Macroprudential policies and CO2 emissions: a comparative analysis of G7 and BRIC countries

This study investigates the impact of macroprudential policies on CO2 emissions in G7 and BRIC countries using country-level panel data from 11 countries, covering the period from 1992 to 2020. The findings indicate that macroprudential policies alleviate CO2 emissions in the sample. Quantile regression results reveal that policies can exacerbate CO2 emissions in countries with high levels of CO2 emissions due to carbon leakage. The positive impact of macroprudential policies on sustainable development can be strengthened by high level of globalisation. Moreover, the influence of macroprudential policies stayed the same based on the basic regression results during the post-global financial crisis (GFC) period, while the impact was positive in the pre-GFC period. Finally, robust tests validated the findings reported in the basic regression model. From this, policymakers should prioritise sustainable economic growth when implementing macroprudential policies and leverage the influence of globalisation to amplify their impact on CO2 emissions. Furthermore, it is crucial to strengthen environmental regulations to prevent carbon leakage that result from industries seeking lenient standards

Content validity-based evaluation criteria system for siting wind-solar plants

The spatial assessment criteria system for hybridizing renewable energy sources, such as hybrid solar-wind farms, is critical in selecting ideal installation sites that maximize benefits, reduce costs, protect the environment, and serve the community. However, a systematic approach to designing indicator systems is rarely used in relevant site selection studies. Therefore, the current paper attempts to present an inclusive framework based on content validity to create an effective criteria system for siting wind-solar plants. To this end, the criteria considered in the related literature are captured, and the top 10 frequent indicators are identified. The Delphi technique is used to subject commonly used factors to expert judgments. Other factors are considered according to expert recommendations. In this context, the assessment tool was a combination of questionnaires and interviews with experts from scientific backgrounds that reflect the measurement target. The item-level content validity index (I-CVI) is applied along with the modified Kappa statistic (k*) to analyze expert ratings and suggestions. The results demonstrate the superiority of 9 and 4 commonly used factors and the suggested factors, respectively. The 13 criteria have achieved high agreement among experts at I-CVIs ≥ 0.78 and k*s > 0.76. The conclusion can be drawn that the modified Kappa statistic used in this analysis has a more significant effect on eliminating irrelevant factors. The current methodology and consequences might pave the way for making informed decisions to locate wind and solar farms

Graft copolymer of tannin and polyvinyl alcohol with acrylic acid for the preparation of hydrophobic biodegradable film

Using CaCl2-H2O2 as the initiator, hydrophobic tannin-acrylic resin (TA) was prepared by grafting acrylic monomers onto tannins through redox reaction. Subsequently, biodegradable TA-polyvinyl alcohol (PVOH) hydrophobic film (TAP) was prepared by blending TA with PVOH at room temperature. The results of Fourier transform infrared spectroscopy (FT-IR) and Electro Spray Ionization Mass Spectroscopy (ESI-MS) showed that polypropylene undergoes graft copolymerization at position 8 of the A-ring in tannin and acrylic monomers. The results of Gel Permeation Chromatography (GPC) showed that the blending of TA resin with PVOH resulted in a higher molecular weight and denser network structure of the film. The preparation process of TAP film is simple, with room temperature curing and no surface pores and cracks. Moreover, the addition of TA transforms hydrophilic PVOH films into hydrophobic films, with a water contact angle of up to 110°. At the same time, TAP films have higher Brinell hardness and elongation at break compared to PVOH film. In addition, thermogravimetric analyzer (TG) and ultraviolet visible light (UV–Vis) measurements showed that the TAP film has good thermal stability and excellent resistance to UV–Vis. More importantly, the TAP film was found to have good biodegradability against Penicillium sp. Scanning electron microscopy (SEM) observed that the TAP film was degraded by Penicillium sp. after 15-days exposure

Enhanced simultaneous voltammetric detection of dopamine and uric acid using Au@Ni-metal–organic framework-modified electrode

A novel conductive Ni‐based metal–organic framework (MOF) decorated with Au nanoparticles (AuNPs; Au@Ni‐MOF) was developed and used as a drop‐cast thin‐film electrode to individually and simultaneously quantify dopamine (DA) and uric acid (UA). The Ni‐MOF composite was synthesized using an in situ growth strategy involving the growth of [Ni3(BTC)2(H2O)6]n (BTC = 1,3,5‐benzenetricarboxylate) with coordinatively unsaturated Ni (II) sites. The Ni‐MOF powder was then mixed with a AuNP solution to produce a Au@Ni‐MOF hybrid material. The synthesized Au@Ni‐MOF hybrid material was employed as a surface modifier for a screen‐printed carbon electrode, and its efficacy for the detection of DA and UA was assessed using differential pulse voltammetry (DPV) and cyclic voltammetry. The developed sensor exhibited remarkable sensitivity, achieving low detection limits of approximately 0.027 and 0.028 μM (S/N = 3) in the simultaneous quantification of DA and UA, respectively. The sensor exhibited an extensive linear range of 0.5 μM to 1 mM, coupled with excellent sensitivities for DA and UA of 1.43 and 1.35 μA μM−1 cm−2, respectively. Furthermore, the sensor performance in human serum and urine samples was successfully validated using DPV, which revealed outstanding recovery rates of 93.8–105.0% with a minimal relative standard deviation below 3%. Moreover, the synthesized Au@Ni‐MOF composite exhibited exceptional dispersion stability, high sensitivity, and remarkable selectivity, which were attributed to the well‐arranged combination of AuNPs and Ni‐MOFs, enabling its use in non‐enzymatic sensing applications targeting DA and UA.</jats:p

Health risk assessment of pollutant emissions from coal-fired power plant: a case study in Malaysia

Coal-fired power plants (CFPPs) are Malaysia’s primary electricity source, but their emissions adversely affect human health, organism growth, climate change, and the environment. The carbon, hydrogen, and sulphur content of coal make it a viable option for electricity generation. However, the by-products from leaching, volatilisation, melting, decomposition, oxidation, hydration, and other chemical reactions significantly negatively impact the environment and human health. This study aims to quantify the emissions from a coal-fired power plant, investigate the interplay between different emissions, simulate the dispersion of emissions, and assess their health impact through a health risk assessment. The results indicate that SO2 is the primary contributor to emissions and its impact on human health is a concern. The health effects, both chronic and acute, are more pronounced in children than in adults. This study combines real-time emissions data and simulations to assess emissions’ health impact, raising awareness about the emissions from coal-fired power plants. Furthermore, the findings can potentially enhance working conditions for employees and promote environmental health

Segmentation of pulmonary cavity in lung CT scan for tuberculosis disease

The complexity of pulmonary tuberculosis (TB) lung cavity lesion features significantly increase the cost of semantic segmentation and labelling. However, the high cost of semantic segmentation has limited the development of TB automatic recognition to some extent. To address this issue, we developed an algorithm that automatically generates a semantic segmentation mask of TB from the TB target detection boundary box. Pulmonologists only need to identify and label the location of TB, and the algorithm can automatically generate the semantic segmentation mask of TB lesions in the labelled area. The algorithm, first, calculates the optimal threshold for separating the lesion from the background region. Then, based on this threshold, the lesion tissue within the bounding box is extracted and forms a mask that can be used for semantic segmentation tasks. Finally, we use the generated TB semantic segmentation mask to train Unet and Vnet models to verify the effectiveness of the algorithm. The experimental results demonstrate that Unet and Vnet achieve mean Dice coefficients of 0.612 and 0.637, respectively, in identifying TB lesion tissue

Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology

Physicochemical impacts on bacterial communities in Putrajaya Lake, Malaysia

This study determines the associations between the bacterial communities and water physicochemical parameters in Putrajaya Lake and Putrajaya Wetlands Park, Malaysia. Bacterial communities were assessed by metagenomics of the 16S rRNA gene from lake water input, central wetlands, and primary lake area. Water samples (n=18) were collected during two different periods: post-high rainfall events (samples collected in May) and dry periods (July). The data revealed that bacterial communities of the three sites were taxonomically distinct and associated with different environmental parameters. However, no significant differences were found between the wet and dry periods. Alpha diversity analyses revealed the highest index in May 2018 in the constructed wetlands (H’= 5.397) than those from water input or primary lake (p<0.05). Overall, 49 phyla, 147 classes, 284 orders, 471 families, 778 genera and 62 species of bacteria were identified. Verrumicrobia and Firmicutes showed a strong positive correlation with ammonia-nitrogen (r = 0.709). Actinobacteria and Cyanobacteria had a moderate positive correlation with nitrate with r value (r = 0.673) and (r = 0.647), respectively. In this study, the metagenomics of the 16S rRNA gene amplicon by Illumina MiSeq has successfully identified the bacterial community assemblage in Putrajaya Lake and wetlands. Bacterial composition was associated with the availability of physicochemical properties of specific sites. The effectiveness of the engineered wetlands of Putrajaya in bioremediation was demonstrated by the marked decrease in certain nutrient concentrations from lake water input to the primary lake area