Secondary attack rates and determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) household transmission in Pakistan: A case-ascertained prospective, longitudinal study

BACKGROUND

Households are considered ideal settings for studying the transmission dynamics of an infectious disease.

METHODS

A prospective study was conducted, based on the World Health Organization FFX protocol from October 2020 to January,2021. Household contacts of laboratory-confirmed index cases were followed up for their symptomatic history, nasal swabs for RT-PCR,and blood samples for anti-SARS CoV-2 antibodies were collected at enrollment and days 7, 14 and 28. We estimated secondary attack rate (SAR), effective household case cluster size and determinants of secondary infection among susceptible household contacts using multivariable logistic regression.

RESULTS

We enrolled 77 index cases and their 543 contacts. Out of these, 252 contacts were susceptible at the time of enrollment. There were 77 household clusters, out of which, transmission took place in 20 (25.9%) giving rise to 34 cases. The acquired secondary attack rate (SAR) was 14.0% (95% CI 9.0-18.0). The effective household case cluster size was 0.46 (95%CI 0.33,0.56). Reported symptoms of nausea and vomiting (aOR, 7.9; 95% CI, 1.4-45.5) and fatigue (aOR, 9.3; 95% CI, 3.8-22.7) were associated with SARS-CoV-2 transmission.

CONCLUSIONS

We observed a low SARS-CoV-2 secondary attack rate in the backdrop of high seroprevalence and asymptomatic transmission among households in Karachi, Pakistan.

Assessing the role of green investments and green innovation in ecological sustainability: From a climate action perspective on European countries

In recent years, economies have been increasingly focused on achieving the United Nations' Sustainable Development Goals, recognizing that their achievement is vital to ecological sustainability and green growth. In this context, this paper focuses on investigating the impact of green innovation, green investment, economic growth, and natural resources on ecological sustainability in the five best-performing European Union countries in terms of the Climate Change Performance Index. This study uses the load capacity factor as a comprehensive proxy of ecological sustainability and also assesses the load capacity curve hypothesis in sample nations. Continuously updated fully modified and continuously updated bias-corrected estimators are used to analyze the data from 1995 to 2020 in the context of climate action perspective. The econometric analysis revealed that the load capacity curve hypothesis is invalid in the sample countries. Natural resources decrease environmental sustainability. However, green investments and green innovations contribute to environmental quality and thereby, can be used for effective climate action. Based on these findings, the study recommends specific policies to achieve the Sustainable Development Goals, with a particular focus on target 13.

Fostering innovation and sustainable thinking in surgery: an evaluation of a surgical hackathon

INTRODUCTION

Surgery represents a major source of carbon emissions, with numerous initiatives promoting more sustainable practices. Healthcare innovation and the development of a digitally capable workforce are fundamental in leveraging technologies to tackle challenges, including sustainability in surgery.

METHODS

A surgical hackathon was organised with three major themes: (1) how to make surgery greener, (2) the future of plastic surgery in 10 years, and (3) improving healthcare outcomes using machine learning. Lectures were given on sustainability and innovation using the problem, innovation, market size, strategy and team (PIMST) framework to support their presentations, as well as technological support to translate ideas into simulations or minimum viable products. Pre- and post-event questionnaires were circulated to participants.

RESULTS

Most attendees were medical students (65%), although doctors and engineers were also present. There was a significant increase in delegates' confidence in approaching innovation in surgery (+20%, p < 0.001). Reducing waste packaging (70%), promoting recyclable material usage (56%) and the social media dimension of public perceptions towards plastic surgery (40%) were reported as the most important issues arising from the hackathon. The top three prizes went to initiatives promoting an artificial intelligence-enhanced operative pathway, instrument sterilisation and an educational platform to teach students research and innovation skills.

CONCLUSIONS

Surgical hackathons can result in significant improvements in confidence in approaching innovation, as well as raising awareness of important healthcare challenges. Future innovation events may build on this to continue to empower the future workforce to leverage technologies to tackle healthcare challenges such as sustainability.

First Measurement Using Elliptically Polarized Photons of the Double-Polarization Observable E for γp→pπ^{0} and γp→nπ^{+}

We report the measurement of the helicity asymmetry E for the pπ^{0} and nπ^{+} final states using, for the first time, an elliptically polarized photon beam in combination with a longitudinally polarized target at the Crystal Ball experiment at MAMI. The results agree very well with data that were taken with a circularly polarized photon beam, showing that it is possible to simultaneously measure polarization observables that require linearly (e.g., G) and circularly polarized photons (e.g., E) and a longitudinally polarized target. The new data cover a photon energy range 270-1400 MeV for the pπ^{0} final state (230-842 MeV for the nπ^{+} final state) and the full range of pion polar angles, θ, providing the most precise measurement of the observable E. A moment analysis gives a clear observation of the pη cusp in the pπ^{0} final state.

Linking clean energy consumption, globalization, and financial development to the ecological footprint in a developing country: Insights from the novel dynamic ARDL simulation techniques

Developing countries have been facing economic difficulties for over three and a half decades due to numerous factors, including fossil fuel consumption and dwindling biocapacity. It is necessary to pinpoint the factors that may be culpable for poor environmental quality leading to a rising ecological footprint (EFP). This study explores the effect of clean energy, financial development (FDV), and globalization on the EFP in a developing country using the novel dynamic ARDL simulation techniques and the bootstrap causality test. The findings suggest that green energy has no meaningful impact on the EFP. Globalization and FDV significantly reduce the EFP by 0.25% and 0.08%, respectively. Besides, the findings confirm the existence of the EKC hypothesis. Furthermore, the causality results affirm a unidirectional causality from globalization and FDV to EFP, while economic growth drives globalization. Also, a one-way causality flows from globalization to FDV, just as FDV Granger causes green energy. In line with the findings, the study recommends that public policies focus on funding environmental-friendly technologies and green innovations. The funding must be on recently developed energy-saving technologies that can ensure complementarity between increased economic growth and environmental deterioration.

Drug-Induced Autoimmune Hepatitis: An Unusual Adverse Event of Atorvastatin Therapy

Drug-induced autoimmune hepatitis (AIH) is characterized by acute or chronic hepatic injury coupled with autoantibody development, hypertransaminasemia, and idiopathic AIH features on liver biopsy. Atorvastatin-induced AIH is a rare but well-documented life-threatening adverse event. We report a case of atorvastatin-induced AIH in a 57-year-old female who presented with worsening fatigue, jaundice, and deranged liver function tests. She had been prescribed atorvastatin 20 mg daily three months prior. Her clinical presentation, imaging findings, and serological testing were suggestive of drug-induced AIH. A liver biopsy confirmed a drug-induced autoimmune picture, and she was diagnosed with atorvastatin-induced AIH after ruling out all other possible causes. Her clinical presentation and liver enzymes improved after prolonged treatment with prednisone.

Assessing the interplay between political globalization, social globalization, democracy, militarization, and sustainable development: evidence from G-7 economies

Sustainable development can act as a catalyst in boosting environmental sustainability and human welfare by alleviating unsustainable production and consumption practices. Political globalization emerges as indispensable in increasing global environmental governance. In addition, social globalization, militarization, and democracy can also affect sustainable development. In light of the overlooked impacts of these crucial variables on sustainable development within prior research studies, this study investigates the heterogeneous effects of political globalization, militarization, social globalization, and democracy on sustainable development from 1990 to 2019 in the G-7 panel. The results obtained from the application of the methods of moment quantile regressions reveal that a one-percentage-point increase in political globalization yields a significant enhancement in sustainable development, ranging from 0.015 to 0.017% across the 10th to 90th quantiles. Contrarily, sustainable development exhibits a decline within the range of 0.025 to 0.028% across the 10th to 90th quantiles, on account of a 1% increase in social globalization. Likewise, militarization hampers sustainable development with a slightly increasing effect from the 10th to 90th quantiles. Gross fixed capital formation decreases sustainable development while the relationship between democracy and sustainable development indicates a negative correlation, which has not achieved statistical significance across the majority of quantiles. These novel outcomes are also verified by using some other regression tests. Subsequently, a detailed policy framework is presented for the purpose of fostering sustainable growth within the G-7 group.

Financial development, resource richness, eco-innovation, and sustainable development: Does geopolitical risk matter?

Financial development and geopolitical risks can significantly affect sustainable development. However, the roles of these factors in sustainable development are rarely investigated. Thus, this study takes into account the role of geopolitical risk while exploring the effects of financial development, natural resource rents, and eco-innovation on sustainable development in the Organization for Economic Co-operation and Development (OECD) countries. To this end, yearly data from 1990 to 2019 is analyzed using advanced econometric tests. The Common Correlated Effects Mean Group (CCEMG) results indicate that financial development and eco-innovation are significantly and positively related to sustainable development. Natural resource rents have a detrimental impact on sustainable development which confirms the presence of the resource curse hypothesis in OECD countries. Furthermore, the results revealed that controlling geopolitical risk is useful in fostering sustainable development. Lastly, the panel Granger causality test unveiled one-way causality from financial development, eco-innovation, natural resource rents, and geopolitical risk to sustainable development. Moreover, causalities are found from geopolitical risk to financial development, eco-innovation and natural resources. These findings suggest that OECD countries should prioritize financial development and eco-innovation policies for sustainable development while mitigating the negative effects of natural resource rents. The geopolitical risk can harm sustainable development, so policymakers should promote international cooperation and risk-sharing.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

Multi-scale ultrasound induced composite coacervates of whey protein and pullulan polysaccharide on emulsion forming and stabilizing mechanisms

A non-destructive technique known as multi-scale ultrasound (MSU) was employed to modify the emulsion consisting of glycosylated bovine whey protein (WP) and pullulan (Pu). To assess the effect on the structural and emulsifying properties of the WP-Pu, the formulated emulsion, was treated with divergent MSU at (single: 20 kHz, 40 kHz, and 60 kHz; dual: 20-40 kHz, 40-60 kHz, and 20-60 kHz; and tri: 20-40-60 kHz) frequency for a duration of 30 min. The tri-frequency, treated emulsion showed improved emulsifying stability compared to the control and MSU-treated single, and dual-frequency samples, as indicated by the particle size, structural morphology, and adsorbed protein. The molecular docking and numerous spectral analysis provided evidence that WP can undergo successful phenolation. This modified form of WP then interacts with Pu through various forces, including H-bonding and other mechanisms, resulting in the formation of a composite emulsion. The rheological properties revealed that both the control emulsion and the MSU-treated emulsion exhibited non-Newtonian pseudoplastic flow behavior. This behavior is characterized by shear thinning, where the viscosity decreases with increasing shear rate. The shear rates tested ranged from 1 to 300 1/s, additionally, the degree of crystallinity increased from 18.2° to 19.4°. Overall, the tri-frequency effect was most pronounced compared to single and dual-frequency. Ultrasonication, an emerging non-thermal technology, proves to be an efficient approach for the formulation of WP-Pu composites. These composites have significant potential for use in drug delivery systems and functional foods.

Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings

Snake venom contains many toxic proteins that can destroy the circulatory system or nervous system of prey. Studies have found that these snake venom proteins have the potential to treat cardiovascular and nervous system diseases. Therefore, the study of snake venom protein is conducive to the development of related drugs. The research technologies based on traditional biochemistry can accurately identify these proteins, but the experimental cost is high and the time is long. Artificial intelligence technology provides a new means and strategy for large-scale screening of snake venom proteins from the perspective of computing. In this paper, we developed a sequence-based computational method to recognize snake toxin proteins. Specially, we utilized three different feature descriptors, namely g-gap, natural vector and word 2 vector, to encode snake toxin protein sequences. The analysis of variance (ANOVA), gradient-boost decision tree algorithm (GBDT) combined with incremental feature selection (IFS) were used to optimize the features, and then the optimized features were input into the deep learning model for model training. The results show that our model can achieve a prediction performance with an accuracy of 82.00% in 10-fold cross-validation. The model is further verified on independent data, and the accuracy rate reaches to 81.14%, which demonstrated that our model has excellent prediction performance and robustness.

The predictive value of CTSI scoring system in non-operative management of patients with splenic blunt trauma: The experience of a level 1 trauma center

Background

The spleen is one of the most injured organs following blunt abdominal trauma. The management options can be either operative or non-operative management (NOM) with either conservative management or splenic artery embolization. The implementation of CT in emergency departments allowed the use of CT imaging as a primary screening tool in early decision-making. Consecutively, new splenic injury scoring systems, such as the CT severity index (CTSI) reported was established.

Aim

The main aim of this study is to evaluate the effect of the implementation of CTSI scoring system on the management decision and outcomes in patients with blunt splenic trauma over 8 years in a level 1 trauma center.

Methods

This is a retrospective study including all adult patients with primary splenic trauma, having NOM and admitted to our hospital between 2013 and 2021.

Results

The analyses were conducted on ninety-nine patients. The average sample age was 32.7 ± 12.3 years old. A total of (63/99) patients had splenic parenchyma injury without splenic vascular injury. There is a statistically significant association between CTSI grade 3 injury and the development of delayed splenic vascular injury (p < 0.05). There is an association between severity of initial CTSI score and the risk of NOM/clinical failure (p = 0.02).

Conclusion

Our findings suggest implementing such a system in a level 1 trauma center will further improve the outcome of treatment for splenic blunt trauma. However, CTSI grade 3 is considered an increased risk of NOM failure, and further investigations are necessary to standardize its management.

Achieving SDG-13 in the Era of Conflicts: The Roles of Economic Growth and Government Stability

Establishing effective climate control and reducing the ecological footprint (EF) are necessary for pursuing Sustainable Development Goals (SDGs), in particular Goal 13. In this context, it is required to enhance the understanding of various factors that can either decrease or enhance the EF. In the literature to date, limited studies on external conflicts (EX) have reported diverse results, and also the impacts of government stability (GS) on EF are less explored. This study explores the roles of external conflicts, economic growth, and government stability on EF in the context of SDG-13. The study also contributes to the literature by examining the environmental effects of government stability and external conflicts for the first time in Pakistan. This research uses time-series methodologies on data from Pakistan from 1984 to 2018 for exploring the long-run relations and causal dynamics. The results unfolded that external conflicts stimulate and Granger cause EF and therefore expand environmental deterioration. Thus, limiting conflicts is in the favor of Pakistan to achieve SDG-13. Surprisingly, government stability also poses harmful impacts on environmental quality by enhancing the EF, indicating that stable governments focus on improving economic conditions rather than environmental quality. Moreover, the study proves the validity of the environmental Kuznets curve. Policy suggestions are made to move forward in achieving SDG-13 and to evaluate the effectiveness of government environmental policies.

Modelling dried noodle quality: Contribution of starch and protein physicochemical properties of 32 wheat cultivars

30 mainstream wheat breeds from China and 2 from Australian were evaluated to analyze the correlation between grain quality traits, protein/starch properties and the comprehensive quality of fine dried noodles (FDN), with a multiple regression analysis conducted to establish predictive equations. Results showed FDN quality was both determined by the protein content and quality, as well as the starch properties, especially pasting characteristics. The balance between gluten strength and starch swelling characteristics was a key point to produce high quality FDN. Zhoumai32 and APW were found to be excellent cultivars for FDN production. Gluten content and index, SDS sedimentation value, dough extensibility, setback and peak viscosity could be served as indicators for specializing FDN flour. The established predictive equations could well explain over 60% of the variation in noodle color, cooking time, hardness, chewiness, and extensibility. These results were hoped to be a fundamental step towards developing the related standards or regulations for specializing FDN flour and rapid noodle quality prediction.

Assessing the multitargeted antidiabetic potential of three pomegranate peel-specific metabolites: An in silico and pharmacokinetics study

Diabetes is a chronic metabolic disorder that occurs due to impaired secretion of insulin, insulin resistance, or both. Recent studies show that the antidiabetic drugs used to control hyperglycemic levels are associated with undesirable adverse effects. Therefore, developing a safe and effective medicine with antidiabetic potential is needed. In this context, in silico studies are considered a rapid, effectual, and cost-effective method in drug discovery procedures. It is evident from the literature that plant-based natural components have shown promising outcomes in drug development to alleviate various diseases and hence have diversified the screening of potential antidiabetic agents. Purposely, in the present study, an in silico approach was performed on three Punica granatum peel metabolites (punicalin, punicalagin, and ellagic acid). All these three compounds were docked against nine protein targets involved in glucose metabolism (GFAT, PTP1β, PPAR-ᵞ, TKIR, RBP4, α-amylase, α-glucosidase, GCK, and AQP-2). These three pomegranate-specific compounds demonstrated significant interactions with GFAT, PTP1β, PPAR-ᵞ, TKIR, RBP4, α-amylase, α-glucosidase, GCK, and AQP-2 protein targets. Specifically, punicalin, punicalagin, and ellagic acid revealed significant binding scores (-9.2, -9.3, -8.1, -9.1, -8.5, -11.3, -9.2, -9.5, -10.1 kcal/mol; -10, -9.9, -8.5, -8.9, -10.4, -9.0, -10.2, -9.4, -9.0 kcal/mol; and -8.1, -8.0, -8.0, -6.8, -8.7, -7.8, -8.3, -8.1, -8.1 kcal/mol, respectively), with nine protein targets mentioned above. Hence, punicalin, punicalagin, and ellagic acid can be promising candidates in drug discovery to manage diabetes. Furthermore, in vivo and clinical trials must be conducted to validate the outcomes of the current study.

Do life expectancy and hydropower consumption affect ecological footprint? Evidence from novel augmented and dynamic ARDL approaches

Human activities threaten the future of the ecosystem by emitting pollution to the air, water, and soil. Considering the increasing ecological footprint (EF), the study focuses on investigating the role of life expectancy and hydropower consumption by controlling also income, trade openness, and globalization on the environment under the environmental Kuznets curve (EKC) hypothesis for Turkey during 1971-2018. In this context, the study performs recently developed augmented autoregressive distributed lag (AARDL) and dynamic ARDL (DARDL) methods. The results show that (i) life expectancy increases the environmental pressure; (ii) hydropower consumption has no effect on the EF; (iii) globalization and trade openness reduce the EF; (iv) the EKC hypothesis is valid, but the estimated turning point lies between USD 19,914 and USD 20,571, which is far from the sample period in Turkey. From the overall results, it can be concluded that Turkey cannot solve environmental problems with insufficient income levels, an increasing elderly population, and ineffective use of hydropower. Hence, Turkey should rely on income much more, use hydropower much more efficiently, and benefit from the spillover effect of technological innovations related to globalization and foreign trade to significantly reduce the EF.

Analyzing economic growth, eco-innovation, and ecological quality nexus in E-7 countries: accounting for non-linear impacts of urbanization by using a new measure of ecological quality

The rising urban population and accelerated resource consumption pose major environmental challenges. Given that around 75% of global resources are consumed in urban areas, understanding the true association between urbanization (UR) and ecological quality is necessary for better urban strategies. Alongside this, eco-innovation (ECO) can limit environmental pollution and thus, it is the cornerstone of environmental policies. On that note, this research discloses the nexus between eco-innovation, economic growth (EGR), and ecological quality by considering the non-linear effects of UR in the emerging seven (E-7) nations from 1992 to 2018. Unlike previous literature, the newly developed indicator of ecological quality (the load capacity factor (LCF)) is adopted to understand the connection between UR, eco-innovation, and ecological quality since this new comprehensive indicator is based on both biocapacity and ecological footprint (EF). In the methodology, the "Continuously Updated-Fully Modified" (CuP-FM) test is used since this method offers diverse benefits for panel data estimation by accounting for major panel data estimation issues including autocorrelation, endogeneity, heteroscedasticity, fractional integration, and cross-sectional dependence. The empirical estimates unveiled that the current levels as well as high levels of UR limit the LCF. This evidence suggests that UR in the E-7 nations expands ecological degradation. This finding contradicts the assertions of ecological modernization theory that expanding urbanization brings some ecological benefits. Similarly, economic growth alleviates ecological quality. However, eco-innovation and agricultural land (AGL) contribute to enhancing the LCF and promoting ecological quality. Further, UR and eco-innovation Granger cause the LCF. Lastly, comprehensive urban environmental strategies are discussed to promote sustainable urbanization backed by eco-innovation.

Evaluating the link between innovative human capital and regional sustainable development: Empirical evidence from China

Innovative human capital (IHC) can boost the economic growth of nations. However, in recent years, economies have become more attuned to sustainable development. In this context, it is important to assess the potential impact of IHC on green growth. Against this background, this study empirically examines the role of IHC on regional green growth in China, considering the spatial spillover effect and focusing on the quantity and quality of human capital and its direct and indirect effects on green growth. To this end, this paper adopts the spatial Durbin model, constructs an indicator system to evaluate green growth, and establishes a calculation formula for the quantity and quality of IHC. The empirical analysis provided some important findings. First, IHC and green growth have strong spatial correlation characteristics. Second, the quantity of IHC has a significant positive impact on regional green growth; however, the quality of IHC does not promote regional green growth. Third, the quantity and quality of IHC indirectly improve the level of regional green growth through technological progress. Finally, the role of IHC and its spatial spillover effect in improving the regional green growth level are most obvious in the central and western regions of China. Therefore, promoting green growth requires enhancing the accumulation of IHC and narrowing the gap between eastern and western China in the accumulation of IHC.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

Green technologies, government stability, and green energy transition in a globalized world: evidence from E-7 nations

Unsustainable development and rising environmental degradation are major challenges for emerging nations that tend to promote human welfare by expanding economic development. Green energy transition (GETR) can help these nations to continue their development, reduce fossil fuel utilization, and achieve environmental sustainability. However, previous literature overlooks the importance of green technologies, government stability, and economic globalization in the GETR process. Accordingly, this research takes a step forward and assesses the impacts of green technologies (GT), government stability (GOV), and economic globalization (EGL) on green energy transition including population density (POP) and economic growth (GDP) in emerging seven (E-7) countries from 1992 to 2020. The research applied the "continuously updated fully modified (CuP-FM)" methodology to acquire the long-run findings robust to endogeneity stationary regressors, autocorrelation, and cross-sectional dependence (CD). The results highlighted that green technologies can be enhanced to accelerate the energy transition process since GETR and green technologies are positively connected. Also, government stability and economic globalization support the green energy transition. However, both population density and economic growth obstruct the energy transition process. The Emirmahmutoğlu and Kose test unveiled that green technologies, economic globalization, and government stability Granger cause the green energy transition. Based on these findings, policies are directed to promote the GETR by enhancing green technologies, economic globalization, and government stability for achieving ecological sustainability.

Geostatistical analysis for spatial distribution of anemia (Hb level) among women of reproductive age and determinant factors

The study was designed to assess the geostatistical spatial distribution of anemia and determinant factors among the women of reproductive age group (RAG) in Gilgit district, Pakistan. The Hb levels for 15-25 RAG, 26-35 RAG, and 36-45 group showed 10.22 g/dL, 10.41 g/dL, and 9.90 g/dL levels, respectively, while the Hb level showed a nugget/sill ratio of 0.21 inferring strong for the 15-25 group, weak for the 26-36, and moderate for 36-45 spatial dependence. Furthermore, 15-25 RAG showed 8% severe and 33.34% sufficient cases and 26-35 showed 12% severe and 29.33% sufficient results in their Hb level, whereas 36-45 had 9.34% severe and 29.33% sufficient.

Determinants of environmental quality in India: evidence using the bootstrapped ARDL model with structural breaks

This current study examines the impact of renewable energy consumption, agriculture, and globalization on carbon emissions in India over the period from 1980 to 2018. For long-run estimates, we apply Gregory-Hansen's co-integration test, bootstrap ARDL approaches, fully modified ordinary least squares, and dynamic OLS. The empirical results of long-run estimates indicate that a 1% increase in renewable energy consumption, agriculture, and economic globalization will increase carbon emissions by 0.764%, 1.675%, and 0.517%, respectively. Moreover, this study confirms the detrimental effect of these variables on carbon dioxide emissions. Economic globalization coefficients indicate that the scale effect is valid in India. The 2002 economic crisis slowed down the country's growth rate, which reduced the ecological pollution. Several policy recommendations are derived from the empirical findings.

Natural Resources Depletion, Financial Risk, and Human Well-Being: What is the Role of Green Innovation and Economic Globalization?

Human well-being is the top priority of all nations in the twenty-first century. However, depletion of natural resources and financial risk can negatively impact human well-being, which in turn can make it difficult to realize human well-being. Also, green innovation and economic globalization may play a significant role in human well-being. In this context, this study assesses the impacts of natural resources, financial risk, green innovation, and economic globalization on human well-being in emerging countries from 1990 to 2018. The empirical results from the Common Correlated Effects Mean Group estimator unveiled that natural resources and financial risk negatively affect the human well-being of emerging nations. Furthermore, the results show that green innovation and economic globalization positively contribute to human well-being. These findings are also verified using alternative methods. In addition, natural resources, financial risk, and economic globalization Granger cause human well-being but not the other way round. Furthermore, bidirectional causality exists between green innovation and human well-being. Considering these novel findings, sustainable utilization of natural resources and controlling financial risk are necessary strategies for realizing human well-being. More resources should be allocated for green innovation, and government should encourage economic globalization to attain sustainable development in emerging countries.

Graphical Abstract

Effect of environmental taxes on environmental innovation and carbon intensity in China: an empirical investigation

Environmental taxes are deemed to be an important carbon mitigating factor and also encourage the producers to adopt environmentally friendly technologies. Therefore, the main objective of the analysis is to evaluate the environmental taxes on environmental innovations and carbon intensity in China. The novel quantile autoregressive distributed lag (QARDL) is applied to investigate the model empirically. From the results, we confer that the short and long run estimated coefficients of environmental taxes are positive and significant at most quantiles in the environmental innovation model, implying that the higher the rate of environmental taxes, the higher the rate of environment-related innovations. However, the estimates of environmental taxes are significant and negative in both short and long run at more than half quantiles in the carbon emissions model, suggesting the positive role of environmental taxes in reducing carbon emissions. In general, our findings imply that an increase in environment-related taxes helps reduce carbon intensity and promote environmental innovation in China. The study suggests that policymakers in China should promote environmental innovation and expand the environmental tax base to achieve the objective of carbon neutrality.

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform.

Global human obesity and global social index: Relationship and clustering

Introduction

Obesity, a complex, multifactorial disease, is considered a global disease burden widely affecting the quality of life across different populations. Factors involved in obesity involve genetics, behavior and socioeconomic and environmental origins, each contributing to the risk of debilitating morbidity and mortality. However, the trends across the world vary due to various globalization parameters.

Methods

This article tends to identify the global social indicators, compiled into a global index, and develop a correlation between the global social index created by using the human development index, social and political globalization, the global happiness index, and the quality of infrastructure, institutions, and individuals using the internet factors and its effect on global obesity.

Results and Discussion

Our results identified a positive correlation between medium human development levels with obesity compared to low and very high human development levels. Economic stability due to rapid industrialization has increased the buying capacity and changed the global food system, which seems to be the major driver of the rise of global obesity.

Conclusion

The results decipher that global social indicators and overall social index have positively affected global obesity, which will help policymakers and governmental organizations monitor the obesity patterns across their regions by a significant contribution from globally influenced social factors.

Transport infrastructure, economic growth, and transport CO2 emissions nexus: Does green energy consumption in the transport sector matter?

Attaining Sustainable Development Goals (SDGs) is important to control the adverse impacts of climate change and achieve sustainable development. Among the 17 SDGs, target 13 emphasizes enhancing urgent actions to combat climate-related changes. This target is also dependent on target 7, which advocates enhancing access to cheap alternative sustainable energy. To accomplish these targets, it is vital to curb the transport CO₂ emissions (TCO₂) which increased by approximately 80% from 1990 to 2019. Thus, this study assesses the role of transport renewable energy consumption (TRN) in TCO₂ by taking into consideration transport fossil fuel consumption (TTF) and road infrastructure (RF) from 1970 to 2019 for the United States (US) with the intention to suggest some suitable mitigation policies. Also, this study assessed the presence of transport environmental Kuznets curve (EKC) to assess the direction of transport-induced growth. The study used the Bayer-Hanck cointegration test which utilizes four different cointegration techniques to decide cointegration along with the Gradual Shift causality test which considers structural shift and fractional integration in time series data. The long-run findings of the Dynamic Ordinary Least Squares (DOLS) test, which counters endogeneity and serial correlation, revealed that the transport renewable energy use mitigates as well as Granger causes TCO₂. However, transport fossil fuel usage and road infrastructure enhance TCO₂. Surprisingly, the transport EKC is invalid in the case of the US, and increased growth levels are harmful to the environment. The association between TCO₂ and economic growth is similar to a U-shaped curve. The Spectral Causality test revealed the growth hypothesis regarding transport fossil fuel use and economic growth connection, which suggests that policymakers should be cautious while decreasing the usage of transport fossil fuels because it may hamper economic progress. These findings call for revisiting growth strategies and increasing green energy utilization in the transport sector to mitigate transport emissions.

Green growth and environmental quality in top polluted economies: the evolving role of financial institutions and markets

An efficient financial system is crucial for the attainment of green growth and superior environmental quality. Therefore, our focus in this analysis is to estimate the effect of financial institutions and markets on green growth and environmental quality in highly polluted economies from 1991 to 2019. Estimates of the variables are collected with the help of the ARDL bounds testing approach. Findings of the ARDL model imply that a financial institution's efficiency helps improve green growth in the USA, China, and Japan in the long-run. However, the efficiency of the financial markets causes the green economy to grow in the long run in China and Russia only. On the other side, in the CO₂ model, the long-run estimated coefficients of a financial institution's efficiency are negatively significant in Japan and China only, implying that a financial institution's efficiency significantly reduces CO₂ emissions. Similarly, the long-run estimates of financial markets are significantly negative in the context of China and Japan only in CO₂ emissions.

How do economic and financial factors influence green logistics? A comparative analysis of E7 and G7 nations

Logistics is an important sector that supports every sector of the economy, and thus, the development of nations is impossible without efficient logistics. Alongside this, the logistics sector generates various kinds of emissions that degrade the quality of the environment. Developed countries are striving to introduce green logistics to avoid environmental damages caused by transportation. Also, developing countries will start focusing on developing green logistics sooner or later. Keeping this in view, a comparative panel study on the group of seven (G7) and Emerging Seven (E7) nations is conducted to study the impacts of various economic and financial variables on green logistics. The data from 1996 to 2018 is analyzed using some reliable methods that produce efficient and consistent results during heterogeneity and cross-sectional dependence. The findings disclosed that cointegration is present in E7 and G7 panels. For E7 countries, the results disclosed that research and development (R&D), FDI, portfolio investment, and trade openness reduce green logistics. However, in the G7, R&D, FDI, portfolio investment, and trade increase green logistics. The empirical analysis did not show any meaningful impact of financial development and economic growth on green logistics in both groups of nations. Lastly, based on the comparative analysis, various policies are suggested for E7 to catch up with the G7 regarding logistics development. Also, policies for G7 are suggested to boost green logistics.

The nexus between environmental strategy and environmental performance: analyzing the roles of green product innovation and mechanistic/organic organizational structure

The relationship between environmental strategy and environmental performance has been extensively analyzed in the environmental management literature. However, this relationship is spurious due to the inconsistence outcomes, lack of intervening mechanism, and scarcity of appropriate context. This study undertakes these considerations by exploring the underlying moderation-mediation mechanism through which proactive corporate environmental strategy affects corporate environmental performance. The hypotheses of the study were empirically tested on the data gathered from 147 ISO 14001:2015 certified firms in Pakistan. The data was statistically validated and then tested with Bootstrapping method using Preacher and Hayes Process Macros. The findings of the study revealed that a proactive corporate environmental strategy predicts corporate environmental performance through green product innovation. Moreover, the moderation hypothesis of the organizational structure variable in the nexus between proactive corporate environmental strategy and green product innovation was not supported in the full sample, whereas the split sample based on organizational size indicated moderation effects in the small firm's sample. The findings of the study carry important implications for firms related to corporate environmental strategy and green product innovation strategy.

A Case Report on Biliary Ascariasis

Biliary Ascariasis occurs when Ascaris lumbricoides worms invade the biliary system. It may cause biliary obstruction, cholangitis, cholecystitis, or acute pancreatitis. We report a case of a 37-year-old female patient who presented with a history of upper abdominal pain, nausea, vomiting, and weight loss for two weeks. Ultrasound showed dilated common bile duct with linear tubular echogenic structure in the common bile duct and bowel loops. Endoscopic Retrograde Cholangio pancreatography (ERCP) revealed large adult worms confirming the diagnosis of Ascariasis.

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

Environmental degradation, renewable energy, and economic growth nexus: Assessing the role of financial and political risks?

Sustainable development goal (SDG), which focuses on affordable and sustainable energy, provides a practical solution to realize sustainable growth. In addition, this target can encourage the realization of SDG 13 (climate action). However, factors like political and financial risk can impact climate actions and renewable energy. Therefore, this research extends the debate on the ecological footprint (EF) mitigation and achievement of SDGs by evaluating the renewable energy, political risk, financial risk, and EF nexus in an Environment Kuznets Curve (EKC) framework from 1986 to 2018. Panel data for the Association of Southeast Asian Nations (ASEAN) is estimated using second-generation approaches. The CuP-FM test results indicated that the EKC is present in ASEAN in the context of renewable energy, financial risk, and political risk. Furthermore, the findings revealed that controlling political and financial risks is a useful mitigation strategy because EF decreases as these risks are reduced. Notably, a decrease in EF has been linked to the use of renewable energy. These results are verified by using CO2 emissions as an alternative proxy for environmental degradation. Moreover, both financial and political risk Granger cause renewable energy and economic growth indicating that controlling financial and political risk is necessary for sustainable development.

Do heavy-duty and passenger vehicle emissions standards reduce per capita emissions of oxides of nitrogen? Evidence from Europe

Oxides of nitrogen are among the most dangerous emissions to human health and to the environment. In European nations, road transportation contributes to approximately 40% of emissions of oxides of nitrogen with the dominant share coming from passenger and freight transport. To help mitigate emissions of oxides of nitrogen, the European Union (EU) has implemented vehicular emissions standards. This paper studies the effect of EU vehicular emissions standards on per capita emissions of oxides of nitrogen in European nations during the period 2000 to 2017, both for on-road vehicular emissions and at the economy level. To do this, pollution is modelled as a byproduct of economic production. After controlling for economic growth, historical per capita levels of emissions of oxides of nitrogen, and a series of geographic and technological factors, it is determined that the vehicular emissions standards put in place by the EU decrease per capita levels of emissions of oxides of nitrogen. More precisely, reducing the heavy duty emissions standard by 1 g/kWh leads to as much as a 7% reduction in per capita on-road emissions of oxides of nitrogen. Reducing the passenger vehicle emissions standards for both diesel and gasoline engines enhances this effect, resulting in an even greater reduction in per capita emissions of oxides of nitrogen. These results further suggest that any rebound effect taking place is outweighed by the reduction in emissions of oxides of nitrogen from lowering emissions standards.

Exploring renewable energy, financial development, environmental quality, and economic growth nexus: new evidence from composite indices for environmental quality and financial development

The association between trade, financial development, consumption of renewable energy, environmental quality, foreign direct investment, and economic growth is important for sustainable growth and environmental strategies. Hence, this research unveils this association in selected low- and high-income economies from 1996 to 2020. Unlike most of the previous literature, this study uses a composite environmental quality index, a composite financial development index, and a composite trade share measure to better represent environmental quality, financial development, and trade openness, respectively. The Continuously Updated Fully Modified and Continuously Updated Bias Corrected estimators along with the Dumitrescu Hurlin causality method are utilized to scrutinize the nature of the linkage between the modeled variables. The long-run estimation provided that consumption of renewable energy and environmental quality augment economic growth in high-income nations, while both these variables do not contribute to the economic growth in low-income countries. Financial development upsurges economic growth in high- as well as low-income nations. Interestingly, trade openness boosts economic growth in high-income countries, while in low-income countries, it obstructs economic growth. In causal linkage, the conservation hypothesis for low-income countries and the feedback hypothesis for high-income countries are confirmed in the context of consumption of renewable energy and economic growth association. The supply-leading hypothesis for low-income countries and the feedback hypothesis for high-income countries are supported regarding the financial development-economic growth nexus. Moreover, one-way causality from growth to environmental quality and bidirectional causality between environmental quality and economic growth for low- and high-income countries are established, respectively. Lastly, exhaustive environmental and economic policies are directed.

A path towards carbon mitigation amidst economic policy uncertainty in BRICS: an advanced panel analysis

Continuing economic progress with less environmental damage and achieving a sustainable environment require switching from fossil fuels to green energy. However, alleviating environmental damage of growth has become a major challenge for BRICS where economic progress amidst rising urbanization pollutes the environment. In this context, the fight against climate change and actions towards environmental sustainability are greatly affected by rising economic policy uncertainty. Hence, this study assesses the role of green energy, urbanization, and economic growth in CO2 emissions in the presence of economic policy uncertainty in BRICS (excluding South Africa) from 1997 to 2020. The study used the cross-sectionally augmented auto-regressive distributive lag technique for revealing the short- and long-run effects of the analyzed variables on environmental quality. The empirical evidence suggested that the environmental Kuznets curve exists according to the recent framework of Narayan and Narayan Energy Policy 38:661-666, (2010) because even though economic growth increases CO2 emissions, its long-run effect is less than the short-run effect. Economic policy uncertainty boosts CO2 not only in the short-run but also in the long-run, evidencing that a sustainable environment requires decreasing the levels of policy uncertainty. For BRICS, switching towards green energy is a vital option to decrease environmental deterioration owing to the negative connection between green energy and CO2. The findings indicated that rapid urbanization is among the causes of high CO2. Furthermore, economic policy uncertainty influences both green energy and economic growth levels. Finally, policies are recommended to mitigate environmental deterioration.

Economic growth, technology, and CO2 emissions in BRICS: Investigating the non-linear impacts of economic complexity

Upgrading economic structures and producing less pollution-intensive goods are indispensable for achieving Sustainable Development Goals (SDGs) in BRICS (Brazil, Russia, India, China, and South Africa) that produce 41% of global CO₂ emissions. Economic complexity (ECC), which measures the sophistication of productivity and economic structure, has important environmental repercussions. Theoretically, the environmental impacts of economic complexity at higher levels and lower levels of complexity vary from each other. However, the majority of previous studies have overlooked these theoretical underpinnings while assessing the environmental repercussions of economic complexity. In addition, technological competencies are necessary to boost the economic complexity levels. Accordingly, this study uncovers the non-linear effects of economic complexity on CO₂ emissions including technology, population density, and economic growth in a STIRPAT model. To this end, the panel data from 1992 to 2018 is analyzed using the Continuously Updated Fully Modified method (CuP-FM) in the context of BRICS. The long-run results uncovered that CO₂ emissions intensify at a lower level of economic complexity. On the flip side, a higher level of economic complexity is beneficial in mitigating CO₂ in BRICS. Hence, the economic complexity and CO₂ connections follow an inverted U-shaped curve. The results also disclosed that expanding the level of technology lessens CO₂ and stimulates the quality of the environment. Further, population density and economic growth are evidenced to intensify CO₂. Moreover, economic complexity and technology Granger cause CO₂. Lastly, strategies are directed in the context of Sustainable Development Goals 9 and 13 to control CO₂ emissions by upgrading technology and products complexity.

COVID-19-Induced Seizures: A Meta-Analysis of Case Series and Retrospective Cohorts

The adverse events and complications of coronavirus disease 2019 (COVID-19) continue to challenge the medical profession despite the worldwide vaccination against the severe acute respiratory coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. Other than typical respiratory manifestations, COVID-19 also presents a wide range of neurological manifestations. This article underlines the pooled incidence of COVID-19-induced seizures in patients with epilepsy and without epilepsy. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols, we conducted a bibliographical search, and an initial search revealed 1,375 articles. In total, 21 articles were included in the final analysis by following the inclusion criteria. A total of 11,526 patients from 21 published articles that met the predetermined search criteria were included. The median age of the patients was 61.9 years, of whom 51.5% were males. A total of 255 patients presented with seizures as the first manifestation of COVID-19 with a prevalence of 2.2% (95% confidence interval = 0.05-0.24, p < 0.01) (I² = 97%), of which 71 patients had previously been diagnosed with epilepsy. Among patients with epilepsy, 49 patients had seizures as an initial presentation of SARA-CoV-2 with an incidence of 72% (0.54-0.85, p = 0.1) (I² = 34). Although the incidence of COVID-19-induced seizures is not high compared to other neurological manifestations, seizure incidence in epileptic patients with COVID-19 is remarkably high. New-onset seizures in any patient should be considered a presentation of COVID-19 in the absence of other causative factors.

876 Veriset Haemostatic Patch, Indications, Benefits and Complications: A Systematic Review

Aim

Achieving haemostasis intraoperatively is important for minimising blood loss, complications, and operation time. Suturing, cauterisation, fibrin glues and patches are used for this purpose. We explore Veriset, a patch consisting of polyethylene glycol and oxidised cellulose, to determine and compare its safety and effectiveness.

Method

Medline, Embase, Web of Science, Scopus, Cinahl and Cochrane databases were searched. Data points collected were Study/subject characteristics/demographics, surgery/specialty, time to haemostasis, proportion of haemostasis achieved, intraoperative adverse events, post operative complications, follow up time, and biochemical/histological analysis. Risk of Bias was assessed by Newcastle-Ottawa Scale

Results

Six studies were included; four human trials (3 RCTs, 1 case series) and two animal trials.

The human trials combined had 250 patients, with 147 using Veriset. In two RCTs, Veriset showed faster time to haemostasis and higher proportion of haemostasis achieved vs suturing and Tachosil haemostatic patch. In all three RCTs, no significant differences in adverse events and complications were seen between Veriset and suturing/Tachosil. Vascular, nephrectomy and hepatic surgery were investigated.

In the animal studies, the pig trial showed similar effectiveness and safety as the human trials. The rat study compared novel experimental patches to Veriset, and showed similar effectiveness to Veriset, at reduced costs.

Conclusions

Although current literature is scarce, Veriset is more effective than alternatives for haemostasis, with similar safety, although there are now other experimental patches that could have better health economic implications. Further clinical trials would be necessary to determine the breadth of applicable surgical fields for Veriset.

541 Pre-Operative Assessment of Patients Undergoing Elective TURBT or Rigid Cystoscopy + Bladder Biopsy: Is a Group & Save Clinically Required?

Aim

Method

Results

Conclusions

Modeling for Insights: Does Fiscal Decentralization Impede Ecological Footprint?

In recent years, the debate on environmental issues has become a hot topic. Fiscal decentralization is believed to be a crucial driver of environmental sustainability. However, the discussion on the effect of fiscal decentralization (FD) on environmental sustainability has not reached a unanimous conclusion. In this study, we inspect the effect of fiscal decentralization, economic development, technological innovation, economic globalization, and energy use on environmental quality in eight Asia-Pacific Economic Cooperation (APEC) member countries. In addition, we analyze the mechanisms through which fiscal decentralization influences the ecological footprint (EF) through the channels of technological innovation and economic growth. Using the STIRPAT framework, this study employed the CS-ARDL method for short-run and long-run analyses that deal with slope heterogeneity and cross-sectional dependence. The empirical results show that fiscal decentralization and technological innovation mitigate ecological footprint, while economic development, energy consumption, and urbanization negatively affect environmental quality. However, economic globalization is not related to the EF in the sample economies. The results further reveal that FD enhances environmental quality through the channel of technological innovation, while it does not affect the EF through the channel of economic growth. Finally, it is recommended to make a reasoned division between the rights and responsibilities of local government and central government in environmental pollution management, and optimize the environmental system. At the same time, policymakers should encourage technological innovation to reduce the adverse impacts of economic development and energy consumption on the environment.

The role of environmental transformational leadership in employees' influencing organizational citizenship behavior for environment well-being: a survey data analysis

Many researchers and intellectuals focused on the topic of organizational citizenship behavior for the environment (OCBE); however, employees' pro-environmental behaviors, such as eco-helping, eco-civic engagement, and eco-initiatives, are often being ignored. Also, the investigation of the stimulating factors behind these behaviors remains weak. Hence, this research aims to explore the role of environmental transformational leadership (ELT) in these three types of organizational citizen behaviors for the environment (OCBE) considering the indirect effects of psychological empowerment and leader-member exchange (LMX). We examined the effects of meditation by using four steps for mediation analysis and the Sobel test. Chi-square (χ²) tests for observing the difference were also applied. The results from a survey of 500 employees from the manufacturing industry in China provide that environmental transformational leadership contributes indirectly and directly to promoting environmental behavior within organizations due to the inspirational nature of transformational leaders. Furthermore, the intentions of employees for organizational environmental behavior stimulate on account of a high sense of leader-member exchange and psychological empowerment. Based on these findings, the study suggests that leadership in organizations should facilitate their employees with psychological empowerment and sharing of information and initiative regarding the environment for boosting OCBE. It is also recommended that at the time of recruitment and selection of employees, they should be given orientations regarding environmental protection and resource conservation. Moreover, organizations should promote the transformational style of leadership to achieve environment-related goals.

The nonlinearity and nonlinear convergence of CO2 emissions: Evidence from top 20 highest emitting countries

Carbon dioxide (CO₂) is the most prevalent greenhouse gas that triggers climate change, which in turn leads to catastrophic effects on trade, business, human health, and other areas. Understanding the characteristics and tendency of CO₂ emissions will improve policy making and mitigation strategies. Understanding the linearity or nonlinearity and convergence or divergence of CO₂ emissions is essential for selecting appropriate modeling techniques and for designing reliable policies. Therefore, this paper investigates the nonlinearity and nonlinear convergence of CO₂ emissions among the world's top 20 highest emitting countries, which account for 80% of the world's total emissions. To check the nonlinearity of CO₂ emissions, the McLeod-Li nonlinearity test, the Terasvirta nonlinearity test, and the Brock-Dechert-Scheinkman-LeBaron nonlinearity test are employed. The convergence or divergence of CO₂ emissions is checked by using the Kilic nonlinear unit root test, the Hu and Chen nonlinear unit root test, and the Park and Shintani nonlinear unit root test. The findings revealed that the CO₂ emissions process in all the 20 countries is nonlinear; 17 countries exhibit convergence in CO₂ emissions while the other 3 countries diverged from 1960 to 2018. Based on the results, the nonlinear nature of CO₂ emissions requires special attention from scholars when selecting estimation techniques for CO₂ emissions. For countries with convergence, emissions trends can be used to forecast future values of CO₂ emissions. Moreover, strong policy actions are required to achieve convergence in the countries with divergence.

Impact of vitamin D on maternal and fetal health: A review

The role of vitamin D in improving maternal health and reducing the risk of developmental disorders in fetus has been an important domain of research since the past few years. Vitamin D, owing to its immunomodulatory, anti‐inflammatory, developmental roles, and regulating calcium homeostasis, is predicted to have a significant influence on maternal and fetal health status. Several observational studies and clinical trials, determining the impact of vitamin D on gestational diabetes, C‐section, postpartum depression, pre‐eclampsia, miscarriages, and preterm delivery, have been elaborated in this review. In addition, fetal birth defects including neurological development, reduced birth weight, respiratory infections, bone development, and altered anthropometrics have also been summarized with available evidences. Other important mechanisms related to the roles of vitamin D in the body are also explained. Furthermore, recent studies determining the effect of vitamin D at genetic level will also help in understanding and future design of research in the area of maternal and fetal health.