Tool For Analysis Of Processes Research Articles

Pengaruh Kualitas Aplikasi Terhadap Keputusan Menggunakan Jasa Transportasi Online Maxim: Survei pada Mahasiswa UNSIKA

This research aims to find out how much influence application quality has on the decision to use Maxim's online transportation services with the application quality variable as the independent variable and the decision to use as the dependent variable. This study uses a quantitative approach. This research was conducted on Unsika students and the number of respondents to this research was 100 customers who had ordered Maxim online transportation services. The data collection tool uses a questionnaire and the data processing analysis tool uses SmartPLS 3.0. The results of the research state that the quality of the application has a significant influence on the decision to use Maxim's online transportation services.

Positron Emission Projection Imaging: A technique for concentration field measurements in opaque industrial systems

A novel Positron Emission Projection Imaging (PEPI) algorithm designed to compute the plane-projected spatial distribution of radiolabelled materials without the need for collimation is introduced. By leveraging improved data efficiency, we have achieved a technique with enhanced spatial resolution and temporal resolution compared to previous PEPI algorithms. Validation of this algorithm was conducted using synthetic data generated from a digital twin of a PET scanner, demonstrating its accuracy for practical applications. The industrial advantage of this novel algorithm was applied in the imaging of laminar flow mixing within a ploughshare mixer, with the experimental results compared against those obtained from validated computational fluid dynamics (CFD) models. This comparison highlights an important use case for PEPI as a robust validation tool for CFD simulations, crucial for enhancing industrial processes. PEPI, which uses deeply penetrating gamma-photons, is now capable of imaging opaque fluids and solids in industrial casing. Future directions for this work include further algorithmic refinements and expanding its application across various industrial systems, establishing PEPI as a robust tool for in-depth industrial process analysis. The advancements presented here allow for optimized mixer design and enhanced process efficiency, extending the frontiers of tomographic imaging in industrial applications.

Linking implementation science and policy: Process and tools for congressionally mandated implementation, evaluation, and reporting.

To describe a process model for assisting partners in addressing requirements of legislation and review policy analysis, planning, and evaluation design processes and tools. Throughout its 25-year history, the United States Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) program has been a forerunner in partnering with organizational leaders to improve health care. The Foundations of Evidence-based Policymaking Act of 2018 provided new opportunities for QUERI and other implementation scientists to support federal agency leaders in implementing, evaluating, and reporting on congressionally mandated programs. Although implementation scientists have the skills to support partnered implementation and evaluation, these skills must be adapted for congressionally mandated projects as many scientists have limited experience in policy analysis and the intersection of data informing organizational policy, programs, and practices (i.e., evidence-based policy). During the conduct of four congressionally mandated projects, our national VA QUERI team developed processes and tools to achieve the goals and aims of our VHA partners and to ensure our collective work and reporting met legislative requirements. Our process model, program planning, and analysis tools were informed by an iterative process of refining and adapting the tools over a period of six years, spanning the years 2017 to 2023. Work to support our partners was conducted across three phases: preparation and planning, conducting implementation and evaluation, and developing the congressionally mandated report. The processes and tools we developed within the context of mutually respectful and honest partnerships have been critical to our QUERI center's success in this area. Lessons we learned may help other scientists partnering in VA or other federal agencies to plan, conduct, and report on congressionally mandated projects.

Unveiling 3-D evolution and mechanisms of ozone pollution in Changzhou, China: Insights from lidar observations and modelling

Ground ozone (O3) pollution has emerged as a prominent environmental concern in eastern cities of China, particularly during the summer and autumn seasons. However, a comprehensive investigation into the three-dimensional (3-D) evolution characteristics of O3 within complicated urban environments, especially in lake-land environment, is notably scarce. To enhance our understanding of the mechanisms underlying elevated O3 concentrations within a 3-D scale, this study employed an ozone lidar to delineate vertical ozone profiles in Changzhou, a typical city in China with complicated anthropogenic and biogenic emissions and complex land cover. The process analysis tool integrated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model was further utilized to analyze the formation processes of O3. The results unveil a persistent O3 pollution episode lasting over 15 days in Changzhou during the study period, with multiple peaks exceeding 200 μg m⁻³. Notably, O3 predominantly accumulated within the boundary layer, confined below 1.2 km. Both ground and vertical contributions to this pollution were mainly due to local chemical reactions, with a maximum near-surface contribution reaching 19 ppb h−1 and a vertical contribution of 10 ppb h−1 at the height of 900 ± 200 m. Furthermore, episodes of the enhanced O3 concentrations on August 9 and August 26, 2021, were influenced by external advection process. Our study also found that local circulation plays an important role in the accumulation of surface O3 during certain periods. There was a temperature difference between the surface of Lake Tai and the adjacent land, resulting in the formation of lake-land breezes that facilitate the transport of O3 from the lake surface to the terrestrial environment during pollution events. Our study emphasizes the necessity of reducing local pollutant emissions and implementing joint emission controls as the primary strategies for mitigating O3 pollution in Changzhou and the surrounding region.

A Case Study on the Operational Performance Evaluation of a Manufacturing Process and System (MaPS) Sustainability Analysis Tool for Engineering Education

As the future product and process design decision-makers, students in engineering should have knowledge of sustainability and its application in industry. However, educators are challenged in training students in these concepts. Existing analysis tools are deficient in supporting sustainability assessment by nonexperts. To address this gap, the Manufacturing Process and System (MaPS) Sustainability Analysis tool was developed under the Constructionism in Learning: Sustainable Life Cycle Engineering (CooL:SLiCE) project. The technical aspects of developing a framework and its associated proof-of-concept MaPS Sustainability Analysis tool are reported in a separate study. Herein, the educational aspects of the MaPS Sustainability Analysis tool and its operational performance in terms of ease of use and usefulness to assess its suitability for the classroom are investigated. Undergraduate and graduate engineering students at Tampere University and Oregon State University were recruited to use and evaluate the tool. Through application of the Technology Acceptance Model (TAM), it was determined that the 24 study participants found the tool easy to use and useful in analyzing product design, manufacturing process, and supply chain sustainability performance. To move this proof of concept toward general use, key software operational characteristics and a GUI should be investigated to improve efficiency, effectiveness, satisfaction, and learnability of the MaPS Sustainability Analysis tool.

Advancing Industrial Process Electrification and Heat Pump Integration with New Exergy Pinch Analysis Targeting Techniques

The process integration and electrification concept has significant potential to support the industrial transition to low- and net-zero-carbon process heating. This increasingly essential concept requires an expanded set of process analysis tools to fully comprehend the interplay of heat recovery and process electrification (e.g., heat pumping). In this paper, new Exergy Pinch Analysis tools and methods are proposed that can set lower bound work targets by acutely balancing process heat recovery and heat pumping. As part of the analysis, net energy and exergy load curves enable visualization of energy and exergy surpluses and deficits. As extensions to the grand composite curve in conventional Pinch Analysis, these curves enable examination of different pocket-cutting strategies, revealing their distinct impacts on heat, exergy, and work targets. Demonstrated via case studies on a spray dryer and an evaporator, the exergy analysis targets net shaft-work correctly. In the evaporator case study, the analysis points to the heat recovery pockets playing an essential role in reducing the work target by 25.7%. The findings offer substantial potential for improved industrial energy management, providing a robust framework for engineers to enhance industrial process and energy sustainability.

Prediction of key quality attributes in Salvia miltiorrhiza standard decoction using a Gaussian process regression model.

Nonstationary, nonlinear mass transfer in traditional Chinese medicine (TCM) extraction poses challenges to correlating process characteristics with quality parameters, particularly in defining clear parameter ranges for the process. The aim of the study was to provide a solution for quality consistency analysis in TCM preparation processes. Salvia miltiorrhiza was taken as an example for 15 batches of standard decoction. Using aqueous extract, alcoholic extract, and the content of salvianolic acid B as herb material key quality attributes, multiple nonlinear regression, Gaussian process regression, and artificial neural network models were employed to predict the key quality attributes including the paste yield, the content of salvianolic acid B, and the transfer rate. The evaluation criteria were root mean square error, mean absolute percentage error, and R2. The Gaussian process regression model had the best prediction effect on the paste yield, the content of salvianolic acid B, and the transfer rate, with R2 being 0.918, 0.934, and 0.919, respectively. Utilizing Gaussian process regression model confidence intervals, along with Shewhart control and intervals optimized through process capability index analysis, the quality control range of the standard decoction was determined as follows: paste yield, 25.14%-33.19%; salvianolic acid B content, 2.62%-4.78%; and transfer rate, 56.88%-64.80%. This study combined the preparation process of standard decoction with the Gaussian process regression model, accurately predicted the key quality attributes, and determined the quality parameter range by using process analysis tools, providing a new idea for the quality consistency standard of TCMprocesses.

Comparison of novel character relationship network mining and drama character relationship shaping algorithms

This study embarks on an interdisciplinary journey to analyze and compare Character Relationships (CR) in two diverse storytelling mediums – the classic novel “Emma” by Jane Austen and the popular TV series “Friends”. Leveraging a blend of Natural Language Processing (NLP) and advanced video analysis tools, this research “sheds light on” the intricate network of CR within these narratives. This study scientifically analyzes these relationships' complete method, creation, and impact using sentiment analysis, object identification, and story coherence algorithms. Qualitative and quantitative metrics such as precision, recall, F-score, and accuracy assist in explaining character updates. The content and TV exhibit distinct storytelling modes, and this study demonstrates that algorithmic analysis of stories is practical. The findings of this study request to contribute to online studies by focusing on understanding character networks and their vital role in TV storytelling.

Analysis of The Use of Nguyen Widrow Algorithm in Backpropagation in Kidney Disease

Fast and accurate diagnosis is very important for kidney disease. This research conducts and analyzes by using Nguyen Widrow Algorithm in Back Propagation method in artificial neural network for kidney disease diagnosis with the aim to improve the accuracy in predicting and time efficiency in diagnosing. The Nguyen Widrow algorithm is very capable of accelerating convergence and stabilizing the learning process in artificial neural networks, which is also expected to present a meaningful contribution to the handling of health data. This study uses MATLAB as a platform for algorithm implementation and a dataset of medical records of kidney disease patients collected from a hospital that specializes in treating kidney disease patients. The data pre-processing and artificial neural network modeling stages use the Nguyen Widrow algorithm, while the model training process uses the Back Propagation method. The results showed that the Nguyen Widrow algorithm was able to improve the accuracy of predicting someone suffering from kidney disease compared to using only the Back Propagation method. Analysis of the performance of the model shows a significant improvement in stability and convergence speed during the learning process. This indicates that data processing and medical decision making becomes more efficient. On the other hand, this research also studied the challenges and limitations that will be faced in terms of implementation of the Nguyen Widrow algorithm. Also the sensitivity of the initialization parameters, the need for the quality of the dataset to be used in training the model.This research reveals the ability of the Nguyen Widrow algorithm to improve the performance of artificial neural networks in diagnosing kidney disease. By implementing this algorithm in MATLAB, the results show that the use of the latest data processing technology and analysis tools can provide significant improvements in accuracy and efficiency in the medical field. In addition, this research is expected to provide a new direction in the development of machine learning algorithms for applications in the healthcare field, especially for diagnosing kidney disease. By further utilizing this technology, it contributes significantly to improving the quality of healthcare and treatment outcomes for patients suffering from kidney disease.

IVIPAT: an in-vehicle information processing analysis tool to optimize user interaction flows

IVIPAT: an in-vehicle information processing analysis tool to optimize user interaction flows

Numerical analysis of maximum force in deep drawing processes of prismatic parts

The high competitiveness of the industrial sector and the short time for developing new products drives the use of process analysis tools that enable speeding up the design phase. In sheet metal stamping projects, numerical simulation has become indispensable. As a replacement for traditional trial-and-error methods, the finite element method (FEM) enables the optimization of process-related variables. Consequently, it is possible to mitigate some of its main problems, such as the emergence of fracture, wrinkling, and springback. A second alternative for analyzing the process, even faster and cheaper, is based on the use of analytical calculations. These can predict, among other parameters, the forces involved in stamping. This study aims to evaluate the degree of accuracy of equations predicted in the literature to estimate the maximum stamping force of non-axisymmetric parts. For this purpose, analytical results of the stamping force of 1 mm thick austenitic stainless steel AISI 304 sheets were compared to results obtained via numerical simulation.

Manufacturing Process and System Sustainability Analysis Tool: A Proof-of-Concept for Teaching Sustainable Product Design and Manufacturing Engineering

Abstract Decision support methods and tools have been developed to aid in improving product sustainability performance during design. However, these approaches are often developed for domain experts and not well-suited for non-expert decision makers (e.g., engineering students and engineering practitioners), who do not possess specialized knowledge in sustainability analysis of product designs and manufacturing processes. The objective of this research is to facilitate the sustainability performance analysis of manufacturing processes and systems through unit manufacturing process (UMP) modeling within an easy-to-use, publicly-available product design, and manufacturing analysis tool. To achieve this objective, a sustainability assessment framework is developed that considers a cradle-to-gate life cycle scope and has four phases: (1) product development, (2) supply chain configuration, (3) manufacturing process design, and (4) manufacturing process and system (MaPS) sustainability analysis. To implement this framework and to address the identified limitations of existing tools, a proof-of-concept MaPS sustainability analysis tool is developed as a spreadsheet software tool. The tool supports the evaluation of environmental (energy and associated carbon footprint), economic (the cost of goods sold), and social (worker safety) impacts. While this study focuses on the technical aspects of the research, the authors investigate associated educational aspects in a separate study and report tool operational performance evaluation by undergraduate and graduate engineering students. Study participants found the tool easy to use and useful in completing sustainability assessment tasks in product design and manufacturing. To build upon this research, the developed framework and tool can be expanded to consider other phases of the product life cycle. Moreover, key software tool operational characteristics and graphical user interfaces should be investigated to improve efficiency, effectiveness, satisfaction, and learnability of the MaPS sustainability analysis tool.

Understanding offshore high-ozone events during TRACER-AQ 2021 in Houston: insights from WRF–CAMx photochemical modeling

Abstract. Mechanisms for high offshore ozone (O3) events in the Houston area have not been systematically examined due to limited O3 measurements over water. In this study, we used the datasets collected by three boats deployed in Galveston Bay and the Gulf of Mexico during the Tracking Aerosol Convection Interactions ExpeRiment – Air Quality (TRACER-AQ) field campaign period (September 2021), in combination with the Weather Research and Forecasting (WRF) coupled Comprehensive Air quality Model with Extensions (CAMx) modeling system (WRF–CAMx), to investigate the reasons for high offshore O3. The model can capture the spatiotemporal variability in the daytime (10:00–18:00 central daylight time, CDT) O3 for the three boats (R > 0.7) but tends to overestimate O3 by ∼ 10 ppb on clean days and underestimate O3 by ∼ 3 ppb during high-O3 events. The process analysis tool in CAMx identifies O3 chemistry as the major process leading to high-O3 concentrations. The region-wide increase in the long-lived volatile organic compounds (VOCs) through advection transits O3 formation to be more sensitive to NOx, leading to more O3 production under a NOx-limited regime. In addition, the VOC-limited O3 formation is also boosted along western Galveston Bay and the Gulf Coast under high-NOx conditions brought by the northeasterly winds from the Houston Ship Channel. Two case studies illustrate that high offshore O3 events can develop under both large- and mesoscale circulations, indicating both the regional and local emissions need to be stringently controlled. Wind conditions are demonstrated to be important meteorological factors in such events, so they must be well represented in photochemical models to forecast air quality over the urban coastal regions accurately.

Fractional Fourier transform associated with polar coordinates

The fractional Fourier transform (FRFT) is a generalized form of the Fourier transform (FT), it is another important class of time–frequency analysis tool in signal processing. In this paper, we study the two-dimensional (2D) FRFT in the polar coordinates setting. First, Parseval theorem of the 2D FRFT in the polar coordinates is obtained. Then, according to the relationship between 2D FRFT and fractional Hankel transform (FRHT), the convolution theorem for the 2D FRFT in polar coordinates is obtained. It shows that the FRFT of the convolution of two functions is the product of their respective FRFTs. Moreover, the fast algorithm for the convolution theorem of the 2D FRFT is discussed. Finally, the sampling theorem for signal is explored.

Design of spherical agglomeration via salt-induced liquid–liquid phase separation

In this study, a novel strategy for spherical agglomeration via salt-induced liquid–liquid phase separation (SI–SA) was developed. SI–SA technology achieves phase separation in the miscible binary anti-solvent crystallization system by adding certain amounts of salts, avoiding the use of toxic bridging liquids. Water–organic solvent–salt systems that induced phase separation were selected using phase separation experiments. The wetting performance of the organic solvent was estimated by droplet experiments, and organic solvents with stronger wettability for crystals than water would be selected for further verification. Spherical agglomeration experiments were designed based on the ternary phase diagrams of these selected systems. Water–organic solvent–salt systems of benzoic acid were selected, starting with six organic solvents and four salts. A total number of 16 water–organic solvents–salt systems were effectively predicted using the SI–SA strategy. The in-situ process analysis tool indicated that the formation of spherical particles of benzoic acid underwent a process of crystallization, phase separation, wetting, two-step agglomeration, and consolidation. The stirring rate and mass ratio of salt to final crystals were found to be important for controlling the morphology and particle size of the spherical crystals. The spherical particles of aspirin and salicylic acid were successfully predicted in different systems via SI–SA technology, demonstrating the universality and potential usage of SI–SA technology in industry.

Synergistic Application of Machine Learning to Microstructural Characterization on Electrode Composites of Solid Oxide Fuel Cells

Solid oxide fuel cells (SOFCs) have been recognized as one of the most potent next-generation energy conversion systems, owing to their higher efficiency, better fuel flexibility, and smaller amount of pollution, unlike their counterpart fuel cells. SOFCs can be analyzed as a highly-complex multi-component materials system consisting of porous electrodes (cathodes and anodes), densified electrolytes, interconnects, and sealing materials, which are sequentially layered and characteristic of heterogeneous and multi-phase materials. The resultant power generation is achieved through the electrochemical reactions involving electronic carriers, ionic carries, and gaseous transports at anodes and cathodes, influencing their electrical, mechanical, and thermal properties. In particular, both cathodes and anodes are typically constructed based on three components: mixed ionic/electronic electrodes, electrolytes, and a porous section. Those three constituents should be optimized at both electrodes with the aim of maximizing the electrochemical reaction sites, i.e., triple-phase boundaries in association with the gaseous transport involved in oxygen reduction reactions at cathodes and hydrogen oxidation reactions at anodes, which leads to high cell performance and guaranteed durability. The electrochemical performances are crucially dependent on their complex microstructures, demanding for highly informative analyses on the electron microscopy images under investigation.This work reports quantitative microstructural analysis methodology incorporating machine learning-assisted microstructural analysis strategy. Quantitative microstructural interpretations were carried out without human involvement through an integrated combination of deep learning and focused ion beam-scanning electron microscopy (FIB-SEM) analytics on Ni/Y2O3-stabilized ZrO2 (Ni/YSZ) cermets. The Ni/YSZ/pore composites were analyzed for the automated extraction of microstructural parameters with the aim to preventing the subjective analysis problems and unavoidable artifacts frequently encountered in lengthy image processing tasks and eliminate biased evaluations. Considering the high volume of image data and future expectations for electron microscopy usage, FIB-SEM was efficiently combined with semantic segmentation targeted at detailed phase recognition on three constituents, i.e, Ni, YSZ, and pores. Traditional image processing analysis tools are in the synergistic manner combined with phase separation predictions by semantic segmentation algorithms, allowing for a quantitative evaluation of microstructural parameters. The combined strategy enables one to significantly enhance poor image quality originating from artifacts in electron microscopy, including charging effects, curtain effects, out-of-focus problems, and unclear phase boundaries encountered in searching for high-efficiency solid oxide fuel cells (SOFCs).

Analysis of Causes and Sources of Summer Ozone Pollution in Rizhao Based on CMAQ and HYSPLIT Models

As a typical coastal city, O3 pollution in Rizhao has become increasingly serious in recent years. In order to explore the causes and sources of O3 pollution, IPR process analysis and ISAM source tracking tools based on the CMAQ model were used, respectively, to quantify the contributions of different physicochemical processes and different source tracking areas to O3 in Rizhao. Additionally, by comparing the differences between O3-exceeding days and non-exceeding days, combined with the HYSPLIT model, the regional transportation path of O3 in Rizhao was explored. The results showed that the concentrations of O3, NOx, and VOCs near the coastal areas of Rizhao and Lianyungang were significantly increased on O3 exceedance days compared with those on non-exceedance days. This was mainly because Rizhao was the convergence zone of western, southwestern, and eastern winds on exceedance days, which facilitated the transport and accumulation of pollutants. Process analysis showed that the transport process (TRAN) contribution to the near-surface O3 near the coastal areas of Rizhao and Lianyungang increased significantly on the exceedance days, whereas the contribution to most areas to the west of Linyi decreased. Photochemical reaction (CHEM) had a positive contribution to the O3 concentration in Rizhao during the daytime at all heights, and TRAN had a positive contribution at 0-60 m above the ground, and mainly had a negative contribution above 60 m. The contributions of CHEM and TRAN at 0-60 m above the ground would increase significantly on exceedance days, which was approximately twice that on the non-exceedance days. Source analysis showed that the local sources in Rizhao were the main contribution sources of NOx and VOCs, with the contribution rates of 47.5% and 58.0%, respectively. O3 mainly came from the contribution outside the simulation area (67.5%). The O3 and precursor contributions of the western cities of Rizhao (Weifang, Linyi, etc.) and the southern cities (Lianyungang, etc.) would increase significantly on the days of exceeding the standard. The transportation path analysis showed that the number of exceedances accounted for the largest proportion (11.8%) in the path from the west of Rizhao, which was the main transportation channel of O3 and precursors in Rizhao. This was verified through process analysis and source tracking results, and such trajectories accounted for 13.0% of the total number of trajectories, and their main routes were in the Shaanxi, Shanxi, Hebei, and Shandong regions.

Exploring mathematical connections in the context of proof and mathematical argumentation: A new proposal of networking of theories

Extended theory of mathematical connections (ETC) and theory of mathematical argumentation (TMA) based on Toulmin’s (1984) model were articulated for the study of mathematical connections activated in the argumentation process. For this purpose, a “networking of theories” was made to obtain the complementarities between both theories. Then, a class episode was selected that dealt with the demonstration of the continuity theorem of functions of real variable “<i>if a function is derivable at a point then it is continuous at that point</i>”, made by an in-service mathematics teacher of differential calculus, who participated in a non-participant observation, where his classes were videotaped. The arguments of this episode were analyzed through with Toulmin’s (1984) model, after with thematic analysis method to identify mathematical connections, and, finally, the connections in the proof and mathematical argumentation were analyzed. The main result of the research reveals that the mathematical connections play a fundamental role in the argumentation process of the episode, given that, connection is important for the establishment and identification the argument and the warrant that supports it. In addition, complementarities were found between both theories, which makes this networking a useful tool for a better analysis of mathematical argumentation processes.

Penunjukan Vendor Prioritas Dalam Penyediaan Karton Dangerous Goods Untuk Proses Re-Packing

The objective of this research is to determine the priority suppliers in the repackaging division. The main problem is that there are no results of the assessment of the existing criteria, namely one criterion with other criteria is considered to be superior in selecting a vendor. This study uses the Analytical Hierarchy Process analysis tool. The results of this assessment are the supplier's performance to the company using the AHP method based on the right criteria, namely, Dynastyindo with very good results and World Expert with quite good results. The Dynastyndo vendor is classified as very good for March 2021, while the World Expert vendor can improve its performance further. The key findings are, the criteria that are considered most important at this time are price and both services. So that these two things can be focused which can then be seen in terms of which sub-criteria or indicators need to be repaired from vendors who have problems.Policy recommendations as a follow-up to research that still has limitations, it is necessary to carry out actual vendor assessments for all vendors that work with PT. DG Pack Wahana Multi Logsitk using actual data from the current month.

PENGARUH KECEMASAN DAN KUALITAS TERHADAP NIAT KONSUMEN MENGGUNAKAN DENGAN SIKAP SEBAGAI MEDIASI BAGI PENGGUNA PRODUK WARDAH LIGHTENING SERUM AMPOULE DI SURABAYA

This research was conducted to obtain empirical evidence of the effect of anxiety on attitudes, quality on attitudes, anxiety on consumer intentions, quality on consumer intentions, and attitudes towards consumer intentions for users of Wardah Lightening Serum Ampoule products in Surabaya. This study uses a quantitative approach. The sampling technique in this study was carried out by non-probability sampling and the sample method used was purposive sampling. The sample in this study were 100 users of the Wardah Lightening Serum Ampoule product in the city of Surabaya. The data processing analysis tool method in this study uses SEM SmartPLS 3.0. The results of this study were H1 was rejected, H2 was accepted, H3 was accepted, H4 was rejected, and H5 was accepted.