Explore the vast range of titles available.

Fault Tree Analysis (FTA) method is a commonly used approach for safety and reliability analysis of fuzes. However, it is rarely applied to the safety and reliability analysis of MEMS S&A of small-caliber grenade fuzes. Considering the structural characteristics and complexity of processing technology of MEMS S&A for small-caliber ammunition fuzes, it is not convenient to obtain all failure modes of MEMS S&A through experimental methods. Therefore, the FTA method is adopted to analyze the premature isolation release and unreliable operation of MEMS S&A respectively, and the probability base values are determined according to the types of base events. The safety failure rates of premature isolation release and unreliable operation of MEMS S&A are calculated and compared with relevant standards to verify whether they meet the design requirments.

This study adopts a Gravity model framework to conduct an empirical analysis of the relationship between intra-African trade, regional integration, and economic development in Sub-Saharan Africa. The study employed a gravity-consistent Vector Autoregressive (VAR) methodology, using time-series data from the World Bank Development Indicators (WDI) for the period 1975-2023. The empirical findings of the study indicated that the gravity model is valid for Sub-Saharan African (SSA) countries. Consistent with the gravity model, the study’s results suggest that intra-African trade increases as the size of GDP increases, and that regional free trade agreements in SSA are strengthened. Conversely, intra-African trade diminishes as distance—measured by import transportation costs—increases within the region. The study proposes that policymakers focus on prudent policies to promote intra-African trade, regional economic expansion, integration, and cooperation through the AfCFTA. The study strongly suggests that AfCFTA provides a significant opportunity and platform for cross-border infrastructure and technology investment; an integrated financial system and monetary union; economic structural reforms that boost intra-investment and regional value chain integration; and economic diversification that would ultimately unlock the optimal economic benefits of intra-African trade.

This paper econometrically assesses the impact on EU goods trade of the EU-Canada Comprehensive Economic and Trade Agreement (CETA), which has been provisionally applied since 2017. As opposed to many other studies, this analysis is carried out at the most disaggregated level of publicly available trade data - the 8-digit level. Furthermore, we control for nomenclature changes and revisions. Most EU trade with Canada are concentrated in a handful of broad product groups which were already duty-free (MFN0 duties). The paper nevertheless finds positive and significant impacts on EU exports to Canada. On the other hand, CETA seems to have increased EU imports from Canada only in goods that were already subject to MFN0 duties. The results suggest that there are positive effects on trade of the agreement beyond tariff reductions, something which requires additional research.

With Brexit completed and the UK's conditions of separation from the EU pending, there is some anticipation for a U.S.-UK FTA. But then there is the Pandemic and the unpredictable variables of Donald Trump and Boris Johnson, and the influence of the residual binding obligations of the UK-EU separation agreement and possible UK-EU FTA, which may cause some pause. Identifying the negotiating agenda of the labor issues may flow easily from each country's recent FTAs – USMCA and UK's obligations under CETA. With that likely agenda, a comparison can be made between each country's current labor laws on these issues to identify possible emerging areas needing further attention. Lingering in the background is the potential U.S.-EU FTA (TTIP) which will set standards and obligations for the UK which can be relevant to the UK FTAs with the U.S. and the EU. This is followed with analysis as to likely outcomes on these labor issues and the U.S.-UK FTA. Although the future cannot be predicted, it can be prepared for.

While the Association of Southeast Asian Nations (ASEAN) and the European Union (EU) relaunched negotiations on ASEAN-EU FTA in 2017, few signs have indicated significant progress in trade talks and EU has been prompted to pursue, in parallel, bilateral free trade agreements (FTAs) with individual ASEANMembers States as building blocks for the region-to-region megaproject. The underlying divergence in economic, political, historical, and social dimensions between ASEAN and EU has continued to condition bi-regional negotiations, and issues of sanitary and phytosanitary (SPS) are of particular importance given the large inter-regional agri-food trade and inherent tensions embedded in SPS measures that are adopted to strike a balance between trade liberalization and public health. Whether and how the two blocs can reconcile different interests and policy agendas in shaping SPS cooperation under the prospective FTA merits in-depth examination. This article analyses the trajectory and dynamics in ASEAN-EU SPS cooperation. It assesses the SPS Chapters of the EUSingapore and EU-Vietnam FTAs, drawing on their relevance to and implications for region-to-region development. Lastly, this article highlights various bottom-up SPS cooperation initiatives between the two—due to EU’s technical assistance, and more critically, ambition to export its normative paradigms globally—emphasizing their role as catalyst for deeper region-to-region SPS cooperation. ASEAN-EU FTA, EU-Singapore FTA, EU-Vietnam FTA, SPS Agreement, sanitary and phytosanitary measures, New Asian Regionalism

Infections caused by arthropod-borne RNA viruses are overrepresented among emerging infectious diseases. Effective methods for collecting, storing, and transporting clinical or biological specimens are needed worldwide for disease surveillance. However, many tropical regions where these diseases are endemic lack analytical facilities and possibility of continuous cold chains, which presents challenges from both a biosafety and material preservation perspective. Whatman® FTA® Classic Cards may serve as an effective and safe option for transporting hazardous samples at room temperature, particularly for RNA viruses classified as biosafety level (BSL) 2 and 3 pathogens, from sampling sites to laboratories. In this study, we investigated the biosafety and perseverance of representative alpha- and flaviviruses stored on FTA® cards. To evaluate the virus inactivation capacity of FTA® cards, we used Sindbis virus (SINV), chikungunya virus (CHIKV), and Japanese encephalitis virus (JEV). We inoculated susceptible cells with dilution series of eluates from viral samples stored on the FTA® cards and observed for cytopathic effect to evaluate the ability of the cards to inactivate viruses. All tested viruses were inactivated after storage on FTA® cards. In addition, we quantified viral RNA of JEV, SINV, and tick-borne encephalitis virus (TBEV) stored on FTA® cards at 4 °C, 25 °C, and 37 °C for 30 days using two reverse transcriptase quantitative PCR assays. Viral RNA of SINV stored on FTA® cards was not reduced at either 4 °C or 25 °C over a 30-day period, but degraded rapidly at 37 °C. For JEV and TBEV, degradation was observed at all temperatures, with the most rapid degradation occurring at 37 °C. Therefore, the use of FTA® cards provides a safe and effective workflow for the collection, storage, and analysis of BSL 2- and 3-virus RNA samples, but there is a risk of false negative results if the cards are stored at higher temperatures for long periods of time. Conscious usage of the cards can be useful in disease surveillance and research, especially in tropical areas where transportation and cold chains are problematic.

Reliability design is critical to the safe operation of complex systems, and the large-tonnage freight elevator (LTFE) is a typical complex system. In order to improve the reliability of LTFE in the textile dyeing and printing industry, a comprehensive analysis method is proposed, which is based on Fuzzy Failure Mode, Effect and Criticality Analysis (FFMECA), Fault Tree Analysis (FTA) and Bayesian Networks (BN). First, FFMECA is analyzed by fuzzy analytic hierarchy Process (FAHP) and entropy-weight method. The fuzzy function was designed according to the characteristics of parameters Severity ( S ), Occurrence ( O ) and Detection ( D ), and the Fuzzy Risk Priority Number (FRPN) are calculated and ranked to identify high-risk failure modes. Next, the key causes of failure are identified by the analysis of FTA and BN. FTA model is constructed by the high-risk failure modes, then the FTA model is mapped into a BN model, and an inferential analysis is executed. Finally, a case study is conducted, and six high-risk failure modes are found, including gate structure fault, tractor failure, electromagnetic braking fault. Improvement measures were proposed and simulation verified that the system reliability is more than 94%, meeting the system design requirements.

Industry 4.0 and advanced technology, such as sensors and human–machine cooperation, provide new possibilities for infusing intelligence into failure analysis. Failure analysis is the process of identifying (potential) failures and determining their causes and effects to enhance reliability and manufacturing quality. Proactive methodologies, such as failure mode and effects analysis (FMEA), and reactive methodologies, such as root cause analysis (RCA) and fault tree analysis (FTA), are used to analyze failures before and after their occurrence. This paper focused on failure analysis methodologies intelligentization literature applied to FMEA, RCA, and FTA to provide insights into expert-driven, data-driven, and hybrid intelligence failure analysis advancements. Types of data to establish an intelligence failure analysis, tools to find a failure’s causes and effects, e.g., Bayesian networks, and managerial insights are discussed. This literature review, along with the analyses within it, assists failure and quality analysts in developing effective hybrid intelligence failure analysis methodologies that leverage the strengths of both proactive and reactive methods.

Stranded animals offer valuable information on marine mammal physiology and pathology; however, the decomposition state of the carcasses and lack of a rigorous cold chain for sample preservation can sometimes discourage diagnostic analyses based on nucleic acid detection. The present paper aims at evaluating the reliability of FTA® card tissue imprints as an alternative matrix to frozen tissues for virological analyses based on biomolecular methods. Given the contribution of Cetacean morbillivirus (CeMV) to strandings and the increase of herpesvirus detection in cetaceans, these two pathogens were selected as representative of RNA and DNA viruses. Dolphin morbillivirus (DMV) and herpesvirus presence was investigated in parallel on tissue imprints on FTA® cards and frozen tissues collected during necropsy of dolphins stranded in Italy. Samples were analysed by nested RT-PCR for DMV and nested-PCR for herpesvirus. Only one animal was positive for herpesvirus, hampering further considerations on this virus. DMV was detected in all animals, both in FTA® card imprints and tissue samples, with differences possibly related to the decomposition condition category of the carcasses. Tissue sampling on FTA® cards seems a promising alternative to frozen tissues for biomolecular analyses, especially when ensuring adequate storage and shipment conditions for frozen tissues is difficult.

Molecular-based detection of pathogens from potato tubers hold promise, but the initial sample extraction process is labor-intensive. Developing a robotic tuber sampling system, equipped with a fast and precise machine vision technique to identify optimal sampling locations on a potato tuber, offers a viable solution. However, detecting sampling locations such as eyes and stolon scar is challenging due to variability in their appearance, size, and shape, along with soil adhering to the tubers. In this study, we addressed these challenges by evaluating various deep-learning-based object detectors, encompassing You Look Only Once (YOLO) variants of YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLO11, for detecting eyes and stolon scars across a range of diverse potato cultivars. A robust image dataset obtained from tubers of five potato cultivars (three russet skinned, a red skinned, and a purple skinned) was developed as a benchmark for detection of these sampling locations. The mean average precision at an intersection over union threshold of 0.5 (mAP@0.5) ranged from 0.832 and 0.854 with YOLOv5n to 0.903 and 0.914 with YOLOv10l. Among all the tested models, YOLOv10m showed the optimal trade-off between detection accuracy (mAP@0.5 of 0.911) and inference time (92 ms), along with satisfactory generalization performance when cross-validated among the cultivars used in this study. The model benchmarking and inferences of this study provide insights for advancing the development of a robotic potato tuber sampling device.