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The present study investigates human behavioral and emotional experiences based on human-built environment interaction with a specific interest in urban graffiti displaying fear and pleasure-inducing facial expressions. Regarding human behavioral and emotional experience, two questions are asked for the outcome of human responses and two hypotheses are formulated. H1 is based on the behavioral experience and posits that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified behavioral fear and pleasure responses. H2 is based on emotional experience and states that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified emotional fear and pleasure responses.

The research design is developed as a multi-method approach, applying a lab-based experimental strategy (N:39). The research equipment includes a mobile electroencephalogram (EEG) and a Virtual Reality (VR) headset. The behavioral and emotional human responses concerning the representational features of urban graffiti are assessed objectively by measuring physiological variables, EEG signals and subjectively by behavioral variables, systematic behavioral observation and self-report variables, Self-assessment Manikin (SAM) questionnaire. Additionally, correlational analyses between behavioral and emotional results are performed.

The findings of behavioral and emotional evaluations and correlational results show that specialized fear and pleasure response patterns occur due to the affective characteristics of the urban graffiti's representational features, supporting our hypotheses. As a result, the characteristics of behavioral fear and pleasure response and emotional fear and pleasure response are identified.

The present paper contributes to the literature on human-built environment interactions by using physiological, behavioral and self-report measurements as indicators of human behavioral and emotional experiences. Additionally, the literature on urban graffiti is expanded by studying the representational features of urban graffiti as a parameter of investigating human experience in the built environment.

Adhesively bonded joints are used in many fields, especially in the automotive, marine, aviation, defense and outdoor industries. Adhesive bonding offers advantages over traditional mechanical methods, including the ability to join diverse materials, even load distribution and efficient thermal-electrical insulation. This study aims to investigate the mechanical properties of adhesively bonded joints, focusing on adherends produced with auxetic and flat surfaces adhered with varying adhesive thicknesses.

The research uses three-dimensional (3D)-printed materials, polyethylene terephthalate glycol and polylactic acid, and two adhesive types with ductile and brittle properties for single lap joints, analyzing their mechanical performance through tensile testing. The adhesion region of one of these adherends was formed with a flat surface and the other with an auxetic surface. Adhesively bonded joints were produced with 0.2, 0.3 and 0.4 mm bonding thickness.

Results reveal that auxetic adherends exhibit higher strength compared to flat surfaces. Interestingly, the strength of ductile adhesives in auxetic bonded joints increases with adhesive thickness, while brittle adhesive strength decreases with thicker auxetic bonds. Moreover, the auxetic structure displays reduced elongation under comparable force.

The findings emphasize the intricate interplay between adhesive type, bonded surface configuration of adherend and bonding thickness, crucial for understanding the mechanical behavior of adhesively bonded joints in the context of 3D-printed materials.

This paper seeks to analyze the extent to which New Zealand domestic and outbound travelers' book components of their trip in advance or at their destination and to explore the factors that influence this. Furthermore, the paper compares the distribution channels used by domestic and outbound travelers to purchase different travel products and to examine why these channels have been selected.

The study involves the collection and analysis of data on the profiles, trip characteristics and distribution behavior of domestic and outbound travelers based on a nationwide telephone survey of 1,000 respondents.

Little variation was found in the profile characteristics of domestic and outbound travelers but significant differences occurred in terms of the trip characteristics, the extent of booking in advance and at the destination, reasons why advance bookings were not made, how bookings were made and the channels used all exhibited significant differences according to domestic or outbound travel. This pattern was less consistent with regards to the factors influencing how the bookings were made and the factors affecting channel choice.

The findings illustrate the complexity of travel decision‐making issues, underline the need to take account of differences between domestic and outbound travel and across trip components, and to examine the factors that underlie distribution related behavior.

The value and originality of this paper lie in the systematic comparison of the booking and channel choice behavior of domestic and outbound travelers, the search for factors influencing this behavior and a sectoral approach that differentiates transport to and at the destination.

This study aims to improve the performance and to regulate the harmful emission from the diesel engine. For this purpose, palm oil biodiesel (POBD), waste cooking biodiesel (WCBD) and animal fat biodiesel (AFBD) are used for examination.

The transesterification process was followed to convert the three raw oils into biodiesels and the experiments are conducted at various loads with fixed 25 rps. Diesel as a reference fuel and three neat biodiesels are tested for emissions and performance. By training the experimental results in an artificial neural network (ANN), the best biodiesel was predicted.

The biodiesels are tested for significant fuel properties with the American Society for testing and materials standards and observed that kinematic viscosity, density and cetane number are recorded higher than diesel fuel. The fatty acid composition (FAC) from chromatography reveals the presence of unsaturated FAC is more in POBD (70.89%) followed by WCBD (57.67%) and AFBD (43.13%). The combustion pressures measured at every degree of crank angle reveal that WCBD and AFBD exhibited on far with diesel fuel. Compared to diesel fuel WCBD and AFBD achieved maximum brake thermal efficiency of 31.99% and 30.93% at 75% load. However, there is a penalty in fuel consumption and NOx emissions from biodiesels. On the other hand, low carbon monoxide, unburnt hydrocarbon emissions and exhaust smoke are reported for biodiesels. Finally, WCBD was chosen as the best choice based on ANN modeling prediction results.

There is no evident literature on these three neat biodiesel applications with the mapping of ANN modeling.

The aim of this study is to examine the significant factors to improve the green nuclear energy investments in the emerging economies. For this purpose, balanced scorecard-based criteria are weighted with DEMATEL methodology. The findings demonstrate that technological improvement and financial issues are the most important issues for the improvement of the green nuclear energy investments in these countries. Nuclear energy working with thorium can also be obtained with proton accelerator technology that is currently quite expensive. Because of this problem, the investors are not willing to make investments for green nuclear energy projects. Hence, emerging countries should make the necessary technological investments to have proton accelerator technologies. With the help of this condition, it will be possible to reduce the cost of green nuclear energy projects which attracts the attention of the investors. This situation has a powerful contribution for the sustainable economic development of these countries.

With the increase in population, the energy needs of countries are also increasing. These countries have difficulties in meeting these increasing needs. Countries that cannot meet this need have to import energy from abroad. This situation adversely affects the current account balance of countries. Nuclear energy investments allow countries to obtain their own energy, although there are some criticisms. In this framework, while some countries in the world increase their nuclear energy investments, some countries do not have any nuclear power plants (NPP). There are 32 such countries where nuclear energy projects are running till date. Therefore, it is very important to determine the socio-economic variables of countries that have nuclear energy investments. In this context, a detailed literature analysis will be made first to determine socio-economic criteria. Then, the importance weights of these factors will be calculated using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The profiles of the countries that make nuclear energy investments demonstrate that education level is the most essential socio-economic factor for the improvement of nuclear energy investments. Also, income inequality is another important variable in this regard. However, consumption behaviour and saving behaviour have the lowest weights.

The purpose of this paper is to analyze the effect of printing parameters on the mechanical properties of standard dog bone specimens manufactured by fused deposition modeling.

Polylactic acid (PLA) specimens were printed and tested according to the ASTM standard. The effect of five important printing parameters, layer height, raster angle, printing speed, nozzle temperature and nozzle diameter, was examined on ultimate tensile strength (UTS), elongation and apparent density. Five levels were attended for each parameter, and a high number of required experiments were reduced by applying the L₂₅ Taguchi design of the experiment.

The effect of each parameter on outputs and optimal values for maximum tensile strength were determined. The most influential parameter is the raster angle of 64.96%. Nozzle temperature has a low effect of 1.76%, but nozzle diameter contribution is 9.77%. The experiment results are validated by analysis of variance analysis, and the optimal predicted level for parameters is 90° raster angle, 0.2 mm layer height, 100 mm/s printing speed, 200°C nozzle temperature and 0.8 mm nozzle diameter. The maximum UTS observed is 48.70 MPa for 0.8 mm nozzle diameter, whereas the minimum is 18.49 for 0.2 mm nozzle diameter.

This paper is a very extensive experimental research report on the effect of the parameters for the tensile property of 3D printed PLA specimens by the Taguchi method. The documented results can be further developed for an optimization model to obtain a desired mechanical property with less variation and uncertainty in a product.

The current Industry 4.0 era is considered not only as a process that dominates technological developments but also as a process that influences the leadership styles. Management 4.0 is essential for businesses to find and apply the appropriate technologies in the age of Industry 4.0. The leadership styles that business managers will adopt in order to be successful in this process and to survive in an intensely competitive environment can play an important role. At this point, a significant problem arises: identifying leadership styles that will bring success. In this context, the primary purpose of this chapter is to explain the modern leadership styles that business managers can adopt or follow in the age of Industry 4.0. In line with this purpose, the chapter first describes the historical development of leadership, leadership theories and modern leadership styles, such as transactional, transformational, technological, strategic, visionary and agile leadership, and all these concepts are discussed based on the Industry 4.0 perspective.

The purpose of this paper is to analyze the correlation between the Twitter activity of two airline companies and their stock performance at the Istanbul Stock Exchange (BIST).

Overall, 113,018 tweets were divided into 34,152 semantic and 78,866 share tweets. Semantic tweets are tweets mentioning company’s products or services and were labeled manually and with deep learning models. Share tweets were divided into 13,618 relevant and 65,248 irrelevant tweets.

A positive correlation was found between share tweets and stock performance. Semantic tweets did not display a correlation with stock performance. Relevant share tweets displayed as a strong correlation as all share tweets for one company. Also, the manual labeling of 8,000 tweets led to the discovery of many insights related to service provision in the airway industry, management of digital support channels, management of reputation on social media and using Twitter as a customer support platform.

Relevant share tweets comprise only 20% of all share tweets for one company and show the same level of correlation with stock performance. This means that the efficiency of business intelligence solutions created to monitor Twitter activity can be improved five times by saving computational power, network bandwidth and data storage.

Previous research has analyzed all Twitter activity taken together. By dividing tweets into semantic and share tweets, this paper illustrates that it is, in fact, share tweets that are correlated with stock performance and not semantic tweets.