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This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target.

The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)¹² model for forecasting were determined based on model identification, estimation and diagnostics.

SARIMA(1, 0, 1) (1, 1, 0)¹² was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target.

This study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna.

The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry.

This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

This paper aimed to shed light on the relationship between blockchain technology intensity and tax evasion and whether this relationship is moderated by good governance.

Data from a sample of 50 European companies selected from the STOXX 600 index between 2010 and 2019 were used to test the model via panel data and multiple regression. Here, we used the generalized least squares method estimated on panel data. A multivariate regression model was used to analyze the moderating effect of good governance on the association between blockchain technology intensity and tax evasion. For the robustness analyses, we included the comparative study of legal systems. We performed an additional analysis by testing the dynamic dimension of the data set using the generalized method of moments to control for the endogeneity problem.

Expectedly, the results showed a negative relationship between blockchain technology intensity and tax evasion. Furthermore, the findings suggest that the moderating variable negatively affects the relationship between blockchain technology and tax evasion.

To our knowledge, this study supports the existing literature. Firstly, it expands the scientific debate on tax evasion. Secondly, it extends the scope of the agency theory, which is used to explain the phenomena associated with tax evasion. This study is one of the first to examine the moderating effect of good governance on the association between blockchain technology intensity and tax evasion.

Additive manufacturing (AM) is growing economically because of its cost-effective design flexibility. However, it faces challenges such as interlaminar weaknesses and reduced strength because of product anisotropy. Therefore, the purpose of this study is to develop a methodology that integrates design for additive manufacturing (AM) principles with fused filament fabrication (FFF) to address these challenges, thereby enhancing product reliability and strength.

Developed through case analysis and literature review, this methodology focuses on design methodology for AM (DFAM) principles applied to FFF for high mechanical performance applications. A DFAM database is constructed to identify common requirements and establish design rules, validated through a case study.

Existing DFAM approaches often lack failure theory integration, especially in FFF, emphasizing mechanical characterizations over predictive failure analysis in functional parts. This methodology addresses this gap by enhancing product reliability through failure prediction in high-performance FFF applications.

While some DFAM methods exist for high-performance FFF, they are often specific cases. Existing DFAM methodologies typically apply broadly across AM processes without a specific focus on failure theories in functional parts. This methodology integrates FFF with a failure theory approach to strengthen product reliability in high-performance applications.