Showing papers by "Yaşar University published in 2019"

Understanding sustainable consumption in an emerging country: The antecedents and consequences of the ecologically conscious consumer behavior model

Abstract: For past few decades, consumers have lately started to adapt sustainable consumption in emerging countries. The increasing importance of sustainable consumption led the researchers to analyze green purchase and ecological behaviors. The aim of this study was to examine the antecedents of ecologically conscious consumer behavior model and show the relationships among ecologically conscious consumer behavior, green purchase conspicuous behavior, and green purchase intention based on the theory of planned behavior in an emerging country. Data were collected from 650 consumers in Turkey, one of the emerging countries, by using face‐to‐face survey technique and analyzed by factor analyses and structural equation modelling. In findings, environmental concern, altruism, and perceived consumer effectiveness were found as the antecedents of the model, and there were significant effects of ecologically conscious consumer behaviors on green purchase conspicuous behaviors and green purchase intentions.

A predictive control strategy for optimal management of peak load, thermal comfort, energy storage and renewables in multi-zone buildings

Abstract: Buildings are responsible for about 40% of the global energy consumption, where heating, ventilation and air conditioning (HVAC) systems account for the most part of it. Continuous increase in the installation of new HVAC systems and higher penetration of renewables and energy storage in the building energy network require more sophisticated control approaches to realize the full potential of these systems. In this paper, an optimal control framework to coordinate HVAC, battery energy storage and renewable generation in buildings is developed. The controller aims to reduce peak load demand while achieving thermal comfort within industry standards. To facilitate this, a simple lumped mathematical model that describes the zone transient thermal dynamics is structured with a minimal data from the building, and is trained with actual thermal and electrical data. Next, a model predictive control algorithm that takes into account building thermal dynamics, battery state of charge, renewable generation status, and actual operational data and constraints, is formulated to regulate HVAC demand, battery power and building thermal comfort . The controller considers the changes in the outside dry-bulb air temperature, electricity price, required energy amount and comfort conditions simultaneously in order to find the proper optimal zone temperatures guaranteeing occupant comfort. The new controller was tested using data from a real building, and preliminary results indicate that significant reduction in peak electrical power demand can be achieved by the proposed approach.

The role of childhood emotional maltreatment and body image dissatisfaction in problematic smartphone use among adolescents.

Abstract: Growing empirical evidence has identified specific psychological and contextual risk factors associated with problematic smartphone use (PSU). However, the potential direct and indirect impact of childhood emotional maltreatment (CEM) on PSU remains largely unexplored, despite the established role of CEM in the onset of other excessive, problematic, and addictive behaviors. Consequently, the purpose of the present study was to test the direct and indirect relationships of emotional abuse and neglect (two facets of CEM) with PSU via specific mediational pathways including body image dissatisfaction (BID), social anxiety, and depression. The sample comprised 443 adolescents who completed a questionnaire that included assessment tools of aforementioned variables. Multiple mediation model results indicated that CEM was directly and indirectly associated with PSU via BID, depression, BID-related depression, and BID-related social anxiety. Results suggested that emotionally traumatic experiences were associated with PSU in adolescents and that this relationship may partially be explained by BID and psychosocial risk factors. The present study draws caution to the amplifying roles of CEM and BID on increased PSU. The results of the study have important clinical and public health implications, but additional research is needed before interventions can be developed and implemented on the basis of present results.

Relational bonding strategies, customer satisfaction, and loyalty in the container shipping market

Abstract: In recent years, the business of container lines has faced severe challenges such as overcapacity and low profitability. To survive in such a competitive market, container lines need to maintain long-term customer relationships by enhancing the satisfaction and loyalty of customers. The purpose of this paper is to adopt a social exchange theory (SET) approach and investigate the impact of relational bonding strategies on the satisfaction and loyalty of customers in container shipping.,Drawing on SET, a theoretical model that specifies the relationships between relational bonding strategies, customer satisfaction and loyalty was proposed. Survey data were collected from 175 freight forwarders. The obtained data were analyzed using structural equation modelling.,The results indicate that financial bonding strategies have the most significant direct effects on customer satisfaction, while social bonding strategies have the strongest direct impact on customer loyalty. Financial bonding strategies, on the other hand, have the strongest total effects on customer loyalty. Intermodal and basic operations are found to have the equal total effects on customer loyalty.,By identifying the most effective relational bonding strategies for enhancing customer satisfaction and loyalty, this study’s findings allow container lines to better allocate their resources and implement effective relational marketing policies to satisfy and retain their customers.,This research analyses and validates the determinants of customer satisfaction and loyalty from a relational lens and empirically contributes to the field of relational marketing in the container shipping industry.

Trait Emotional Intelligence and Problematic Social Media Use Among Adults: The Mediating Role of Social Media Use Motives

Abstract: There are many contributing factors to problematic social media use including personality differences, psychosocial factors, and specific use motivations. The present study (N = 444 emerging adults, 75% women) investigated the direct and indirect relationships between trait emotional intelligence and problematic social media use via social media use motives by testing a complex mediation model. Path analyses suggested that trait emotional intelligence was directly and indirectly associated with problematic social media use via two social media use motives: (i) expressing or presenting a more popular self, and (ii) passing time. Results of the present study indicate that trait emotional intelligence may have a role in the motives for using social media as well as the development and maintenance of problematic social media use. Moreover, future studies should focus mediator risk factors between trait emotional intelligence and problematic social media use.

Perspectives on the Gap Between the Software Industry and the Software Engineering Education

Abstract: The gap between the software industry and software engineering education was first mentioned three decades ago, in 1989. Since then, its existence has been regularly reported on and solutions to close it have been proposed. However, after thirty years this gap resists all efforts for closure. In this study we assert that the gap between industry and academia exists for several reasons that are related and intertwined. To take a broader look at the problem from the perspective of all related entities, we (i) provide a detailed overview of the profession and identify the entities, (ii) extract the causes that stem from these entities and discuss what each entity should do, (iii) report and analyze the results of a questionnaire that has been conducted with students and recent graduates, (iv) emphasize the highlights of the interviews conducted with students, recent graduates and academics, (v) and compile a list of skills that are sought by the industry by analyzing the software engineering job advertisements. We further contribute to finding solutions by considering all entities involved, which provides an opportunity to access all of them, so that each can find out what they can do to acknowledge and narrow the gap. Our study concludes that the gap requires constant attention and hard work for all of the entities involved, and therefore all should be on the lookout for new technologies, learn to embrace the changes and adapt to them, so that the gap is kept at a minimum.

Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques

Abstract: This paper presents the results of a comparison between multivariate adaptive regression splines (MARS) and random forest (RF) techniques in pipe failure prediction in two water distribution networ...

Emotion Classification from EEG Signals in Convolutional Neural Networks

Abstract: The objective of this research is to classify EEG (electroencephalography) signal recordings of the subjects evoked by visual stimulus by using CNN (Convolutional Neural Networks). EEG records the electrical activity of brain signals. In medicine, EEG is used to diagnose some neurological disorders but moreover the classification of the emotions is also possible from EEG recordings. Emotion recognition is an important task for the computers in machine perception. Therefore, in this study the participants are presented with a video containing funny, scary and sad excerpts and simultaneously EEG signal is measured by Neurosky Mindwave EEG Headset. The spectrogram of EEG signals is supplied to CNN and three emotions are classified using brain signal spectrogram images.

Festivals and destination marketing: An application from Izmir City

Abstract: Festivals are among the growing global city and destination attraction factors and they are very trendy. Studies indicate that there is a direct relationship between the successful organizations of festivals and the number of visitors and tourism income of a city destination. Thus, festival marketing efforts usually have a direct impact on city marketing efforts. Due to this fact, the study tries to figure out the festival marketing potential of Izmir city that seems to lag behind the general tourism development pattern of Turkey contrary to the great potential it has. The study comprises of two main sections, first one being a literature review on festival marketing, followed by the second section on a qualitative research on Izmir’s festivals and their marketing potential. It tries to determine the positive impacts of Izmir’s festivals on destination marketing and by grouping and categorizing them under similar characteristics figuring out the most promising ones. The research consists of four open-ended interview questions on festivals held in Izmir city. As practical implications, Destination Management Organizations, Destination Marketing Organizations, destination and city marketers, festival managers and destination tourism developers may benefit from the findings of this study.

Applying the Right Relationship Marketing Strategy through Big Five Personality Traits

Abstract: In this study, Structural Equation Modeling was utilized in an exploratory manner to answer the following questions: (1) Can relationship marketing practices be predicted by Big Five personality tr...

Self-cleansing design of sewers: Definition of the optimum deposited bed thickness.

Abstract: Sediment deposits may influence the performance of the sewer systems. Sediments are the main store of pollutants which causes sewer systems overflows. In order to prevent the deposition of sediment in sewer systems, self-cleansing design criteria are used. Among various criteria, "self-cleansing with deposited bed" criterion is found appropriate for designing of large sewers. Allowing small thickness of deposited bed in large sewers decreases the required channel bed slope and construction costs. This study presents a bed load self-cleansing model established on wide ranges of experimental data. Comparison of developed model with the existing models in the literature shows its higher performance on variety of data sources. Examples for the application of the model are presented, and design graphs and charts for different pipe sizes are proposed. As a result, the optimum deposited bed thickness is found 1%-5% of the pipe diameter; however, 1% is recommended for sewer pipes design. PRACTITIONER POINTS: Nondeposition sediment transport condition with a bed deposit criterion is found appropriate for large sewer pipe design. The precision of a model is linked to the ranges of experimental data and parameters used for the models development. The optimum range of deposited bed thickness can be adopted in the range of 1% to 5% of the pipe diameter.

Energy dissipation and performance assessment of double damped outriggers in tall buildings under strong earthquakes

Abstract: The use of a single set of outriggers equipped with oil viscous dampers increases the damping ratio of tall buildings in about 6–10%, depending on the loading conditions. However, could this ratio be further increased by the addition of another set of outriggers? Should this additional set include dampers too? To answer these questions, several double damped outrigger configurations for tall buildings are investigated and compared with an optimally designed single damped outrigger, located at elevation 0.7 of the total building's height (h). Using free vibration, double outrigger configurations increasing damping up to a ratio equal to the single-based optimal are identified. Next, selected configurations are subjected to several levels of eight ground motions to compare their capability for avoiding damage under critical excitations. Last, a simplified economic analysis highlights the advantages of each optimal configuration in terms of cost savings. The results show that, within the boundaries of this study, combining a damped outrigger at 0.5h with a conventional outrigger at 0.7h is more effective in reducing hysteretic energy ratios and economically viable if compared with a single damped outrigger solution. Moreover, double damped outrigger configurations for tall buildings exhibit broader display of optimal combinations, which offer flexibility of design to the high-rise architecture.

Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication

Abstract: We present a comparative study of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), 1-Dimensional Convolutional Neural Network (1-D CNN), and Long-Short Term Memory (LSTM) models on the problem of forecasting the traffic generation patterns of individual Internet of Things (IoT) devices in Machine-to-Machine (M2M) communication. We classify IoT traffic into four classes: Fixed-Bit Periodic (FBP), Variable-Bit Periodic (VBP), Fixed-Bit Aperiodic (FBA), and Variable-Bit Aperiodic (VBA). We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error (sMAPE) measure for devices in the VBP class in our simulations. Furthermore, we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN. While the training time per IoT device is the highest for LSTM, all of the forecasting models have reasonable training times for practical implementation. Our results suggest an architecture in which an IoT Gateway predicts the future traffic of IoT devices in the FBP, VBP and FBA classes and pre-allocates the uplink wireless channel for these classes in advance in order to alleviate the Massive Access Problem of M2M communication.

ECG Arrhythmia Classification By Using Convolutional Neural Network And Spectrogram

Abstract: In this study, the electrocardiography (ECG) arrhythmias have been classified by the proposed framework depend on deep neural networks in order to features information. The proposed approaches operates with a large volume of raw ECG time-series data and ECG signal spectrograms as inputs to a deep convolutional neural networks (CNN). Heartbeats are classified as normal (N), premature ventricular contractions (PVC), right bundle branch block (RBBB) rhythm by using ECG signals obtained from MIT-BIH arrhythmia database. The first approach is to directly use ECG time-series signals as input to CNN, and in the second approach ECG signals are converted into time-frequency domain matrices and sent to CNN. The most appropriate parameters such as number of the layers, size and number of the filters are optimized heuristically for fast and efficient operation of the CNN algorithm. The proposed system demonstrated high classification rate for the time-series data and spectrograms by using deep learning algorithms without standard feature extraction methods. Performance evaluation is based on the average sensitivity, specificity and accuracy values. It is also worth to note that spectrogram increases the performance of classification since it extracts the useful time-frequency information of the signal.