-like dimensions.

Mortality analysed by propensity score matching: an application to national neonatal audit

Mortality, a key outcome variable in many population studies and studies of healthcare and its interventions, is commonly analysed by regression of the survival status on a set of relevant background variables. We describe an alternative based on the potential outcomes framework, in which we ask how a particular group of subjects, or a population, whose outcomes were realised in one condition, would have fared had they been treated or cared for in different circumstances. The method is applied to neonatal mortality in the operational delivery networks in England and Wales. The performance of a network is assessed by the difference of the mortality rates of the network and of a matched set of babies drawn from the entire domain of the study. The outlier status of a network is established by a decision-theoretical approach.

Stochastic inversion of geophysical data by a conditional variational autoencoder

Recovering geologically realistic physical property models by geophysical inversion is a long-standing challenge. Generative neural networks offer a promising path to meet this challenge because they can produce spatially complex models that exhibit the characteristics of a set of training models, even when those characteristics are difficult to quantify. In the context of geophysical inversion, these characteristics may include faults, layers, and sharp contacts between rock units. Here, we develop a framework for incorporating prior geologic knowledge into geophysical inversions using conditional variational autoencoders (CVAEs). We train a CVAE to reconstruct training density models while honoring relative gravity data. Once trained, the decoder network of the CVAE inverts gravity data. The inputs to the decoder are observed gravity data and a set of latent variables that are sampled from a standard normal distribution. The decoder maps from the observed data and latent variables to density models such that the resulting models are consistent with the training models and the input data. Consequently, the inversion fits the observed data and incorporates the information embedded in the training models. The decoder can produce many inverted models instantaneously, sampling an approximation to the posterior model distribution efficiently. We find that the latent variables correspond to independent interpretable ways in which the model can vary while still honoring the observed data. We draw a connection to linear inverse theory, positing that the latent variables are analogous to the principal components of the local posterior model covariance.

DYNAMIC RELATIONSHIPS: E-COMMERCE SALES AND KEY EXOGENOUS VARIABLES IN THE PHILIPPINES

This study delves into the complex and evolving landscape of e-commerce in the Philippines, focusing on the relationship between E-Commerce Sales as the endogenous variable and a set of influential exogenous variables, including Digital Marketing Spending, GDP Growth, Internet Penetration, and Mobile Phone Ownership. This research employs a flexible spline modeling approach, uncovers non-linear associations, and offers significant implications for academic understanding and practical applications. The findings underscore the growing impact of Digital Marketing Spending on E-Commerce Sales, revealing the paramount role of online advertising and promotional strategies in the digital marketplace. Moreover, the study explains the intricate interplay between GDP Growth, Internet Penetration, Mobile Phone Ownership, and E-Commerce Sales, highlighting the non-linear nature of these relationships. As the Philippines continues its economic expansion and technological integration, these associations exhibit insightful implications for policymakers, businesses, and e-commerce stakeholders.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0075/a.php" alt="Hit counter" /></p>