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The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency.

The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially.

By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards.

The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

This study aims to examine the illicit capital movement through trade misinvoicing in Burundi, at disaggregated levels by major trading partners and by major export and import commodities.

Trade misinvoicing is estimated by comparing the trade values declared by Burundi with those declared by trading partners in a bilateral international transaction, after adjusting for the cost of freight and insurance. Disaggregated trade misinvoicing by major trading partners is computed using the Direction of Trade Statistics database of the International Monetary Fund over the period 1970–2019. Disaggregated trade misinvoicing by major trading commodities is computed using the UN-COMTRADE database over the period 1993–2019.

Exports of Burundi to most of its major trading partners are found to be underinvoiced. The top destinations for export underinvoicing are United Arab Emirates, Belgium and Germany. However, exports to UK and Switzerland are found to be overinvoiced. The major export commodities considered, coffee and gold, are found to be affected by trade misinvoicing to a great extent. On the import side, the estimation results indicate that imports of Burundi from its major trading partners are in general overinvoiced. High import overinvoicing is observed in the trade with Saudi Arabia, China and Japan. At commodity level, for the top 6 commodities considered, imports were to a great extent found to be overinvoiced. Cases of illicit capital outflows and inflows through trade misinvoicing are highlighted.

Some policy implications are drawn from this study. First, in collaboration with its development partners, the Government of Burundi should put in place measures to reduce the trade misinvoicing phenomenon, which undermines poverty reduction efforts. The study has shown which trade partners are involved and which commodities are mostly affected. Policy efforts could then be focused in that regard. Investigations at the company and transaction levels can be made to identify the mechanisms of trade misinvoicing. Second, more effort is needed in ensuring systematic and transparent reporting of international trade transactions. To fight trade misinvoicing, transparency in international trade is key, through coordinated enforcement of reporting rules.

Previous studies analyzed the problem of trade misinvoicing at an aggregated level. However, this leaves out essential information on trading partners involved in the phenomenon as well as trading commodities affected. This study investigates trade misinvoicing at disaggregated levels, at product level and by trading partner.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

Digital technologies (DTs) have emerged as a major driving force, transmuting the ways Supply Chains (SCs) are managed. The integration of DTs in supply chain management (SCM), Digital Supply Chain Management (DSCM), has fundamentally reshaped the SCM landscape, offering new opportunities and challenges for organizations. This chapter provides a comprehensive overview of modern DTs and the way they impact modern SCM. This chapter has twofold objectives. First, it illustrates the major changes that DTs have brought to the supply chain landscape, unraveling their multifaceted implications. Second, it offers readers a deeper and comprehensive understanding of the challenges and opportunities arising from the incorporation of DTs into supply chains. By going through the chapter, readers will be able to have a comprehensive grasp of how DTs are reshaping SCM and how organizations can survive and thrive in the digital age. This chapter commences by shedding light on how DTs have and continue to redefine SCM, improving supply chain resilience, visibility, and sustainability in an increasingly complex and interconnected world. It also highlights the role of DTs in enhancing SC visibility, agility, and customer-centricity. Furthermore, this chapter briefly highlights the challenges related to the adoption (pre and post) of DTs in SCM, elucidating on issues related to talent acquisition, data security, and regulatory compliance. It also highlights the ethical and societal implications of this digital transformation, emphasizing the significance of responsible and sustainable practices. This chapter, with the help of three cases, illustrates how the adoption of DTs in SC can impact the various SC performance indicators.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

This paper aims to examine the effects of the real exchange rate on trade balance in Algeria and investigates whether it represents a viable tool to sustain and improve trade performance using the nonlinear autoregressive distributed lag (NARDL) estimation technique and data from Algeria over the period 1980–2018. This study also highlights the role of trading partners with large income endowments in enhancing the trade balance.

The NARDL model is used to unveil potential short and long run nonlinear responses of the trade balance to shocks in real exchange rates and detect whether these responses are different in terms of sign and magnitude. The paper also provides a dynamic multiplier analysis that tests the existence of a J-Curve pattern in Algeria with several policy recommendations.

The findings confirm the existence of a J-curve pattern in Algeria where domestic currency depreciation will worsen the trade balance in the short run and improve it in the long run. The authors also find that the asymmetrical effect of real exchange rate on trade balance is different in sign and magnitude. Finally, the results indicate that an increase in trade partners' income increases the trade balance in Algeria. The findings are of utmost importance with several policy implications.

While some works investigated the nonlinear response of trade balance to real exchange rate movements, their results remain inconclusive and seem to depend on the characteristics of the country/region of study. Moreover, the role of trade partners and their potential impact on trade balance has been relatively overlooked in the literature. The authors fill this gap by examining the asymmetric impacts of real exchange rate and the effect of trade partners' income on trade balance in Algeria.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

Purpose: The COVID-19 pandemic is the most severe threat we have faced since World War II. So far, there have been about 5 million recorded cases, with over 300,000 fatalities globally. The epidemic is also wreaking havoc on the corporate world. People are losing their jobs and money, and no one knows when normalcy will return. So, addressing the VUCA Leadership Strategies Model is important to get more insight into this topic.

Need for the Study: According to the International Labor Organization, the pandemic might cost 195 million jobs. Even when the immediate impacts wear off, the long-term economic impact will reverberate for years. All four volatile, unpredictable, complex, and ambiguous (VUCA) characteristics apply to the issues we confront due to the coronavirus.

Methodology: Changes caused by COVID-19 occur daily, and are unpredictable, dramatic, and quick. No one can predict precisely when the epidemic will end or when a treatment or immunisation will be available. The pandemic impacts many parts of society, including health care, business, the economy, and social life. There is no ‘best practice’ that enterprises may utilise to tackle the pandemic’s issues. The VUCA leadership strategy models will be discussed and compared in this research study.

Findings: In this moment of transition, leaders must adhere to their fundamental values, core purpose, and ambition for big, hairy, and audacious goals.

Practical Implications: In this chapter, VUCA leadership strategy models will be discussed in detail for pre- and post-pandemic scenarios and their impact on different sectors, which will be very important for researchers in the same field.