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The present research is trying to construct the network of relationships between different department of an organization during the design and development of car engine. Analyzing the structure of the network, finding the patterns of collaborations, and determining the important departments are the main purposes of the present research.

Improving relationships during a project life is an effective way to enhance employee performance in project-oriented organizations. This paper examines the collaborative relationships between internal project stakeholders through social network analysis (SNA) in a project for the design and development of car engine. In the first step of the research, the network of internal stakeholders was studied based on collaboration in the common activities performed by the resources. Then, the network of correspondences between internal stakeholders was studied. Finally, the two networks were integrated into a single network.

In the integrated network, the “fuel and combustion department” had the largest degree centrality (i.e. highest collaboration with others). The “integration department” was found to have the highest closeness centrality (i.e. more rapid access to other nodes). Furthermore, the “procurement department” had the highest betweenness centrality (i.e. the most strategic department). Our results revealed the potential capabilities of SNA method for the project management in the vehicle industry.

The obtained results of the present research show us the value of applying SNA methods and concepts to analyze the inter-organizational network of the Project Stakeholders relationship.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of this study is to assess the performance of fuel blends containing ethanol and gasoline in spark ignition engines. The aim is to explore alternative fuels that can enhance performance while minimizing or eliminating adverse environmental impacts, particularly in the context of limited fossil fuel availability and the need for sustainable alternatives.

The authors used the Ricardo Wave software to evaluate the performance of fuel blends with varying ethanol content (represented as E0, E10, E25, E40, E55, E70, E85 and E100) in comparison to gasoline. The assessment involved different composition percentages and was conducted at various engine speeds (1,500, 3,000, 4,500 and 6,000 rpm). This methodology aims to provide a comprehensive understanding of how different ethanol-gasoline blends perform under different conditions.

The study found that, across all fuel blends, the highest brake power (BP) and the highest brake-specific fuel consumption (BSFC) were observed at 6,000 rpm. Additionally, it was noted that the presence of ethanol in gasoline fuel blends has the potential to increase both the BP and BSFC. These findings suggest that ethanol can positively impact the performance of spark-ignition engines, highlighting its potential as an alternative fuel.

This research contributes to the ongoing efforts in the automotive industry to find sustainable alternative fuels. The use of Ricardo Wave software for performance assessment and the comprehensive exploration of various ethanol-gasoline blends at different engine speeds add to the originality of the study. The emphasis on the potential of ethanol to enhance engine performance provides valuable insights for motor vehicle manufacturers and researchers working on alternative fuel solutions.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

There is a lot of talk about the electric car today, but these vehicles are not new. Indeed, thebeginning of the 20th century saw electricity and the automobile take hold in North American society, so that by 1910, the electric car was everywhere. Until the turn of the 1920s, a new era dawned for transportation in the USA, but without the electric car. The purpose of this study is to question Why did it happen.

This paper develops such a comparison, not of the cars themselves, through a detailed engineering analysis, but rather of the marketing of electric vehicles in the USA in 1910 and 2010, as it appeared in the marketing strategies of the manufacturers.

There are many technical and economic reasons for this, but not only; there are also commercial strategy reasons. The position of manufacturers, especially through advertising and the press, can tell us about this golden age of the electric car, what precipitated its fall, and its reappearance a century later.

It is a comparison of images, of how electric vehicles had been and are proposed to the public, through the exploration of mainly promotional material and newspaper articles.

This study explores how titanium oxide (TiO₂) filler influences the specific wear rate (SWR) in flax fiber-reinforced epoxy composites (FFRCs) through a Taguchi approach. It aims to boost abrasive wear resistance by incorporating TiO₂ filler, promoting sustainable and eco-friendly materials.

This study fabricates epoxy/flax composites with TiO₂ particles (0–8 wt%) using hand layup. Composites were tested for wear following American Society for Testing and Materials (ASTM) G99-05. Statistical analysis used Taguchi design of experiments (DOE), with ANOVA identifying key factors affecting SWR in abrasive sliding conditions.

The study illuminates how integrating TiO2 filler particles into epoxy/flax composites enhances abrasive wear properties. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, wt% of TiO₂ and sliding distance. Grit size has the highest effect at 43.78%, and wt% TiO₂ filler contributes 15.61% to SWR according to ANOVA. Notably, the Taguchi predictive model closely aligns with experimental results, validating its reliability.

This paper integrates TiO₂ filler and flax fibers to form a novel hybrid composite with enhanced tribological properties in epoxy composites. The use of Taguchi DOE and ANOVA offers valuable insights for optimizing control variables, particularly in natural fiber-reinforced composites (NFRCs).

The purpose of this study is to investigate the impact of titanium oxide (TiO₂) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO₂ filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials.

This study focuses on the fabrication of epoxy/bamboo composites infused with TiO₂ particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF).

The study illuminates how integrating TiO₂ filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO₂ and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO₂ and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO₂-filled composites. The inclusion of TiO₂ improved wear resistance, as evidenced by reduced surface damage and wear debris.

This research paper aims to integrate TiO₂ filler and bamboo fibers to create an innovative hybrid composite material. TiO₂ micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the physical, mechanical, tribological and water resistance properties of 5% NaOH-treated bamboo fiber–reinforced composites.

In this research, the epoxy/bamboo/TiO₂ hybrid composite filled with 0–8 Wt.% TiO₂ particles has been fabricated using simple hand layup techniques, and testing of the developed composite was done in accordance with the American Society for Testing and Materials (ASTM) standard.

The results of this study indicate that the addition of TiO₂ particles improved the mechanical properties of the developed epoxy/bamboo composites. Tensile properties were found to be maximum for 6 Wt.%, and impact strength was found to be maximum for 8 Wt.% TiO₂ particles-filled composite. The highest flexural properties were found at a lower TiO₂ fraction of 2 Wt.%. Adding TiO₂ filler helped to reduce the water absorption rate. The studies related to the wear and friction behavior of the composite under dry and abrasive wear conditions reveal that TiO₂ filler was beneficial in improving the wear performance of the composite.

This research paper attempts to include both TiO₂ filler and bamboo fibers to develop a novel composite material. TiO₂ micro and nanoparticles are promising filler materials; it helps to enhance the mechanical and tribological properties of the epoxy composites and in literature, there is not much work reported, where TiO₂ is used as a filler material with bamboo fiber–reinforced epoxy composites.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.

The share of fast-moving consumer goods (FMCG) in e-commerce has increased, particularly after COVID-19, alongside the growing volume of e-commerce transactions. The increased number of orders necessitates the optimization of delivery operations. This situation has made using different technologies in last-mile delivery processes necessary. Currently, used electric vehicles (EVs) play a significant role in integrating these technologies into delivery operations. Adapting to new technologies emerges as a critical need for small enterprises to survive in the face of advancing technologies. In this context, the main purpose of this paper is to examine and identify attitudes of small businesses toward EVs’ usage in last-mile delivery for FMCG.

The theoretical frameworks commonly used in studies investigating new technologies, namely the theory of planned behavior (TPB) and technology acceptance model (TAM) have been employed. The analysis of the study was conducted using partial least squares-structural equation modeling (PLS-SEM) and the Smart PLS software package.

When examining the results of the study, a significant and positive relationship was found between compatibility and enjoyment with perceived usefulness. Furthermore, a significant and positive relationship was identified between attitude toward use, perceived behavioral control and perceived usefulness. Ultimately, a strong relationship was found between intention, which is a precursor to active usage and attitude. Based on the obtained data, it can be concluded that EVs can be actively used in last-mile delivery in the future.

In FMCG processes in Türkiye, EVs are limitedly used in last-mile delivery operations. However, there are very few studies on the use of EVs in FMCG. Therefore, it is expected that the current research will contribute to the literature by providing information on the factors that influence the acceptance of EV usage and their implications. The TAM and TPB models were used in the current study.