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The purpose of this study is to explore the genocidal impacts of uranium mining for Native Americans in the Northwest and Northern Plains, as well as their resistance to historical and contemporary acts of colonialism.

Using a case study approach, this study gathered qualitative data from various government, tribal and news sources to investigate the extent of ecological violence experienced by Native Americans specific to uranium mining processes on Spokane Indian Reservation, Pine Ridge Reservation and Wind River Reservation.

Native Americans in the Northwest and Northern Plains are victimized by the capitalism-genocide involved in uranium production. The consequences of the uranium industry boom in the 1950s–1980s has left Native Americans with degraded lands, polluted water sources and a legacy of adverse health effects, including some of the highest rates of cancer.

The work discussed in this paper offers possibilities for collaborating with Native Americans to develop more sustainable energy options for the USA to make the necessary shift away from fossil fuels and nuclear energy.

Prior research has addressed the genocidal impacts of uranium mining for Native Americans in the Southwest USA and claimed these actions were direct consequences of toxic colonialism, capitalistic agendas and the treadmill of production (Fegadel, 2023). Most uranium was recovered from ore deposits within the Colorado Plateau, and most abandoned uranium mines (AUMs) are located within the same region. Tribes residing in the Northwest and Northern Plains have, however, experienced similar plights as those in the Southwest, but these issues have not been widely examined.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Tanzania is rich in small hydropower (SHP) potentials. However, many of these potentials have yet to be fully used, and more than two-thirds of its rural population lacks access to electricity. The purpose of this paper is to explore the role of SHP stations in improving rural welfare in the southern highlands of Tanzania. It further explores the history, cost-effective analysis and threats to the sustainability of SHP as one of the renewable energy sources.

The study uses a qualitative research design to explore respondents’ views on the role of SHP stations in facilitating rural electrification and welfare improvement. Primary data were gathered using semi-structured interviews with the 27 key informants and beneficiaries of SHP stations from the Southern Highlands of Tanzania. In addition, the study used documentary research to complement the information from the field survey.

The findings found that SHP stations enhance rural electrification and welfare by providing electricity in remote areas with sparse populations. They operate as standalone off-grids, often by church communities and individuals. However, the sustainability of SHP stations is hampered by challenges such as climate change impacts, high capital investment costs, heavy siltation of small reservoirs, skilled manpower shortages, limited local manufacturing capabilities and infrastructural issues.

The study contributes to the ongoing debate on renewable energy supply and uses, focusing on how SHP stations could contribute to sustainable rural electrification and achieve the 2030 United Nations agenda for sustainable development, which, among other things, aims to safeguard access to sustainable and modern energy and alleviate energy poverty.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Given the growing concern about employees' well-being, numerous researchers have investigated the causes and effects of occupational stress. However, a review study on identifying existing research topics and gaps is still deficient in the extant literature. To fill this gap, this review study aims to present a bibliometric and science mapping approach to review the state-of-the-art journal articles published on occupational stress in the construction industry.

A three-fold comprehensive review approach consisting of bibliometric review, scientometric analysis and in-depth qualitative discussion was employed to review 80 journal articles in Scopus.

Through qualitative discussions, mainstream research topics were summarized, research gaps were identified and future research directions were proposed as follows: versatile stressors and stress model; an extended subgroup of factors in safety behavior; adaptation of multiple biosensors and bio-feedbacks; evaluation and comparison of organizational stress interventions; and incorporation of artificial intelligence and smart technologies into occupational stress management in construction.

The findings of this review study present a well-rounded framework to identify the research gaps in this field to advance research in the academic community and enhance employees' well-being in construction.