Number Of Variables Research Articles

Modelling and optimizing the impact resistance of engineered cementitious composites with Multiwalled carbon nanotubes using response surface methodology

Engineered Cementitious Composites (ECC) are highly regarded in construction owing to their tensile ductility and crack control capabilities, making them suitable for various structural applications. The accumulation of multi-walled carbon nanotubes (MWCNTs) further enhances their mechanical properties. However, there’s a significant knowledge gap concerning MWCNTs-ECC impact resistance. The objective of this study is to tackle the challenges associated with evaluating, optimizing, and predicting MWCNTs-ECC impact resistance to ensure its safe and widespread use in critical infrastructure by applying response surface methodology (RSM). Moreover, the 13 mixtures of ECC combined with several quantities of PVA fiber and MWCNTs as input elements were utilized to calculate the first (E1) and final (E2) impact energies. The findings demonstrated that the MWCNTs-ECC combinations’ impact resistance improved as the input ingredient concentrations increased. Besides, the optimum E1 and E2 of ECC combined with 1% of PVA fiber were noted by 1398 Joules and 12,956 Joules at 0.065% of MWCNTs on 28 days respectively. Furthermore, Response prediction models for E1 and E2 were created, and after being validated with an analysis of variance (ANOVA), it was determined that they had high R2 readings of 99.30% and 99.07%, correspondingly. The optimization process produced an ideal number of input variables for MWCNTs and PVA fiber, respectively, of 0.066% and 1%, with a desirability value of 100%. Moreover, it is recommended that the usage of 0.066% of MWCNTs in ECC combined with 1.0–1.50% PVA fiber provides optimum results for the construction industry.

Stature estimation equations from fragmentary long bones based on a modern Eastern Mediterranean assemblage.

Stature estimation is central in forensic anthropology and very important in bioarchaeology. For this reason, several different methods have been proposed, employing different skeletal elements and statistical approaches. A major issue with skeletonized individuals is that their bones are often found fragmented, a taphonomic parameter that limits the application of many available methods. As a result, attempts have been made to create equations to predict either directly stature or long bone length (which can then be used with current stature prediction equations) from bone fragments. The current paper is a contribution in this direction. The femur, tibia and humerus of 76 individuals from a modern Greek skeletal collection were divided into different segments using a landmark approach. Subsequently, univariate and multivariate equations were created to predict both maximum long bone length and stature from the "bone fragments". The models varied in performance depending on the specific bone fragment used, the number of variables simultaneously employed for prediction and the sex of the individuals. Although the models used to directly predict stature from bone fragment dimensions should be treated cautiously because the stature of the assemblage from the Athens Collection had itself been anatomically estimated, the results are valuable towards highlighting the complex association between bone dimensions, long bone length and living/estimated stature.

Case Analysis About Demand Changes of New Energy Vehicles and Its Stock Returns

The industry for new energy vehicles (NEVs) has experienced giant growth which draws interest from investors, decision-makers, and scholars. This research examines the relationship between changes in market demand and the stock returns of NEV firms. The method we use is to take into account a number of variables including international relations, governmental regulations, and developments in related industries. This article investigates the cases of Tesla Inc. and NIO Inc. on the performance of NEV stocks by the following ideas: the impact of foreign relations, government policies, and shifts in associated industries. For investors in the NEV industry, the results highlight how crucial it is to understand market dynamics as well as possible dangers and possibilities. Also, the article suggests topics for further investigation, such as the effects of NIO's global initiatives like growing product lines and Tesla's growing product range. Stakeholders hoping to properly manage the changing NEV ecosystem will find great insights from this thorough investigation.

Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data

Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database's size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables.

Economical management of diesel generator sets by means of MATLAB digital technologies

A numerical algorithm for optimizing the technological processes of diesel generator sets operating on various grades of fuel, implemented by means of CVX-technologies, is considered. Traditional algorithms have generally proven to be positive, but can be problematic for optimization due to the large number of variables and the nonlinearity of the model. In addition, due to the huge computational load, they can “stall” or become damaged when used to search for non-standard designs, such as asymmetric D-optimal and large-dimensional spatial structures. It is noted that an alternative to them is calculations in the CVX format, which are widespread and available for solving convex optimization problems. However, their application for the control of technological processes of energy saving in autonomous electric power complexes in transport is still limited. Based on the tools of the CVX package in MATLAB, using their usefulness and flexibility in comparison with traditional optimization methods, this paper proposes technical solutions aimed at developing energy-saving technologies for energy complexes of autonomous systems in the context of digital transformation. The application of energy saving management methods is presented in this study on the example of fuel consumption management by a group of diesel generators with different consumption characteristics of primary engines, for which optimal modes have been obtained at various options of active power consumption by the network. An example of calculating optimal modes in parallel operation of five DGA with CVX program codes is given, confirming the correctness of the proposed solutions. It is shown that the use of CVX programs in MATLAB demonstrates their usefulness and flexibility in comparison with traditional algorithms in the statistics of searching for various types of optimal approximate constructions by convex criterion for nonlinear models.

Predictors of adolescent childbearing among Ethiopian women with spatial effect adjustment

BackgroundChildbearing under the age of 20 is referred to as teenage childbearing. Compared to high-income countries, it is significantly higher in low-income countries. Adolescent childbearing is influenced by a number of variables, including economic, demographic, and social factors, and these vary geographically. Thus, this study aimed to determine the predictors of adolescent childbearing among Ethiopian women with spatial effect adjustment.MethodsA total weighted sample of 4712 women aged 15 to 49 were included. The data were obtained from the 2019 Ethiopia Demographic and Health Survey. A generalized Geoadditive model which accounts for spatial effect and the non-linear effect of continuous variables was adopted to determine the associated factors of adolescent childbearing among Ethiopian women.ResultsThe spatial pattern of adolescent childbearing was non-random in Ethiopia with Moran’s index statistics 1.731999 (P-value < 0.001). Based on the evidence of spatial variation in a model, the highest risk of adolescent childbearing was observed in Jijiga, Shinilie, Welwel and Walder, Afar (Zone1 and Zone 5), Assosa, Metekel, and Gambela (Zone1). We also noted that women not intending to use a contraceptive method, Muslim religion, living in a rural area, and large household family size were significantly associated with a high risk of adolescent childbearing. Furthermore, our model results also confirmed that higher educational levels, older household age, and good economic status significantly reduced the risk of adolescent childbearing.ConclusionsThis study revealed that adolescent childbearing distribution was significantly clustered in the Eastern and Southwestern parts of Ethiopia. Intervention programs aimed at the prevention of early marriage and raising awareness of sexual activity are essential to reducing adolescent childbearing.

Fast and Compact Partial Differential Equation (PDE)-Based Dynamic Reconstruction of Extended Position-Based Dynamics (XPBD) Deformation Simulation

Dynamic simulation is widely applied in the real-time and realistic physical simulation field. How to achieve natural dynamic simulation results in real-time with small data sizes is an important and long-standing topic. In this paper, we propose a dynamic reconstruction and interpolation method grounded in physical principles for simulating dynamic deformations. This method replaces the deformation forces of the widely used eXtended Position-Based Dynamics (XPBD), which are traditionally derived from the gradient of the energy potential defined by the constraint function, with the elastic beam bending forces to more accurately represent the underlying deformation physics. By doing so, it establishes a mathematical model based on dynamic partial differential equations (PDE) for reconstruction, which are the differential equations involving both the parametric variable u and the time variable t. This model also considers the inertia forces caused by acceleration. The analytical solution to this model is then integrated with the XPBD framework, built upon Newton’s equations of motion. This integration reduces the number of design variables and data sizes, enhances simulation efficiency, achieves good reconstruction accuracy, and makes deformation simulation more capable. The experiment carried out in this paper demonstrates that deformed shapes at about half of the keyframes simulated by XPBD can be reconstructed by the proposed PDE-based dynamic reconstruction algorithm quickly and accurately with a compact and analytical representation, which outperforms static B-spline-based representation and interpolation, greatly shortens the XPBD simulation time, and represents deformed shapes with much smaller data sizes while maintaining good accuracy. Furthermore, the proposed PDE-based dynamic reconstruction algorithm can generate continuous deformation shapes, which cannot be generated by XPBD, to raise the capacity of deformation simulation.

Combinatorial Order Pre-processing Search (COPS): A new pre-processing strategy for large-scale interpretable data analysis in process analytical technologies

Combinatorial Order Pre-processing Search (COPS), a novel approach for optimizing data pre-processing is proposed in this work. Unlike simultaneous hyperparameter optimization, COPS employs a priori optimization to reduce computational time while refining the search space for preprocessing sequences and combinations. It allows for setting a maximum number of pre-processing methods, while efficiently searching through combinations of methods with chemically relevant knowledge. In this work, 67 calibration datasets across various analytical techniques, including fluorescence spectroscopy, gas chromatography (GC), near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), visible-near-infrared spectroscopy (Vis-NIR), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and voltammetry were evaluated. COPS yielded significant improvements over existing methodologies based on design of experiment and compounded pre-processing approaches. The COPS outperformed the other methods, resulting in an average root mean square error of prediction (RMSEP) reduction of 31.7%, while also reduced the complexity (number of latent variables) of the model which allows for easier interpretation. This underscores the importance of combinatorial order set theory for the search of pre-processing method combinations (without fixing the sequence of pre-processing methods) to enhance model performance and interpretation. The novel COPS approach can be employed in process analytical technology (such as inline, online or at-line chemical sensing analytics) to enhance predictive accuracy and operational efficiency, fundamentally transforming the quality and reliability of chemical process monitoring and control.

A dissection of the monotonicity property of binary operations from a dominance point of view

In this paper, we expound weaker forms of increasingness of binary operations on a lattice by reducing the number of variables involved in the classical formulation of the increasingness property as seen from the viewpoint of dominance between binary operations. We investigate the relationships among these weaker forms. Furthermore, we demonstrate the role of these weaker forms in characterizing the meet and join operations of a lattice and a chain in particular. Finally, we provide ample generic examples.

Influence of Infrastructural Choices Strategy on Organizational Performance of Cements Manufacturing Firms in Kenya

Purpose: To determine the influence of infrastructural choices strategy on organizational performance of cements manufacturing firms in Kenya. Methodology: A mixed methods approach was used in the investigation. The study utilized a concurrent triangulation research design. The unit of analysis comprised of seven (7) cement manufacturing firms, targeting all the 4192 employees from where a sample size of 365 employees was drawn. The data collection tools comprised of semi-structured questionnaires and interview guides. The interview guide was administered to the CEOs while the questionnaires were administered to the managers and non-managers. The study tools were piloted to assess their validity and reliability. The data was then analyzed both descriptively and inferentially using SPSS version 27. Quantitative data was analyzed using means and standard deviations while qualitative data was analyzed using content analysis. To make the number of variables more manageable, the data was subsequently subjected to exploratory factor analysis. Findings: The results of the study revealed that infrastructural choice strategies have been employed by cement manufacturing firms to a good extent. The regression results obtained also led to the rejection of the null hypotheses since infrastructural choice strategies were found to have a positive and significant influence on organizational performance. Unique Contribution to Theory, Policy and Practice: The study recommended, based on these results that the management of individual cement firms need to identify appropriate infrastructural choice strategies and leverage on them in order to achieve efficient and effective utilization of their scarce resources at organizational level.

Approximate and Exact Optimization Algorithms for the Beltway and Turnpike Problems with Duplicated, Missing, Partially Labeled, and Uncertain Measurements.

The Turnpike problem aims to reconstruct a set of one-dimensional points from their unordered pairwise distances. Turnpike arises in biological applications such as molecular structure determination, genomic sequencing, tandem mass spectrometry, and molecular error-correcting codes. Under noisy observation of the distances, the Turnpike problem is NP-hard and can take exponential time and space to solve when using traditional algorithms. To address this, we reframe the noisy Turnpike problem through the lens of optimization, seeking to simultaneously find the unknown point set and a permutation that maximizes similarity to the input distances. Our core contribution is a suite of algorithms that robustly solve this new objective. This includes a bilevel optimization framework that can efficiently solve Turnpike instances with up to 100,000 points. We show that this framework can be extended to scenarios with domain-specific constraints that include duplicated, missing, and partially labeled distances. Using these, we also extend our algorithms to work for points distributed on a circle (the Beltway problem). For small-scale applications that require global optimality, we formulate an integer linear program (ILP) that (i) accepts an objective from a generic family of convex functions and (ii) uses an extended formulation to reduce the number of binary variables. On synthetic and real partial digest data, our bilevel algorithms achieved state-of-the-art scalability across challenging scenarios with performance that matches or exceeds competing baselines. On small-scale instances, our ILP efficiently recovered ground-truth assignments and produced reconstructions that match or exceed our alternating algorithms. Our implementations are available at https://github.com/Kingsford-Group/turnpikesolvermm.

EMSA-IK: a real-time evolutionary multi-objective semi-analytical inverse kinematics algorithm for redundant manipulators

Purpose This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators. Design/methodology/approach Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints. Findings Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application. Originality/value The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

Machine learning for predicting device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients

This study aimed to preliminarily develop machine learning (ML) models capable of predicting the risk of device-associated infection and 30-day outcomes following invasive device procedures in intensive care unit (ICU) patients. The study utilized data from 8574 ICU patients who underwent invasive procedures, sourced from the Medical Information Mart for Intensive Care (MIMIC)-IV version 2.2 database. Patients were allocated into training and validation datasets in a 7:3 ratio. Seven ML models were employed for predicting device-associated infections, while five models were used for predicting 30-day survival outcomes. Model performance was primarily evaluated using the receiver operating characteristic (ROC) curve for infection prediction and the survival model’s concordance index (C-index). Top-performing models progressively reduced the number of variables based on their importance, thereby optimizing practical utility. The inclusion of all variables demonstrated that extreme gradient boosting (XGBoost) and extra survival trees (EST) models yielded superior discriminatory performance. Notably, when restricted to the top 10 variables, both models maintained performance levels comparable to when all variables were included. In the validation cohort, the XGBoost model, with the top 10 variables, achieved an area under the curve (AUC) of 0.810 (95% CI 0.808–0.812), an area under the precision-recall curve (AUPRC) of 0.226 (95% CI 0.222–0.230), and a Brier score (BS) of 0.053 (95% CI 0.053–0.054). The EST model, with the top 10 variables, reported a C-index of 0.756 (95% CI 0.754–0.757), a time-dependent AUC of 0.759 (95% CI 0.763–0.775), and an integrated Brier score (IBS) of 0.087 (95% CI 0.087–0.087). Both models are accessible via a web application. The internally evaluated XGBoost and EST models demonstrated exceptional predictive accuracy for device-associated infection risks and 30-day survival outcomes post-invasive procedures in ICU patients. Further validation is required to confirm the clinical utility of these two models in future studies.

On nonnegative invariant quartics in type A

The equivariant nonnegativity versus sums of squares question has been solved for any infinite series of essential reflection groups but type A. As a first step to a classification, we analyse An-invariant quartics. We prove that the cones of invariant sums of squares and nonnegative forms are equal if and only if the number of variables is at most 3 or odd.

Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimization

Surrogate-based optimization (SBO) approach is becoming more and more popular in the expensive aerodynamic design of aircraft. However, with increasing number of design variables required for parameterizing a complex shape, SBO is suffering from the serious difficulty of the curse of dimensionality. To ameliorate this issue, a supervised nonlinear dimensionality-reduction surrogate modelling method was proposed. Such a method combines the supervised kernel principal component analysis (SKPCA) and polynomial chaos-Kriging (PCK) techniques into the jointly surrogate modelling process and adaptively establishes the accurate mapping from the high-dimensional inputs to the output of the system. This SKPCA-PCK method, which fully considers the effect of inputs on outputs and adaptively trains these hyper-parameters in the surrogate modelling process, escapes from the low prediction accuracy and instability of the surrogate model in conjunction with current linear or unsupervised dimensionality-reduction methods. Further, an efficient SKPCA-PCK-based global optimization method for high-dimensional aerodynamic design was developed. The performance of the proposed method is examined by investigating two numerical examples, the transonic RAE2822 airfoil and the wing of the NASA Common Research Model. Results demonstrate that the proposed SKPCA-PCK method significantly improves the modelling efficiency and accuracy compared to the unsupervised linear PCA-Kriging method. More importantly, the proposed SKPCA-PCK-based optimization method provides better performance and an appreciably higher optimization efficiency for expensive single-point and robust aerodynamic design involving high-dimensional design variables compared to the Kriging-based optimization method. These results provide further evidence that the proposed method provides a promising approach for mitigating the curse of dimensionality in SBO.

Demonstration of potential DNA contamination introduced by laboratory consumables using Fluorescein

The development of increasingly efficient DNA extraction and profiling kits has increased the amount of allelic information obtained from trace DNA samples, but also inadvertently, increased the detection of DNA contamination. This study aimed to evaluate the potential of DNA transfer using fluorescein, fluorescent under an alternate light source, in the use of a range of forensically relevant DNA profiling consumables. An evaluation of two pre-lysis methods adopting three different sample tubes, some with deliberate seal damage, showed the PrepFiler™ Automated Forensic DNA Extraction Kit caused leakage and crusting when the rim of the PrepFiler™ LySep column was compromised, but no leakage was observed under the same conditions using the Investigator STAR Lyse&Prep kit. The AutoLys tube showed minimal leakage using the PrepFiler™ chemistry. A DNA extract tube with an external thread, similar to the AutoLys tube, showed no leakage after fridge or freezer storage. However, it highlighted that a centrifugal spin does not guarantee all the DNA will pool at the base of the tube. A comparison of adhesive plate sealing films to 8-well strip caps for sealing 96-well PCR plates showed the adhesive plate sealing films presented a lower risk of DNA transfer, largely due to the adhesion of dispersed liquid on the sticky surface of the film. Overall, this study highlighted a number of variables that may be considered in the development of more refined contamination minimisation protocols in respect to increased sensitivities of DNA profiling.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Mathematical models of C-type and N-type inactivating heteromeric voltage gated potassium channels.

Voltage gated potassium channels can be composed of either four identical, or different, pore-forming protein subunits. While the voltage gated channels with identical subunits have been extensively studied both physiologically and mathematically, those with multiple subunit types, termed heteromeric channels, have not been. Here we construct, and explore the predictive outputs of, mechanistic models for heteromeric voltage gated potassium channels that possess either N-type or C-type inactivation kinetics. For both types of inactivation, we first build Markov models of four identical pore-forming inactivating subunits. Combining this with previous results regarding non-inactivating heteromeric channels, we are able to define models for heteromeric channels containing both non-inactivating and inactivating subunits of any ratio. We simulate each model through three unique voltage clamp protocols to identify steady state properties. In doing so, we generate predictions about the impact of adding additional inactivating subunits on a total channel's kinetics. We show that while N-type inactivating subunits appear to have a non-linear impact on the level of inactivation the channel experiences, the effect of C-type inactivating subunits is almost linear. Finally, to combat the computational issues of working with a large number of state variables we define model reductions for both types of heteromeric channels. For the N-type heteromers we derive a quasi-steady-state approximation and indicate where the approximation is appropriate. With the C-type heteromers we are able to write an explicit model reduction bringing models of greater than 10 dimensions down to 2.

Rational Expectations Models with Higher-Order Beliefs

Abstract We develop a method of solving rational expectations models with dispersed information and dynamic strategic complementarities. In these types of models, the equilibrium outcome hinges on an infinite number of higher-order expectations which require an increasing number of state variables to keep track of. Despite this complication, we prove that the equilibrium outcome always admits a finite-state representation when the signals follow finite ARMA processes. We also show that such a finite-state result may not hold with endogenous information aggregation. We further illustrate how to use the method to derive comparative statics, characterize equilibrium outcomes in HANK-type network games, reconcile with empirical evidence on expectations, and integrate incomplete information with bounded rationality in general equilibrium.

THE EFFECT OF SERVICE QUALITY AND PRICE ON PURCHASE DECISIONS AT D'CREPES PONDOK INDAH MALL SOUTH JAKARTA

This study aims to analyze the effect of service quality and price on consumer purchase decisions at D'Crepes Pondok Indah Mall, South Jakarta. The research employs a quantitative method with an associative approach, where service quality and price serve as independent variables, and purchase decisions act as the dependent variable. Data was collected through questionnaires distributed to 100 respondents. The findings indicate that both service quality and price significantly influence consumer purchase decisions, both partially and simultaneously. Simple linear regression analysis reveals that service quality has a strong impact on purchase decisions, while price also exerts a considerable influence. Additionally, multiple linear regression confirms that service quality and price together have a very strong correlation with purchase decisions, accounting for 76.4% of the variation in consumer behavior. This research emphasizes the importance of providing high-quality service and setting appropriate prices to improve consumer purchase decisions. However, the study acknowledges several limitations, including the restricted number of variables analyzed and the sole use of questionnaires for data collection. Further studies are recommended to explore additional factors such as location, promotion, and brand image, and to expand the sample size for broader insights.