Month: May 2024

Use of face masks for COVID-19 prevention: a qualitative study on barriers and motivators in Zimbabwe

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Abstract

Introduction

To mitigate the impact of the COVID-19 pandemic, face mask use has been a key component of public health measures. Research in most settings has focused on understanding the effectiveness of this intervention in reducing COVID-19 transmission. This study aimed to identify the barriers and motivators of face mask use in the Zimbabwean population.

Methods

Thirty key informant interviews (KIIs) and 10 focus group discussions (FGDs) were conducted with homogenous study groups of health workers, village health workers, church leaders, traditional healers, teachers, women leaders, transporters, youth leaders and the general population selected in 10 districts across the country from September–October 2022. Each study group consisted of key informants and FGD participants. Interviews and FGDs were captured using digital recording devices, transcribed verbatim, and translated into English. The data were analysed manually via thematic analysis.

Findings

Six themes were generated in this study. The four themes identified as barriers were individual factors (low risk perception in rural areas and as the number of cases declined due to vaccination, lack of conviction and lack of knowledge on the importance of face masking resulting in practices such as sharing and improper wearing of masks), access challenges (due to scarcity and affordability resulting in reusing dirty masks or washing surgical masks), concern about side effects (breathing difficulties and other respiratory complications), and sociocultural and religious beliefs (resulting in removal of masks by traditional healers during consultations, removal of masks in church). Two themes that were identified as motivators included perceived benefits (confidence in the effectiveness of facemasks for the prevention of COVID-19 transmission) and environmental factors (fear of law enforcement agents and village health workers).

Conclusions

The study findings underscore the need of awareness campaigns, improvement of accessibility and affordability of masks, sensitivity to religious and cultural beliefs to increase the usage and effectiveness of face mask during pandemics of respiratory diseases.

Innovative application of artificial intelligence in a multi-dimensional communication research analysis: a critical review

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Abstract

Artificial intelligence (AI) imitates the human brain’s capacity for problem-solving and making decisions by using computers and other devices. People engage with artificial intelligence-enabled products like virtual agents, social bots, and language-generation software, to name a few. The paradigms of communication theory, which have historically put a significant focus on human-to-human communication, do not easily match these gadgets. AI in multidimensional touch is the subject of this review article, which provides a comprehensive analysis of the most recent research published in the field of AI, specifically related to communication. Additionally, we considered several theories and models (communication theory, AI-based persuasion theory, social exchange theory, Frames of mind, Neural network model, L-LDA model, and Routine model) to explain a complex phenomenon and to create a conceptual framework that is appropriate for this goal and a voluntary relationship between two or more people that lasts for an extended period. Communication and media studies focus on human–machine communication (HMC), a rapidly developing research area. It is our intention to continue investigating the beneficial and detrimental effects of artificial intelligence on human communication as well as to identify novel concepts, theories, and challenges as the research process develops.

Enhancing drought monitoring through spatial downscaling: A geographically weighted regression approach using TRMM 3B43 precipitation in the Urmia Lake Basin

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Abstract

Efficient drought monitoring in the Urmia Lake basin (ULB) is imperative to protect its ecosystem, agriculture, and the livelihoods of local communities relying on its water resources, considering the lake's susceptibility to changes in water availability. This study presents a novel approach to address the pressing issue of precise drought monitoring in regions with limited and unevenly distributed weather stations. By utilizing Tropical Rainfall Measuring Mission (TRMM) satellite-derived data and a Geographically Weighted Regression (GWR) model, we have significantly refined the spatial resolution of TRMM-3B43 precipitation (5 km) compared with the original TRMM data (about 25 km). This innovative methodology, which uses the globally available MODIS data of Normalized Difference Vegetation Index (NDVI) and day-night difference in land surface temperature (LSTdn) as independent variables, achieves spatial downscaling, enhancing the spatial resolution to 5 km for the period 2001–2019. The downscaled precipitation data from three models (M1: GWR_NDVI, M2: GWR_LSTdn, and M3: GWR_NDVI-LSTdn) were applied for drought assessment based on the Standardized Precipitation Index (SPI) and modified Rainfall Anomaly Index (mRAI). The findings demonstrate that the M2 model is more accurate than the other models for spatial downscaling of TRMM 3B43 data, with reductions in RMSE and MAE values by 6.04 and 4.16 mm, respectively. Additionally, this downscaling model significantly improves KGE values from 0.56 to 0.92 while achieving the lowest percentage bias. Monthly downscaled TRMM data based on LST across 12 synoptic stations in the basin reveals enhanced accuracy post-downscaling. Spatial analysis of average monthly precipitation maps illustrates a descending rainfall trend from June to September, followed by an ascending trend from October, with peak rainfall in November and December, notably in the western and southwestern regions. This analysis of precipitation trends offers valuable insights into the spatial distribution of rainfall within the basin, revealing variations across regions crucial for effective water resource management. The concentration of higher precipitation levels in the western and southwestern sectors underscores the significance of targeted water conservation and storage efforts. Drought severity analysis unveiled persistent and escalating drought conditions, causing significant impacts across the basin. A comparative assessment of severity classes using SPI and mRAI indices at 12 synoptic stations demonstrated strong agreement. Drought occurrences were a near-annual affair, attaining severe levels during specific years, notably 2008, 2011, and 2019. Spatial analysis revealed widespread drought events affecting nearly half of the basin area, with a noticeable worsening in severity during critical years. This highlights the practical need for adopting drought mitigation strategies and water resources management.

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

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Abstract

Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0–125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0–25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

A support vector machine model of landslide susceptibility mapping based on hyperparameter optimization using the Bayesian algorithm: a case study of the highways in the southern Qinghai–Tibet Plateau

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Abstract

Recent advancements have seen a pervasive application of machine learning methodologies in assessing the susceptibility of geological hazards. A pivotal element influencing the accuracy of model predictions resides in the prudent selection of model parameters within machine learning frameworks. The objective of this study is to develop a robust landslide susceptibility assessment model by refining the support vector machine (SVM) model through the employment of the Bayesian algorithm for hyperparameter optimization. The southern part of the Qinghai-Tibet Plateau, focusing on major highways, is selected as the study area. Nine influencing factors, namely the elevation, slope, aspect, profile curvature, lithology, topographic wetness index, normalized difference vegetation index, distance to faults, and distance to rivers, are selected as the conditioning variables instrumental in evaluating the likelihood of collapse occurrences. Secondly, data from field surveys involving 351 landslides and randomly generated non-landslide data are utilized in a balanced 1:1 ratio to construct the training and testing datasets. Next, the cross-validation loss rate of the SVM model is selected as the objective function, and the Bayesian algorithm is used to optimize the BoxConstraint and KernelScale parameters of the SVM model, resulting in a Bayesian optimization-based SVM model. The results show that, within a five-fold cross-validation framework, the model yields 99.15% and 96.32% accuracy for the training and testing datasets, respectively. Concurrently, the area under the receiver operating characteristic curve values are recorded at 99.76% and 98.67% for the respective datasets, highlighting a notable level of predictive proficiency. Furthermore, factor importance ranking reveals lithology and elevation as the most influential, with partial dependence plots identifying high susceptibility areas between elevations of 2916 and 3954 m under soft lithology conditions. A collapse susceptibility map encompassing the entire study area is encompassing, categorizing the study area into extremely high (7.79%), high (13.38%), moderate (29.99%), and low (48.84%) susceptibility zones.

Digital strategies in wildfire management: social media analytics and Web 3.0 integration

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Abstract

This study proposes the integration of specific social media analytics (SMA) metrics into existing U.S. wildfire management systems to enhance their ability to accurately predict, monitor, and respond to wildfires in a timely manner. In addition, the examination of SMA's influence on shaping wildfire-related policies is addressed in our analysis with respect to the mitigation of the extent and effects of such disasters. Furthermore, the potential of Web 3.0 technologies in achieving these objectives is analyzed as part of this work. The results highlight that advaa analytics (SMA) metrics to wildfire management and along with Web 3.0 integration.

Global projections of heat exposure of older adults

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Abstract

The global population is aging at the same time as heat exposures are increasing due to climate change. Age structure, and its biological and socio-economic drivers, determine populations’ vulnerability to high temperatures. Here we combine age-stratified demographic projections with downscaled temperature projections to mid-century and find that chronic exposure to heat doubles across all warming scenarios. Moreover, >23% of the global population aged 69+ will inhabit climates whose 95th percentile of daily maximum temperature exceeds the critical threshold of 37.5 °C, compared with 14% today, exposing an additional 177–246 million older adults to dangerous acute heat. Effects are most severe in Asia and Africa, which also have the lowest adaptive capacity. Our results facilitate regional heat risk assessments and inform public health decision-making.

A survey on students’ use of AI at a technical university

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Abstract

We report the results of a 4800-respondent survey among students at a technical university regarding their usage of artificial intelligence tools, as well as their expectations and attitudes about these tools. We find that many students have come to differentiated and thoughtful views and decisions regarding the use of artificial intelligence. The majority of students wishes AI to be integrated into their studies, and several wish that the university would provide tools that are based on reliable, university-level materials. We find that acceptance of and attitudes about artificial intelligence vary across academic disciplines. We also find gender differences in the responses, which however are smaller the closer the student’s major is to informatics (computer science).