Month: August 2024

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

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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.

WhatsApp-propriate? Exploring “WhatsApp” as a Tool for Research Among Ghanaian Immigrants in the United States

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Abstract

African immigrants remain underrepresented in research due to challenges in recruitment. Mobile instant messaging applications, such as WhatsApp, present novel, and cost-effective opportunities for conducting health research across geographic and temporal distances, potentially mitigating the challenges of maintaining contact and engagement in research with migrant populations. Moreover, WhatsApp has been found to be commonly used by African immigrant communities. However, little is known about the acceptability and use of WhatsApp as a tool for health research among African immigrants in the US. In this study, we examine the acceptability and feasibility of WhatsApp as a tool for research among Ghanaian immigrants- a subset of the African immigrant population group. We used WhatsApp to recruit 40 participants for a qualitative interview about their use of the mobile messaging application. Three distinct themes related to the acceptability and feasibility of WhatsApp emerged from the interviews: (1) preference for using WhatsApp as a medium of communication; (2) positive perception of WhatsApp; and (3) preference for using WhatsApp for research. The findings indicate that for African immigrants in the US, WhatsApp is a preferred method for recruiting and collecting data. It remains a promising strategy to utilize in future research involving this population.

Assessing Food Access, Exercise, and Dietary History among Older African American Parishioners During the COVID-19 Pandemic (C-FED Study): Design, Opportunities, Challenges, and Lessons Learned

Latest Results

Abstract

Objectives

Unhealthy diets and inadequate exercise are associated with chronic health conditions and excess mortality. Older African Americans do not meet dietary and exercise guidelines, and this may have worsened during the COVID-19 pandemic due to individual and environmental factors, including food insecurity. Studies evaluating these dynamics are essential for developing interventions. This narrative details a study protocol and data collection experiences during the pandemic.

Methods

Participants > 55 years African American old completed detailed food frequency, exercise, and food access questionnaires between October 2020 and July 2021. Observations of the study administrators (authors of this manuscript) for the duration of the study are presented. Details on the study design and reflections on the opportunities, challenges, and lessons learned are summarized. Future manuscripts will report data analysis of study findings.

Results

A total of 123 older African American adults participated in the study, and 118 (70% female) completed all three questionnaires. More than 50% of the participants had at least two primary chronic conditions. About 85% were fully vaccinated against COVID-19. Applying community-based participatory approaches, leveraging partnerships, and exercising flexibility approaches were pivotal to successfully implementing the study protocol.

Conclusions

Despite challenges related to the COVID-19 pandemic, detailed data on older African American adults’ diet and exercise habits were obtained. Our study design and experiences will benefit future researchers. More importantly, results from our study will inform interventions and policies aimed at minimizing consequences associated with poor diet and exercise habits during the pandemic among this vulnerable population.

Assessing Food Access, Exercise, and Dietary History among Older African American Parishioners During the COVID-19 Pandemic (C-FED Study): Design, Opportunities, Challenges, and Lessons Learned

Latest Results

Abstract

Objectives

Unhealthy diets and inadequate exercise are associated with chronic health conditions and excess mortality. Older African Americans do not meet dietary and exercise guidelines, and this may have worsened during the COVID-19 pandemic due to individual and environmental factors, including food insecurity. Studies evaluating these dynamics are essential for developing interventions. This narrative details a study protocol and data collection experiences during the pandemic.

Methods

Participants > 55 years African American old completed detailed food frequency, exercise, and food access questionnaires between October 2020 and July 2021. Observations of the study administrators (authors of this manuscript) for the duration of the study are presented. Details on the study design and reflections on the opportunities, challenges, and lessons learned are summarized. Future manuscripts will report data analysis of study findings.

Results

A total of 123 older African American adults participated in the study, and 118 (70% female) completed all three questionnaires. More than 50% of the participants had at least two primary chronic conditions. About 85% were fully vaccinated against COVID-19. Applying community-based participatory approaches, leveraging partnerships, and exercising flexibility approaches were pivotal to successfully implementing the study protocol.

Conclusions

Despite challenges related to the COVID-19 pandemic, detailed data on older African American adults’ diet and exercise habits were obtained. Our study design and experiences will benefit future researchers. More importantly, results from our study will inform interventions and policies aimed at minimizing consequences associated with poor diet and exercise habits during the pandemic among this vulnerable population.

Identifying Core Issues for Basin Management: The Issue Generating Assessment (IGA) Methodology

Latest Results

Abstract

Effective stakeholder engagement is essential for basin management, requiring structured approaches to foster collaboration and consensus. This paper applies the Issue Generating Assessment (IGA) method, which identifies core issues for stakeholder discussion, to basin management. Focusing on the Israeli part of the Hadera Basin, we identify the core issues that should be discussed by stakeholders using the IGA method. To this end 39 participants across 14 sectors evaluating three generic basin management strategies were asked to qualitatively explain their evaluations. By analyzing these explanations utilizing the IGA method, four core issues emerged: (1) Managing uncertainty: addressing climate change and land use impacts on stream flow; (2) Mutual impacts management: handling interactions between the stream and its surroundings; (3) Integration of uses: balancing various stream utilization priorities; (4) Defining natural system functions: determining the role of natural systems. For each core issue, we proposed questions to guide stakeholder discussions. The IGA method is thus found to be useful, and has the potential to foster meaningful dialogue in structured stakeholder meetings, thereby focusing discussions and allowing understandings among stakeholders to be reached as a basis for basin management plans. Such early understandings may contribute to the development of strategies for sustainable basin management.

In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach

Latest Results

Abstract

Numerous efforts in the additive manufacturing literature have been made toward in-situ defect prediction for process control and optimization. However, the current work in the literature is limited by the need for multi-sensory data in appropriate resolution and scale to capture defects reliably and the need for systematic experimental and data-driven modeling validation to prove utility. For the first time in literature, we propose a data-driven neural network framework capable of in-situ micro-porosity localization for laser powder bed fusion via exclusively within hatch strip of sensory data, as opposed to a three-dimensional neighborhood of sensory data. We further propose using prior-guided neural networks to utilize the often-abundant nominal data in the form of a prior loss, enabling the machine learning structure to comply more with process physics. The proposed methods are validated via rigorous experimental data sets of high-strength aluminum A205 parts, repeated k-fold cross-validation, and prior-guided validation. Using exclusively within hatch stripe data, we detect and localize porosity with a spherical equivalent diameter (SED) smaller than \(50.00\,\upmu \) m with a classification accuracy of \(73.13\pm 1.57\%\) This is the first work in the literature demonstrating in-situ localization of porosities as small as \(38.12\,\upmu m\) SED and is more than a five-fold improvement on the smallest SED porosity localization via spectral emissions sensory data in the literature. In-situ localizing micro-porosity using exclusively within hatch-stripe data is a significant step towards within-layer defect mitigation, advanced process feedback control, and compliance with the reliability certification requirements of industries such as the aerospace industry.