Universiti Tunku Abdul Rahman

BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. METHODS: We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). FINDINGS: Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6-58·8) of global deaths and 41·2% (39·8-42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. INTERPRETATION: Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. FUNDING: Bill & Melinda Gates Foundation.

BACKGROUND: Many causes of vision impairment can be prevented or treated. With an ageing global population, the demands for eye health services are increasing. We estimated the prevalence and relative contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020. We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error. METHODS: We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals [UIs]) of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness (<3/60 or less than 10° visual field around central fixation) by cause, age, region, and year. Because of data sparsity at younger ages, our analysis focused on adults aged 50 years and older. FINDINGS: Global crude prevalence of avoidable vision impairment and blindness in adults aged 50 years and older did not change between 2010 and 2019 (percentage change -0·2% [95% UI -1·5 to 1·0]; 2019 prevalence 9·58 cases per 1000 people [95% IU 8·51 to 10·8], 2010 prevalence 96·0 cases per 1000 people [86·0 to 107·0]). Age-standardised prevalence of avoidable blindness decreased by -15·4% [-16·8 to -14·3], while avoidable MSVI showed no change (0·5% [-0·8 to 1·6]). However, the number of cases increased for both avoidable blindness (10·8% [8·9 to 12·4]) and MSVI (31·5% [30·0 to 33·1]). The leading global causes of blindness in those aged 50 years and older in 2020 were cataract (15·2 million cases [9% IU 12·7-18·0]), followed by glaucoma (3·6 million cases [2·8-4·4]), undercorrected refractive error (2·3 million cases [1·8-2·8]), age-related macular degeneration (1·8 million cases [1·3-2·4]), and diabetic retinopathy (0·86 million cases [0·59-1·23]). Leading causes of MSVI were undercorrected refractive error (86·1 million cases [74·2-101·0]) and cataract (78·8 million cases [67·2-91·4]). INTERPRETATION: Results suggest eye care services contributed to the observed reduction of age-standardised rates of avoidable blindness but not of MSVI, and that the target in an ageing global population was not reached. FUNDING: Brien Holden Vision Institute, Fondation Théa, The Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg.

IMPORTANCE: Cancer is among the leading causes of death worldwide. Current estimates of cancer burden in individual countries and regions are necessary to inform local cancer control strategies. OBJECTIVE: To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 28 cancers in 188 countries by sex from 1990 to 2013. EVIDENCE REVIEW: The general methodology of the Global Burden of Disease (GBD) 2013 study was used. Cancer registries were the source for cancer incidence data as well as mortality incidence (MI) ratios. Sources for cause of death data include vital registration system data, verbal autopsy studies, and other sources. The MI ratios were used to transform incidence data to mortality estimates and cause of death estimates to incidence estimates. Cancer prevalence was estimated using MI ratios as surrogates for survival data; YLDs were calculated by multiplying prevalence estimates with disability weights, which were derived from population-based surveys; YLLs were computed by multiplying the number of estimated cancer deaths at each age with a reference life expectancy; and DALYs were calculated as the sum of YLDs and YLLs. FINDINGS: In 2013 there were 14.9 million incident cancer cases, 8.2 million deaths, and 196.3 million DALYs. Prostate cancer was the leading cause for cancer incidence (1.4 million) for men and breast cancer for women (1.8 million). Tracheal, bronchus, and lung (TBL) cancer was the leading cause for cancer death in men and women, with 1.6 million deaths. For men, TBL cancer was the leading cause of DALYs (24.9 million). For women, breast cancer was the leading cause of DALYs (13.1 million). Age-standardized incidence rates (ASIRs) per 100 000 and age-standardized death rates (ASDRs) per 100 000 for both sexes in 2013 were higher in developing vs developed countries for stomach cancer (ASIR, 17 vs 14; ASDR, 15 vs 11), liver cancer (ASIR, 15 vs 7; ASDR, 16 vs 7), esophageal cancer (ASIR, 9 vs 4; ASDR, 9 vs 4), cervical cancer (ASIR, 8 vs 5; ASDR, 4 vs 2), lip and oral cavity cancer (ASIR, 7 vs 6; ASDR, 2 vs 2), and nasopharyngeal cancer (ASIR, 1.5 vs 0.4; ASDR, 1.2 vs 0.3). Between 1990 and 2013, ASIRs for all cancers combined (except nonmelanoma skin cancer and Kaposi sarcoma) increased by more than 10% in 113 countries and decreased by more than 10% in 12 of 188 countries. CONCLUSIONS AND RELEVANCE: Cancer poses a major threat to public health worldwide, and incidence rates have increased in most countries since 1990. The trend is a particular threat to developing nations with health systems that are ill-equipped to deal with complex and expensive cancer treatments. The annual update on the Global Burden of Cancer will provide all stakeholders with timely estimates to guide policy efforts in cancer prevention, screening, treatment, and palliation.

BACKGROUND: To contribute to the WHO initiative, VISION 2020: The Right to Sight, an assessment of global vision impairment in 2020 and temporal change is needed. We aimed to extensively update estimates of global vision loss burden, presenting estimates for 2020, temporal change over three decades between 1990-2020, and forecasts for 2050. METHODS: We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. Only studies with samples representative of the population and with clearly defined visual acuity testing protocols were included. We fitted hierarchical models to estimate 2020 prevalence (with 95% uncertainty intervals [UIs]) of mild vision impairment (presenting visual acuity ≥6/18 and <6/12), moderate and severe vision impairment (<6/18 to 3/60), and blindness (<3/60 or less than 10° visual field around central fixation); and vision impairment from uncorrected presbyopia (presenting near vision <N6 or <N8 at 40 cm where best-corrected distance visual acuity is ≥6/12). We forecast estimates of vision loss up to 2050. FINDINGS: In 2020, an estimated 43·3 million (95% UI 37·6-48·4) people were blind, of whom 23·9 million (55%; 20·8-26·8) were estimated to be female. We estimated 295 million (267-325) people to have moderate and severe vision impairment, of whom 163 million (55%; 147-179) were female; 258 million (233-285) to have mild vision impairment, of whom 142 million (55%; 128-157) were female; and 510 million (371-667) to have visual impairment from uncorrected presbyopia, of whom 280 million (55%; 205-365) were female. Globally, between 1990 and 2020, among adults aged 50 years or older, age-standardised prevalence of blindness decreased by 28·5% (-29·4 to -27·7) and prevalence of mild vision impairment decreased slightly (-0·3%, -0·8 to -0·2), whereas prevalence of moderate and severe vision impairment increased slightly (2·5%, 1·9 to 3·2; insufficient data were available to calculate this statistic for vision impairment from uncorrected presbyopia). In this period, the number of people who were blind increased by 50·6% (47·8 to 53·4) and the number with moderate and severe vision impairment increased by 91·7% (87·6 to 95·8). By 2050, we predict 61·0 million (52·9 to 69·3) people will be blind, 474 million (428 to 518) will have moderate and severe vision impairment, 360 million (322 to 400) will have mild vision impairment, and 866 million (629 to 1150) will have uncorrected presbyopia. INTERPRETATION: Age-adjusted prevalence of blindness has reduced over the past three decades, yet due to population growth, progress is not keeping pace with needs. We face enormous challenges in avoiding vision impairment as the global population grows and ages. FUNDING: Brien Holden Vision Institute, Fondation Thea, Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg.

Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.

Introduction: Several vaccine candidates are being clinically tested in response to the 2019 coronavirus disease (COVID-19) pandemic. This study was conducted to assess the acceptance of a 50% or 95% effective COVID-19 vaccine, when it becomes available in southeast Asia, among the general population in Indonesia. Methods: A cross-sectional online survey was conducted between March 25 and April 6, 2020. Participants were asked if they would accept a free vaccine which was 95% or 50% effective. Using a logistic regression model, we assessed the associations between sociodemographic characteristics, exposure to COVID-19 information, or perceived risk of infection and acceptance of a hypothetical COVID-19 vaccine. Results: Among 1,359 respondents, 93.3% of respondents (1,268/1,359) would like to be vaccinated for a 95% effective vaccine, but this acceptance decreased to 67.0% (911/1,359) for a vaccine with 50% effectiveness. For a 95% effective vaccine, being a healthcare worker and having higher perceived risk of COVID-19 infection were associated with higher acceptance, adjusted odds ratio (aOR): 2.01; 95%CI: 1.01, 4.00 and aOR: 2.21; 95%CI: 1.07, 4.59, respectively; compared to civil servants, being retired was associated with less acceptance (aOR: 0.15; 95%CI: 0.04, 0.63). For a 50% effective vaccine, being a healthcare worker was also associated with greater acceptance, aOR: 1.57; 95%CI: 1.12, 2.20. Conclusion: Acceptance of a COVID-19 vaccine was highly influenced by the baseline effectiveness of the vaccine. Preparing the general population to accept a vaccine with relatively low effectiveness may be difficult.

The current rapid growth in the economy and the patterns of consumers’ consumption and behavior worldwide are the main cause of environmental deterioration. As the environment continues to worsen, it has become a persistent public concern in the developed countries and has recently awakens developing countries to the green movement. This paper is essentially exploratory in nature and has two objectives. The first objective is to compare gender with attitudes towards the environment and green products. The second objective is to investigate the relationship between attitude towards the environment and green products. Result from the independent sample t-test shows that there were no significant differences between gender in their environmental attitudes and attitudes on green products. The rotated factor matrix validated the underlying dimensions of environmental attitudes into three major dimensions (environmental protection, government’s role, and personal norm). Results from the multiple linear regression analysis revealed that consumer attitudes on the government’s role and their personal norm towards the environment contributed significantly to their attitude on green product. Further investigation revealed that personal norm was the most important contributor to the attitude towards green product. However, environmental protection did not contribute significantly to consumers’ attitudes on green product.Keywords: Environmental attitude; green products; consumer behaviour; Malaysia

Purpose Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria. Design/methodology/approach A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field. Findings The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM. Originality/value This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

Silver nanoparticles (AgNPs) used in this study were synthesized using pu-erh tea leaves extract with particle size of 4.06 nm. The antibacterial activity of green synthesized silver nanoparticles against a diverse range of Gram-negative foodborne pathogens was determined using disc diffusion method, resazurin microtitre-plate assay (minimum inhibitory concentration, MIC), and minimum bactericidal concentration test (MBC). The MIC and MBC of AgNPs against Escherichia coli, Klebsiella pneumoniae, Salmonella Typhimurium, and Salmonella Enteritidis were 7.8, 3.9, 3.9, 3.9 µg/mL and 7.8, 3.9, 7.8, 3.9 µg/mL respectively. Time−kill curves were used to evaluate the concentration between MIC and bactericidal activity of AgNPs at concentrations ranging from 0× MIC to 8× MIC. The killing activity of AgNPs was fast acting against all the Gram-negative bacteria tested; the reduction in the number of CFU mL−1 was >3 Log10 units (99.9%) in 1-2 h. This study indicates that AgNPs exhibit a strong antimicrobial activity and thus might be developed as a new type of antimicrobial agents for the treatment of bacterial infection including multidrug resistant bacterial infection.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

Mesenchymal stem cells (MSCs) can be widely isolated from various tissues including bone marrow, umbilical cord, and adipose tissue, with the potential for self-renewal and multipotent differentiation. There is compelling evidence that the therapeutic effect of MSCs mainly depends on their paracrine action. Extracellular vesicles (EVs) are fundamental paracrine effectors of MSCs and play a crucial role in intercellular communication, existing in various body fluids and cell supernatants. Since MSC-derived EVs retain the function of protocells and have lower immunogenicity, they have a wide range of prospective therapeutic applications with advantages over cell therapy. We describe some characteristics of MSC-EVs, and discuss their role in immune regulation and regeneration, with emphasis on the molecular mechanism and application of MSC-EVs in the treatment of fibrosis and support tissue repair. We also highlight current challenges in the clinical application of MSC-EVs and potential ways to overcome the problem of quality heterogeneity.

Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored. Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem. The two main approaches used to mitigate multicollinearity are variable selection methods and modified estimator methods. However, variable selection methods may negate efforts to collect more data as new data may eventually be dropped from modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to better mitigate multicollinearity.

The advancement of the World Wide Web has resulted in the creation of a new form of retail transactions- electronic retailing (e-tailing) or web-shopping. Thus, customers’ involvements in online purchasing have become an important trend. As such, it is vital to identify the determinants of the customer online purchase intention. The aim of this research is to evaluate the impacts of shopping orientations, online trust and prior online purchase experience to the customer online purchase intention. A total of 242 undergraduate information technology students from a private university in Malaysia participated in this research. The findings revealed that impulse purchase intention, quality orientation, brand orientation, online trust and prior online purchase experience were positively related to the customer online purchase intention.

Purpose This study aims to empirically examine the factors that affect the consumer intention to use (IU) mobile commerce (m‐commerce) in Malaysia. The five factors examined in this study are perceived usefulness (PU), perceived ease‐of‐use (PEOU), social influence (SI), perceived cost and trust. Design/methodology/approach The study sample consists of 222 respondents with a response rate of 84.09 per cent. Data were analyzed by employing correlation and multiple regression analysis. Findings The findings revealed that PU, SI, perceived financial cost and trust are positively associated with consumer IU m‐commerce in Malaysia. In addition, PEOU and trust were found to have an insignificant effect on consumer IU m‐commerce in Malaysia. Research limitations/implications The generalizability of the findings is limited as the study focuses only on Malaysia. Practical implications Based on the findings, companies involved in m‐commerce should focus on improving the usefulness of the system, trust (i.e. security and privacy protection) and reducing the cost of m‐commerce services to improve the adoption of m‐commerce. Originality/value The findings made a contribution in terms of allowing us to understand the factors that can contribute to the adoption of mobile commerce. This study successfully extend the TAM model in the context of mobile commerce by incorporating one trust‐based construct (trust), one behavioural control construct (perceived financial cost) and one subjective norm construct (SI). This extended TAM model provides a greater understanding of user acceptance of mobile commerce in Malaysia.

Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated; Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models’ performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm.

The richness of high-value bio-compounds derived from microalgae has made microalgae a promising and sustainable source of useful product. The present work starts with a review on the usage of open pond and photobioreactor in culturing various microalgae strains, followed by an in-depth evaluation on the common harvesting techniques used to collect microalgae from culture medium. The harvesting methods discussed include filtration, centrifugation, flocculation, and flotation. Additionally, the advanced extraction technologies using ionic liquids as extractive solvents applied to extract high-value bio-compounds such as lipids, carbohydrates, proteins, and other bioactive compounds from microalgae biomass are summarized and discussed. However, more work needs to be done to fully utilize the potential of microalgae biomass for the application in large-scale production of biofuels, food additives, and nutritive supplements.

Scientific studies have demonstrated that it is possible to generate a wide variety of bioenergy from biomass residues and waste, and however its cost is not competitive with petro-fuels and other renewable energy. On-going efforts are continued extensively to improve conversion technologies in order to reduce production costs. The present review focuses on the conversion technologies for transforming biomass residues and waste to biofuels, specifically their technological concepts, options and prospects for implementation are addressed. The emerging developments in the two primary conversion pathways, namely the thermochemical (i.e. gasification, liquefaction, and pyrolysis) and biochemical (i.e. anaerobic digestion, alcoholic fermentation and photobiological hydrogen production) conversion techniques, are evaluated. Additionally, transesterification, which appears to be the simplest and most economical route to produce biodiesel in large quantity, is discussed. Lastly, the strategies for direct conversion of biomass residues and waste to bioelectricity including the use of combustion and microbial fuel cells are reviewed.

This study explored the correlates of climate anxiety in a diverse range of national contexts. We analysed cross-sectional data gathered in 32 countries (N = 12,246). Our results show that climate anxiety is positively related to rate of exposure to information about climate change impacts, the amount of attention people pay to climate change information, and perceived descriptive norms about emotional responding to climate change. Climate anxiety was also positively linked to pro-environmental behaviours and negatively linked to mental wellbeing. Notably, climate anxiety had a significant inverse association with mental wellbeing in 31 out of 32 countries. In contrast, it had a significant association with pro-environmental behaviour in 24 countries, and with environmental activism in 12 countries. Our findings highlight contextual boundaries to engagement in environmental action as an antidote to climate anxiety, and the broad international significance of considering negative climate-related emotions as a plausible threat to wellbeing.

Purpose – The purpose of this paper is to uncover the effects of perceived transaction convenience (PTC) and perceived transaction speed (PTS) on unified theory of acceptance and use of technology (UTAUT) in the context of m-payment. Design/methodology/approach – A predictive analysis approach was used to examine the PTC and PTS using a two-stage partial least square (PLS) and neural network (NN) analyses. Findings – The findings reveal that only effort expectancy (EE) and facilitating conditions (FC) were discovered to significantly influence BI. More importantly, PTC was found to have positive significant relationship with EE and performance expectancy (PE). Moreover, PTS also supported the positive relationship with BI and EE. Practical implications – The findings of the study provided further insights to mobile payment service providers, online banking industry players, and all decision makers and stakeholders involved. Originality/value – Despite of many attempts devoted to understand m-payment adoption, the effects of PTC and PTS on m-payment are not well understood.

Commercial and environmental benefits have made carbon dioxide (CO 2 ) methanation one of the topmost research projects all over the world both at the pilot plant and commercial scale. Mitigation of CO 2 via carbon capture and storage (CCS) routes have less motivation from a commercial point of view. Therefore, an integrated system is of paramount importance to convert CO 2 into value-added products such as methane (CH 4 ) using solar energy (photosynthesis) or surplus electrical energy in hydrolysis for production of reactant hydrogen to use in CO 2 methanation. To date, great efforts have been made to investigate both the reaction mechanism and catalysts development for methanation. Here in this review, up to date references have been cited, which are aimed at giving researchers a comprehensive overview of CO 2 methanation with respect to the recent advancements in reaction mechanism, catalytic materials, and the novel combination of metal active phase and its synergy. Both thermochemical and electrochemical routes of CO 2 methanation have been discussed, mainly focusing on thermochemical routes. Among the two routes, the thermochemical route seems to be a promising technique for producing an energy carrier due to the high selectivity of CH 4 .