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This paper empirically tests the impact of capital sudden stops on the economic growth using quarterly data from 49 emerging economies.

This paper applies the GMM dynamic panel estimation method.

The results show that capital sudden stops can significantly inhibit the economic growth of emerging economies. It was also found that the inhibiting effect on low-savings-rate economies is greater, but less on high-savings-rate economies. In addition, this paper examined the impact of different types of capital sudden stops on economic growth in emerging economies. The results reveal that the impact of sudden stops of direct investment is not significant.

Little existing research considers the impact of capital sudden stops through the perspective of savings rate differences. Based on our research using the GMM model, we argue that capital sudden stops will lead to a decline in investment kinetic energy in emerging economies, and therefore, a decline in economic growth. There are also few studies on the economic effects of capital sudden stops. And the time series model is generally used in a single economy. This paper, however, uses the data from 49 emerging economies and takes the panel approach to more comprehensively study the capital sudden stops of emerging economies.

The need for an efficient enterprise risk management (ERM) has never been greater than today when organisations face complex and interconnected risks targeting their business models. Macroeconomics and geopolitical uncertainties, digital transformations of industries and sectors, cybersecurity, and climate change, among other trends, present significant uncertainties. This article aims to analyse the scientific papers on research specific to ERM and review the links between the researched area and market or corporate governance topics. Risk management is underdeveloped in many organisations; the current standard for risk management is a reactive approach. It is usually treated in isolation rather than as a core competency and a strategic asset. As a result, risk management processes are ineffective and seen as adding value to decision-making and responding to uncertainties. Based on the literature, the scope is to set up the framework for future research on ERM by building a bibliometric analysis and examining articles collected from the Web of Science Core Collection database. The study identified the essential research on this topic based on the citations of the papers and the author’s countries with the highest number of publications and citations. VOSviewer software analysed the ERM system based on keywords, citations, geographical distribution, and authorships. The research proves a strong connection between the ERM and corporate governance topics considering the stage where most countries are regarding this subject.

This paper adopts a multi-tier perspective and aims to explore challenges of small and medium-sized enterprises (SMEs) in collaborative manufacturing amid the emergence of dedicated B2B platforms. Original equipment manufacturers welcome formation of demand-driven collaborations between SME suppliers to facilitate ramp-up of production capacity. While being potentially beneficial to suppliers, such collaborations face various barriers.

An exploratory study of 17 suppliers within the European Union’s aerospace industry was undertaken. The study comprised two stages. In the first stage, suppliers’ answers to self-administered interviews were analysed using thematic analysis. In the second stage, interactions between the barriers were determined through interviews with experienced SME collaboration facilitators. The authors apply system dynamics modelling to analyse the links between barriers and identify re-enforcing and balancing loops of other factors.

The authors establish five major groups of barriers to collaboration impeding: market transparency, access to orders, partner trust, contracting and (e) data sharing and coordination. The authors model application of four enablers that facilitate barrier removal for technology-enabled supply chains: digital platforms, supplier development, smart contracts and Industry 4.0.

The study is limited by the data collection from the aerospace industry; validation of the models in other low-volume high-variability manufacturing sectors is needed.

The reader will learn about the barriers which impede demand-driven SME collaboration within manufacturing supply chains, interrelationships between these barriers and suggestions about how to remove them. SME cluster managers will find managerial implications particularly interesting as they will help them to overcome collaboration concerns and better prepare cluster members for Industry 4.0.

The models developed within this study can be used to explore the effects of intervening at critical points in the model to create virtuous improvement cycles between key barriers and related variables in the model. This can help decision-making and policymaking in the area of supply chain integration.

There is currently a lack of studies about how the existing barriers amplify and de-amplify themselves and what the managerial approaches to tackle the barriers are. It is unclear how far companies will go in terms of information sharing, given the trust levels, power dynamics and governance structures evident in supply chains. This study contributes by explaining the reinforcing interaction between the barriers and showing ways to overcome these using enablers.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

This paper aims to analyze the morphology transformation on the Cu₃Sn grains during the formation of full Cu₃Sn solder joints in electronic packaging.

Because of the infeasibility of analyzing the morphology transformation intuitively, a novel equivalent method is used. The morphology transformation on the Cu₃Sn grains, during the formation of full Cu₃Sn solder joints, is regarded as equivalent to the morphology transformation on the Cu₃Sn grains derived from the Cu/Sn structures with different Sn thickness.

During soldering, the Cu₃Sn grains first grew in the fine equiaxial shape in a ripening process until the critical size. Under the critical size, the Cu₃Sn grains were changed from the equiaxial shape to the columnar shape. Moreover, the columnar Cu₃Sn grains could be divided into different clusters with different growth directions. With the proceeding of soldering, the columnar Cu₃Sn grains continued to grow in a feather of the width growing at a greater extent than the length. With the growth of the columnar Cu₃Sn grains, adjacent Cu₃Sn grains, within each cluster, merged with each other. Next, the merged columnar Cu₃Sn grains, within each cluster, continued to merge with each other. Finally, the columnar Cu₃Sn grains, within each cluster, merged into one coarse columnar Cu₃Sn grain with the formation of full Cu₃Sn solder joints. The detailed mechanism, for the very interesting morphology transformation, has been proposed.

Few researchers focused on the morphology transformation of interfacial phases during the formation of full intermetallic compounds joints. To bridge the research gap, the morphology transformation on the Cu₃Sn grains during the formation of full Cu₃Sn solder joints has been studied for the first time.

An elasto‐viscoplastic analysis of anisotropic plates and shells is undertaken by means of the finite element displacement method. A thick shell formulation accounting for shear deformation is considered and a layered approach is adopted in order to model property changes through the shell thickness. In order to avoid ‘locking’ behaviour as the shell thickness is reduced, the nine‐node Lagrangian and heterosis elements are introduced into the present model. Viscoplastic yielding is based on the Huber—Mises criterion extended by Hill for anisotropic materials. Time integration of the strain rate equations is accomplished by both explicit and implicit algorithms and special consideration is given to the evaluation of the viscoplastic strain increment for anisotropic situations. The computer code developed is demonstrated by application to a range of numerical examples.

The purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes.

To improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal.

The experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method.

The proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages.

A K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

A generalized displacement method has been previously presented for the analysis of thin plate‐shell structures with the use of bilinear 4‐node isoparametric shell elements. Following this approach, a procedure for the geometrically non‐linear analysis of thin plates and shells based on both updated and total Lagrangian formulations is developed. The results of some numerical examples are presented to show the versatility and effectiveness of the method.

This paper aims to investigate the volatility transmission across stocks, gold and crude oil markets before and during the novel coronavirus (COVID-19) crisis.

A multivariate vector autoregression (VAR)-Baba, Engle, Kraft and Kroner generalized autoregressive conditional heteroskedasticity model (BEKK-GARCH) is used to assess volatility transmission across the examined markets. The sample is divided as follows. The first period ranging from 02/01/2019 to 10/03/2020 defines the pre-COVID-19 crisis. The second period is from 11/03/2020 to 05/10/2020, representing the COVID-19 crisis period. Then, a robustness test is used using exponential GARCH models after including an exogenous variable capturing the growth of COVID-19 confirmed death cases worldwide with the aim to test the accuracy of the VAR-BEKK-GARCH estimated results.

Results indicate that the interconnectedness among the examined market has been intensified during the COVID-19 crisis, proving the lack of hedging opportunities. It is also found that stocks and Gold markets lead the crude oil market especially during the COVID-19 crisis, which explains the freefall of the crude oil price during the health crisis. Similarly, results show that Gold is most likely to act as a diversifier rather than a hedging tool during the current health crisis.

Although the recent studies in the field focused on analyzing the relationships between different markets during the first quarter of 2020, this study considers a larger data set with the aim to assess the volatility transmission across the examined international markets Amid the COVID-19 crisis, while it shows the most significant impact on various financial markets compared to other diseases.

Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.

First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.

Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.

A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.