Model Analysis Method Research Articles

The Influence of Halal Certification, Halal Awareness, and Brand Image on Interest in Buying Halal Food Products: An Empirical Study of Consumers in Indonesia

This study aims to determine the effect of variables on halal certification, halal awareness, brand image, and intention to buy halal food products. This research is a study of consumers of halal products. The data in this study were obtained by distributing online questionnaires to respondents who were consumers of halal products in Indonesia. The quantitative approach uses surveys as primary data and data analysis using structural equation modelling (SEM). Respondents in this study were consumers of halal products, with a total of 480 respondents who were consumers of halal food products. The method used in the sampling of respondents was purposive sampling with the criteria of having bought halal food. Collecting data by using research instruments, data analysis is quantitative. The data analysis method used in this study uses descriptive analysis to identify characteristics and the Structural Equation Modeling (SEM) analysis method with SmartPLS 3.2.6 software. The analysis in the SEM model can be divided into two stages, namely, the measurement model and the structural analysis model. The results showed that halal certification has a positive effect on purchase intention. Halal certification has a positive effect on brand image. Brand image has a positive effect on purchase intention. Halal awareness has a positive effect on purchase intention. Halal certification positively and significantly affects purchase intention through brand image.

Anticipated Attributes of Chief Executive Officers Based on Newly Developed CEO Matrix

Purpose: To evaluate deep into the multifaceted realm of Chief Executive Officer (CEO) attributes, aiming to comprehensively explore and analyze the diverse qualities that define effective leadership at the highest echelons of organizations. Central to this exploration is the utilization of a newly developed CEO Matrix, designed as a comprehensive tool to categorize and assess CEO attributes across a spectrum of competencies. Methodology: A conceptual model development and analysis method using an exploratory research method and Bloom's higher-order thinking is adopted. Further analysis of the newly developed CEO matrix is made using the ABCD analysis framework. Findings: A CEO matrix is developed based on analysis of various attributes of a CEO as manager, leader, visionary, technocrat, financial acumen, decision maker, emotional hero, role model, etc. Low and High values of Leadership Skills and Financial Acumen are selected as two parameters to represent the matrix. CEO falls into each quadrant is named suitably as (1) Visionary Leader (High Leadership, Low Financial Acumen) (2) Financial Strategist: (Low Leadership, High Financial Acumen), (3) Master/Super Strategist: (High Leadership, High Financial Acumen), and (4) Developing Leader: (Low Leadership, Low Financial Acumen). The CEO matrix is further discussed using Bloom’s Higher-order research skills. Originality/Values: A 2x2 CEO matrix is created with two parameters (Leadership effectiveness and financial acumen. The CEO matrix is analysed based on Higher order thinking research skills and the ABCD analysis framework. Research Type: Exploratory.

The impact of disposition effect, herding and overconfidence on investment decision making moderated by financial literacy

Behavioral finance elucidates how investors rationally commit errors in the process of making investment decisions as a result of cognitive and emotional biases, ultimately resulting in unfavorable investment choices. People are unable to avoid a variety of behavioral biases that have an impact on investors' investment decision-making processes. The human mind employs shortcuts and emotional filters to interpret information, leading to errors in the decision-making process. The objective of this study is to investigate and assess the impact of behavioral biases such as disposition effects, herding, and overconfidence on investment decision-making. Additionally, we will examine how financial literacy moderates these biases. The research employed a purposive sample strategy to select generation Z investors in Kediri City. The sample size of 400 respondents was determined using the Krejcie & Morgan table. This research employs the Partial Least Squares (PLS) Structural Equation Model (SEM) analysis method using the SmartPLS version 3 data processing application. The research findings indicate that the disposition effect and overconfidence exert a substantial impact on investment decision-making; however, herding does not exhibit a significant influence on investment choice-making. Financial knowledge enhances the impact of the disposition effect on investment decision-making in a positive way. Financial literacy has a negative moderating effect on the influence of herding and overconfidence on investment decision-making among Generation Z investors in Kediri City.

The effect of nurse engagement and affective commitment on nurse performance in the health sector through self-efficacy as a mediating variable: a study at Malang City hospital

Malang City Hospital is one of the hospitals appointed by the Malang City government in handling Covid-19 patients. This study aims to analyze the effect of nurse engagement and affective commitment on nurse performance in the health sector through self-efficacy as a mediating variable. Data were taken from 102 nurses at the Malang City Hospital using the Structurals Equation Model analysis method using the Variances Based SEM approach or better known as Partial Least Square (PLS). The results of this study indicate that nurse engagement and affective commitment have no effect on nurse performance. Self-efficacy is able to mediate the effect of nurse engagement and affective commitment on nurse performance.

Optimization design of optical windows for deep-sea pressure-resistant structures based on transition materials

IntroductionThe design of high-pressure optical windows is one of the key tasks in deep-sea exploration, as it directly determines the reliability and maximum operating depth of underwater optical equipment.MethodIn this paper, mechanical modeling and finite element analysis methods are employed to study optical windows.Results and discussionResults show that changes in boundary conditions during loading significantly affect the stress distribution of the window. Based on this conclusion, an optimization design method is proposed, which involves the use of transition materials to reduce the impact of base deformation on the window. The study further analyzes the effects of the transition material’s Young’s modulus, Poisson’s ratio, thickness, and friction coefficient on the stress of the optical window. A calculation method for material selection criteria is proposed to select appropriate transition materials, and actual materials are used for verification. Finally, the reliability of the optimization design scheme based on transition materials is confirmed through high-pressure experiments.

Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology

The advent of the Internet of Vehicles (IoV) has led to a significant surge in data generation from vehicles, prompting the innovative utilization of data transactions within the IoV environment. However, due to the inherent trustless nature of data transactions in IoV, concerns have arisen regarding the lack of trust between involved parties and the potential compromise of user privacy. To address these issues, blockchain technology emerges as a suitable decentralized distributed storage and security management solution, offering transparency and security in data transactions. In this study, we leverage blockchain to integrate with the IoV, devising a robust trust management framework, and devising a privacy protection scheme to safeguard user privacy concerns. Additionally, we employ the performance evaluation process algebra (PEPA) method for system modeling and performance analysis to assess the efficacy of our proposed solution. Empirical findings demonstrate that our approach effectively enhances the performance of data transactions within the IoV while ensuring that the privacy of users remains intact.

SISO impedance modeling and stability comparison of grid-connected inverter control system in different time domains

Due to the effects of grid impedance and the negative impedance from the phase-locked loop, the inverter may become unstable during the grid connection process. In order to analyze the small interference stability of grid-connected systems, the analysis method of impedance modeling came into being, which is particularly important for accurately analyzing and judging the oscillation instability of the system under weak grid. However, conventional impedance modeling methods often neglect the discrete characteristics of the control system, resulting in certain limitations when examining stability concerns. To solve this problem, a stability analysis approach based on single-input single-output (SISO) hybrid impedance model is proposed. Firstly, SISO continuous impedance model of system is constructed. Then, the discrete characteristics of the control system are considered, resulting in the derivation of a SISO hybrid impedance model. Furthermore, stable regions of two models are compared under different sampling frequencies. The results indicate that the hybrid impedance model proposed exhibits reduced conservatism, offering a more accurate assessment of stability and stability margins. This finding holds particular significance in guiding the design of controller parameters.

Experimental Study and Seismic Design of Swing Story-Lateral Force Resisting Structural System

A seismic vibration control system, consisting of a swing story substructure, a lateral force resisting substructure and connecting dampers, is recently proposed. The analytical model and optimization method based on the stability maximization and H2 criteria have been established. In this paper, excellent vibration control effects of the proposed system are verified through the small-scale shaking table tests on a two-story model. Experimental results demonstrate the accuracy of the analytical model and the validity of the vibration control strategy. Through numerical analyses of benchmark structures, the input energy of the optimally designed system is remarkably mitigated, and favorable vibration control effects for reducing both inter-story drifts and absolute floor accelerations under earthquake ground motions are verified by comparing with conventional moment-resisting frame structures. In addition, significant control effects can be achieved when the optimal stiffness is greater than the critical buckling stiffness. A seismic design flow chart of the intensity-reduced design method is proposed based on the numerical analyses and the Chinese Seismic Design Code of Buildings. The results indicate that the seismic intensity-reduced design for the proposed system is feasible.

An analytical model for seismic response analysis of electrical equipment-steel support structure

Electrical equipment mounted on supporting structures is typical in a substation but vulnerable to earthquakes from previous field investigations. To evaluate the seismic performance of such an electrical equipment-support coupling system, a modal space analytical model was developed using the vibration partial differential equation (PDE) with distributed parameters and joint nodes. In the analytical model, resonant frequencies and mode shapes were obtained through numerical algorithms based on the boundary condition of each segmented beam. Then seismic responses of the coupling system can be attained by the modal decomposition method and superposition principle. A full-scale shaking table test was conducted on a 550 kV bushing-support coupling structure, and the accuracy of the analytical model was verified by the experimental results. Furthermore, a ceramic breakage inside the connecting flange at the base cross-section of the bushing specimen was observed after the seismic qualification test, which means its seismic performance can not meet the demand in the IEC seismic design specifications. To figure out this problem, parametric analysis was carried out using the analytical model to study the influence of parameters of the supporting structure and flange connection on the seismic responses of the equipment. In light of the analytical results, halving the original flange's flexural rigidity can reduce the bottom stress of the bushing by about 20% without over-magnifying its top displacement for this bushing-support coupling system. Application of the presented analytical modelling method can assist in the seismic optimization design of other equipment-support coupling systems in substations.

Analysis of the Trend Characteristics of Air Pollutants in the Fenwei Plain Based on the KZ Filter

In order to improve air quality, China has implemented a series of the most stringent control measures ever in recent years. Quantitatively analyzing the contribution of emissions to the trend change in air pollutants is an essential scientific basis for verifying the effectiveness of air pollution control. We based our study on the air quality online monitoring data and meteorological element data of 11 cities in the Fenwei Plain from 2018 to 2022. We quantitatively investigated the changing patterns of NO2, O3, PM10, and PM2.5 and their influencing factors in the major cities of the Fenwei Plain by using the KZ filtering and MLR modeling analysis methods. The results show the following: (1) The long-term fractions of NO2, PM10, and PM2.5 in the Fenwei Plain decreased by 10.5, 33.1, and 17.1 μg·m−3, with decreases of 25.8%, 29%, and 28.8%, respectively, from 2018 to 2022. The long-term fractions of O3 showed the characteristics of decreasing and then increasing, with 2020 as the dividing line. (2) The short-term components of NO2, PM10, and PM2.5 contributed the most to the total variance, with the proportion of short-term components ranging from 34.7% to 69.8%, 53% to 73%, and 43% to 58%, respectively. The seasonal components of O3 contributed the most to the total variance, with the proportion of short-term components ranging from 54% to 70.7%. (3) Most cities in the Fenwei Plain had unfavorable meteorological conditions with regard to NO2, PM10, and PM2.5 in 2018–2020 and favorable meteorological conditions in terms of NO2, PM10, and PM2.5 in 2020–2022. O3 showed different characteristics from the other three pollutants. Most cities in the Fenwei Plain had meteorological conditions in 2018–2019 that were unfavorable for improving O3 levels. In 2019–2021, meteorological conditions were favorable for improving O3 levels, while in 2021–2022, meteorological conditions were unfavorable for improving O3 levels.

Novel Data-Driven Deep Learning Assisted CVA for Ironmaking System Prediction and Control

Effective control of blast furnace ironmaking system (IS) is crucial to maintain the metallurgical industry in smooth operation. Also, the quality of molten iron (MIQ) can be assessed using indices of phosphorus P, sulfur S, and silicon Si contents, which provide insight into the current state of the molten iron and internal operations of the blast furnace. In this paper, we propose a novel deep learning assisted canonical variate analysis (DLaCVA) method for modeling and predicting MIQ and optimizing IS control. Due to the highly complex physical and chemical reactions within IS, we build a nonlinear state-space model and estimate its state, utilizing DLaCVA. Moreover, we theoretically analyze of how deep learning can aid in state-space modeling, such as deriving the corresponding optimization objective and learning gradient, laying the basis for exploring deep learning in system identification further. Ultimately, we devise a predictive control strategy based on a quadratic performance index to attain optimal MIQ control performance. Experiments utilizing genuine IS data display that DLaCVA outperforms other methods concerning modeling accuracy and control effectiveness.

Pengaruh Harga Emas Dunia, Jumlah Uang Beredar, Inflasi, dan Zakat terhadap Indeks Saham Syariah Indonesia (ISSI)

This research aims to determine the long-term and short-term impact of world gold prices, money supply, inflation, and zakat on the Indonesian shariah stock indices (ISSI). The dependent variables applied in this study are the world's gold price, money supply, inflation, and zakat. By applying the vector error correction model (VECM) analysis method in the series, data was interpolated to monthly data for 2017-2021. Secondary data is the form of data applied in this study using the Eviews 10 program. This is due to several factors influencing the index that changes the Islamic capital market by representing Indonesian stocks. Based on the results of hypothesis testing, the concluding are accepted in the form of long-term relationships between world gold prices and money supply influence it. In the short term, each dependent variable does not affect the ISSI.

Respon Ekspor Akibat Shock Nilai Tukar Dan Jumlah Uang Beredar di Indonesia

The rupiah exchange rate against the US dollar has always fluctuated, due to the instability of economic conditions both at home and abroad. The macroeconomic conditions of the two countries have an influence on the level of exports, especially in Indonesia. The response to export developments that occur in the country is not only influenced by the rupiah exchange rate with the dollar, the money supply (JUB) in Indonesia is also related to the level of exports that occur. If the JUB increases, the number of exports will also increase. So that the relationship between exports, exchange rates, and the money supply has a close relationship with the country's economic conditions. This study aims to analyze how the response in the level of exports in Indonesia is related to the shocks that occur in the exchange rate between the rupiah and the dollar, and also to the money supply in Indonesia through several variable data such as exports, exchange rates, inflation, BI rate, and money. circulating (M2), using data for 2018:M1-2022:M12 using the Vector Error Correction Model (VECM) analysis method. The results show that the response to inflation (INF), the exchange rate (KURS), the money supply (M2), and the bi rate on exports in Indonesia show that the variable response results are positive and the average is stable.

An analytical and numerical modeling method for predicting drilling force and hole expansion of a BTA drill processing Inconel 625 and FeCr alloy laminated materials

This paper aims to predict the drilling force and hole expansion of Boring and Trepanning Association (BTA) drill processing Inconel 625 and FeCr alloy laminated tube sheets, essential in petrochemical and nuclear industries. The paper proposes a combined analytical and numerical modeling approach that integrates discrete forces on multiple cutting edges and guide pads of the BTA drill, experimentally determines the mathematical relationship between the discrete forces, and predicts the drilling force using a calibrated finite element simulation of the cutting edge. Then, a comprehensive evaluation of the drilling forces revealed the torque and feed force distribution for cutting, guide pad burnishing, and friction. Furthermore, a precise finite element simulation model for guide pad burnishing hole wall is developed and combined with the burnishing force identified by the analytical model, enabling hole expansion prediction. The proposed model is validated by drilling experiments on Inconel 625 and FeCr alloy-laminated materials with average prediction errors of 8.4 %, 4.7 %, and 20.5 % for torque, feed force, and hole expansion, respectively, demonstrating the accuracy and industrial applicability of the modeling method.

Digital Transformation Capacity of Small and Medium Enterprises: The Case of Hai Phong Province, Vietnam

The objective of this article focuses on studying the internal factors affecting the digital transformation capabilities of small and medium-sized enterprises in Hai Phong Province to explain the reasons for the limitations in seizing opportunities and implementing digital transformation. These factors include: (1) Leadership; (2) Digital-oriented business strategy; (3) Employee capabilities; (4) Corporate culture; (5) Technological infrastructure. From there, solutions to enhance the digital transformation capabilities of small and medium-sized enterprises in Hai Phong Province can be proposed based on solid grounds. The article utilizes a linear regression model analysis method to assess the impact of these factors on the digital transformation capabilities of businesses based on survey data from 427 samples, including experts and managers of small and medium-sized enterprises in Hai Phong Province. The research results indicate the factors that influence the digital transformation capabilities of businesses at various levels. Consequently, businesses will have appropriate solutions to enhance their digital transformation capabilities.

Green hospital practices on patient’s satisfaction in Indonesian hospital: medical specialist department case investigation

PurposeThis study aims to explore the empirical correlation between patient flow issues, quality of green health services and patient satisfaction in specialist medical department factors from patients’ perspectives as service consumers.Design/methodology/approachThis research is a type of nonintervention empirical research that uses an open survey to explore the views and experiences of users of specialist medical department services. The targeted population is hospital patients included in the top five national PERSI (Indonesian Hospital Association) Award 2022 Green Hospital Category, with a total number of respondents of 572 people. This study uses the partial least square-structural equation modeling analysis method with the SmartPLS application.FindingsPatient flow problems generally affect the quality of eco-friendly health services, except for the waiting time problem, which affects service quality. It should be understood as a top priority for patients to receive services from medical specialists without risking time as a core service aspect from the patient’s perspective. In addition, all variables in eco-friendly hospital services affect patient satisfaction, except in the case of visits to specialist medical departments, which do not affect medical support services and hospital practices that are responsive to the delivery of care services resulting from medical support services that are inseparable in integrated services as well as health care following medical ethics.Originality/valueThis study has a novelty in understanding the implications of green practice in determining patient satisfaction in medical specialist department as the epicenter of hospital services and the main object of assessment for the quality of hospital services.

Unified AC/DC hybrid admittance modelling and oscillation stability analysis for interlinking voltage source converter system

AbstractThe voltage source converter (VSC) interlinking AC and DC power networks is a core equipment in AC/DC hybrid power systems. To analyze the stability of this system based on the impedance method, it is necessary to establish the hybrid AC/DC admittance model of interlinking VSC. This paper presents a unified and systematic AC/DC hybrid admittance modelling framework which is widely applicable to different control methods of interlinking VSCs. The hybrid admittance models for interlinking VSCs under PQ control, VdcIq control, and Vf control are explicitly formulated, with their elements individually reflecting the dynamics of the AC side, DC side, and the interaction between them. Subsequently, the impedance network model of interlinking VSC systems is formulated based on the hybrid admittance, and furthermore the frequency‐domain modal analysis is performed to analyze the system oscillation stability. Finally, frequency scanning and time‐domain simulation results in MATLAB/Simulink are provided to validate the effectiveness of the modelling and oscillation stability analysis method.

Assessment of optimal dam release for anadromous fish migration in estuary

Dams play a crucial role in meeting growing water demands, generating energy, and flood protection. However, the controlled water release from dams can significantly impact the ecosystem of downstream areas, especially estuaries. This study investigated the effects of Sardar Sarovar Dam (SSD) on downstream flow and estimated the flow requirements necessary to support anadromous fish (in this study Tenualosa ilisha) migration in the Narmada River estuary. The study used hydrodynamic modeling and environmental flow requirement analysis methods to arrive at conclusions. The results indicated that the SSD significantly altered the downstream flow patterns, particularly during the monsoon season. A reduced freshwater flow resulted in increased tidal influence. The study found that the optimal discharge rates for fish migration range from 1500 to 2250 m3/s, and the minimum flow required to prevent tidal dominance in the estuary is 500 m3/s. The study underscores the importance of appropriate freshwater release from dams to preserve downstream ecology, support fish migration, and avoid adverse effects on fishing communities.

Study on temperature rise characteristics of DC charging cable for electric vehicles

In order to accurately calculate the relationship between the loading current and temperature rise of DC charging cable of electric vehicles, this article establishes the equivalent thermal circuit model of DC charging cable of electric vehicle based on the thermal circuit model analysis method, and refers to the principle of adaptive filtering, proposes a method to derive the thermal circuit model parameters of DC charging cable of electric vehicle from the actual temperature rise data, and designs a comparison experiments to verify the accuracy of the thermal circuit model of DC charging cable obtained by this method. The experimental results show that the calculation temperature rise and actual temperature rise error of the EV charging cable thermal circuit model obtained by deriving from the actual temperature rise data in this article is small, which verifies that the EV DC charging cable thermal circuit model obtained by this method has a certain accuracy in analyzing the temperature rise of this type of cable.

Surface ozone in global cities: A synthesis of basic features, exposure risk, and leading meteorological driving factors

Long-term exposure to high surface ozone (O3) concentrations, a complex oxidative atmospheric pollutant, can adversely impact human health. Based on O3 monitoring data from 261 cities worldwide in 2020, generalized additive model (GAM) and spatial data analysis (SDA) methods were applied in this study to quantitatively evaluate the spatiotemporal distribution of O3 concentration, exposure risk, and dominant meteorological factors. Results indicated that over 40% of the cities worldwide were exposed to harmful O3 concentration ranges (40–60 µg/m3), with most cities distributed in China and India. Moreover, significant seasonal variations in global O3 concentrations were observed, presenting as summer (45.6 µg/m3) > spring (47.3 µg/m3) > autumn (38.0 µg/m3) > winter (33.6 µg/m3). Exposure analysis revealed that approximately 12.2% of the population in 261 cities were exposed to an environment with high O3 concentrations (80–160 µg/m3), with about 36.32 million people in major countries. Thus, the persistent increase in high O3 levels worldwide is a critical factor contributing to threats to human health. Furthermore, GAM results indicated temperature, relative humidity, and wind speed as primary determinants of O3 variability. The synergy of meteorological factors is critical for understanding O3 changes. Our findings are important for enforcing robust air quality policies and mitigating public risk.