Modeling Analysis Research Articles

Modeling and Performance Analysis of Hydrogen Powered Hybrid Bike

Modeling and Performance Analysis of Hydrogen Powered Hybrid Bike

NUMERICAL TREATMENT OF LINEAR VOLTERRA INTEGRO DIFFERENTIAL EQUATIONS USING VARIATIONAL ITERATION ALGORITHM WITH COLLOCATION

To numerically solve the linear Volterra integro-differential equation, this study employs fourth-kind Chebyshev polynomials and the variational iteration algorithm with collocation, which is a combination of the variational iteration strategy and collocation technique. By applying fourth-kind Chebyshev polynomials to the variational iteration method with collocation for solving Volterra integro-differential equations, mathematical problems with a broad range of multidisciplinary applications are addressed, and numerical techniques that produce more accurate and efficient results are developed. The recommended method is then used, and the fourth-kind Chebyshev polynomials generated for the given integro-differential equation serve as the trial functions for the approximation. As a result, the suggested method's significance probably goes beyond a particular equation or application, as it contributes to the larger field of mathematical modeling and numerical analysis. Research methods employing a variational iteration algorithm with collocation aim to provide general techniques that can be applied to a wide range of problems. Additionally, numerical examples were provided to highlight the applicability and dependability of the proposed methodology. The mathematical computations were carried out using the Maple 18 software.

Beware of cyberbullying! Evidence from high school students in Indonesia

Cases of cyberbullying among high school students have become the center of attention of educational institutions as a new form of bullying using information technology. Many studies examine cyberbullying as a new form of bullying. However, studies on school climate, social support, and social self-efficacy in reducing cyberbullying behavior among high school students are still limited. Therefore, this study investigates the effect of school climate, social support, and social self-efficacy on cyberbullying behavior. This study involved 290 high school students in three schools in Kupang City, Indonesia. We used a simple random sampling technique to determine which respondents were involved. SPSS version 20 and Amos 18 software were used to analyze the data for this study. Data analysis in this study used structural equation modeling (SEM) analysis. The study’s results revealed that school climate and social self-efficacy significantly negatively affected high school students’ cyberbullying behavior. Meanwhile, social support has no direct influence on students’ cyberbullying behavior. Social self-efficacy has also been shown to mediate the effect of school climate on high school students’ cyberbullying behavior. An in-depth discussion is presented in this paper to provide an overview of the critical implications for educational practitioners.

Environmental heterogeneity caused by large-scale cultivation of Pyropia haitanensis shapes multi-group biodiversity distribution in coastal areas

The response of marine biodiversity to mariculture has long been a research focus in marine ecology. However, the effects of seaweed cultivation on biological community assembly are poorly understood, especially in diverse communities with distinct ecological characteristics. In this study, we used environmental DNA metabarcoding to investigate the spatial distribution patterns of bacterial, protistan, and metazoan diversity, aiming to reveal the mechanisms of community assembly in the Pyropia haitanensis cultivation zone along the Fujian coast, China. We found that, compared with the biological communities in control zones, those in P. haitanensis cultivation zones exhibited stronger geographic distance-decay patterns and displayed more complex and stable network structures. Deterministic processes (environmental selection) played a more important role in the assembly of bacterial, protistan, and metazoan communities in P. haitanensis cultivation zones, especially metazoan communities. Variance partitioning analysis showed that environmental variables made greater contributions to the diversity of the three types of communities within the P. haitanensis cultivation zones than in the control zones. Partial least squares path modeling analysis identified nitrate‑nitrogen (NO3-N), pH, particulate organic carbon (POC), and dissolved organic carbon (DOC) as the key environmental variables affecting biodiversity. Overall, the environmental heterogeneity caused by the large-scale cultivation of P. haitanensis could be the crucial factor influencing the composition and structure of various biological communities. Our results highlight the importance of the responses of multi-group organisms to the cultivation of seaweed, and provide insights into the coexistence patterns of biodiversity at the spatial scale.

Consumer preference and willingness to pay for 3D-printed chocolates: A discrete choice experiment

This study explores the emerging field of 3D food printing, specifically examining consumer preferences for 3D-printed chocolate bars. While 3D printing has revolutionized various industries, the consumer acceptance of 3D-printed food remains a relatively underexplored topic. A discrete choice experiment was conducted to investigate consumer preferences and willingness to pay for 3D-printed chocolate bars in the UK. Key attributes included in the study were price, chocolate type, and the use of 3D printing technology. Using a mixed logit model, preference heterogeneity was assessed, revealing a general inclination towards 3D-printed chocolates, with an estimated increase in willingness to pay of £0.21 per 100g. Furthermore, the influence of individual (participants’) characteristics, such as age, gender, prior knowledge of 3D food printing, and food technology neophobia, on perceived benefits associated with this technology and willingness to pay was also explored, using a structural equation modeling analysis. The results indicated that only perceived hedonic benefits, such as improved texture, positively impacted the willingness to pay, while food technology neophobia acted as a significant barrier. The results of this study therefore contribute valuable insights into the consumer acceptance of 3D-printed foods, specifically chocolates, highlighting potential avenues for market adoption and emphasizing the importance of effective communication in shaping consumer attitudes toward novel food processing technologies.

Design of Two-Wheeler Hybrid Electric Vehicle using MATLAB-Simulink

Abstract: This report presents the design and structural analysis of a go-kart using PTC Creo for CAD modelling and ANSYS for finite element analysis (FEA). The aim is to develop a low-cost, comfortable, and durable go-kart with an optimized frame design. The study involves the application of design principles and advanced simulation tools to ensure the go-kart meets safety and performance standards. The best frame design was identified through iterative testing and analysis, focusing on weight distribution, material selection, and structural integrity. The objective of this work is to demonstrate the design procedure and good strength for a Roll cage. This project emphasizes on the practical and engineering applications of the subjects Vehicle Dynamics and Automotive Technology.

Unravelling Stock Market Patterns: Analysis and Predictive Modelling Using Time Series and Deep Learning

Abstract: Within the dynamic landscape of the financial markets, where fortunes can shift with the wind, the precision of future prediction holds immense value. This research embarks on a quest to enhance the accuracy of stock market forecasts, venturing into the domain of advanced time series models. We shall closely examine three prominent methodologies: the venerable ARIMA, the multifaceted VARMA, and the potent deep learning architecture known as LSTM. Through a rigorous investigation of their strengths and limitations, we shall subject these models to both univariate and multivariate data extracted from the Indian stock market. This comparative analysis aims to glean invaluable insights into their predictive capabilities, ultimately paving the path for the development of even more sophisticated forecasting tools. By wielding these instruments with confidence, investors can navigate the intricate dynamics of the market with a newfound certainty and optimize their decision-making for greater returns. Our endeavor transcends mere numerical analysis; it seeks to illuminate the complex dance of the market, unravel its hidden patterns, and ultimately empower investors to gain the upper hand in this challenging yet potentially rewarding financial sphere.

Factors affecting brand attachment in the social media channels: Case of university in Vietnam

Originated from the reason that Hanoi University of Industry (HaUI)’s current social media network is still limited, specifically HaUI's Fanpage has not yet been green-checked, thereby leading to weak brand attachment have little impact on university admissions. From collecting data from information sources such as newspapers, social networking sites, the author has inherited previous studies to propose a research model and conduct quantitative research analysis. Through designing a survey questionnaire and randomly sending it to individuals who are studying and working at HaUI, specifically lecturers, staff and especially the university students. The study obtained 338 valid responses that were coded and cleaned using SPSS and AMOS software. After the cleaning and coding step, reliability testing, exploratory factor analysis, confirmatory factor analysis and structural equation modelling were conducted. The research has made a significant theoretical contribution in terms of the important mediation role of brand awareness, perceived quality and university reputation in the relationship between social media communication and brand attachment. From there, it also provided the practical contribution to improve the association of the brand with students, to perfectionate the social networking sites,to share positive things, interesting, attractive and relevant lessons.

Make a Charitable Birthday Wish on Facebook: An Extended Theory of Planned Behavior

ABSTRACT Social media fundraising has become an important tool for nonprofits to raise money. Focusing on one specific fundraising tool, Facebook birthday fundraiser, the current study developed an extended Theory of Planned Behavior (TPB) model and included three external variables, relationship strength with the fundraiser creator, perceived risk regarding personal information safety, and personal relevance of the fundraising cause. An online survey was conducted to investigate their influences on Facebook users’ donation intention and behaviors. Confirmatory factor analysis and structural equation modeling were conducted, and hypothetical correlations were tested. Results showed that relationship strength significantly influenced subjective norms, attitude toward the fundraiser, self-efficacy, and perceived behavior control. Perceived risk positively influenced subjective norms. Personal relevance positively affected donation intention. Relationships among factors from previous TBP studies were also tested and verified. Implications and limitations of the study were discussed.

Soil Physicochemical Properties and Rhizosphere Microorganisms Mediates the Growth of Cigar Tobacco Across Different Ecoregions in Yunnan, Southwest China

The quality of cigar tobacco is influenced by various factors, among which the ecological environment plays a crucial role. However, it remains uncertain whether Yunnan, known for its diverse ecological environment, is suitable for the growth of cigar tobacco. Therefore, the objective of this study was to investigate the disparities in soil physicochemical properties and soil microbial community across three ecoregions in Yunnan, namely Longyang, Ruili and Tengchong, located in southwest China. Furthermore, we aimed to analyze the correlation of these factors with tobacco growth. The results showed that Longyang exhibited higher values in soil pH (7.78±0.11), available potassium (735.72±198.96), cellulase (1.07±0.23) and polyphenol oxidase (0.39±0.001) compared to Ruili and Tengchong, promoting the growth of cigar tobacco (P<0.05). Additionally, Longyang demonstrated greater soil fungal diversity but lower soil bacterial diversity (P<0.05). Correlation analysis and structural equation modeling further confirmed that soil pH was the primary factor influencing tobacco growth (r=0.802, P< 0.01). Moreover, bacteria exhibited higher sensitivity to soil physicochemical properties compared to fungi, with bacterial structure significantly influencing plant growth (P< 0.05). In summary, the alkaline soil in Longyang, enriched with organic matter and available potassium, exhibited favorable conditions for promoting the soil microbial community and thereby proved more suitable for the growth of cigar tobacco.

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models are determined by the subjectively assigned scales of the latent variables. Thus, conclusions in existing studies comparing structural equation modeling (SEM) and path analysis with weighted composites (PAWC) on the accuracy and precision of the estimates of the indirect effect in mediation analysis have little validity. Instead of comparing the size on estimates of the indirect effect between SEM and PAWC, this article compares parameter estimates by signal-to-noise ratio (SNR), which does not depend on the metrics of the latent variables once the anchors of the latent variables are determined. Results show that PAWC yields greater SNR than SEM in estimating and testing the indirect effect even when measurement errors exist. In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. Results of a real-data example also support the conclusions.

Facilitating the Smooth Migration of Inhabitants of Atoll Countries to Artificial Islands: Case of the Maldives

The vulnerability of atoll countries to sea level rise underscores the pivotal connection between climate change and migration. This study examines the multifaceted challenges faced by such countries, including land loss, economic disruption, water contamination, and increased vulnerability to extreme weather events, and potential adaptation strategies, such as migration to developed countries and other islands, land reclamation, and floating platform development. The situation of the Maldives, particularly the creation of the artificial island of Hulhumalé, is explored as a case study. Hulhumalé is designed to alleviate congestion while addressing concerns about rising sea levels. This study employed a questionnaire survey and analyzed the data using importance analysis of permutation features and structural equation modeling following the Wilcoxon–Mann–Whitney tests. The results revealed that the key factors influencing resident satisfaction after migration were clean housing, resilience to natural disasters, sports facilities, and environmental quality. Adaptation strategies must be tailored to each country’s unique circumstances, considering the interconnectedness of environmental and socioeconomic factors in addressing climate-induced migration. Considering Hulhumalé as a model for climate change adaptation, concerted global action is necessary to mitigate the impacts of climate change and ensure the security and well-being of vulnerable populations.

Clinical value of serum tumor markers in assessing the efficacy of neoadjuvant chemotherapy in advanced ovarian cancer: single-center prospective clinical study

ObjectiveThis study aimed to assess the clinical importance of various biomarkers, including NLR, CEA, CA199, CA125, CA153, and HE4, through dynamic testing to evaluate the effectiveness of neoadjuvant chemotherapy (NACT) for individuals facing advanced ovarian cancer. This provides valuable information for tailoring treatment plans to individual patients, thereby leading to a more personalized and effective management of individuals facing ovarian cancer.MethodsThe levels of NLR, CA125, CA199, CEA, CA153, and HE4 were detected before chemotherapy and after 3 courses of chemotherapy. Patients were categorized into ineffective and effective groups according to the effectiveness of NACT. To evaluate the factors influencing NACT’s effectiveness in individuals facing advanced ovarian cancer, receiver operating characteristic (ROC) curves, predictive modeling, and multifactorial regression analysis were employed.ResultsIn the effective group, the patients’ age, maximum tumor diameter, and CEA and HE4 levels of the patients were significantly higher compared to those in the ineffective group (P <.05). Additionally, the difference in HE4 levels before and after treatment between the effective and ineffective groups was statistically significant (P<.05). Multifactorial analysis showed that age and maximum tumor diameter were independent risk factors impacting the effectiveness of NACT in individuals facing advanced ovarian cancer (P<.05). The ROC curve for predicting the effectiveness of NACT in individuals facing advanced ovarian cancer showed a sensitivity of 93.3% for NLR and a specificity of 92.3% for CA199. HE4 emerged as the most reliable predictor, demonstrating a specificity of 84.6% and a sensitivity of 75.3%. The area under the curve of the combined CA125 and HE4 assays for predicting the ineffectiveness of NACT in individuals facing advanced ovarian cancer was 0.825, showcasing a specificity of 74.2% and a sensitivity of 84.6%.ConclusionThe predictive capacity for the effectiveness of NACT in individuals facing advanced ovarian cancer is notably high when considering the sensitivity of NLR and the specificity of CA199. Additionally, the combination of CA125 and HE4 assays can obtain a better predictive effect, which can accurately select patients suitable for NACT, determine the appropriate timing of the interval debulking surgery (IDS) surgery, and achieve a satisfactory tumor reduction effect.

Aerodynamic modeling and performance analysis of model predictive controller for fixed wing vertical takeoff and landing unmanned aerial vehicle

This research focuses mainly on the aerodynamic modelling and performance analysis of a model predictive controller for a hybrid fixed-wing vertical takeoff and landing of unmanned aerial vehicle. The aerodynamics, which comprises several aerodynamic characteristics including the lift, drag, and thrust coefficients, is modelled using Newton’s second law of motion. The force and moment equations were obtained, and they were then converted into matrix and state equation form, together with the transformation matrices. The essential equations with six degrees of freedom (6 DoF) and 12 state matrices including the control input and manipulating variables were obtained. The proposed model predictive controller (MPC) was designed using the optimal model predictive controller design parameters, such as a sampling time of 0.1, a prediction horizon of 15, a control horizon of 3 and additional controller settings. With a settling period of 3 s, an overshoot of 0.5865, and a steady state inaccuracy of 0.00785, the MATLAB simulation demonstrates that the system variables, including roll, pitch, and yaw, are stabilized. This MPC control is more effective in anticipating and optimizing the UAV than other control strategies. Eventually, the controller’s simulation on MATLAB Simulink demonstrates the controller’s ability to stabilize and control the system in a real-time application. Autonomous vertical takeoff and landing operations depend heavily on the mathematical model and architecture of the flight controller. Among the uses are inspection, monitoring, and rescue.

Enhancing thermal management efficiency in robotics engineering: Exploring mechanisms, techniques, and modeling approaches

Thermal management is pivotal in robotics engineering, ensuring optimal performance and reliability of robotic systems. This comprehensive review explores various heat dissipation mechanisms, cooling techniques, and thermal modeling approaches employed in robotics engineering. Key topics include conduction-based, convection-based, and radiation-based heat transfer mechanisms, alongside liquid cooling, air cooling, and phase-change cooling techniques. Analytical thermal models, computational fluid dynamics (CFD) simulations, and finite element analysis (FEA) modeling are discussed for their roles in predicting thermal behaviors and optimizing heat dissipation strategies. By synthesizing these advancements, this review provides valuable insights into enhancing thermal management efficiency and ensuring the longevity of robotic systems.

Analytical detection of “ Smart Stock Trading System” utilizing AI-model

In the realm of finance, stock trading stands out as one of the most significant activities. Predicting stock prices involves analyzing available market data to forecast the future value of a particular stock or share. In our research, we developed a machine learning model along with LSTM (Long Short-Term Memory) to predict stock prices. Our model follows the 70:30 principle, allowing the model to improve its accuracy over time. We collected a decade's worth of data from Yahoo Finance and employed customized feature engineering and deep learning techniques to analyze the stock market trends. Our approach has shown promising accuracy in predicting stock market trends. By carefully optimizing prediction term lengths, feature engineering methods, and data preprocessing techniques, our work contributes to advancing research in both financial analysis and technical modeling within the stock market domain. Keywords— LSTM, Machine learning, RNN, Data Prediction, finance, linear regression.

Assessing the Key Factors Influencing Renewable Energy Generation in Mozambique: A Structural Equation Modelling Analysis

Structural equation modelling analysis was performed to identify various variables that influence the growth of Renewable Energy Production (REG) in Mozambique. The study revealed that the government's policies regarding renewable energy generation in Mozambique are of moderate quality. However, by implementing skillfully crafted policies and plans, there is a possibility for additional progress and growth. The progress of renewable energy initiatives in Mozambique is significantly influenced by the investment climate for such projects. Although the government has established a favourable environment for investors, it primarily caters to foreign investors rather than local and private investors. Environmental considerations significantly influence the growth of renewable energy generation (REG) in Mozambique. This study highlighted the need to consider environmental issues throughout the planning and execution of REG projects. The lack of technical and managerial proficiency in a particular area can be resolved by importing foreign specialists, either on a temporary or permanent basis. Concurrent with the global goal of shifting to clean energy-producing technologies, this study attempts to identify and assess the variables that influence the growth and development of renewable energy businesses in Mozambique. Setting priorities for energy efficiency can uncover several economic prospects, fostering the growth of a market for innovative solutions and facilitating the construction of specialized firms in this field. Consequently, this will entice investment from both domestic business owners and investors from other countries.

Reaching Malaysian ESL students through Instagram stories - perceived effectiveness, language exposure and learner engagement: A structural equation modelling analysis

Students often find discontinuation between classroom learning and out-of-school practice. As Generation Z and millennials are avid users of Instagram and Instagram Stories, this research aims to study the perceived effectiveness of social media on language exposure and learner engagement, with a learner’s perceived proficiency as a moderating effect. A total of 202 Malaysian Chinese Independent Secondary School Senior 2 and Senior 3 students took part in the Google Form survey. This quantitative study employed partial least squares structural equation modelling for data analysis. The results showed that Instagram Stories are perceived to be effective and useful as it contributes to language exposure and learner engagement agentically, cognitively, and emotionally. However, a learner’s perceived proficiency was not found to be a significant moderator. Future studies point towards exploring English learning outside of the classroom context using other engaging social media tools such as TikTok and more.

Nonlinear dynamical modeling and response analysis of complex structures based on assumed mode weighting

The assumed mode method (AMM) is widely used for dynamical modeling but faces increasing challenges in developing low-dimensional and high-precision models of complex structures for investigations of nonlinear dynamical responses and active vibration control. This is mainly because the current complex structure has an increasing number of components, each of which needs to be discretized by multiple assumed modes in dynamical modeling, leading to increasing degrees of freedom for the system. To address this issue, this paper proposes a dimension-reduced method based on the assumed mode weighting method, referred to as Mode Weighting Method (MWM). The modeling process of a multi-beam structure is fully presented as an example to show the strategy of the MWM. During this process, the assumed mode (AM) model (the dynamic model derived in terms of a set of assumed modes) is developed first where the joints of the structure between its components are considered as artificial springs and each component is discretized by its vibration modes without constraints. The components of the eigenvector of the characteristic equation derived from the AM model are taken as the weighting coefficients to get the approximate analytical global modes of the structure by multiplying the corresponding assumed modes. Consequently, a nonlinear dynamic model with a few degree of freedom is obtained by discretizing the structure with the weighted modes. Moreover, a technique for simply obtaining the weighted mode (WM) models by directly processing the AM model through a transformation matrix is presented. To validate the proposed method, the simulations on the natural characteristics and the dynamical responses for both linear and nonlinear models, are performed respectively based on the WM and the finite element models, where both results are well matched each other. This work provides a new way of developing advanced dynamical modeling methods for structural dynamics design, optimization, and active vibration control.

Rural tourism in India and pro-tourism behavior

Purpose This paper aims to understand tourist behavior in Indian rural tourism by integrating the stimuli-organism-response (SOR) framework, service quality model (SERVQUAL) model and theory of planned behavior. It investigates the role of positive tourist-host interactions, destination image, trust and attitude in shaping pro-tourism intentions. Additionally, it explores the moderating effect of Indian traditionality on these relationships. Design/methodology/approach This study uses a mixed-methods approach, combining qualitative data from 15 respondents with quantitative analysis involving 580 tourists engaged in Indian rural tourism. Confirmatory factor analysis and structural equation modeling are used to examine relationships among tourist-host interactions, destination image, trust, attitude and pro-tourism intentions. Findings The study reveals that positive interactions and a positive destination image play crucial roles in building trust and shaping favorable attitudes toward rural tourism. Trust and attitude, in turn, significantly drive pro-tourism intentions. Furthermore, tourist attitude partially mediates the relationship between trust and intentions. The study introduces the novel concept of Indian traditionality as a moderator, highlighting its impact on trust, attitude and pro-tourism intentions. Research limitations/implications Limitations include the focus on Indian rural tourism, which may limit generalizability, and potential response bias due to self-reported data. Future research could explore other cultural contexts and use diverse data collection methods. Practical implications This research offers practical insights for policymakers and stakeholders, emphasizing the importance of fostering positive tourist–host interactions and cultivating a positive destination image to enhance the overall tourist experience and promote sustainable rural tourism practices. Originality/value This study’s innovation lies in harmonizing theoretical frameworks to shed light on Indian rural tourism’s multifaceted aspects. It introduces Indian traditionality as a cultural moderator, enriching understanding of tourist behavior. The research contributes to academia and industry by guiding the development of sustainable tourism practices and improving the tourist experience.