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The purpose of this paper is to present the results of acoustic emission (AE) and ultrasonic inspection of two H₂S storage tanks carried out in a heavy water plant, in order to characterize point type defects observed during earlier ultrasonic inspection and to ensure that these defects are not growing during hydrotesting of the tanks.

Using multiple AE sensors and AE source location methodology, the entire tank could be covered to detect and locate any dynamic sources of AE associated with local plastic deformation and/or growing discontinuities from any part of the tank during the hydrotest. For confirmation of the results obtained by AE, ultrasonic inspection on the tanks and on virgin plates from which the tanks were manufactured, were carried out.

The AE signals generated during first pressurisation are attributed to the micro yielding of the material of the tanks. A few scattered AE events were observed at a few locations during the hydrotesting of the tanks and these are due to structural and rubbing noise. During hold periods and repressurising cycle of the hydrotesting, no detectable AE events were observed and this confirmed the absence of any growing discontinuity in the tanks during the hydrotesting. Ultrasonic inspection on the tanks and on virgin plates confirmed that the point type defects detected are manufacturing defects and not formed during service life.

The combined results from AE and ultrasonic techniques confirmed the structural integrity of the tanks and ensured their healthiness for continued operation.

The paper brings out the use of AE and ultrasonic techniques for monitoring hydrotesting of storage tanks of a heavy water plant. The storage tanks where point type defect indications were reported during previous ultrasonic inspection and whether these defects are growing during hydrotesting of the tanks or not, were required to be known before the tanks are put in to further service. AE signals collected during pressurising and repressurising cycles of the hydrotest and subsequent inspection by ultrasonic confirmed the vessels to be free from growing defects during the hydrotest and provided baseline data for future inspection.

The purpose of this paper is to determine the feasibility of identifying the creep rupture of reactor cladding tubes using acoustic emission technique (AET).

The creep rupture tests were carried out by pressuring stainless steel capsules upto 6 MPa at room temperature and then heating continuously in a furnace upto rupture. The acoustic emission (AE) signals generated during the creep rupture tests were recorded using a 150 kHz resonant sensor and analysed using AE Win software.

When rupture occurs in the pressurized capsule tube representing the cladding tube, AE sensor attached to a waveguide captures the mechanical disturbance from the capsule and these data can be advantageously used to identify the creep rupture event of the cladding tube.

The creep rupture data of fuel clad tube is very important in design and for smooth operation of nuclear reactors without fuel pin failure in reactors.

AE is an advanced non-destructive evaluation technique. This technique has been successfully applied for on-line monitoring of creep rupture of the reactor cladding tube which otherwise could be detected by thermocouple readings only.

The recent pandemic of Covid-19 has substantially changed people’s daily lives. They work and interact even more based on information and communication technologies (ICT). The use of new technologies and the interconnectivity specific to smart cities have intensified in the context of the pandemic. A significant part of the population works from home, participates in concerts and other remote social activities, organizes online parties, communicates virtually with friends and family, etc. These transformations required an extended and more stable infrastructure, significant investments in the development of software applications dedicated to remote activities (streaming, contact tracing, security, online ordering and delivery, telemedicine, etc.), in specific services (data storage and applications, electronic signature services, etc.) and the integration of subsystems used in smart cities. This chapter examines the role of SARS-CoV-2 pandemic in the acceleration of digital transformations in smart cities due to the need and desire to digitize communities and public administrations. It has become a top priority for both private and public companies from smart cities in the context created by the pandemic.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

The progress of a nation is quite closely linked with the quality of education it offers its citizens. The onus of nurturing future leaders, the students, lies significantly with higher educational institutions (HEIs) and the academic staff associated with such institutions. Therefore, HEIs need so that these engaged employees may go on to look beyond regular extrinsic motivators to ensure employee engagement to mentor students effectively. In this study, we attempted to investigate the influence of an important predictor of employee engagement, leadership, specifically servant leadership style mediated through job satisfaction. A structured questionnaire was administered to the academic staff of the top 25 universities in India. The data collected and the proposed hypotheses were tested using structural equation modeling. The results confirmed that job satisfaction mediates the relationship between servant leadership and work engagement. The study offers insights into the importance of servant leadership to foster employee engagement and thereby institutional effectiveness in the educational sector.

The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies.

The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy.

The results of the experiment showed that FSC produced the best F₁-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system.

Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness.

This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.

The purpose of this paper is to analyze the long-term impacts of health capital on economic growth in Singapore from 1980 to 2013.

Autoregressive distributed lag (ARDL)-ECM methodology and several diagnostic and specification tests were used to estimate the impact of health capital on economic growth on time series data covering the period 1980-2013.

The results confirm that health capital (measure by health expenditure per capita) positively and significantly affects Singapore’s economic growth in the long run. In addition, the equilibrium error correction coefficient lagged by one in the short-run is approximately 83.25 percent for all estimated variables, implying a considerably high speed of long-term adjustment to equilibrium following a short-term shock. Moreover, the Toda-Yamamoto’s Granger causality estimation reveals that there is a unidirectional causality from health expenditure per capita to GDP per capita.

The findings imply that Singapore’s economic growth could be improved significantly if expenditure on health capital is increased. This eventually would have a substantial impact on human productivity which leads to improved output per capita. Thus, policy makers and/or the government should strive to create institutional capacity to improve basic health service by strengthening the health institutions infrastructure that produces healthy and quality manpower.

Grounded on the premises that there are little or no studies on the impact of health capital on Singapore economy, this paper provides new evidence on the potential effect of health capital on Singapore’s economic growth over the last three decades. Also, this study explore the causal effect (unidirectional or bidirectional) between health capital and economic growth.

The purpose of this paper is to examine the effect of reciprocating compressor speeds and valve conditions on the roor-mean-square (RMS) value of burst acoustic emission (AE) signals associated with the physical motion of valves. The study attempts to explore the potential of AE signal in the estimation of valve damage under varying compressor speeds.

This study involves the acquisition of AE signal, valve flow rate, pressure and temperature at the suction valve of an air compressor with speed varrying from 450 to 800 rpm. The AE signals correspond to one compressor cycle obtained from two simulated valve damage conditions, namely, the single leak and double leak conditions are compared to those of the normal valve plate. To examine the effects of valve conditions and speeds on AE RMS values, two-way analysis of variance (ANOVA) is conducted. Finally, regression analysis is performed to investigate the relationship of AE RMS with the speed and valve flow rate for different valve conditions.

The results showed that AE RMS values computed from suction valve opening (SVO), suction valve closing (SVC) and discharge valve opening (DVO) events are significantly affected by both valve conditions and speeds. The AE RMS value computed from SVO event showed high linear correlation with speed compared to SVC and DVO events for all valve damage conditions. As this study is conducted at a compressor running at freeload, increasing speed of compressor also results in the increment of flow rate. Thus, the valve flow rate can also be empirically derived from the AE RMS value through the regression method, enabling a better estimation of valve damages.

The experimental test rig of this study is confined to a small pressure ratio range of 1.38–2.03 (free-loading condition). Besides, the air compressor is assumed to be operated at a constant speed.

This study employed the statistical methods namely the ANOVA and regression analysis for valve damage estimation at varying compressor speeds. It can enable a plant personnel to make a better prediction on the loss of compressor efficiency and help them to justify the time for valve replacement in future.

The purpose of this paper is to investigate the relationship between monetary policy and economic growth in the light of a developing economy, with the main focus on Malaysia. Primarily, the research will concentrate on the interactions between interest rates, inflation, money supply and growth in GDP, which will serve as the instrument for measuring economic growth.

The research will apply quantitative analysis to determine the relationship between GDP growth and monetary policy instruments, particularly interest rate, money supply and level of inflation. Given the advancement and achievement in econometric analysis and computer software creation, the least-squares estimates analysis will be used to investigate the relationship and significance between these variables.

It is observed that relationship between economic growth and inflation is positive. This entails that a 1 percent change in inflation will result in a 77 percent increase in the level of economic growth in this economy. The linkage between economic growth and interest rates has also been observed to be positive. A positive nexus can be observed between economic growth and money supply. The coefficient value of 0.02 for money supply growth shows that it has the smallest effect on economic growth amongst the variables tested in the model.

Based on the findings of this study, the following recommendations can be made, which could serve as policies instruments for Malaysian economic development. This does not mean that the findings can be generalized for other developing economies.

Observations from the test for economic application significance are based on the signs of the parameters. It was observed that inflation, interest rates and money supply all have a positive relationship with economic growth, which is in line with the a priori expectations. This means that monetary policy has positively affected the economic growth.

The results of the OLS analysis reveal that the monetary policy instruments used for the model demonstrated that monetary policy has a positive relationship with economic growth in Malaysia. A breakdown of the individual monetary policy instruments shows that the interest rate, inflation and money supply all have individual positive relationships with economic growth.

A positive relationship exists between economic growth in Malaysia and all selected monetary instruments, namely, inflation, money supply and interest rate. The results show that the results show that inflation, interest rate and money supply will cause the economy to grow but their contribution to the developments is affected from other policy instruments which are used by the governments.

The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.