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It was mid-March 2014, and GE's John F. Welch Technology Centre in Bangalore, India was brimming with activity. GE had developed an advanced, scalable positron emission tomography-computed tomography (PET/CT) scanner as part of its global Healthymagination initiative to provide better healthcare for more people at a lower cost. Munesh Makhija, Managing Director, GE India Technology Centre and Chief Technology Officer (CTO), GE South Asia, was thumbing through a report prepared by the PET/CT product development team and GE's healthcare market research team. In another office, Suresh Kumar R.(Kumar), General Manager of the Essential PET Segment, was putting the finishing touches on a presentation outlining a commercialisation strategy for the new PET/CT product, Discovery IQ (Exhibit 1).

Discovery IQ was a revolutionary product that would be useful for staging, treatment planning and post-treatment planning assessment. Early reviews from nuclear physicians had been positive. However, the product was still too costly for the bottom of the pyramid (BoP) market. Kumar and his team were scheduled to meet with Makhija the following morning to discuss a “go-to-market strategy”. Kumar knew that Makhija would want to talk about their segmentation strategy and the underlying needs of various customer types. He also expected Makhija to focus on return on investment (ROI) projections because diagnostic centres in India first looked at various financial return measures before investing in any new equipment. Kumar wanted to present a commercialisation strategy for Discovery IQ, which required a significant commitment of resources to tackle supply and distribution challenges across tier II and tier III cities^a in India.

This paper aims to investigate whether Islamic and conventional equity indices offer some alpha. These indices are expected to offer no alpha being value-weighted, passive and unmanaged.

This paper used monthly data from 1996 to 2016 of four Dow Jones (DJ) and one financial times stock exchange (FTSE) Islamic equity indices and five conventional Morgan Stanley Capital International (MSCI) equity indices. This study used a simple ordinary least square (OLS) rolling window regressions to generate the alphas and risk loadings when adjusting for prominent pricing factor models.

The findings from OLS regressions suggest that DJ Islamic indices of Japan, Europe and World generate significant alphas, whereas, MSCI conventional indices of Asia/Pacific, USA and World generate significant alpha when risk-adjusted for pricing factor models. However, in 36-month rolling window regressions, all Islamic indices generate significant alpha and factor loading. The magnitude of alpha and factor loading changes over time.

The finding shows that the Shari’ah-compliant investment fund’s alpha must be adjusted with the respective benchmark index alpha to measure the fund manager’s skill performance quantitatively.

To the best of the author’s knowledge, this is the first study that investigates and compares the Islamic, as well as conventional indices for abnormal returns, which are adjusted for both Fama–French five and q-theory-based four assets pricing risk factors and as a benchmark for Shari’ah-compliant fund’s performance.

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

This paper investigates the causality among gold prices, crude oil prices, bitcoin and stock prices by using daily data from January 2014 to December 2021. The study also examines the data during the COVID-19 outbreak from January 2020 to December 2021.

To estimate the long- and short-run causality, this study considers the nonlinear autoregressive distributed lag (NARDL) cointegration test.

The analysis found the existence of an asymmetric long-run cointegration among selected assets. Findings indicate that positive changes in bitcoin do not affect stock market in the long term. Changes in crude oil prices have a significant impact on stock prices. Moreover, it is observed that variations in the stock prices trigger a negative impact on gold prices. During the COVID-19 period, the study notices the presence of an asymmetric long-term cointegration between selected assets except bitcoin. Besides, findings revealed that negative price adjustments in gold lead to significant positive shocks in stock market.

These results provide critical information for policy performers and researchers to develop new strategies. Policy regulators can also consider the potential effects of the COVID-19 outbreak while developing strategies for investment decisions.

During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.

This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in the damaged and retrofitted state through vibration response signals.

The analysis of the vibration response signals is performed in time and time-frequency domains using statistical features-root mean square, impulse factor, crest factor, kurtosis, peak2peak and Stockwell transform. The proposed methodology uses the Hilbert transform in combination with spectral kurtosis and bandpass filtering technique for obtaining robust outcomes of modal frequencies.

The absence or low amplitude of considered mode shape frequencies is observed both before and after retrofitting of bridge indicates the deficient nodes. The kurtosis feature among all statistical approaches is able to reflect significant variation in the amplitude of different nodes of the bridge. The Stockwell transform showed better resolution of present modal frequencies but due to the yield of additional frequency peaks in the vicinity of the first three analytical modal frequencies no decisive conclusions are achieved. The methodology shows promising outcomes in eliminating noise and visualizing distinct modal frequencies of a steel truss bridge.

The findings of the present study help in analyzing noisy vibration signals obtained from various structures (civil or mechanical) and determine vulnerable locations of the structure using mode shape frequencies.

The literature review gave an insight into few experimental investigations related to the combined application of Hilbert transform with spectral kurtosis and bandpass filtering technique in determining mode frequencies of a steel truss bridge.

This paper aims to test for volatility spillovers among Islamic stock markets with the exogenous impact of geopolitical risk (GPR) to check the risk transmission among Saudi Arabia, Malaysia, Indonesia and Turkey. Researchers test for both the symmetric and asymmetric risk transmission.

For the symmetric response of volatility, the study uses simple generalized autoregressive conditional heteroscedastic (GARCH) and for the asymmetric response of volatility with the exogenous impact of GPR, the exponential GARCH models have been adopted.

The results suggest spillover effects exist from Turkey to Saudi Arabia, Indonesia to Malaysia and Saudi Arabia and Malaysia to Indonesia. The findings of volatility spillover from GPR to sample countries suggest that only Malaysia and Indonesia experience volatility spillovers from GPR.

The present study is limited to the context of four countries and Islamic equities; the study contributes to the literature on volatility spillover, Islamic finance, GPR and asset pricing.

This study contributes to individual, institutional investors’ policymakers’ knowledge in determining security prices, trading plans, investment hedging and policy regulation.

The extant literature disregards the GPR index to examine the volatility spillover effects among Islamic stock markets, which allow researchers to justify the mechanism of risk transmission due to GPR across the Islamic stock market.

To the best of the authors’ knowledge, this is the first research of its type to look at volatility spillover and GPR transmission in Islamic stock markets.

Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.

Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.

This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.

This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.

This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

Mental illness presents a huge individual, societal and economic challenges, currently accounting for 20% of the worldwide burden of disease. There is a gap between the need for and access to services. Digital technology has been proven effective in e-mental health for preventing and treating mental health problems. However, there is a need for cross-disciplinary efforts to increase the impact of e-mental health services. This paper aims to report key challenges and possible solutions for cross-disciplinary and cross-sectorial research teams within the domain of e-mental health.

The key challenges and possible solutions will be discussed in light of the literature on effective cross-disciplinary research teams.

Six topics have been key challenges in our cross-disciplinary and cross-sectorial research team: to develop a shared understanding of the domain; to establish a common understanding of key concepts among the project participants; to involve the end-users in the research and development process; to collaborate across sectors; to ensure privacy and security of health data; and to obtain the right timing of activities according to project dependencies.

This study focuses to increase knowledge and training in cross-disciplinary and cross-sectorial research, as this is often referred to as an important tool when developing sustainable solutions for major societal challenges.

This study needs to include theory and skills training in cross-disciplinary research in research training.

Cross-disciplinary teams have the potential to address major societal challenges, including more perspectives and more stakeholders than single disciplinary research teams.

Major societal challenges require complex and sustainable solutions. However, there is a lack of knowledge about how cross-disciplinary and cross-sectorial research teams may work productively to solve these challenges. This paper shares experiences regarding the challenges and possible solutions for productive collaboration in cross-disciplinary and cross-sectorial research teams within the domain of e-mental health services.

Recent literature has shifted to examining whether exchange rate volatility symmetrically or asymmetrically affects the trade flows. This study aims to extend the existing literature by examining the effects of extremely large to extremely small changes in exchange rate volatility series on the US imports from Brazil, India, Mexico and South Africa.

For examining the effects of extreme changes, multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model is used and the exchange rate volatility series is divided into quintiles and deciles. It helps to examine the effects of each quintile/decile of exchange rate volatility series on the US imports.

Findings indicate that the effects of extremely large changes in the exchange rate volatility series significantly differ from the effects of extremely small changes in the exchange rate volatility series on the US imports.

The findings of this study are very important. These findings help to consider the effect of extreme changes before devising policies related to trade flows.

This study mainly focuses on US imports from Brazil, India, Mexico and South Africa. In addition, this study extends the existing literature by using a novel methodology called MTNARDL model.