Month: August 2024

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

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Abstract

Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.

Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

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Abstract

Due to various technical issues, existing numerical weather prediction (NWP) models often perform poorly at forecasting rainfall in the first several hours. To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting, we propose a deep learning-based approach called UNetMask, which combines NWP forecasts with the output of a convolutional neural network called UNet. The UNetMask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting. The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask. The UNetMask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask, which provides the corrected 6-hour rainfall forecasts. We evaluated UNetMask on a test set and in real-time verification. The results showed that UNetMask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI scores. Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNetMask’s forecast performance. This study shows that UNetMask is a promising approach for improving rainfall forecasting of NWP models.

Wild edible plants: diversity, use pattern and livelihood linkage in Eastern India

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Abstract

Wild edible plants (WEPs) are of great important source of nutrition and ensuring food security for local tribal and rural poor communities of India. In this context, documenting wild edible plants genetic resources associated with livelihood linkages is most urgent not only for improving the socio-economic condition of the people but also for bioprospecting genetic resources for their sustainable development. Therefore, the present study aimed to document the diversity, use pattern and livelihood linkage of WEPs in Nayagarh district of Odisha, Eastern India. To fulfill the objectives, an extensive field survey was conducted in different forest pockets of the district following plant species collection, photographs, semi-structured interviews/group discussions with local tribal and rural people. WEPs were collected, identified and quantitative ethnobotanical methods were applied for data analysis. A total of 103 wild edible plant species belonging to 69 genera and 48 families were reported during the study. Dioscoreaceae (8), Moraceae (7), Fabaceae (6) and Amaranthaceae (6) were the most dominant families and tree (39%) was the most dominant life form. Fruits account for 61 species, and were followed by leaves (27 species). The relative frequency of citation for maximum used plant species were Dioscorea bulbifera (0.97) followed by Drimia indica (0.96), Glinus oppositifolius (0.95), Dioscorea pentaphylla (0.95) and Cycas circinalis (0.93).The availability of summer fruits dominated over other seasonal fruits. Pickled fruits and collected fresh tubers during winter were preserved to fulfill the need during food scarcity. WEPs were significantly contributing about 14% of the livelihood of rural and tribal communities of the district. The study provides baseline information on wild food plant genetic resources of Nayagarh district that will be helpful for prioritization and conservation of wild edible plants. Furthermore, the present study provides the nutritional profiles analysis, domestication and cultivation of frequently cited wild food plants for food and nutritional security.

The Affective Scaffolding of Grief in the Digital Age: The Case of Deathbots

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Abstract

Contemporary and emerging chatbots can be fine-tuned to imitate the style, tenor, and knowledge of a corpus, including the corpus of a particular individual. This makes it possible to build chatbots that imitate people who are no longer alive — deathbots. Such deathbots can be used in many ways, but one prominent way is to facilitate the process of grieving. In this paper, we present a framework that helps make sense of this process. In particular, we argue that deathbots can serve as affective scaffolds, modulating and shaping the emotions of the bereaved. We contextualize this affective scaffolding by comparing it to earlier technologies that have also been used to scaffold the process of grieving, arguing that deathbots offer some interesting novelties that may transform the continuing bonds of intimacy that the bereaved can have with the dead. We conclude with some ethical reflections on the promises and perils of this new technology.

How Do Bangladeshi Secondary School Students Conceptualise Well-Being in School

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Abstract

Despite the growing importance of understanding student well-being for students’ holistic development, it is still a relatively neglected concept in low and middle-income countries such as Bangladesh. Quantitative metrics such as students’ enrolment rate and academic grades have been prioritised at school and considered as the proxy of well-being at school. In contrast, students’ quality of school experience and well-being remain neglected. This qualitative study explores the conceptualisation of well-being experiences perceived by secondary school students in Bangladesh. Online focus groups and one-on-one interviews in conjunction with arts-based methods (i.e., drawings) were employed to elicit the views of 40 Grades 7–10 students (aged 13–16 years) about their well-being. Grounded theory approaches were used to analyse the data. Findings revealed that the students conceptualise well-being at school as a multidimensional but relational concept. Six interrelated and constitutive dimensions were identified including a positive sense of self and the future, sense of school resource sufficiency, a sense of relatedness, a sense of school engagement, a sense of accomplishment at school, and a sense of purpose in attending the school. The findings have implications for informing future research and enhancing understanding of student well-being from students’ standpoint within the context of a country from the global south.

What matters for the economic synchronization of the Baltic States

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Abstract

Three small Baltic economies of Estonia, Latvia and Lithuania have undergone extreme economical system change from the planned economy to the market one. The institutional infrastructure have been reorganized and all three countries joined the EU and Euro area. We aim to answer which channels of economic integration are of the largest importance for the small open European economies. We showed that all three countries could be treated as one region due to development, institutional and economic similarities. Secondly, we explore whether the trade or common currency is the main channel for the business cycle synchronization across the region of three small Baltic economies. The business cycle synchronization and trade intensity (TI) between the Baltic States and their main trading partners before and after joining the EU have been investigated as an example of an ex-post case for the small economies. We have observed a large increase in TI with the trading partners from EMU and EU countries, irrespective of the TI calculation method. The analysis of business cycle synchronization of the Baltic States with their main trading partners is captured by the correlations of the cyclical component of GDP series, using the quarterly real and de-trended GDP growth data from 1995 Q1 to 2019 Q4. The panel model has indicated an important empirical feature that the common currency strongly and significantly impacted the business cycle synchronization whilst the bilateral trade intensity between the Baltic States and their main trading partners have a significant negative effect on the business cycle synchronization when controlling for time effects. The Granger causality test confirmed that the most robust impulses to the Baltic States are coming from EU trading partners.

Intelligent detection method of microparticle virus in silkworm based on YOLOv8 improved algorithm

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Abstract

The presence of microparticle viruses significantly impacts the quality of silkworm seeds for domestic sericulture, making their exclusion from detection in silkworm seed production crucial. Traditional methods for detecting microparticle viruses in silkworms, such as manual microscopic observation, molecular biology, and immunological approaches, are cumbersome and unable to achieve intelligent, batch real-time detection. To address this challenge, we employ the YOLOv8 algorithm in this paper. Firstly, NAM attention is introduced in the original algorithm’s Backbone component, allowing the model to extract more generic feature information. Secondly, ODConv replaces Conv in the Head component of the original algorithm, enhancing the model’s ability to identify microparticle viruses. Finally, NWD-LOSS modifies the CIoU loss of the original algorithm to obtain a more accurate prediction box. Experimental results demonstrate that the NN-YOLOv8 model outperforms mainstream detection algorithms in detecting silkworm microparticle diseases. With an average detection time of 22.6 milliseconds per image, the model shows promising prospects for future applications. This model improvement enhances detection efficiency and reduces human resource costs, effectively realizing detection intelligence.

MoPeD meets MITO: a hybrid modeling framework for pedestrian travel demand

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Abstract

Transport demand models were initially designed for simulating car trips. Nowadays researchers and planners are considering pedestrian travel and its health and safety impacts in the regional transport models. However, the existing transport models lack the knowledge and experience in pedestrian modeling for health assessment. This paper contributes to the modeling practice by developing an integrated model called the MITO/MoPeD. The model builds upon previous model development and integrates the fine-grained pedestrian modeling tool into the agent-based transport model. The MITO/MoPeD model is applied to the Munich metropolitan area. Model performances are analyzed based on travel measures (e.g., walk share, trip length distribution, and pedestrian flow) and physical activity volumes. Results show that the MITO/MoPeD model can better represent pedestrian travel behavior than the existing Munich Model. It performed better in simulating the spatial distribution of walk shares and the distribution of walk trip lengths. Moreover, it overcomes the issue of overestimating physical activity volumes. These findings suggest that the MITO/MoPeD model can deliver more precise travel outcomes. More importantly, it is valuable for addressing pedestrian planning issues such as transportation infrastructure investments, land use planning, assessment of safety and health outcomes, and evaluation of environmental impacts.

Social Determinant of Health Framework to Examine the Impact of COVID-19 on Latino Health

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Abstract

Objectives

Evaluated how COVID-19 impacted Latino health across social, economic, and emotional dimensions and differentiated whether adverse COVID-19-related effects persisted across respondents.

Methods

In both English and Spanish, a cross-sectional survey was conducted in the USA from June 2021 to April 2022. Chi-square tests, Z-tests, and T-tests were used to test for significant differences between Spanish- and English-speaking respondents. Multiple linear regressions were carried out to understand whether previously established determinants of health for Latinos accounted for greater COVID-19-related adversity across social, economic, and mental health dimensions. English as a primary language was significantly related to greater adverse emotional/mental health COVID-19 experiences after controlling for other social determinants of health factors (β = − 0.355, p < 0.001). Individuals who reported worrying about housing loss were significantly more likely to experience more adverse economic adversity due to COVID-19 (β = − 0.234, p < 0.001). Household income < $35,000 (β = 0.083, p < 0.05), having more than 5 people living in the same home (β = −0.102, p < 0.05), and work-related transportation barriers (β = − 0.114, p < 0.05) all increased the likelihood of household-related stressors occurring because of the pandemic.

Conclusions

The study highlights the heterogeneity in the Latino community and the key social, economic, and community-level factors most strongly correlated with adverse COVID-19-related outcomes.

The Association of Race and Ethnicity with Mortality in Pediatric Patients with Congenital Heart Disease: a Systematic Review

Latest Results

Abstract

Context

Congenital heart disease (CHD) is a common condition with high morbidity and mortality and is subject to racial and ethnic health disparities.

Objective

To conduct a systematic review of the literature to identify differences in mortality in pediatric patients with CHD based on race and ethnicity.

Data Sources

Legacy PubMed (MEDLINE), Embase (Elsevier), and Scopus (Elsevier)

Study Selection

English language articles conducted in the USA focused on mortality based on race and ethnicity in pediatric patients with CHD.

Data Extraction

Two independent reviewers assessed studies for inclusion and performed data extraction and quality assessment. Data extraction included mortality based on patient race and ethnicity.

Results

There were 5094 articles identified. After de-duplication, 2971 were screened for title and abstract content, and 45 were selected for full-text assessment. Thirty studies were included for data extraction. An additional 8 articles were identified on reference review and included in data extraction for a total of 38 included studies. Eighteen of 26 studies showed increased risk of mortality in non-Hispanic Black patients. Results were heterogenous in Hispanic patients with eleven studies of 24 showing an increased risk of mortality. Results for other races demonstrated mixed outcomes.

Limitations

Study cohorts and definitions of race and ethnicity were heterogenous, and there was some overlap in national datasets used.

Conclusion

Overall, racial and ethnic disparities existed in the mortality of pediatric patients with CHD across a variety of mortality types, CHD lesions, and pediatric age ranges. Children of races and ethnicities other than non-Hispanic White generally had increased risk of mortality, with non-Hispanic Black children most consistently having the highest risk of mortality. Further investigation is needed into the underlying mechanisms of these disparities so interventions to reduce inequities in CHD outcomes can be implemented.