Indonesia ranks as the second-largest source of plastic garbage in marine areas, behind China. This is a critical problem that emphasises the need for synergistic endeavors to safeguard the long-term viability of marine ecosystems. The objective of this work is to examine the implementation of the Penta Helix model in the management of marine plastic trash. For this purpose, a Systematic Literature Review (SLR) was carried out, utilizing scholarly papers sourced from the Science Direct, Scopus, and Web of Science databases. The analysis centred on evaluating the Penta Helix model as a cooperative framework for tackling plastic waste management in the marine environments of Indonesia and China. The results suggest that the Penta Helix methodology successfully enables the amalgamation of many interests and resources, making a valuable contribution to the mitigation of plastic pollution in the waters of both nations. In order to advance a more comprehensive and sustainable approach to plastic waste management, this multidisciplinary plan brings together stakeholders from government, academia, business, civil society, and the media. Under this framework, the government is responsible for formulating laws, guidelines, and programs to decrease the use of disposable plastics and improve waste management infrastructure, all while guaranteeing adherence to environmental constraints. Simultaneously, the industrial and academic sectors are responsible for creating sustainable technology and pioneering business strategies, while civil society, in collaboration with the media, has a crucial role in increasing public consciousness regarding the destructive effects of plastic trash. This comprehensive strategy emphasizes the need of synergistic endeavors in tackling the intricate issues of marine plastic contamination.

The profession of tourist guide has recently been subject to a significant loss of prestige in Hungary. There have been many career leavers who have been prevented from working due to an unregulated legal framework or a lack of government support during and in the post-COVID-19 period. The first problem - an ineffective and poorly regulated regulatory environment - has led to a significant increase in unauthorised tourism-related activities, undermining the reputation of the profession. As a result of the unregulated legal environment, the country - and Budapest in particular - is losing significant revenue and the situation is damaging the city’s image. Today, personal knowledge and experience are likely to be rendered worthless by the development of new technologies, tools and fast-paced lifestyles. Many people do not even know who exactly a tourist guide is, what their duties are and what regulations apply to their activities, despite the fact that tourist guides spend a lot of quality time with tourists visiting our country, providing them with information and acquainting them with our traditions. The transfer of value, which is the essence of their activity, is an important factor in shaping the image of the country and the perception of Hungary by visitors. Most people may not be aware of the remarkable difference between a qualified and licensed guide and an unqualified and unlicensed guide. The former presents a place authentically. This study aims to present the legal and professional background of this activity and the importance of this work in the light of current regulations, highlighting the important role of guides in the transmission of values today. It also focuses on the main changes and reactions brought about by the COVID-19 pandemic, as well as the uncertainties and concerns created by the legislative background. In order to illustrate the unique situation in Hungary, regulatory procedures and tourist management practices are also covered.

The purpose of this study is to address the issue of low local participation in ecotourism management in Indonesia, specifically at the Malela Waterfall ecotourism site in Cicadas Village, Rongga District, West Bandung Regency, West Java, Indonesia. The research method is action research, which includes observation data gathering, in-depth interviews, and Focus Group Discussions. The findings of the study show that by carrying out the process of developing social infrastructure, namely development that prioritizes strengthening human resources in carrying out social service functions in ecotourism activities such as skill training of residents in the field of ecotourism, massive ecotourism outreach, and strengthening social communities—Non-Governmental Organizations (NGOs) and youth organizations as ecotourism actors. This type of development serves to raise awareness and participation among local inhabitants in Malela Waterfall ecotourism in West Bandung Regency. This promotes harmony and mutually beneficial partnerships among all Malela Waterfall ecotourism stakeholders. Furthermore, increasing community participation benefits the well-being of residents in the tourist region.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.

Raising public awareness of maritime risk and disseminating information about disaster prevention and reduction are the most frequent ways that the government incorporates citizens in marine disaster risk management (DRM). However, these measures are deemed to be insufficient to drive the participation rate. This study aims to understand the participation trend of citizens in marine DRM. On the basis of the theory of citizen participation’s ladder, public participation within marine DRM is categorized into non-participation, tokenistic participation, and substantive participation. Using organization theory, the government’s strategies for encouraging participation are classified into common approach (raising awareness), structural approach (innovating instruments), and cultural approach (developing citizenship). Considering the vignette experiment of 403 citizens in a coastal city of China that has historically been subject to marine disasters, it was found that effectiveness of the strategies, from highest to lowest, are citizenship development, risk education, and instruments innovation. At the individual level, psychological characteristics such as trust in the government, past disaster experience, and knowledge of marine DRM did not significantly influence citizens’ participation preferences. At the government level, even when citizens are informed about new participatory mechanisms and tools, they still tend to be unwilling to share responsibilities. However, self-efficacy and understanding the beneficial outcomes of their participation in marine (DRM) can positively impact the willingness to participate. The results show that to encourage public participation substantively in the marine DRM, it is important to cultivate a sense of civic duty and enhance citizens’ sense of ownership, fostering a closer and more equitable partnership between the state and society.

Given the issues of urban-rural educational inequality and difficulties for children from poor families to succeed, this study explores the impact mechanism of internet usage on rural educational investment in China within the context of the digital divide. Using data from the 2019 China Household Finance Survey (CHFS), this study analyzed the educational investment decisions of 2064 rural households. Results indicate that in the Eastern region, a high level of educational investment is primarily influenced by the per capita income of the family, with social capital and internet usage also playing supportive roles. In the Northeastern region, the key factor is the diversity of internet usage, specifically using both a smartphone and a computer. In the Central region, factors such as the diversity of internet usage, subjective risk attitudes, the appropriate age of the household head, and per capita income of the family contribute to higher levels of educational investment. In the Western region, the dominant factors are the diversity of internet usage, subjective usage and per capita income of the family. These factors enhance expected returns on the high level of educational investment and boost farmers’ confidence. High internet usage rates significantly promote diverse and stable educational investment decisions, providing evidence for policymakers to bridge the urban-rural education gap.

A serious problem in the workplace is incivility, which impacts especially vulnerable groups like single mothers who hold jobs and experience subtle unfair or damaging treatment. As the number of single working mothers continues to rise in today’s workforce, this study aims to clarify third-party perceptions about incivility against them at work and subsequent influences on individuals as well as the organization. Because the analysis is embedded in theories of social role expectations and organizational justice, it explores third-party observers’ perceptions (such as coworkers or supervisors) of whether incivility directed at single working mothers differs from that experienced by their comparison group—professionally equivalent peers who do not share equal caregiver responsibilities. The researchers employed a mixed-methods approach, incorporating both quantitative surveys and in-depth qualitative interviews to collect rich data from participants who represented several fields. They report their results that third-party observers are less likely to experience vicarious justification of incivility against single working mothers but may be equally unlikely or even more reluctant than in the case of other employees and furthermore find this data account for these differences. The results illustrate the intricate interplay of gender, family structure and work dynamics on workplace outcomes—all leading to lower job satisfaction rates, a high level of stress or even stagnation in career progression for single working mothers. Our findings also extend the workplace incivility literature by demonstrating ways in which single working mothers are particularly vulnerable to this form of mistreatment and a broader need for organizational policies that cultivate an inclusive, supportive environment. Implications for human resource management, organizational culture and policy based on these findings are discussed as it may provide some recommendations for handling incivility in the workplace environment.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.