Structural Relationship between Ecotourism Motivation, Satisfaction, Place Attachment, and Environmentally Responsible Behavior Intention in Nature-Based Camping

This study aims to make an investigation on the relationship between ecotourism motivation (EM), satisfaction (SA), place attachment (PA), and environmentally responsible behavior intention (ER) based on the analysis of camping tourism andstudy the mediating roles of SA and PA in the relationship between EM and ER. A questionnaire-based survey was conducted on 400 camping tourists who visited the Yunnan Province of China within the last year. The distribution of survey questionnaires range from 12 February 2023–24 February 2023. The results reveal that EM positively influences SA, PA, and ER, whereas, although SA positively impacts PA, it does not significantly affect ER. In contrast, PA positively affects ER. Furthermore, PA has a positive mediating impact on the relationship between EM and ER, whereas, SA does not significantly mediate this relationship. Finally, SA and PA act as chain mediators between EM and ER. The findings contribute to the tourism literature by shedding light on the complex relationships between EM, SA, PA, and ER in the context of camping tourism, thereby, enhancing tourist satisfaction and promoting environmentally responsible behavior, thus, contributing to the growth of a sustainable camping industry.

APPLICATION AND FEASIBILITY OF VARIOUS TEACHING TOOLS USED IN ONLINE CLASSES DURING COVID-19 IN TERTIARY EDUCATION

The study compares the effectiveness, popularity, and ease of applicability of different learning tools in virtual classrooms among university teachers and students concerning the users' technological literacy and training, as well as equipment support offered by the universities during the pandemic. Comparisons between face-to-face teaching in classrooms and online virtual classrooms will be drawn concerning limitations, incentives, motivation, and effectiveness toward learning. This study also leads to the question of future course development by exploring the possibility of course design and assessment restructuring with a switch to online education with the new mode of technology as the trend.

An Electric Vehicle Assisted Charging Mechanism for Unmanned Aerial Vehicles

The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV.

Advanced Dance Choreography System Using Bidirectional LSTMs

Recently, the craze of K-POP contents is promoting the development of Korea's cultural and artistic industries. In particular, with the development of various K-POP contents, including dance, as well as the popularity of K-POP online due to the non-face-to-face social phenomenon of the Coronavirus Disease 2019 (COVID-19) era, interest in Korean dance and song has increased. Research on dance Artificial Intelligent (AI), such as artificial intelligence in a virtual environment, deepfake AI that transforms dancers into other people, and creative choreography AI that creates new dances by combining dance and music, is being actively conducted. Recently, the dance creative craze that creates new choreography is in the spotlight. Creative choreography AI technology requires the motions of various dancers to prepare a dance cover. This process causes problems, such as expensive input source datasets and the cost of switching to the target source to be used in the model. There is a problem in that different motions between various dance genres must be considered when converting. To solve this problem, it is necessary to promote creative choreography systems in a new direction while saving costs by enabling creative choreography without the use of expensive motion capture devices and minimizing the manpower of dancers according to consideration of various genres. This paper proposes a system in a virtual environment for automatically generating continuous K-POP creative choreography by deriving postures and gestures based on bidirectional long-short term memory (Bi-LSTM). K-POP dance videos and dance videos are collected in advance as input. Considering a dance video for defining a posture, users who want a choreography, a 3D dance character in the source movie, a new choreography is performed with Bi-LSTM and applied. For learning, considering creativity and popularity at the same time, the next motion is evaluated and selected with probability. If the proposed method is used, the effort for dataset collection can be reduced, and it is possible to provide an intensive AI research environment that generates creative choreography from various existing online dance videos.

The digital communication tools and citizens' relationship with local governments: a comparison of Georgian and Polish cities

This article aims to explore the impact of digital communication tools application by citizens and the perceived usefulness of social media on the relationship between citizens and local authorities. The data were gathered in April–September 2020 through a survey (CAWI) among citizens of Poznan, Poland (n=502), and Kutaisi, Georgia (n=504), and were analyzed with structural equation modeling. The findings show that the intensity of digital communication tools usage for participation in the city branding and the perceived usefulness of social media contribute to the lasting relationships between citizens and local authorities in both countries. The novelty of this research concerns comparing two countries with different levels of development. Georgia is a developing economy in Europe and is in the process of modernizing the local governance across the cities. Poland, however, is a mature economy with a post-transformation heritage, where its cities benefit from considerable experience in building and developing citizen participation policies. Furthermore, the research was conducted amid the COVID-19 pandemic and evidenced the growing popularity of digital tools adoption by citizens in city matters. This study contributes to understanding the impact of digital tools on the relationship between citizens and local authorities in terms of city brand management. Citizens' participation in the city branding process via various digital communication tools increased citizen commitment towards long-lasting collaboration with local authorities. Moreover, citizens' perception of social media usefulness positively influences their desire to engage in the city branding process online, supporting the trust-building and collaboration between citizens and local authorities. Points for practitioners The intensiveness of digital tools usage – governments should identify the tools already trusted and popular among their audience and employ those tools to a greater extent to maximize the chances of feedback, high citizen participation, and commitment. Citizens' perception of social media – such characteristics as ease of use, transparency, ease of communication with the municipality, and safety encourage citizens to get involved in the city brand management process. Consequently, local authorities should consider the features mentioned above and develop the online tools quality.

Market reaction to macroeconomic anouncements: green vs conventional bonds

There has been an increased interest in literature to examine the risk and returns between green and conventional bonds during the last decade. However, the existing literature is silent regarding investigating green bonds and their reactions to regional and global shocks. We attempt to close this gap by gathering green and conventional bonds data issued by the same firms. Using data from 262 firms that issue both sets of bonds and trade in the same market, we are able to control/eliminate for firm-specific factors that can impact the bond spreads. Upon introducing US and EU macroeconomic announcements and economic uncertainty, we find that the green bonds are more resilient from specific shocks, when compared to conventional bonds. We further expand our study to systematically assess the impact of the Covid-19 pandemic on both bonds. Our comprehensive findings suggest an increase in green bonds' uptake post-Covid-19 pandemic, and its significant evolvement compared to pre-Covid-19 proves green bonds popularity. Our result shows greater resilience of green bonds from specific Covid-19 shocks and uncertainties.

ONLINE STREAMING E-COMMERCE INFLUENCE ON ECONOMY IN CHINA

Online streaming e-commerce refers to the practice of selling products online via streaming directly to the customers watching the streaming. It gains growing popularity in China in past five years. During the COVID-19 with lockdowns and the sharp decline of in-store visiting, more farmers and retailers began to sell their products online, and more customers turn to the digital platforms and watch online streaming. In this essay, I discuss how online streaming selling becomes popular in China and get supported by the state, and how it contributed to the job market and China's GDP. The analysis shows that online streaming e-commerce plays an important role in boosting Chinese economy.

Micropublishing During and After the COVID-19 Era

In the COVID-19 situation, social and behavioral science evidence is accumulating rapidly through online data collection, but the options to share and publish this information are scarce. As a remedy, I recommend the adoption of micropublishing in the fields of social and behavioral sciences. While micropublishing has been gaining popularity, it is not yet widely accepted or utilized by existing academic journals. Greater implementation would improve the availability of data in the immediate COVID-19 era and establish a post-COVID-19 publishing methodology that could increase researcher and practitioner engagement in real time. I recommend micropublishing in a specific manner that bifurcates an experiment's methodology or survey method from the subsequently published data based on that experiment protocol. Published findings could be presented in a series and edited as new data emerges. This publishing system promotes cumulative science. To provide a visual example that supports my argument, I created a demo journal with sample papers organized according to the structure I recommend. The demo journal has features-except a Digital Object Finder (DOI)-that make it possible to publish social and behavioral sciences research. It could be replicated for a newly established journal. Alternatively, existing journals could add a section dedicated to micropublication. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Traveler There is No Path, the Path is Made by Negotiating: The Story of the Abraham Path, a Decade and a Half of Negotiations, and Lessons Learned

The Abraham Path Initiative (API) has substantially reduced its close-in support of path-building work on the ground. The paths in Palestine, Jordan, and Egypt are run, as they should be, by local partners. It was always part of the strategy to pull back once the Path was established and local communities were shepherding it. These trails have gained in popularity thanks to a newfound tourism industry centered around walking and hiking as well as to a great deal of publicity related to the Path. While in the early years. International travelers predominated in using the Path, many local hiking groups later emerged and were very active along different parts of the Path prior to the Covid pandemic. What were the keys to progress toward that UTA? They can be summed up by seeing the Path through the prism of a very long-term negotiation campaign. Within that campaign, two steps were particularly essential--first, carefully thinking about negotiation sequencing at the outset and revisiting it frequently as the Path developed, and second, taking an iterative approach that harnessed people's learnings and forced them to constantly adapt to the situation that presented itself.

Thailand in 2022: The Decline and Resilience of Thailand's Conservative Establishment

Thailand's military-aligned government saw its popularity plummet in polls throughout 2022. Yet former head of the armed forces and current prime minister Prayut Chan-o-cha was resilient in the face of mass party defections, a falling-out with his fellow party leader and long-time friend Prawit Wongsuwon, a censure debate, and even a constitutional court case threatening to remove him from office. The mass street protests that were a constant feature of the previous three years evaporated as conservative forces used the controversial Section 112 Royal Defamation Law to silence the various movements' leaders. Political attention was focused on the impending elections, which shook up the party system with rampant party mergers and switching. Meanwhile a return to the 2011 electoral rules fueled talk of another Pheu Thai landslide in the next elections. The economy began to improve with the relaxing of COVID rules and the return of tourism, throwing a lifeline to a rapidly sinking Prayut, who himself switched to a new political party before the year's end.

Social Media and COVID-19 Pandemic: Accelerating the Learning of English as a Foreign Language (EFL)

COVID-19 began in China (2019) and spread to other nations and become a world pandemic (2020). The news media and social media platforms have played an important role in information dissemination about the Coronavirus throughout the world. During COVID-19 Pandemic, social media has become a key tool for enhancing social networking and information sharing via the web. As more people turn to the internet for health information, social media platforms are helping to spread life-saving precautions that individuals, families, and communities may take to reduce their chance of contracting the virus. Language learners are linked to interactive social media platforms in Social Media Language Learning. The goal of this study was to look at the role of social media during COVID in foreign language acquisition. The participants in this study were Saudi Arabian college students. The study's sample comprised 200 (n=200) college students, and the social media platforms investigated included Facebook, Instagram, Twitter, LinkedIn, Youtube, WhatsApp, Telegram, Snapchat and other platforms. Questionnaires were used to collect the data. The results indicated that the students used various social media platforms in order to get the appropriate information about the virus that indirectly helped and motivated them learning a new language. These learners lack the self-motivation needed to use social media to develop their English language abilities. The study discovered that adopting online social media during a pandemic for English language learning had a significant impact on interactions with peers and teachers. © 2022 Aesthetics Media Services. All rights reserved.

Predicting the impact of online news articles - is information necessary?: Application to COVID-19 articles.

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features - ranging from the semantic relations through readability measures to the article's digital content - are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

HIIT and Workout Place: A Study on Influence of Social Media on the Lifestyle of Millennials in India: A Quarterly Peer Reviewed Multi-Disciplinary International Journal

People across the globe have been showing tremendous enthusiasm as far as adopting a process-based fitness workout is concerned. There are various ways of keeping oneself fit and fine even during the most hectic daily schedules. Since the last few decades, High Intensity Interval Training, popularly abbreviated as HIIT, has found its place among the citizens of different regions as one of the finest ways of keeping one healthy and sportive. In India, the millennials have been steadily accepting this popular form of workout thanks to their exposure to various media. However, the workout place becomes an important consideration due to various reasons including the impact of COVID pandemic restrictions. The current study is an attempt to bring out the factors behind the preference of HIIT in offering the people a systematic workout process choosing an appropriate place. Conducted in the twin cities of Bhubaneswar and Cuttack in Odisha, the focus of the study was to understand the role played by social media while guiding the millennials to maintain a change with the adoption of HIIT emerging as a trendy workout process. Six factors emerged from the study and the results of its corresponding analysis with the preferred workout places can help the professionals and trainers of HIIT to devise innovative methods to make the workout practice popular and preferred in developing markets.

Increasing Consumption of Korean Dramas during the COVID-19 Pandemic: Cases of Indonesian Viewers

This qualitative study focuses on Indonesian transnational fans' consumption of Korsan dramas (hereafter K-dramas) during the COVID-19 pandemic. This study highlights the fans' lived experiences, thoughts, and interpretation of Korean culture in watching K-dramas as well as how they construct identities through their consumption in their everyday lives. This study used qualitative questionnaire as the instrument to collect the data for the study. Using thematic analysis, the study reveals that there were four emerging motivating factors to watch K-Dramas among Indonesian viewers: fresh substitutive choice, easy access, interesting stories and visuals, and cultural factors. Additionally, the study also shows four outcomes of their consumptive behavior: language acquisition and motivation to learn Korean, cultural understanding and new perceptions of Korea, pursuit of education, and pure entertainment and stress-reliever. Hence, this study contributes to the body of literature on K-Dramas consumption, especially in the era of COVID-19 pandemic.

The urge to search: A multinational model of prepurchase behavior during COVID-19

In this study I proposed a multinational model to explore how COVID-19 mortality salience influences countries' prepurchase online keyword search behavior for products during the pandemic. I used the COVID-19 death toll in 75 countries and extracted Google Trends data to develop a global search ranking that I called the religious searching popularity index (RSPI) as well as a prepurchase index (PPI) for the test products. I used terror management theory to investigate how the RSPI and product type (home use or nonhome use) moderated the relationship between the COVID-19 death toll and the PPI for each of eight products. The results suggested that purchasing did not increase for every home-use product, nor did purchasing of every non-home-use product decrease during the COVID-19 pandemic. According to the PPIs, countries with a higher RSPI had lower search impulses for Nintendo Switch, perfume, treadmills, and watches. The death toll significantly affected the PPIs of some specific home-use and non-home-use items, mainly in countries with lower RSPIs. Replication of this model will assist multinational businesses and researchers in identifying market opportunities for products.

Internet Memes as Stress Buster in the Time of COVID-19 Pandemic

Internet memes can be shared from one virtual social networking site to another. The natural selection of behavior and operant conditioning is a joint product in the evolutionary system of learning. The unique feature of Internet memes is that they are 'half-baked joke', internet users need to understand with their conscious (person's awareness). Internet memes are highly visual and intersexual;they are not only still images, but they also consist videos, GIF (Graphic Interchange Format), editorial cartoons, animated image or songs clip with image or punch line or aphorism quotes, catchphrases of movies or statement without added text [3, 4].

Rumor Localization, Detection and Prediction in Social Network

With the global epidemic of the COVID-19, various rumors spread wantonly on social networks, which has seriously affected the stability and harmony of the entire society. To purify the network environment, some researchers have proposed to fight rumors from the perspectives of tracing the source of rumors, detecting the authenticity of information, and predicting explosive fake news. But their works are fragmented, and their performance are not significant. So we need strong antirumor methods to fight rumors. To this end, this article proposes a more comprehensive antirumor mechanism, which can realize rumors source location, rumor detection, and popularity prediction (RLDP). In particular, in the task of localization, we propose graph neural network-based method, which does not need to specify the underlying propagation mode and the number of rumor sources;in the task of detection, utilizing lightGBM, we construct a rumor detection model;in the task of popularity prediction, we construct a model based on contrastive learning while considering user engagements and information propagation, and the text of rumor. Finally, we verify the performance of the proposed RLDP by conducting extensive experiments. IEEE

Clustered Bert Model for predicting Retweet Popularity

This work aims to predict retweet popularity of covid19 tweet corpus. Our work fuses unsupervised and supervised learning techniques to create retweet popularity model. In the first phase, we use a Clustered Bert model, which works on clustering the Bert embeddings using clustering algorithms on the textual data to generate novel and meaningful feature set for our model. In the second phase, we use the output of Clustered Bert model as an input to the Supervised Regression models intending to predict retweet popularity. Our work also draws a comparison between features from numeric model;emotions/sentiment model;and Clustered Bert model. Three different Regression models, belonging to different categories: Nearest Neighbors, Ensemble and Stacked models are then tested on the final feature-set to generate predictions for our model. The results show higher accuracy when the Clustered Bert model is used in combination with numerical and emotion/sentiment model. The experiment shows better results for Stacked Regression models out of all the three regressors used for our study. © 2022 IEEE.

THE INFLUENCE OF INSTAGRAM LIKES ON CONSUMER ATTITUDE AND PURCHASE INTENTION OF E-COMMERCE USERS IN BOSNIA AND HERZEGOVINA

The popularity of social media for business and marketing is on a definite rise. Although researchers are increasingly studying various aspects of SNS (social networking sites) marketing and e-commerce, there are still few studies into the impact of Instagram due to the relatively new nature of the application. Therefore, the main objective of this study is to provide an analysis of the statistical significance of Instagram likes on consumer attitude and purchase intention on a linked e-commerce site. The secondary objective is to compare demographic data based on age, gender, and educational categories of the respondents. The final aim is to bridge the gap in literature and provide practical implications which would be useful to professionals in brand and marketing management. Based on a systematic literature review, first order structural equation model was proposed and tested. The empirical data was derived from a survey of 166 subjects in Bosnia and Herzegovina. Upon the collection of data, factor analysis was conducted in SPSS to ensure the validity and reliability through items' loadings and Cronbach's Alpha values. Furthermore, the empirical hypothesis testing was conducted in SmartPLS 3 in order to investigate direct effects of variables in the model. The results indicated that there is a significant statistical impact of Instagram likes on consumer attitude and purchase intention. Currently, due to the relatively recent use of the app for business and marketing purposes, there is a very limited amount of research on consumers' involvement and attitude on Instagram and its impact on the likelihood of purchase. Therefore, one can consider the theoretical and practical implications of this study to be evident.

TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks

As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased. Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient solution to meet the phenomenal growth of the global mobile data traffic by bringing multimedia content closer to the users. Although massive connectivity enabled by MEC networks will significantly increase the quality of communications, there are several key challenges ahead. The limited storage of edge nodes, the large size of multimedia content, and the time-variant users' preferences make it critical to efficiently and dynamically predict the popularity of content to store the most upcoming requested ones before being requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much research attention to predict the content popularity in proactive caching schemes. Existing DNN models in this context, however, suffer from long-term dependencies, computational complexity, and unsuitability for parallel computing. To tackle these challenges, we propose an edge caching framework incorporated with the attention-based Vision Transformer (ViT) neural network, referred to as the Transformer-based Edge (TEDGE) caching, which to the best of our knowledge, is being studied for the first time. Moreover, the TEDGE caching framework requires no data pre-processing and additional contextual information. Simulation results corroborate the effectiveness of the proposed TEDGE caching framework in comparison to its counterparts. © 2022 IEEE.