In the current context of multicultural collision, online information is impacting traditional gender values. To analyze the changes in gender role attitudes and gender awareness among Chinese Generation Z college students under the influence of various social factors, the study focuses on Generation Z college students and explores the impact of cultural, media, educational, and family factors on gender role attitudes and gender awareness among Chinese Generation Z college students through questionnaire surveys and quantitative analysis methods. The research results show that Generation Z college students exhibit extremely favorable gender perspectives, with the proportion of bisexual gender roles approaching 38%, surpassing the number of students with traditional understanding of single sex gender roles. At the same time, in school gender awareness education, research has found that the proportion of bisexual gender roles is the highest among students who accept open mindedness, at 46.6%. In family gender awareness education, students who receive parental gender awareness sharing education have the highest proportion of bisexual gender roles, accounting for 48.5%. Therefore, the current gender education for the new Generation of students in China needs to abandon traditional avoidance-based teaching methods and adopt an open and supportive attitude to guide students’ gender values.

Lately, there is a progressive assimilation of sustainable and green development principles into the collective conscience of individuals. Companies have received considerable attention from all sectors of life when it comes to the environment, society and governance (ESG). This study uses a bidirectional fixed effects model to investigate the influence and the mechanism of green innovation on company ESG information, using a research sample composed of data from the A-share listed companies in China spanning the period from 2011 to 2021. The findings indicated that green innovation exerted a substantial positive influence on ESG information disclosure, and the effect was more substantial, especially in mature and declining companies. Financing constraints and analysts’ attention played a mediating role between green innovation and ESG information disclosure. The results of heterogeneity analysis showed that green innovation played a more significant role in promoting ESG information disclosure among state-owned companies, large-scale companies, manufacturing companies and heavy pollution companies. Furthermore, implementing green development policies had facilitated the reinforcement of the promotion impact of ESG information disclosure through green innovation. Additionally, the instrumental variable method was employed to conduct a robustness test. This study enhances the understanding of the theoretical framework about green innovation and the disclosure of ESG information, and offers valuable insights for advancing the sustainable development of companies.

Transit-oriented development is a concept that focuses on developing areas in and around transit nodes to create added value. The concept concentrates on integrating mass public transport networks with non-motorized modes of transport, minimizing the usage of motorized vehicles, and fostering the growth of dense, mixed-use areas with medium to high spatial intensity. This research examines the effects of altering the business model to create Transit Oriented Development (TOD) in Jakarta, contrasting it with PT Moda Raya Transports (PT MRT). We collected data by conducting in-depth interviews with experts and distributing questionnaires to seven respondents who work at this We used the Business Model Canvas (BMC) to identify business models and the internal resources needed for the implementation process. process. Therefore, six elements in BMC were used to conduct changes, and based on the results, RBV analysis was pe PT MRT needs to enhance its internal power to a competitive advantage level in order to effectively manage changes. We need to conduct further research on how the business model can influence the creation of transit-oriented development areas.

Floods have always been an unavoidable natural disaster globally. Due to that, many efforts have been taken in order to alleviate the effect, especially in protecting the victims from losing their lives as well as their belongings. This study focuses on ensuring a smooth allocation process for flood victims to the relief centres considering the nature of their location, near the river, inland, and coastal. The finding indicated that a few implications have been highlighted for disaster management, such as changes in flood victim allocation patterns, classification of prone areas based on three areas, identification of most disaster areas, and others. Thus, to enhance the efficiency of allocation and to avoid any bad incidents happening during the flood occurrence, the allocation of flood victims is proposed to be started at a more critical area like the river area and followed by other areas. The finding also indicated that the proposed allocation procedure yielded a slightly lower average travel distance than the existing practice. These findings could also provide valuable information for disaster management in implementing a more efficient allocation procedure during a disaster.

The purpose of Vehicular Ad Hoc Network (VANET) is to provide users with better information services through effective communication. For this purpose, IEEE 802.11p proposes a protocol standard based on enhanced distributed channel access (EDCA) contention. In this standard, the backoff algorithm randomly adopts a lower bound of the contention window (CW) that is always fixed at zero. The problem that arises is that in severe network congestion, the backoff process will choose a smaller value to start backoff, thereby increasing conflicts and congestion. The objective of this paper is to solve this unbalanced backoff interval problem in saturation vehicles and this paper proposes a method that is a deep neural network Q-learning-based channel access algorithm (DQL-CSCA), which adjusts backoff with a deep neural network Q-learning algorithm according to vehicle density. Network simulation is conducted using NS3, the proposed algorithm is compared with the CSCA algorithm. The find is that DQL-CSCA can better reduce EDCA collisions.