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World trade has been increasing rapidly and much faster than world output. This study analyzes the trade structures of major dynamic East Asian countries as well as regional subgroups such as ASEAN members and Northeast Asian countries. Emphasis will be on the complementarities that would enhance integration among them through international trade. In addition, potential trade levels for each combination of East Asian countries are estimated by applying the gravity model of trade to the trade flows of21 APEC members, as a reference group. It is estimated to have significant potentiality by regional subgroup, ASEAN or Northeast Asia, and not between the two regional subgroups. However, the potential integration between East Asian countries in different regional subgroups is more significant by considering complementarities in trade compared with the results from the basic gravity model. To enhance economic cooperation between East Asian countries, expanding relationships such as inter-industry trade in natural resources trade and industrial goods between the regional subgroups needs to occur. They should also utilize complementary relationships from intra-industry trade in industrial goods such as electric and electronic equipment, related parts and accessories. And they should focus on the implementation of trade facilitation measures based on global standards.

Exchange rate uncertainty leads to an indecisive environment for imports and exports that would condense international trade, foreign direct investment, trade earnings, trade volumes, economic growth and welfare. This study aims to examine, empirically, the effect of exchange rate uncertainty on bilateral trade performance, focusing on eight SAARC member economies using the popular modified gravity model of trade.

The paper includes eight SAARC members – Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan and Sri Lanka panel data set over the period 2005–2018. The authors consider both standardized value (standard deviation) and conditional variance model to determine volatility of exchange rate. Primarily, ordinary least squares, random effects and fixed effects estimation techniques are employed to investigate the impact of exchange rate volatility. Endogeneity and robustness of the findings have been tested using the simultaneity-adjusted model and dynamic panel data two-step system GMM estimation techniques.

Empirical findings endorse the view that exchange rate volatility lowers trade flows in the SAARC regions. However, this adverse effect of exchange rate uncertainty on trade is pretty small. The negative correlation between exchange rate volatility and bilateral trade remains consistent and significant after controlling of simultaneous causality, autocorrelation, year effects, country-pair heterogeneity and endogeneity irrespective of panel data estimation techniques and different measures of volatility.

The present paper is original work.

The purpose of this paper is to analyze the impact of corruption on trade.

The authors estimate gravity equations with the last econometric advances on a wide sample of countries and years using three different measures of corruption. Two of them belong to the so-called perception-based indexes and the third is derived from a structural model that takes into account the causes and indicators of corruption across countries.

A negative effect of corruption on trade appears with perceptions, but it is not widespread. However, the authors find sensible evidence of the “grease the wheels” view with the structural index if low and middle income countries are implicated. Additionally, when using this measure, differences in corruption levels negatively impact trade. Both results are in line with expectations.

Moreover, membership in regional trade agreements does not seem to significantly alter these results.

This article examines the impact of the ASEAN Free Trade Area (AFTA) on Korean exports to ASEAN countries by using the system generalized method of moments. The data covered 15 sectors according to their relative importance in Korean exports and spanned from 1980 to 2006. The estimated results suggest that Korea’s exports were diverted to ASEAN members as a result of the AFTA formation. In 5 of the 15 sectors, the AFTA exerted a significant negative effect on Korean exports to ASEAN countries, but for the remaining 9, the results were mixed and statistically insignificant. The results also indicate that the sectoral approach yields more robust and clear-cut results than the aggregate one.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

The assessment of Autism Spectrum Disorders (ASD) in childhood has two essential aspects: the identification of the risk (under 30 months of age) and the definition of a diagnosis that takes into account its core areas as well as further non-specific aspects. The purpose of this paper is to present an approach that considers the combination of clinical evaluation with the use of tools that analyse the various levels of the child’s functioning as fundamental.

The comprehensive assessment at the Institute of Ortofonologia in Rome provides the ADOS-2 and the Leiter-R for the evaluation of the symptomatology, the severity level, the non-verbal cognitive functioning and the fluid reasoning; the TCE and the UOI are used to identify, respectively, the child’s emotional skills and the ability to understand the intentions of others, as precursors of the theory of mind. Within this assessment, the Brief-P, the Short Sensory Profile and the RBS are also included for the evaluation of executive functions, sensory pattern and of restricted and repetitive behaviours, as observed by parents.

How to define a reliable development profile, which allows to plan a specific intervention calibrated on the potential of the child and on his development trajectory, is described. Two clinical cases are also presented.

The entire process is aimed both at a detailed assessment of the child’s functioning and at identifying a specific therapeutic project and predictive factors for achieving an optimal outcome.

This purpose of this paper is to quantify the effect of local instability arising from high shear loading on response of steel girders subjected to fire conditions.

A three-dimensional nonlinear finite element model able to evaluate behavior of fire-exposed steel girders is developed. This model, is capable of predicting fire response of steel girders taking into consideration flexural, shear and deflection limit states.

Results obtained from numerical studies show that shear capacity can degrade at a higher pace than flexural capacity under certain loading scenarios, and hence, failure can result from shear effects prior to attaining failure in flexural mode.

The developed model is unique and provides valuable insight (and information) to the fire response of typical hot-rolled steel girder subjected to high shear loading.