Black Death is a virosis caused by the Tomato Spotted Wilt Virus (TSWV), transmitted by thrips, and represents a complex problem since weed hosts for thrips vectors and the virus is accentuated as virus reservoir and vector sustenance. The objective was to generate, from a list of weeds that act as hosts for the four vector thrips species in the horticultural belt of La Plata, a relative risk categorization as an epidemiological component. Between 2000 and 2003, three sites were selected within the horticultural belt of La Plata (Buenos Aires, Argentina) where flowers of 21 weed hosts of Frankliniella occidentalis, Frankliniella schultzei, Frankliniella gemina and Thrips tabaci were sampled monthly (60 in total). For analysis, the sampling results were grouped into three annual seasons, corresponding to the phenology of greenhouse crops in the region. For the four thrips vectors, the abundance of adult thrips and the presence of their larvae were considered using an unsupervised hierarchical cluster analysis and the DGC multivariate mean comparison test to obtain the number of significant groups. From this base grouping, three risk groups (RG) were defined as a source of inoculum for these vectors: high (H), medium (M) and low (L) according to the status of the reproductive host (RH). The groups that emerged were: (H): RH of F occidentalis, (M): RH of F. schultzei and T. tabaci, and (L): RH of F. gemina or non-vector thrips. Periodic survey and early flowering suppression of nine weed species categorized as high risk is proposed. This implies the continuous monitoring of three weed species, to which other companion weeds are added according to the growing season.

This study aims to identify key strategies and tactics necessary to effectively implement national social security in a democratic Indonesia. Indonesia established the Law on the National Social Security System in 2004. However, the national social security programs did not commence until 2014. The national social security implementation has faced significant obstacles. These challenges include recurring delays, legal disputes, appeals, judicial reviews, and deviations from the original policy objectives, all threatening the long-term viability of the national social security programs. This article applies a qualitative approach by critically analyzing regulations, government reports, and publicly available data and observing open public meetings and hearings concerning implementing national social security programs. Our findings indicate that implementing national social security policies in a democratic Indonesia depends on effectively managing the dynamic processes involved in policy formulation and adoption. We propose a risk-based decision-making model to assist policymakers in mitigating policy-related risks and enhance the effectiveness of future policy agendas in social security.

This study examines the interaction between foreign direct investment (FDI), idiosyncratic risk, sectoral GDP, economic activity, and economic growth in ASEAN countries using structural equation modeling (SEM) performed using AMOS software. The analysis uses data from the ASEAN Statistics Database 2023 to distinguish the significant direct and indirect impacts of FDI on idiosyncratic risks, sectoral GDP, economic activity and aggregate economic growth can. ASEAN, which includes ten Southeast Asian countries, has experienced rapid economic growth and increasing integration in recent decades, making it an interesting area to study these relationships. The study covers a comprehensive period to capture trends and differences among ASEAN member states. Applying SEM with AMOS allows a detailed examination of complex relationships between important economic variables. The results show a clear link between FDI inflows, idiosyncratic risks, industry GDP performance, economic activity, and overall economic growth. More specifically, FDI inflows have a notable direct influence on idiosyncratic risks, which then impact GDP growth by sector, and the level of economic activity and ultimately contribute to economic growth trends. economy more broadly in ASEAN countries. These findings highlight the importance of understanding and effectively managing the dynamics between FDI and various economic indicators to promote sustainable economic development across ASEAN. This information can inform policymakers, investors, and stakeholders in developing targeted strategies and policies that maximize the benefits of FDI while minimizing related risks to promote strong and inclusive economic growth in the region. This study highlights the multifaceted relationships in the ASEAN economic context, emphasizing the need for strategic interventions and policy frameworks to exploit the potential of foreign investment directed at ASEAN, to the Sustainable Development Goals and long-term economic prosperity in the region.

Relying on the D-Vine copula model, this paper delves into the hedging capabilities of Brent crude oil against the exchange rate of oil-exporting and oil-importing nations. The results affirm Brent crude oil’s role as a safeguard and a refuge against the fluctuations of major currencies. Furthermore, we reaffirm that oil retains its robust hedging and safe-haven attributes during times of crisis, with currency co-movements across all countries exhibiting greater correlation than during the entire dataset. Additionally, our empirical findings highlight an unusually positive correlation between Brent crude oil and the Russian exchange rate during the Russia-Ukraine conflict, demonstrating that oil functions as a less effective hedge and a less dependable refuge for the Russian exchange rate in such geopolitical turbulence.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers’ capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.