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This study deals with changes in European Union's (EU's) trade potential in Machinery (HS 84–85) and Transportation (HS86-89) products.

The study uses a Structural Gravity model, Poisson Pseudo Maximum Likelihood (PPML) estimation together with panel data for the years 2002–2018 and a two-step procedure that employs predicted values of bilateral trade to compare potential to actual trade.

Results for Machinery products suggest a potential to expand trade with existing Regional Trade Agreements (RTAs) in the American continent, and countries of the IGAD region in Africa. In Transportation, a high trade potential with RTAs is found in the Americas, Africa and the Middle East. Policy suggestions concentrate on opportunities for enhancing trade relations through trade liberalization and agreement proliferation.

There are no studies to date, that examine “collective” measure of EU trade potential, that treats the EU as a single country. Changes in existing opportunities to expand trade, common for EU members, are of special interest for policy formulation, especially after the recent turmoil presented by the Global Financial Crisis (GFC) and the Greek Economic Crisis (GEC). Treating the EU as a single entity, is necessary for the formulation of an effective, common, EU trade policy. This study concentrates on the manufacturing sector to examine existing opportunities for the EU to expand trade, after the GFC and the GEC. This article deals with Machinery (HS 84 and 85) and Transportation (HS 86 through 89) products as they comprise a significant part of total EU exports, reaching 41% of total exports in 2016. Finally, this study offers a unique illustration of results through trade potential heat maps.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Money laundering (ML) is a major criminal offence stemming from unethical practices by personnel on the ground at Chattogram Port, an important import and export facility in Bangladesh. Because money can be more easily laundered through imports, it is necessary to investigate the dubious process in this sector. This study aims to identify the items most regularly used for easy ML and the factors contributing to their vulnerability.

This research uses a qualitative approach and analyses information from primary sources. Data is obtained from customs officials, port authority personnel, importers and customs brokers through semi-structured questionnaires. Although there are many techniques for ML, this study only found three most overwhelming: under-invoicing, over-invoicing and misdeclaration. A few case studies have been used based on newspaper reports and the internet to triangulate the qualitative data.

Four import items – food products, garments, capital machinery and chemicals – have a higher risk of ML. This study also revealed that money launderers prefer under-invoicing food and garment items. Misdeclaration is more commonly associated with capital machinery and chemical items. Over-invoicing, on the other hand, is only prevalent in government purchases. The port authorities need to pay particular attention to these issues.

As ML is an ongoing activity that changes over time, the findings of this research are circumscribed by the data collected at a single point in time. Additionally, this research did not consider alternative laundering methods.

The research results can provide a basis for creating effective anti-money laundering (AML) strategies to assist with sustainable economic growth.

Developing effective AML measures can help combat corruption and establish good governance in the country and support human well-being.

This paper presents original research findings based on technical analysis. The Chattogram Port Authority and the National Board of Revenue have accepted and used the main findings in a collaborative action plan to tackle ML. The Bangladesh Bank, the country’s central bank, has also incorporated the necessary guidelines and regulations into the Money Laundering Prevention Act, 2012.

This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.

The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.

The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.

This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.

This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency.

The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution.

LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between.

This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

This study investigates whether, how and under what circumstances off-farm work induces farmland abandonment, which is of great importance for developing countries to cope with food security.

Exploiting large-scale panel data from the newly released Chinese Family Database, this study employs a two-way fixed effects model to empirically estimate the causal relationship between off-farm work and farmland abandonment.

In the context of large-scale labor migration in rural China, current off-farm work leads to an increase in the probability and area of farmland abandoned due to insufficient agricultural labor. However, off-farm work does not harm farm households in plain areas, or villages with land rental markets, abundant agricultural labor, and agricultural machinery, while it harms others. Moreover, farmers who work off-farm in the local area are less likely to abandon their farmland than those in other areas. Additionally, when the number of off-farm workers in a household exceeds two, the probability and area of farmland abandonment will miraculously decline, as the household will no longer live entirely on agriculture.

This study may fill the gap in clarifying the relationship between off-farm work and farmland abandonment, and identify scenarios where off-farm work may not cause farmland abandonment through multiple dimensions, providing insights into the governance of farmland abandonment during rural-urban transformation in developing countries.

African agricultural mechanization could lead to a mechanization divide, where only large farms have access to machines. Technological solutions such as scale-appropriate machines and institutional solutions like service markets offer hope for more inclusive mechanization. Two-wheel tractor-based service markets combine both technological and institutional elements, but there is limited research on their economic viability and challenges.

We analyze the economic viability of two-wheel tractor-based service provision based on data from service providers in Ethiopia, Burkina Faso, and Zimbabwe. We also examine the institutional framework conditions for such service providers based on qualitative interviews with these service providers and stakeholders such as machinery dealers, spare parts providers, and banks.

Two-wheel tractor-based service provision is economically highly viable, largely due to multifunctionality. Post-production services such as threshing and transportation are particularly lucrative. However, the emergence and economic sustainability of service providers can be undermined by bottlenecks such as access to finance, knowledge and skills development, access to fuel and spare parts, and infrastructure problems.

This is the first study on the economics of two-wheel tractor-based service provider models. Past studies have focused on large four-wheel tractors, but two-wheel tractors are different in many aspects, including regarding investment costs, repair and maintenance costs, capacity, and multifunctionality.