Validity and reliability of sustainable supply chain management frameworks in Indian smart manufacturing industries

PurposeSustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.Design/methodology/approachValidity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.FindingsThe work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.Research limitations/implicationsThis study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.Originality/valueThe main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

Time-Frequency Analysis of COVID-19 Shocks and Energy Commodities

In a time-frequency biwavelet framework, we analysed the short-, medium-, and long-term impacts of COVID-19-related shocks on ten energy commodities (i.e., Brent, crude oil, coal, heating oil, natural gas, gasoline, ethanol, naphtha, propane, and uranium) from January 2020 to April 2022. We document intervals of high and low coherence between COVID-19 cases and the returns on energy commodities across the short-, medium-, and long-term horizons. Low coherence at high frequencies indicated weak correlation and signified diversification, hedging, and safe-haven potentials in the short term of the pandemic. Our findings suggest that energy markets' dynamics were highly driven by the pandemic, causing significant changes in market returns, particularly across the medium- and low-frequency bands. Furthermore, the empirical results indicate dynamic lead-lag relationships between COVID-19 cases and energy returns between the medium- and long-term horizons, signifying that diversification could be sought through crossinvestment in different energy commodities. The results have significant implications for market participants, regulators, and practitioners.

Understanding the COVID-19 Pandemic's Impact on E-Learner Satisfaction at the Tertiary Level

E-learning has recently gained considerable interest among stakeholders, including educators, students, and policymakers. During the pandemic, organized online learning is critical to an effective e-learning system because it helps both teaching and learning. Thus, the current study intends to explore the factors contributing to e-learners' satisfaction during the COVID-19 pandemic. A questionnaire survey was conducted to gather data from 650 university students selected through convenience sampling. The data were analyzed using the Statistical Package for the Social Sciences (SPSS). The factors essential to boosting e-learner satisfaction were identified using confirmatory factor analysis (CFA). Frequency distribution and percentages were used to identify the demographic characteristics of respondents, and a reliability test was conducted to test the internal consistency of the data. This study employed structural equation modeling (SEM) to trace the relationship between the six independent variables and e-learner satisfaction. Regression results revealed that psychological factors, educational materials and design, access to technological devices, instructor attributes, and perceptions and expectations significantly influence e-learner satisfaction. However, students' engagement had no significant influence on the same. Because, most respondents had a clear preference for physical learning. The findings of this study will help educationists and policymakers take necessary steps in enhancing learners' satisfaction and improving their academic performance.

COVID-19 Detection via Fusion of Modulation Spectrum and Linear Prediction Speech Features

The coronavirus disease 2019 (COVID-19) pandemic has drastically impacted life around the globe. As life returns to pre-pandemic routines, COVID-19 testing has become a key component, assuring that travellers and citizens are free from the disease. Conventional tests can be expensive, time-consuming (results can take up to 48h), and require laboratory testing. Rapid antigen testing, in turn, can generate results within 15-30 minutes and can be done at home, but research shows they achieve very poor sensitivity rates. In this paper, we propose an alternative test based on speech signals recorded at home with a portable device. It has been well-documented that the virus affects many of the speech production systems (e.g., lungs, larynx, and articulators). As such, we propose the use of new modulation spectral features and linear prediction analysis to characterize these changes and design a two-stage COVID-19 prediction system by fusing the proposed features. Experiments with three COVID-19 speech datasets (CSS, DiCOVA2, and Cambridge subset) show that the two-stage feature fusion system outperforms the benchmark systems of CSS and Cambridge datasets while maintaining lower complexity compared to DL-based systems. Furthermore, the two-stage system demonstrates higher generalizability to unseen conditions in a cross-dataset testing evaluation scheme. The generalizability and interpretability of our proposed system demonstrate the potential for accessible, low-cost, at-home COVID-19 testing. IEEE

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Comparing media coverage of the pharmaceutical industry: pre- and post-pandemic lockdown

PurposeThe purpose of this paper is to analyze media coverage of the pharmaceutical industry before and after the COVID-19 lockdown to determine whether the coverage changed in light of a global health-care crisis and the fast-track development of vaccines and antiviral treatments.Design/methodology/approachThe top five US newspapers were audited, comparing the 12-month periods before and after March 2020 coinciding with the pandemic lockdown, yielding 493 front-page articles and editorials. Each headline and full-text article was separately analyzed and categorized as either positive, negative or neutral toward the pharmaceutical industry. A frequency analysis of the hot button issues covered in each article was conducted.FindingsYear 1 and Year 2 audit results were compared to identify changes in media coverage pre- and post-lockdown. The amount of coverage of the industry increased 145% and the tone of both headlines and articles shifted dramatically. Only one of the five newspapers had a net positive article rating of the industry pre-lockdown, four of five were net positive post-lockdown. The proportion of positive headlines increased 165%. The top issues discussed in the coverage shifted from persistent challenges for the industry (e.g. opioid crisis, high cost of drugs) to the emergence of the virus and status of vaccine development.Originality/valueThis research establishes how media coverage of the pharmaceutical industry changed as the industry responded to a global health-care crisis and identifies implications for industry stakeholders.

Dynamic Frequency Analysis of the Jordanian Power System with Significant Penetration of Renewables: Lessons Learnt from the COVID-19 Lockdowns

The increasing dependence on renewable energy particularly solar Photovoltaic (PV) to supply energy consumption needs in Jordan has placed operational challenges on the power system operator to cope with the significant drop in the system's net-demand and the reduction in synchronous inertia. These challenges were not expected to become critical until the penetration of renewables increases to meet future national energy targets in the forthcoming years. However, the adoption of lockdowns to restrict the outbreak of COVID-19 combined with PV injections reduced the system's net-demand particularly during daytime in spring 2020 like expected levels in the future with high PV penetration. Thus, the implications of future significant penetration of renewables on system security could be better understood based on the operating conditions during lockdowns. In particular, it is important to assess the system's frequency adequacy during emergency events that might be occurred whilst running a low-inertia power system. To do so, this paper provides a detailed dynamic frequency analysis of the Jordanian power system during lockdowns using Power Factory software. The results highlight the importance of energy curtailment of renewables to maintain adequate level of synchronous inertia to maintain security when the system is islanded without interconnections to neighboring countries. However, deciding the proper level of curtailment requires performing dynamic analysis to ensure that both the Rate of Change of Frequency (RoCoF) and the minimum frequency level during generation contingency events will not trigger the Under Frequency Load Shedding (UFLS) relays. © 2022 IEEE.

Improving societal benefit through transformative consumer research: A descriptive review

This paper assesses the societal benefit of transformative consumer research (TCR) through a descriptive review of articles published in the special issues associated with the TCR conferences, held every two years from 2009 to 2021. Frequency analysis and directed content analysis reveal (1) various societal benefits (knowledge, enhanced awareness, capacity building, and recommendations for improvement or redirection), (2) facilitators and barriers for solution implementation (e.g., definition of a problem-solving orientation to research problems, collaboration with practitioners, and testing and refining of solutions), and (3) well-being issues over time (e.g., climate change and sustainability education, food wastage reduction, healthy food consumption and production, and vulnerability during the COVID-19 pandemic). This research contributes to the literature in four ways by (1) synthesizing the types and natures of societal benefits of TCR, (2) identifying the most discussed consumer well-being issues over time, (3) assessing the barriers and facilitators for the implementation of solutions that influence societal benefits, and (4) providing a research agenda for improving consumer well-being and enhancing societal benefit. © 2023 Elsevier Inc.

Continuous Wavelet Transform of Time-Frequency Analysis Technique to Capture the Dynamic Hedging Ability of Precious Metals

Exploring the hedging ability of precious metals through a novel perspective is crucial for better investment. This investigation applies the wavelet technique to study the complicated correlation between global economic policy uncertainty (GEPU) and the prices of precious metals. The empirical outcomes suggest that GEPU exerts positive influences on the prices of precious metals, indicating that precious metals could hedge against global economic policy uncertainty, which is supported by the inter-temporal capital asset pricing model (ICAPM). Among them, gold is better for long-term investment than silver, which is more suitable for the short run in recent years, while platinum's hedging ability is virtually non-existent after the global trade wars. Conversely, the positive influences from gold price on GEPU underline that the gold market plays a prospective role in the situation of economic policies worldwide, which does not exist in the silver market. Besides, the effects of platinum price on GEPU change from positive to negative, suggesting that the underlying cause of its forward-looking effect on GEPU alters from the investment value to the industrial one. In the context of the increasing instability of global economic policies, the above conclusions could offer significant lessons to both investors and governments.

Cardiac Studies Diagnostic Data Informative Features Investigation based on Cumulative Frequency Analysis

The development of information technology in the modern world affects the public health sector on the one hand and accumulates enormous amounts of data on the other hand. The global COVID-19 pandemic has contributed to the digitalization of healthcare. Heart disease is a global problem that causes death worldwide. Therefore, this study proposes a model for determining the information content of signs of diagnostic data of heart diseases based on the cumulative frequency method. The software implementation of the model has been completed. A database of 303 patients, consisting of 14 attributes, was used for the experiments. As a result of the model's work, the features with the most significant information content were identified. The study is promising and can apply diagnostic models in public health practice. ©2022 Copyright for this paper by its authors.

The use of emoji to establish student wellbeing: does the image reflect the reality?

Introduction: The impact of COVID-19 social restrictions on mental wellbeing of health professional students during placement is largely unknown. Conventional survey methods do not capture emotional fluctuations. Increasing use of smartphones suggests short message service (SMS) functionality could provide easy, rapid data. This project tested the feasibility and validity of gathering data on Therapeutic Radiography student mental wellbeing during clinical placement via emoji and SMS. Methods: Participants provided anonymous daily emoji responses via WhatsApp to a dedicated mobile phone. Additional weekly prompts sought textual responses indicating factors impacting on wellbeing. A short anonymous online survey validated responses and provided feedback on the method. Results: Participants (n = 15) provided 254 daily responses using 108 different emoji;these triangulated with weekly textual responses. Feedback concerning the method was positive. 'Happy' emoji were used most frequently;social interaction and fatigue were important wellbeing factors. Anonymity and opportunity to feedback via SMS were received positively;ease and rapidity of response engendered engagement throughout the 3-week study. Conclusions: The use of emoji for rapid assessment of cohort mental wellbeing is valid and potentially useful alongside more formal evaluation and support strategies. Capturing simple wellbeing responses from a cohort may facilitate the organisation of timely support interventions. © The Author(s), 2022. Published by Cambridge University Press.

The nexus between oil and airline stock returns: Does time frequency matter?

This paper investigates the relationship between oil and airline stock returns under different time frequencies. First, we propose an Autoregressive moving average model with mixed frequency exogenous variable to analyse the different impacts of oil on airline stock returns on daily, weekly, and monthly basis. We consistently find a negative oil-airline stock return nexus on a daily basis, but a positive relationship on a weekly basis. While the former supports the economic-based channel, the latter is in line with the market inertia channel. Our findings help explain mixed results reported in the literature. Further, our time frequency connectedness analysis shows that the economic-based channel dominates the market inertia channel since the connectedness is more pronounced in the short-run compared to the medium- and long-run. Our block connectedness results highlight that business models of airline firms can play a significant role in affecting the connectedness, in which the low-cost airlines are more sensitive to the oil price changes. It is worth noting that there are distinguished drivers of the oil-airline stock return nexus in different time frequencies. The drivers also vary between the Global Financial Crisis and the COVID-19 pandemic. Our results are consistent under a battery of robustness checks and deliver important implications to investors, portfolio managers, and executives of airline firms. © 2022 Elsevier B.V.

Time-frequency correlations and extreme spillover effects between carbon markets and NFTs: The roles of EPU and COVID-19

This paper examines the correlations and spillover effects between carbon markets and NFTs, and explores the roles of EPU and COVID-19, utilizing the rolling window wavelet correlation and the quantile frequency connectedness approach. We find, first, strong correlations between returns mainly exist in the long term. Second, the extreme volatility spillover in the carbon-NFT system is greater and faster than in normal case. Third, major international events increase the total connectedness of the system. Fourth, COVID-19 inhibits carbon-NFTs' extreme spillover effect, while China's EPU has positive impacts. Our results also provide valuable references and policy implications for investors and policymakers.

Do the green bonds overreact to the COVID-19 pandemic?

This paper explores the impacts of the COVID-19 pandemic on the global green bond and conventional assets, including commodity, treasury, stock and clean energy markets, using Diebold and Yilmaz (2012) and Baruník and Krehlík, 2018b spillover framework. The results show that spillover transmitted from COVID-19 is relatively strong over a medium- and long-term horizon, and the spillover effects sharply increased when the pandemic became severe. Furthermore, green bonds are most affected by the COVID-19 pandemic, followed by the treasury, while the other conventional assets are only slightly affected. Additionally, our findings also contain a low-risk portfolio during COVID-19 pandemic.

Internet of Things: A systematic Literature Review

Internet of things (IoT) is an emerging technology that is being used widely. The literature has no agreement regarding the factors that affect the adoption of IoT. The purpose of this study is to review the literature systematically using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Following this method, 69 articles were included in this review. A frequency analysis was conducted. The findings showed that number of articles reduced during COVID19. Higher education has the highest numbers of articles. Emerging economies are active in research about IoT. Technology acceptance model (TAM) is still the dominant adoption theory with majority of the reviewed articles are using quantitative method and large sample size to meet the assumption of using structural equation modeling. The most important predictors are the perceived usefulness, perceived ease of use, social influence, privacy, security, and trust. Other factors also included the variables of UTAUT. Decision makers are recommended to focus on usefulness and simplifies the process of using IoT as well as to create awareness about the application of IoT. Future studies are recommended to narrow the scope to one industry and to conduct more studies using mixed method or qualitative approach. More studies in developing countries are needed to explain the adoption of IoT. © 2022 Slovene Society Informatika. All rights reserved.

Have Patients' Attitudes towards Doctors Changed with COVID-19? Insights from Online Doctor Reviews in Beijing, China

The COVID-19 pandemic has brought many challenges, including doctor-patient relationships (DPRs). This study aims to investigate the change in patients' attitudes towards doctors during the pandemic. We collected 24,000 reviews in Beijing, China from an online health platform, with half of the data from 2019 and another half from 2020. These data were compared using sentiment and word frequency analyses. Results show that the number of negative reviews has reduced significantly, and the salient topics of negative reviews have shifted from doctor-related to administration-related. These findings suggest that the DPRs have improved but there is room for managerial improvements in the health sector. © 2022 ACM.

COVID-19 Vaccination Policy: A Study on Street Level Bureaucracy and Local Government

The explain relationship between health workers and local governments, to accelerate vaccination and its consequences for achieving Herd immunity in Indonesia. This article describes how local governments and street-level bureaucracies support the implementation of policies to accelerate COVID-19 vaccination. This research is based on the cluster analysis feature and the word frequency analysis feature on the NVivo-12 software based on reliable online news data. The results show that health workers and local governments place stigma and the front line, equipment support, and protection as dominant factors in implementing vaccination policies. Likewise, the community has a relative advantage after getting coordination, communication, and education about the benefits of vaccination. From an online media perspective, the implication is to offer insight into the unique dynamics between street-level bureaucrats and local government. It also allows us to investigate its contribution to policy outcomes as applied at the street level.