Month: January 2025

The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development

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Abstract

Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses. The review begins by discussing the impact of a pandemic on emerging countries worldwide, elaborating on the critical significance of AI in epidemiological modelling, bringing data-driven decision-making, and enabling forecasting, mitigation and response to the pandemic. In epidemiology, AI-driven epidemiological models like SIR (Susceptible-Infectious-Recovered) and SIS (Susceptible-Infectious-Susceptible) are applied to predict the spread of disease, preventing outbreaks and optimising vaccine distribution. The review also demonstrates how Machine Learning (ML) algorithms and predictive analytics improve our knowledge of disease propagation patterns. The collaborative aspect of AI in vaccine discovery and clinical trials of various vaccines is emphasised, focusing on constructing AI-powered surveillance networks. Conclusively, the review presents a comprehensive assessment of how AI impacts epidemiological modelling, builds AI-enabled dynamic models by collaborating ML and Deep Learning (DL) techniques, and develops and implements vaccines and clinical trials. The review also focuses on screening, forecasting, contact tracing and monitoring the virus-causing pandemic. It advocates for sustained research, real-world implications, ethical application and strategic integration of AI technologies to strengthen our collective ability to face and alleviate the effects of global health issues.

Landsat ETM + imagery and SRTM data application to hydrological analysis: a case study in the As-Saquia El Hamra watershed (Saharan Plateau, Morocco)

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Abstract

The As Saquia El Hamra watershed is located in the southern part of Morocco, covers an area of 81 000 km2. Toward the west, the Oued As Saquia El Hamra flows into the Atlantic Ocean. During flood periods (1987, 2003, and 2016), the study area experienced severe flooding, causing in significant material and human damage, especially in Laâyoune city. The physiographic and geometric characteristics of this watershed basin significantly contribute to the amplification of this natural disaster. Many tools were used to solve the mentioned problem, including spatial Remote Sensing (RS) and Geographic Information System (GIS) techniques. Spatial analysis of the Oued As Saquia El Hamra has provided comprehensive geological, geomorphological, hydrological, and climatic information about the region. This present study aims to investigate the relationship between the drainage network, geomorphology, and geology of the watershed. It seeks to determine the origin, evolution, and extent of this network in relation to various lithological facies and paleo-climatic conditions. As a results, the hydrological Analysis indicate the presence of a main river that spans 400 km. Its exorheic drainage features a dendritic network, intermittent tributaries, wide riverbeds, and branched, anastomosing or meandering patterns in the riverbed. Regarding the lithology data, itinfluences the configuration of the drainage network and divides the watershed into two sub-watershed. On the other hand, sea level fluctuations and climate change play a crucial role in reconstructing of the geological history for hydrographic networks and the drainage systems that have developed since the post-Miocene. Additionally, mechanisms such as such as tectonic effects and anthropogenic factors influence the formation of these drainage networks. Thus, digital data and modeling can be used in various areas of geo-prevention, including natural hazard mapping and land use planning.

Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling

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Abstract

The increasing availability of coarse-scale climate simulations and the need for ready-to-use high-resolution variables drive the climate community to the challenge of reducing computational resources and time for downscaling purposes. To this end, statistical downscaling is gaining interest as a potential strategy for integrating high-resolution climate information obtained through dynamical downscaling over limited years, providing a clear understanding of the gains and losses in combining dynamical and statistical downscaling. In this regard, several questions can be raised: (i) what is the performance of statistical downscaling, assuming dynamical downscaling as a reference over a shared time window; (ii) how much the performance of statistical downscaling is affected by changes in the number of years available for training; (iii) how does the climate normal considered for the training affect the predictions. This study addresses these issues by applying a statistical downscaling procedure based on the empirical quantile mapping bias adjustment, obtaining finer-resolution climate variables. This procedure was adopted in order to downscale temperature and precipitation from ERA5 climate reanalysis, having as reference both for training and validation, the respective variables obtained through the dynamical downscaling of ERA5 over Italy for about 30 years. The availability of such a long simulation allows us to define several long time windows, used to calibrate the statistical relationships and evaluate the performance of statistical downscaling versus dynamical downscaling over a shared blind prediction period, taking advantage of a set of spatial and temporal metrics. The study shows that (i) the statistical downscaling successfully represents mean values and extremes of temperature and precipitation; (ii) its performance remains satisfactory regardless of the number of years used as training; (iii) the shorter is the time window considered for the training, the higher is the sensitivity to changes in the time interval due to the inter-annual variability. Nevertheless, the performance deviations are somehow not so remarkable.

Spatiotemporal analysis of land surface temperature and land cover changes in Prešov city using downscaling approach and machine learning algorithms

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Abstract

In recent decades, global climate change and rapid urbanization have aggravated the urban heat island (UHI) effect, affecting the well-being of urban citizens. Although this significant phenomenon is more pronounced in larger metropolitan areas due to extensive impervious surfaces, small- and medium-sized cities also experience UHI effects, yet research on UHI in these cities is rare, emphasizing the importance of land surface temperature (LST) as a key parameter for studying UHI dynamics. Therefore, this paper focuses on the evaluation of LST and land cover (LC) changes in the city of Prešov, Slovakia, a typical medium-sized European city that has recently undergone significant LC changes. In this study, we use the relationship between Landsat-8/Landsat-9-derived LST and spectral indices Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) derived from Landsat-8/Landsat-9 and Sentinel-2 to downscale LST to 10 m. Two machine learning (ML) algorithms, support vector machine (SVM) and random forest (RF), are used to assess image classification and identify how different types and LC changes in selected years 2017, 2019, and 2023 affect the pattern of LST. The results show that several decisions made during the last decade, such as the construction of new urban fabrics and roads, caused the increase in LST. The LC change evaluation, based on the RF classification algorithm, achieved overall accuracies of 93.2% in 2017, 89.6% in 2019, and 91.5% in 2023, outperforming SVM by 0.8% in 2017 and 4.3% in 2023. This approach identifies UHI-prone areas with higher spatial resolution, helping urban planning mitigate the negative effects of increasing urban LSTs.

Observational uncertainty in the added value of regional climate modelling over Australia

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Abstract

The evaluation of dynamical downscaling of Global Climate Models (GCMs) using Regional Climate Models (RCMs) is complicated and challenging because of observational errors and uncertainties. This study assesses the Added Value (AV) for monthly precipitation against multiple observational datasets using the multi-model RCM ensemble outputs for the Coordinated Regional Climate Downscaling Experiment (CORDEX) Australasia domain. These results are further compared against multiple gridded observational datasets to evaluate the impact of observational uncertainties in the evaluation of RCMs and the associated AV. The results show that observational uncertainty plays an important role in the model performance evaluation and, consequently, the AV particularly at local scales. The RCMs produced positive AV over the peak of Australian Alps but not over the steep slopes of Alps largely because of underestimated observed precipitation. Notably, the RCMs show enhanced performance against observational dataset that combines in situ data and satellite-reanalysis estimates, and accounting for precipitation undercatch corrections. Overall, the RCMs consistently shows better performance once the observational uncertainty is included using the Observational Range Adjusted (ORA) statistics. We find that explicitly accounting for the observation uncertainty does not cause substantial changes to the AV at continental scales. However, at local scales the effects of observational uncertainty on the AV can be substantial especially over complex terrain where the observational uncertainty can be large.

The Watershed Health Assessment Framework: Integrating Geospatial Data and System Science to Advance Natural Resource Management in Minnesota

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Abstract

The Watershed Health Assessment Framework (WHAF) is a structured, evidence-based approach for improving the health of watersheds and water resources. This transparent, repeatable framework brings together current data and analysis to generate health scores for watersheds and water resources organized within a representative set of ecological components. The data and scores are delivered through interactive online applications that allow for place-based explorations of watershed health. This framework helps resource professionals and communities work together to build a common understanding of complex natural resource systems to consider actions that might offer holistic benefits to watershed health.

Millets in India: exploring historical significance, cultural heritage and ethnic foods

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Abstract

This review paper offers a comprehensive exploration of the historical significance of millets in India, their role in preserving cultural heritage and embodiment in a diverse array of ethnic foods. In-depth online literature searches were conducted to assess the data, and the information was retrieved from official government reports, journals and books. The study explores the archaeological evidence and historical records of millet cultivation in India, highlighting their importance in Vedic era, ancient civilizations and Mughal rule. Studies showed a diversity of cultures in India and the importance of millets in religious ceremonies, festivals, literature, and folklore, showcasing their deep-rooted presence in Indian traditions. Further, the inclusion of millets in various ethnic dishes of different states demonstrates the diverse culinary applications of millets in India. Recent processing technologies for millet need to be studied for producing various millet-based food products. Additionally, the paper briefly discusses the challenges of millet consumption and promotion in India along with its future prospects. The study suggests that promoting millets and reviving traditional millet-based ethnic food and cultural practices can help preserve India’s rich heritage.

Nature conservation and poverty alleviation through sustainable ecotourism: the case of Lolab Valley, India

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Abstract

Ecotourism is an important link between environmental conservation and poverty alleviation, as it generates income, empowers local communities, and provides educational benefits. This study focuses on the Lolab Valley in the remote Himalayan regions, emphasising its potential as one of India's premier sustainable tourism destinations. The primary goal of this study is to look into the future development of ecotourism in the area and to understand local perceptions and awareness of sustainable tourism between 2020 and 2023. To accomplish this, fieldwork was carried out that involved focus groups and semi-structured interviews, enabling a thorough investigation of the viewpoints of locals on sustainable tourism. To create a baseline understanding of the different types of tourism in the area, such as mountain, water, culinary, heritage, ethnographic, and creative tourism, the qualitative data that were gathered were examined. Based on our findings, travellers strongly favour sustainable travel over mass tourism. In particular, the option that participants preferred the most was mountain tourism (44%), followed by heritage tourism (20%), anthropological tourism (14%), water tourism (13%), and creative tourism (9%). This suggests that the community has undergone a substantial change in favour of sustainable tourism practices.  This study demonstrates the significance of sustainable tourism over mass tourism and underscores the strong support of the local community for sustainable initiatives. Furthermore, it underscores the role of shared economies in mitigating poverty in the region. The Lolab Valley can make the most of its natural resources and maintain the welfare of its communities by promoting sustainable tourism.

Using natural language processing to analyse text data in behavioural science

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Abstract

Language is a uniquely human trait at the core of human interactions. The language people use often reflects their personality, intentions and state of mind. With the integration of the Internet and social media into everyday life, much of human communication is documented as written text. These online forms of communication (for example, blogs, reviews, social media posts and emails) provide a window into human behaviour and therefore present abundant research opportunities for behavioural science. In this Review, we describe how natural language processing (NLP) can be used to analyse text data in behavioural science. First, we review applications of text data in behavioural science. Second, we describe the NLP pipeline and explain the underlying modelling approaches (for example, dictionary-based approaches and large language models). We discuss the advantages and disadvantages of these methods for behavioural science, in particular with respect to the trade-off between interpretability and accuracy. Finally, we provide actionable recommendations for using NLP to ensure rigour and reproducibility.

Archaeology, Activism, and Protest: Mobilizing the Past for Social Change

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Abstract

This paper is a reflection on how archaeology can intersect in dynamic ways with activism and protest, reflecting the evolving socio-political-economic-cultural landscape globally. Across diverse regions, from rural spaces to urban centers, activism within archaeology confronts systemic issues such as politics, gender inequality, social disparities, migrations, climate change, and cultural heritage preservation, among many others, challenging power structures. Moreover, archaeologists increasingly recognize their role in addressing broader societal challenges, engaging in activism to promote inclusion. The relationship between archaeology and activism is complex, with scholars navigating ethical considerations and power dynamics inherent in their work. This activism extends beyond academic circles, resonating with diverse communities fighting for social justice and environmental sustainability. Through collaborative efforts, archaeologists and communities strive to amplify marginalized voices, challenge oppressive systems, and foster meaningful change.