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Anatolia - Unveiling Its Multidisciplinary Landscape and Future Orientations
No full textKozak, Metin/0000-0002-9866-7529; Kaurav, Rahul Pratap Singh/0000-0001-9851-6854; Singhania, Shubham/0000-0001-8473-9563Using methods such as Latent Dirichlet Allocation (LDA) and content analysis, this paper does a comprehensive analysis of Anatolia's contributions to tourism and hospitality research over the last 27 years, analyzing publishing trends, regional distribution, and topic evolution. The results show a significant increase in worldwide contributions, covering 92 nations, and an interdisciplinary strategy in line with the UN Sustainable Development Goal (UNSDG) 11. Important subject clusters are identified by the study, underscoring the journal's growing influence and its part in determining the course of future research. Through the identification of emergent topics, the study highlights Anatolia's engagement in varied topics and offers a strategic framework for future research.FORE School of Management, New DelhiAuthor 1: The infrastructural support provided by FORE School of Management, New Delhi, in completing this article is gratefully acknowledged.Emerging Sources Citation Inde
An Ultra Efficient 2:1 Multiplexer Using Bar-Shaped Pattern in Atomic Silicon Dangling Bond Technology
No full textAs CMOS technology approaches its physical and technical limits, alternative technologies such as nanotechnology or quantum computing are needed to overcome the challenges of lithography, transistor scaling, interconnects, and miniaturization. This article introduces a novel nanotechnology that uses atomic-scale silicon dangling bonds (ASDB) to create high-performance, low-power, nanoscale logic circuits. DBs are atoms that can form basic logic gates on a silicon surface using a scanning tunneling microscope device. ASDB can also be an alternative to the existing complementary metal oxide semiconductor (CMOS) technology. The article also proposes a new bar-shaped pattern to design gates and logic circuits with ASDB nano tecnolgoy. The bar-shaped pattern improves the reliability of the output, reduces the area and power consumption, and solves the problem of interatomic energy effects of ASDB. The article demonstrates the efficiency of the bar-shaped pattern by implementing two-input gates such as AND, NAND, OR, NOR, XOR, XNOR, and a 2:1 multiplexer with ASDB. The article also uses a powerful tool called SiQAD to simulate and verify the performance of the proposed structures with ASDB. According to the simulation results, the proposed logic gates are more energy efficient, stable, and compact than the previous structures. They consume 35% and 24.34% less energy and have 14.18% more stability, respectively
Mental Health Research in Tourism and Hospitality: a Horizon 2050 Paper
No full textWen, Jun/0000-0002-1110-824XPurpose - This paper aims to cover mental health research related to tourism and hospitality, starting in 1984, and track its development until 2020. Relevant research published between 2020 and 2023 during the COVID-19 pandemic is also reviewed to determine how this research streamis evolving. Design/methodology/approach - A detailed search of Scopus and Google Scholar yielded 4,790 mental health studies in tourism and hospitality; 102 were ultimately retained for systematic review. VOSviewer was used to visualize cluster analysis results. Findings - Research onmental health in the context of tourismand hospitality is limited and can be classified into four themes. The most prominent involves mental health in relation to COVID-19. Thematic differences between studies published before and after the onset of the pandemic are also specified. The findings inform a critical reflection on the conceptual framework linking tourism and mental health, as well as potential research avenues, covering research populations, topics, methods, data sources and outcome-measures. Practical implications - This in-depth analysis of the extant literature provides a foundation for stakeholders to better understand, address and promote mental health in tourism and hospitality. Such insights can steer future research and enlighten industry practitioners, thus contributing to sustainable industry development. Originality/value - This paper represents a pioneering effort to systematically review mental health studies in tourism and hospitality. It offers a holistic perspective and unique insights, bridging substantial knowledge gaps. This paper is also meant to prompt academics and practitioners to contemplate mental health-related research and practice.CSC (China Scholarship Council) - ECU (Edith Cowan University) Joint PhD Scholarship [202109327004]This work was supported by Fangli Hu's CSC (China Scholarship Council) - ECU (Edith Cowan University) Joint PhD Scholarship (No. 202109327004).Social Science Citation Inde
Multimodal Communication in Virtual and Face-To Gesture Production and Speech Disfluency
No full textThe COVID-19 pandemic has made online data collection a popular choice. It is important to evaluate howcomparable online studies are to face-to-face studies, particularly in multimodal language research wheremodes of communication significantly impact the results. In this study, we examined individuals' rates andpatterns of speech disfluency and gesture use across face-to-face and online videoconferencing settings asthey described their daily routines (N= 64). We asked whether and how multimodal language is affected acrossdifferent communication settings and gesture use, particularly iconic gestures, is associated with speech fluencyregardless of the context. Our results have showed that the participants' overall disfluency rate was higherfor the speech communicated via videoconferencing than the speech communicated face-to-face. However,the type of disfluencies changed across contexts, such that filled pauses and repairs were more commonin online communication, whereas silent pauses were more common in face-to-face communication. Thesefindings signal an interplay between the cognitive functions of different disfluency types and communicativestrategies. Results indicate that the overall gesture frequency and iconic gesture use were similar in bothsettings. Furthermore, the use of iconic gestures was found to negatively predict the overall disfluency rate,regardless of the setting. This finding suggests that using iconic gestures might facilitate cognitive processes,paving the way for a more fluent speech. This study demonstrates that multimodal language and communicationstrategies may vary across different communication settings and nuanced understanding of the differences inmultimodal language between online and face-to-face communication can be gained using different contexts.The findings contribute to understanding the impact of increasingly widespread online communication onmultimodal language production processes and provide foundation for future research.Emerging Sources Citation Inde
Reflections on the International Noise Awareness Day 2023 Activities
No full textThis paper presents an overview of the experiences and outcomes derived from the activities organized for International Noise Awareness Day 2023 in Turkey with the theme "Keep your hearing, keep your health". Collaboratively organized by Turkish Acoustical Society, and Occupational Hygienists Society, the initiatives aimed to explore the role of sound in educational facilities, with a particular focus on the perspectives of students and teachers. These activities were a student drawing competition, an online International Symposium, a research project titled "Assessment of Acoustic Conditions in Schools and Health Effects on School," and an "Acoustics Workshop in Educational Buildings" conducted at the Izmir BLX Acoustics Laboratory. Drawing from our experiences and findings, we highlight the significance of actively involving researchers, acoustic consultants, occupational hygienists, teachers, students, and designers in such endeavours. Our findings indicate that inclusive approaches not only boost public engagement but also enhance awareness of behavioural, emotional, educational, administrative and design aspects related to sound. By promoting collaboration and participation in knowledge exchange, such activities significantly improve understanding of sound-related issues in schools
Populist Hyperpersonalization and Politicization of Foreign Policy Institutions
No full textThis article explains how right-wing populist leaders in Hungary, Poland, Russia and Turkey have transformed their states' foreign policy institutions through personalization and politicization. We examine the transformation of foreign policy institutions in the four cases and make two contributions. First, we differentiate between disparate types of personalization by proposing the term 'hyperpersonalization'-populist leaders' reliance on security institutions in foreign policy decision-making-which distinguishes the populist transformation of foreign policy institutions in Russia and Turkey. We argue that lower levels and speed of autocratization lead to politicization combined with milder cases of personalization of the foreign policy bureaucracy, while higher levels and speed of autocratization lead to higher levels of personalization in the foreign policy institutions. Second, we lay out the steps and patterns of populist politicization and hyperpersonalization that bring 'deinstitutionalizing restructuring' to foreign policy institutions. As we illustrate, this deinstitutionalizing restructuring involves concurrent bureaucratic expansion and bureaucratic retrenchment. The process is accompanied by a populist narrative that this restructuring is done to realize the 'popular will' or to regain 'full sovereignty'. We conclude the article with the policy implications of this populist transformation of foreign policy institutions.Social Science Citation Inde
Modeling Neuronal Growth Dynamics Using Artificial Neural Networks
No full textNöronlar ve sinir ağları üzerinde çalışarak, hesaplamalı sinirbilim ve yapay zeka teknikleri son birkaç on yılda beynin işleyişini anlama ve modelleme konusunda muazzam bir ilerleme kaydetmiştir. Bu çalışmada, yapay sinir ağları (ANNs) kullanarak, bilişsel aktivitelerde önemli rol oynayan iki temel sinir hücresi türü olan kortikal ve hipokampal nöronların büyüme desenlerini modelledim ve tahmin ettim. Bildiğimiz kadarıyla, daha önce hiçbir araştırmada nöron çoğalmasını tahmin etmek için sinir ağları kullanılmamıştır. Çalışmamız, nöron büyümesi tahmini için YSA tabanlı bir model oluşturarak bu bilgi açığını kapatmayı ve bu alanda yeni bilgiler eklemeyi amaçlamaktadır. Hipokampal ve kortikal nöronlar doğum sonrası (0-1. gün) fare beyinlerinden toplanmış ve 100 mm'lik bakteriyolojik sınıf bir petri kabında kültürlenmiştir. Hücreler 15 gün boyunca 37 °C'de inkübe edilmiş ve büyüme her altı saatte bir kontrol edilmiştir. Nöronal büyüme, Carl Zeiss Axiovert A1 invert floresan mikroskop kullanılarak izlenmiştir. Altı katmanlı bir Multi-Layer Perceptron (MLP) sinir ağı tasarlanmıştır. The Exponential Linear Unit (ELU) aktivasyon fonksiyonu, alfa değeri 1 ve öğrenme oranı 0.01 olacak şekilde kullanılmıştır. Bu ağ, günlük büyüme için 15 gün boyunca kortikal nöron verileri üzerinde eğitilmiş ve hipokampal nöron büyümesini tahmin etmek için test edilmiştir. Bu çalışmada hedef kortikal nöronun vücut büyümesi laboratuvarda gözlemlenmiştir. Her gün için nöronun alan değerleri elde edildikten sonra ANN modeli eğitilmiştir. Eğitimin ardından model hipokampal nöron üzerinde test edilmiştir. Yapının hassasiyetini doğrulamak için hipokampal nöron üzerindeki ANN tahmini, laboratuvardan elde edilen deneysel verilerle karşılaştırıldı ve çok yakın bir eşleşme bulundu. Bu çalışma, ANN'nın doğru modellendiği takdirde nöronların büyüme modelini tahmin edilebileceğini göstermiştir.Through studying neurons and neural networks, computational neuroscience and artificial intelligence techniques have made tremendous progress in the past several decades in comprehending and simulating the brain's workings. In this work, I employed artificial neural networks (ANNs) to model and forecast the growth patterns of cortical and hippocampal neurons, two important types involved in many cognitive activities. To our knowledge, no earlier research has used neural networks to predict neuron proliferation. By creating an ANN-based model for neuron growth prediction, our work seeks to close this knowledge gap and add new insights to the field. Hippocampal and cortical neurons were collected from postnatal (day 0-1) mouse brains and cultured in a 100-mm bacteriological-grade petri dish. The cells were incubated at 37 °C for 15 days, with growth checked every six hours. Neuronal growth was monitored using Carl Zeiss Axiovert A1 inverted fluorescent microscope. A Multi-Layer Perceptron (MLP) neural network with six layers was designed. The Exponential Linear Unit (ELU) activation function was used with an alpha value of 1 and a learning rate of 0.01. The network was trained on cortical neuron data for 15 days for daily growth and tested to predict hippocampal neuron growth. In this study the body growth of target cortical neuron was monitored in the laboratory. After obtaining the area values of the neuron for each day, the ANN model was trained. After the training, the model was tested on hippocampal neuron. To confirm the precision of the structure, ANN prediction on hippocampal neuron was compared with the experimental data obtained from the laboratory and a very close match was found. This study showed that ANN could predict the growth pattern of the neurons if modeled properly
Intellectual Property in the Age of Machine Creativity: Understanding the Legal Landscape and Emerging Issues
No full textThis chapter explores the intersection of intellectual property (IP) law and generative artificial intelligence (AI), focusing on the complex challenges and emerging issues presented by these technologies. It provides a structured taxonomy of IP concerns related to AI, including authorship and ownership, copyright infringement, fair use. By analyzing the legal and ethical implications of these challenges, the chapter aims to offer insights into current IP frameworks and propose potential solutions and best practices for stakeholders. Through a multidisciplinary approach that includes legal texts, academic literature, and case studies, this chapter contributes to the development of new theoretical frameworks and informs policy, practice, and public understanding in the evolving landscape of machine creativity. © 2025 by IGI Global Scientific Publishing. All rights reserved
A Possible Transformation of Tourism Education: A Chaos Theory Perspective
No full textRecently, there has been a likely transformation from traditional face-to-face education to distance education and hybrid models. Higher tourism education has undergone these changes concordantly as it incorporates an applied field. Therefore, an atmosphere of uncertainty and chaos has arisen in universities. The study approaches the effects of the pandemic on the education system through the perspective of chaos theory. The data were collected from tourism academics, one of the pillars of the higher education system. Semi-structured in-depth interviews were conducted with 21 lecturers. Study findings revealed that the lack of compulsory attendance reduced student participation in online classes. Accordingly, low student attendance resulted in the lack of lecturer-student interaction in courses, negatively impacting lecturer motivation and highlighting the inefficiency of distance education. The study also provides clues regarding differences and managerial implications experienced by public and foundation universities during the pandemic.Social Science Citation Inde
Enhancing Malware Classification: a Comparative Study of Feature Selection Models With Parameter Optimization
No full textThis study assesses the impact of seven feature selection algorithms (Minimum Redundancy Maximum Relevance (MRMR), Mutual Information (MI), Chi-Square (Chi), Leave One Feature Out (LOFO), Feature Relevance-based Unsupervised Feature Selection (FRUFS), A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization (AGRM), and BoostARoota) across two malware datasets (Microsoft and API call sequences) using three machine learning models (Extreme Gradient Boosting (Xgboost), Random Forest, and Histogram-Based Gradient Boosting (Hist Gradient Boosting)). The analysis reveals that no feature selection algorithm uniformly outperforms the others as their effectiveness varies based on the dataset and model characteristics. Specifically, BoostARoota demonstrated significant compatibility with the Microsoft dataset, especially after parameter optimization, whereas its performance varied with the API call sequences dataset, suggesting the need for customized parameter selection. This study highlights the necessity of tailored feature selection approaches and parameter adjustments to optimize machine learning model performance across different datasets. © 2024 IEEE