Month: August 2024

Landslide Mitigation of Urbanized Slopes for Sustainable Growth: A Summary of Recent Developments in Structural and Non-structural Countermeasures to Manage Water-Triggered Landslides

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Abstract

This paper summarizes recent developments made in terms of structural and non-structural solutions to manage the safety of urbanized slopes. The paper gives an overview of the pioneering effort to integrate the climate modeling chain into landslide susceptibility assessment using TRIGRS, application of virtual reality to improve the landslide risk awareness, the advancement of upstream flexible barrier system and debris flow screens to reduce the entrainment and impact on terminal barriers, and finally, internal seepage-induced progressive failure of reservoir rim slopes. These advancements are done using numerical modeling, simulations of real cases and physical modeling using small- and large-scale models.

Structural knowledge and subjective knowledge, not factual knowledge, promotes corrective and restrictive actions towards healthy eating misinformation in China: a multigroup comparison of extended cognitive mediation model based on altruism

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Abstract

Based on the cognitive mediation model (CMM), this study seeks to examine how attention to different media platforms influenced different knowledges via reflective integration, ultimately motivating individuals to perform corrective and restrictive actions against misinformation in the context of healthy eating misinformation. Using data collected from a national survey of 563 Chinese citizens, the findings of this study are threefold. First, attention to television and social media stimulated elaboration and interpersonal communication, while attention to websites only elicited elaboration. Second, only structural and subjective knowledge, not factual knowledge, were found to motivate individuals to perform corrective and restrictive actions. Third, a multigroup analysis demonstrated that the effects of (a) attention to TV news on elaboration, (b) attention to websites on elaboration, (c) interpersonal communication on factual knowledge, and (d) structural knowledge on restrictive actions differed among participants with different levels of altruism. Theoretically, whereas previous studies have focused on single dimension of knowledge, this study uncovered the multi-dimensional nature of knowledge by exploring factual knowledge, structural knowledge, and subjective knowledge in the CMM framework. Moreover, based on the O-S-R-O-R model, the CMM could be extended to behavioral outcomes, which have been overlooked by most CMM studies. In response, this study extends the CMM by integrating corrective and restrictive actions as behavioral outcomes. Lastly, rather than assuming individuals as homogenous in previous research, this study delves into exploring how individuals at the average age of 33.37 (SD = 8.46) with different levels of altruism engaged in different processes of cognitive mediation.

A theoretical framework for polarization as the gradual fragmentation of a divided society

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Abstract

We propose a framework integrating insights from computational social science, political, and social psychology to explain how extreme polarization can occur in deeply divided societies. Extreme polarization in a society emerges through a dynamic and complex process where societal, group, and individual factors interact. Dissent at different levels of analysis represents the driver of this process, where societal-level ideological dissent divides society into opposing camps, each with contrasting collective narratives. Within these opposing camps, further dissent leads to the formation of splinter factions and radical cells—sub-groups with increasingly extreme views. At the group level, collective narratives underpinning group identity become more extreme as society fragments. At the individual level, this process involves the internalization of an extreme group narrative and norms sanctioning radical behavior. The intense bonding within these groups and the convergence of personal and group identities through identity fusion increase the likelihood of radical group behavior.

Hydrological responses of three gorges reservoir region (China) to climate and land use and land cover changes

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Abstract

Three Gorges Dam is the largest hydraulic infrastructure in the world, playing a pivotal role in flood mitigation. The hydrological responses of the Three Gorges Reservoir Region (TGRR) to climate change and human activities are unclear, yet critical for the Three Gorges Dam’s flood control and security. We simulated streamflow and water depth by coupling the Variable Infiltration Capacity model and the CaMa-Flood model. Daily discharge at the outlet of TGRR was well modeled with a relative error within 2% and a Nash-Sutcliffe efficiency coefficient of approximately 0.81. However, the flood peak was overestimated by 2.5–40.0% with a peak timing bias ranging from 5 days earlier to 2 days later. Runoff and water depth in the TGRR increased from 2015 to 2018 but decreased during flood seasons. Land use and land cover changes in 2015 (LUCC2015) and 2020 (LUCC2020) were analyzed to quantify their hydrological impacts. During the 2015–2018 period, land use conversion increased in built-up areas (+ 0.6%) and water bodies (+ 0.1%), but decreased in woodland grassland (-0.7%) and cropland (-0.1%). This led to a slight increase in runoff and inflow of less than 4‰ across the TGRR, a 7.70% decrease in average water depth, and a 15.4‰ increase in maximum water depth. Water depths in the TGRR decreased during flood seasons, and increased during non-flood seasons. Increasing water depth was identified in northern TGRR. This study clarifies the historical TGRR’s hydrological features under LUCC and climate changes, aiding regional flood mitigation in the TGRR.

Ensemble modeling of extreme seasonal temperature trends in Iran under socio-economic scenarios

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Highlights

A new ensemble model was introduced and evaluated for projecting minimum and maximum temperatures in Iran.

Trends in minimum and maximum temperatures in the near term (2021–2040) were obtained using socio-economic scenarios of five models at 95 synoptic stations.

The ensemble technique reduced the error of the models used in projection to an optimal extent.

On singularity and the Stoics: why Stoicism offers a valuable approach to navigating the risks of AI (Artificial Intelligence)

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Abstract

The potential benefits and risks of artificial intelligence technologies have sparked a wide-ranging debate in both academic and public circles. On one hand, there is an urgent call to address the immediate and avoidable challenges associated with these tools, such as accountability, privacy, bias, understandability, and transparency; on the other hand, prominent figures like Geoffrey Hinton and Elon Musk have voiced concerns over the potential rise of Super Artificial Intelligence, whose singularity could pose an existential threat to humanity. Coordinating the efforts of thousands of decentralized entities to prevent such a hypothetical event may seem insurmountable in our intricate and multipolar world. Thus, drawing from both perspectives, this work suggests employing the tools and framework of Stoic philosophy, particularly the concept of the dichotomy of control—focusing on what is within our power. This Stoic principle offers a practical and epistemological approach to managing the complexities of AI, and it encourages individuals to organize their efforts around what they can influence while adapting to the constraints of external factors. Within this framework, the essay found that Stoic wisdom is essential for assessing risks, courage is necessary to face contemporary challenges, and temperance and tranquility are indispensable; and these lessons can inform ongoing public and academic discourse, aiding in the development of more effective policy proposals for aligning Narrow AI and General AI with human values.

Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic

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Abstract

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.

Generative artificial intelligence: a systematic review and applications

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Abstract

In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.

“Democratizing AI” and the Concern of Algorithmic Injustice

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Abstract

The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine three notable notions of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations.

Continental scale spatial temporal interpolation of near-surface air temperature: do 1 km hourly grids for Australia outperform regional and global reanalysis outputs?

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Abstract

Near-surface air temperature is an essential climate variable for the study of many biophysical phenomena, yet is often only available as a daily mean or extrema (minimum, maximum). While many applications require sub-diurnal dynamics, temporal interpolation methods have substantial limitations and atmospheric reanalyses are complex models that typically have coarse spatial resolution and may only be periodically updated. To overcome these issues, we developed an hourly air temperature product for Australia with spatial interpolation of hourly observations from 621 stations between 1990 and 2019. The model was validated with hourly observations from 28 independent stations, compared against empirical temporal interpolation methods, and both regional (BARRA-R) and global (ERA5-Land) reanalysis outputs. We developed a time-varying (i.e., time-of-day and day-of-year) coastal distance index that corresponds to the known dynamics of sea breeze systems, improving interpolation performance by up to 22.4% during spring and summer in the afternoon and evening hours. Cross-validation and independent validation (n = 24/4 OzFlux/CosmOz field stations) statistics of our hourly output showed performance that was comparable with contemporary Australian interpolations of daily air temperature extrema (climatology/hourly/validation: R2 = 0.99/0.96/0.92, RMSE = 0.75/1.56/1.78 °C, Bias = -0.00/0.00/-0.03 °C). Our analyses demonstrate the limitations of temporal interpolation of daily air temperature extrema, which can be biased due to the inability to represent frontal systems and assumptions regarding rates of temperature change and the timing of minimum and maximum air temperature. Spatially interpolated hourly air temperature compared well against both BARRA-R and ERA5-Land, and performed better than both reanalyses when evaluated against the 28 independent validation stations. Our research demonstrates that spatial interpolation of sub-diurnal meteorological fields, such as air temperature, can mitigate the limitations of alternative data sources for studies of near-surface phenomena and plays an important ongoing role in supporting numerous scientific applications.