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Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system's capacity to document instances of malicious behavior. In response to these exigencies, this paper presents a novel approach to Web3.0 intrusion detection, predicated on the utilization of cycle-consistent generative adversarial networks (CycleGANs). Leveraging the data transformation capabilities of its generator, this method facilitates bidirectional conversion between normal Web3.0 behavioral data and potentially intrusive behavioral data. This transformative process not only augments the diversity and volume of recorded intrusive behaviors but also clandestinely simulates various attack scenarios. Furthermore, through fostering mutual competition and learning between the discriminator and generator, the approach enhances the ability to discern the defining characteristics of potential intrusive behaviors, thereby bolstering the accuracy of intrusion detection. To substantiate the efficacy of the CycleGAN-based intrusion detection method, simulation experiments were conducted utilizing public datasets, including KDD CUP 1999 (KDD), CIC-DDOS2019, CIC-IDS2018, and SR-BH 2020. The experimental findings evince the method's remarkable accuracies across the four datasets, attaining rates of 99.81%, 97.79%, 89.25%, and 95.15%, respectively, while concurrently maintaining low false-positive rates. This research contributes novel insights and methodologies toward the advancement of Web3.0 intrusion detection through the application of CycleGAN technology, which is poised to play a pivotal role in fortifying the security landscape of Web3.0. [ABSTRACT FROM AUTHOR]

College students are a unique population occupying a distinct life-course and transition period between adolescence and adulthood. Although not monolithic in experiences, knowledge, and demographics, this diverse population is particularly susceptible to immediate, short-term, mid-term, and long-term disaster impacts. Recently, disaster research focusing on college students has rightly focused on the ongoing COVID-19 pandemic. Although the pandemic was a public health disaster interrupting social, developmental, and educational processes for students on a global scale, the climate crisis and related environmental disasters continuously threaten college students' individual development, health, and well-being. Thus, it is critical to understand current knowledge focusing on environmental disasters and college students in order to determine future research needs. This article used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to examine research on college students and disasters over the past ten years (2014–2023). We identified 67 articles, which we analyzed through a mixed methods approach, including descriptive statistics and thematic analysis. Results indicate that disaster impacts on college students are an understudied topic in the social sciences, especially in an era of more-frequent and -intense environmental hazards. Our findings demonstrate a need to engage college students in disaster research worldwide so that trade schools, colleges, and universities can collaborate with policymakers to build this unique and disproportionately impacted population's capacity to mitigate against, respond to, and recover from environmental hazards in an ever-changing climate. [ABSTRACT FROM AUTHOR]