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China seems ambitious to achieve a "carbon emissions peak" before 2030 and "carbon neutrality" before 2060. To deal with this emissions mitigation plan, technology innovations are regarded as a crucial factor. However, considering its rebound effect (CO 2 emissions driving effect) through economic growth, technology innovations might not prove a promising contributor to CO 2 reduction. Therefore, there is a need to investigate further the nexus between technology innovations and CO 2 emissions for conclusive debate. Based on the data of 215 cities in China, this paper uses mediating effects model to investigate the direct and indirect impacts (through economic growth and industrial structure transformation) of technology innovations on CO 2 emissions from a microeconomic perspective. The main results suggest that technology innovations generally increase CO 2 emissions in China both directly and indirectly. The impact of technology innovations and economic growth on CO 2 emissions indicated the EKC characteristics. Furthermore, the contributions of technology innovations to CO 2 emissions are distinguished in different regions. Thus, there is an urgent need for China to promote innovations in "clean technology" and to transform industrial structure to the tertiary industry to achieve the targets of carbon neutrality and emissions peaking., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Energy security, job development, and minimal environmental damage are just a few of the reasons why green energy technologies have gained popularity. Still, developing these technologies in underdeveloped countries is sluggish due to several bottlenecks. To find and prioritize the bottlenecks to development of green energy technologies (GETs), this study has used the case of South Africa. In this study, technical, financial, social, regulatory, geographical, and institutional bottlenecks are six headings under which 22 bottlenecks are further subdivided. For prioritizing these bottlenecks, a Multi-Objective Optimization based on Ratio Analysis (MOORA) strategy combined with an Evaluation based on Distance from Average Solution (EDAS) method was used. The top three bottlenecks impacting the GETs' development involved social, institutional, and regulatory, whereas in sub-headings, corrupt political systems, knowledge of the public, and fixed investments were revealed to be the most significant hurdles. In addition to that, a framework is also developed for assessing how each bottleneck hinders the development of green energy technologies. The results of this study recommend that formulation of policy using the bottom-up approach is important. Notably, politicians at the highest levels should work together to bolster government agencies so that they can monitor and carry out the policies designed to advance GETs. Building the institutional capacity of government offices is crucial for eliminating corruption in political offices and progressing GETs in South Africa., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study deals with a novel perspective on how natural resource dependence (NRD) and natural resource abundance (NRA) moderate the effects of fiscal decentralization and financial inclusion on energy and carbon intensity. Our work develops on the Stochastic Impacts by Regression on Population, Affluence, and Technology framework, considering the selected seven Organization for Economic Co-operation and Development members as the investigative laboratory from 1995 through 2018. Employing a panel Method of Moments Quantile Regression with fixed effects, we find that fiscal decentralization is beneficial for environmental sustainability, especially across the countries with a higher level of energy and carbon intensity; however, enhanced financial inclusivity is detrimental to the environmental quality, with more visible effects in more energy-efficient economies. As per the direct effects, NRD and NRA mitigate energy and carbon efficiency, with more substantial contributions in less energy- and carbon-intensive countries. Concerning the indirect effects, NRD and NRA positively moderate the influence of fiscal decentralization and financial inclusion on energy and carbon intensities, displaying more substantial effects in more energy-efficient economies. Among other control variables, environmental innovation, renewable electricity, employment to population ratio, and economic progress enhance environmental sustainability. We suggest fiscal decentralization should be built on a more transparent and accountable subnational governmental setup to prevent rent-seeking and fragile environmental protection. We also recommend inclusive finance should enhance the access to and affordability of financial services to economic agents for green consumption and investment ventures to achieve environmental sustainability, among other Sustainable Development Goals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Mining activities provide a pathway for the entry and accumulation of various heavy metals in soil, which ultimately leads to severe environmental pollution. Utilization of various immobilizing agents could restore such contaminated soils. Therefore, in this study, date palm-derived biochars (BCs: produced at 300 °C, 500 °C and 700 °C) and magnetized biochars (MBCs) were employed to stabilize heavy metals (Cd, Pb, Cu and Zn) in mining polluted soil. Metal polluted soil was amended with BCs and MBCs at w/w ratio of 2% and cultivated with wheat ( Triticum aestivum L.) in a greenhouse. After harvesting, dry and fresh biomass of plants were recorded. The soil and plant samples were collected, and the concentrations of heavy metals were measured after extracting with water, DTPA (diethylenetriaminepentaacetic acid), EDTA (ethylenediaminetetraacetic acid), and acetic acid. BCs and MBCs resulted in reduced metal availability and uptake, with higher fresh and dry biomass (>36%). MBCs showed maximum decrease (>70%) in uptake and shoot concentration of metals, as these reductions for Cd and Pb reached below the detection limits. Among all single-step extractions, the DTPA-extractable metals showed a significant positive correlation with shoot concentrations of tested metals. Thus, the synthesized BCs and MBCs could effectively be used for stabilizing heavy metals and improve plant productivity in multi-contaminated soils. However, future studies should focus on long term field trials to restore contaminated mining soils using modified biochars.

We have previously described our novel surgical technique in addressing the anomalous aortic origin of the left coronary artery with a transseptal course behind the right ventricular outflow tract. By means of the Najm procedure, we performed complete unroofing of the transseptal anomalous aortic origin of the left coronary artery and elongated the right ventricular outflow tract posteriorly using an autologous pericardial patch. This report documents modifications to address challenging anatomic variants and highlights the importance of managing septal arteries and myocardial bridges. We also describe the excellent midterm outcomes of 14 patients who have undergone the Najm procedure., (Copyright © 2023 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.

This research intends to identify influential factors in adopting and diffusing solar energy technology (SET) by micro-, small-, and medium-sized enterprises (MSMEs) in two tehsils of Multan district in Pakistan's Punjab province. To this end, the influential factors are identified through studying literature surveys and conducting questionnaires. Following that, partial least squares-based path modeling is employed. The results showed that (1) enterprises' size, perceived SET's ease of use, and perceived SET's reliability are the top three driving factors. (2) The perceived SET's price, perceived level of competition's pressure, and MSME's energy cost intensity are the barriers to the adoption and diffusion of SET, while the lack of technical knowledge about SET is a neutral factor. (3) Perceived SET's price, MSMEs' energy cost intensity, and enterprises' size are among the most important factors based on the effect size and path coefficients, while the lack of technical knowledge about SET, preferences of MSMEs' customers, and eco-labels and green stickers have lower importance. Since the adoption of SET is a rational decision that is concretely dependent on economic incentives, it is recommended to lower the price of SET to scale up the adoption and diffusion of SET by Pakistani MSMEs., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

We investigate the validity of the environmental Kuznets curve (EKC) hypothesis for the NAFTA countries. In this investigation, we approach this hypothesis from a different methodology and propose employing the ARMEY curve hypothesis since the mathematical-functional propositions of both hypotheses were constructed on the same inverted U-shaped relationships. Thus, for the first time, it can be interpreted that the EKC hypothesis is empirically tested through a transmission mechanism of the ARMEY curve hypothesis in a single composite model. Therefore, this approach makes our study different from all empirical studies in the relevant literature. We apply the Augmented Mean Group (AMG) estimator to this aim. Empirical findings indicate that the ARMEY curve hypothesis was verified only for the USA. However, this new approach proposed in this study cannot test the EKC hypothesis through the ARMEY curve model for any NAFTA country since this approach requires verification of the ARMEY curve hypothesis and a significant composite model for the same NAFTA country. If our composite model was significant, it might make it possible to numerically determine a maximum real GDP per capita level that would minimize or maximize CO 2 emission levels for the USA. Therefore, this study introduces-proposes this new methodology as an alternative way of testing the EKC hypothesis in the relevant literature for future empirical studies., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Nanoparticles have numerous applications related to human uses. Titanium dioxide nanoparticles (TiO 2 -NPs) are extensively used in many daily utilities. The small size particles and larger uses in the industry have led them to become a threatening entity to the living organisms. The unchecked use and dumping in the environment poses a significant toxicological risk to the developing mammalian embryo. The present study was conducted to determine the developmental toxicity and teratogenic effects of TiO 2 -NPs in murine embryos. The TiO 2 -NPs were introduced intravenously into pregnant mice graded as T1 (0.52 mg/g BW), T2 (0.7 mg/g BW), and T3 (1.05 mg/g BW) along with control with no dose administration T0 (0.00 mg/g BW). Results recorded after 14 days were resorbed fetuses, dropped wrist, hemorrhages, sacral hygromas, and kinked tails. It was concluded that the exposure of TiO 2 -NPs in mentioned doses from any source may lead to deleterious effects on the development of an embryo., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The aim of this study is to identify and highlight the positive and negative indirect environmental impacts of COVID-19, with a particular focus on the most affected economies (USA, China, Spain, and Italy). In this respect, the empirical and theoretical dimensions of the contents of those impacts are analyzed. Research findings reveal a significant relationship between contingency actions and positive indirect impacts such as air quality improvements, clean beaches, and the decline in environmental noise. Besides, negative indirect impacts also exist, such as the rise in waste level and curtailment in recycling, further threatening the physical spaces (land and water), besides air. It is expected that global businesses will revive in the near future (though slowly), but the reduction in greenhouse gas emissions during this short time span is not a sustainable way of environmental mitigation. Thus, long-term mitigation policies should be strengthened to cope with the undesirable deterioration of the environment. Research findings provide an up-to-date glimpse of the pandemic from the perspectives of current and future indirect environmental impacts and the post-pandemic situation. Finally, it is suggested to invent and prepare action plans to induce a sustainable economic and environmental future in the post-pandemic world scenario.

Sources and levels of heavy metals (HMs) in soil and dust of urban and suburban areas in Riyadh (industrial city) and Mahad AD'Dahab (mining area) cities in Saudi Arabia were reported in this study. Additionally, the concentrations of HMs in different soil particle size fractions (> 250, 63-250 and < 63 µm) were reported. Pollution extent, and ecological and human health risks associated with collected soil and dust samples were explored. Contamination levels of HMs were higher in dust as compared to soil samples at all sites. The average integrated potential ecological risk in dust samples of urban area of Mahad AD'Dahab was 139, and thus characterized as a very-high-risk criterion. Enrichment factor (EF), correlation analyses, and principal component analysis showed that aluminum (Al), cobalt (Co), chromium (Cr), iron (Fe), manganese (Mn), nickel (Ni), titanium (Ti), and zinc (Zn) had mainly the lithogenic occurrence (EF < 2). However, Zn, copper (Cu), and lead (Pb) in Riyadh, and cadmium (Cd), Cu, Zn, and Pb in the Mahad AD'Dahab were affected by industrial and mining activities, respectively, that were of anthropogenic origins (EF > 2). The hazard index values of dust and soil (< 63 µm) samples in both urban and suburban areas in Mahad AD'Dahab were > 1, suggesting non-carcinogenic risk. Therefore, the dust and soil samples from the mined area of Mahad AD'Dahab had a higher pollution levels, as well as ecological and human health risks than those from Riyadh. Hence, the pollution of such residential environments with HMs (especially Cd, Cu, Zn, and Pb) needs to be monitored., (© 2022. The Author(s).)

Precipitation in any form-such as rain, snow, and hail-can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.

Optimal management of critical aortic stenosis (AS) in infants depends on the left ventricle's (LV's) ability to maintain adequate output. Determining feasibility of biventricular repair may be difficult, particularly in those with mitral disease, endocardial fibroelastosis (EFE), multi-level obstruction, and uncertain physiologic capacity. We report a case of a three-month-old with critical AS, severely reduced LV function, EFE, and moderate mitral regurgitation (MR), who underwent a Ross-Konno procedure with concomitant EFE resection and mitral valve repair. Although the technical sequence is challenging, definitive surgery completely relieved multi-level obstruction and MR with markedly improved LV function.

The COVID-19 epidemic is impeding energy development in developing countries and exacerbating the problems associated with energy planning in off-grid locations. To address such complicated decision-making issues and consider scenarios during this long-lasting pandemic, this study proposes a novel integrated MCDM (Multi-Criteria Decision Making) approach using the Delphi based FO-BWM (Fuzzy Optimistic Best-Worst Method), IDOCRIW (Integrated Determination of Objective Criteria Weights) and the Aggregated Weighting Method integrated with the CoCoSo method under different normalization methods based on a case study of the off-grid areas in Bangladesh. The results of Delphi analysis showed that a total of five criteria were agreed upon by the expert panel. After integrating 5 normalization methods with CoCoSo and using three weighting methods separately, a total of 15 MCDM models were constructed. Finally, the 8 sorted MCDM models demonstrate that Solar Home System (SHS) and Mini-Grid systems need to be prioritized, and the criterion Opportunity of Local Funding (OLF) is essential for choosing between SHS and Mini-Grid systems. Sensitivity analysis showed that the proposed method is effective for easing the dilemmas of energy planning in off-grid areas and provides useful insight to address the impacts of future pandemics on energy planning., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier Ltd. All rights reserved.)

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this work., (Copyright © 2022 Taher M. Ghazal et al.)

This study reinvestigates the EKC hypothesis for US states with a new methodology that differs from all previous empirical studies using traditional EKC models. To this aim, this methodology, for the first time, unifies two seemingly different but strongly interrelated hypotheses (models), namely the Armey curve (AC) and traditional EKC models, into one single composite model. The rationale for creating this composite model is twofold. First, the functional propositions of these two hypotheses are depicted with inverted U-shaped curves. Second, they also have economically interrelated-causal relationships. This means that rising government spending (through the AC hypothesis) increases real GDP per capita (RGDPPC) and, consequently, increases in RGDPPC (through the EKC hypothesis) increase CO 2 emissions. The composite model created may also allow US state policymakers to determine a single maximum spending level that will maximize or minimize CO 2 emissions. Empirical findings indicate that the composite model is capable of testing the EKC hypothesis for 7 US states. Additionally, for 7 US states, maximum spending level was calculated to be around 15% of their RGDPPCs. Hence, with this calculated spending level, policymakers of these states may be able to determine-adjust their golden spending levels so as not to cause environmental degradation and declines in GDP., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Based on the panel data of 46 countries in "Belt and Road" (B&R) from 2004 to 2016, this paper studies the impact of innovation investment and institutional quality on green total factor productivity (GTFP). Firstly, the ICRG database, World Bank WDI database, Traditional Foundation database, and Wind database are matched to obtain the balanced panel data of 46 countries along the B&R from 2004 to 2016. Secondly, the Malmquist-Luenberger index, which can be included in the unexpected output, is used to calculate the GTFP of countries along B&R. Thirdly, the evaluation system of national institutional quality of B&R is constructed from three dimensions (political institutional quality, economic institutional quality, and legal institutional quality), and the overall system quality of different countries is measured by entropy method. Finally, an empirical study is made on the relationship among innovation investment, institutional quality, and green total factor productivity. The results show that innovation investment has significantly promoted the GTFP of the B&R countries. It is worth noting that there is a non-linear relationship between innovation investment and GTFP in the B&R countries. With the improvement of overall system quality, political system quality, economic system quality, and legal system quality, the promotion effect of innovation investment on GTFP is further enhanced. In addition, the heterogeneity regression results show that the impact of innovation investment on GTFP is significantly heterogeneous in different regions of the B&R countries. Specifically, innovation investment has the greatest impact on GTFP in South Asia, followed by East Asia and Pacific, Europe and Central Asia, Middle East, and North Africa., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A rapid process of industrialization, on the one hand, transformed the economies from agrarian to industrial societies to improve the living standards and welfare of people. On the other hand, the urbanized and industrialized economies have posed challenging threats to environmental sustainability. The query at hand is whether the growing environmental emissions are driven by industrialization and urbanization or not. This research aims to empirically examine the combined role of industrialization and urbanization in achieving carbon neutrality in Pakistan by considering foreign direct investment and economic growth as control variables in the model. The core empirical results are the following: firstly, industrialization and economic growth exhibit negative but statistically insignificant impacts on CO 2 emissions, imparting a neutral role in determining the environmental degradation in Pakistan. Secondly, urbanization and foreign direct investment disclose positive and statistically significant (at 1% level of significance) impacts on CO 2 emissions, manifesting an environmental degradation driving impact in the country. Thirdly, given the slope coefficients of urbanization and foreign direct investment (0.058 and 0.035), urbanization proved to be a stronger driver than foreign direct investment. Finally, foreign direct investment is revealed to make the Pakistani economy a "Pollution Haven" for the foreign enterprises in the country. Based on empirical results, none of the variables predicted the support for carbon neutrality in Pakistan., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Carbon nanodots (CNDs)-embedded pullulan (PUL) nanofibers were developed and successfully applied for sulfathiazole (STZ) removal from wastewater streams for the first time. The CNDs were incorporated into PUL at 0.0%, 1.0%, 2.0%, and 3.0% ( w / w ) to produce M1, M2, M3, and M4 nanofibers (PUL-NFs), respectively. The produced PUL-NFs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), thermal gravimetric analysis (TGA) and Differential scanning calorimetry (DSC) and applied for STZ removal from aqueous solutions through pH, kinetics, and equilibrium batch sorption trials. A pH range of 4.0-6.0 was observed to be optimal for maximum STZ removal. Pseudo-second order, intraparticle diffusion, and Elovich models were suitably fitted to kinetics adsorption data ( R 2 = 0.82-0.99), whereas Dubinin-Radushkevich, Freundlich, and Langmuir isotherms were fitted to equilibrium adsorption data ( R 2 = 0.88-0.99). STZ adsorption capacity of PUL-NFs improved as the amount of embedded CNDs increased. Maximum STZ adsorption capacities of the synthesized PUL-NFs were in the order of: M4 > M3 > M2 > M1 (133.68, 124.27, 93.09, and 35.04 mg g -1 , respectively). Lewis acid-base reaction and π-π electron donor-acceptor interactions were the key STZ removal mechanisms under an acidic environment, whereas H-bonding and diffusion were key under a basic environment. Therefore, CNDs-embedded PUL-NFs could be employed as an environmentally friendly, efficient, and non-toxic adsorbent to remove STZ from wastewater streams.

Background: Frontline workers (FLWs) are at a higher risk of COVID-19 infection during care interactions than the general population. Personal protective equipment (PPE) is regarded as an effective intervention for limiting the transmission of airborne viruses. However, research examining FLWs' intention to use PPE is limited., Objectives: This study addresses this research gap and also contributes by expanding the conceptual mechanism of planned behavior theory by incorporating three novel dimensions (perceived benefits of PPE, risk perceptions of the epidemic, and unavailability of PPE) in order to gain a better understanding of the factors that influence FLWs' intentions to use PPE., Method: Analysis is based on a sample of 763 FLWs in Pakistan using a questionnaire survey, and the structural equation modeling approach is employed to evaluate the suppositions., Results: Study results indicate that attitude, perceived benefits of PPE, and risk perceptions of the epidemic have positive influence on FLWs' intention to use PPE. In comparison, the unavailability of PPE and the cost of PPE have opposite effects. Meanwhile, environmental concern has a neutral effect., Conclusions: The study results specify the importance of publicizing COVID-19's lethal impacts on the environment and society, ensuring cheap PPE, and simultaneously enhancing workplace safety standards., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Irfan, Salem, Ahmad, Acevedo-Duque, Abbasi, Ahmad, Razzaq and Işik.)

Objectives: Widespread acceptance of the COVID-19 vaccine will be the next important step in fighting the novel coronavirus disease. Though the Pakistani government has successfully implemented robust policies to overcome the COVID-19 pandemic; however, studies assessing public intention to get COVID-19 vaccination (IGCV) are limited. The aim of this study is to deal with this literature gap and has also expanded the conceptual framework of planned behaviour theory. We have introduced three new considerations (risk perceptions of the pandemic, perceived benefits of the vaccine, and unavailability of vaccine) to have a better understanding of the influencing factors that encourage or discourage public IGCV., Methods: Results are based on a sample collected from 754 households using an inclusive questionnaire survey. Hypotheses are tested by utilizing the structural equation modelling approach., Results: The results disclose that the intention factors, that is, attitude, risk perceptions of the pandemic, and perceived benefits of the vaccine, impart positive effects on public IGCV. In contrast, the cost of the vaccine and the unavailability of the vaccine have negative effects. Notably, environmental concern has an insignificant effect., Conclusions: Research findings emphasize the importance of publicizing the devastating impacts of COVID-19 on society and the environment, ensuring vaccination availability at an accessible price while simultaneously improving public healthcare practices., (© 2021 John Wiley & Sons Ltd.)

As the digital economy develops rapidly and the network information technology advances, new development models represented by the network economy have emerged, which have a crucial impact on green economic growth. However, the relevant previous studies lacked the role of analyzing the direct and indirect effects of internet development on green economic growth at the prefecture-level city level. For this purpose, this paper aims to examine the intrinsic mechanism of the impact of internet development on green economic growth and provide empirical support for cities and regions in China to increase internet construction. Furthermore, the mixed model (EBM), which includes both radial and non-radial distance functions, is applied to calculate the green economic growth index. Fixed effect model and mediation effect model are also employed to test influence mechanisms of the internet development on green economic growth using panel data of 269 prefecture-level cities in China from 2004 to 2019. The statistical results reveal that internet development has contributed significantly to green economic growth. When the internet development level increases by 1 unit, the green economic growth level increases by an average of 5.0372 units. However, regional heterogeneity is evident between internet development and green economic growth, that is, the promoting effect of internet development on green economic growth is gradually enhanced from the eastern region to the western region. We also find that internet development guides industrial structure upgrading improves environmental quality and accelerates enterprise innovation, which indirectly contributes to green economic growth. And internet development mainly achieves green economic growth through enterprise innovation. Based on the above findings, we concluded that policymakers should not only strengthen the guiding role of social actors to promote the stable development of the internet industry, but also foster the construction of the three models of "internet+industry integration," "internet+environmental governance," and "internet+enterprise innovation" to promote green economic growth., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this work., (Copyright © 2022 Muhammad Mazhar Bukhari et al.)

Since climate change is the paramount concern of recent literature, macroeconomic factors such as green growth and economic globalization may play an integral role in achieving ecological sustainability in the presence of eco-innovation. However, the combined contributions of green growth, economic globalization, and eco-innovation towards achieving ecological sustainability are missing from the existing knowledge. Therefore, we investigate the combined influence of these variables in the presence of human capital, financial development, and gross domestic product on ecological footprint per capita for a panel of 20 Organization for Economic Co-operation and Development (OECD) countries from 1990 through 2017. The method of panel quantile regression is used to produce sound results across varying levels of the ecological footprint of OECD nations. The main results are as follows: firstly, green productivity growth linearly and non-linearly mitigates the ecological degradation, presenting a more pronounced ecological protection impact for higher quantiles, followed by medium and lower quantiles. Secondly, economic globalization manifests mixed effects: it induces ecological deterioration impact in the absence of its interaction with eco-innovation, while it brings about an ecological protection impact with the interaction term. Thirdly, eco-innovation demonstrates an ecological protection impact for all quantiles, with the most influential impact in countries with higher ecological footprint per capita, followed by medium and lower footprints. Based on empirical results, we propose productive utilization of environmental resources for ecological sustainability through product and process innovation and efficient management practices. Besides, we recommend cultivating energy-efficient and clean environmental technologies for long-term ecological sustainability., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future., Competing Interests: Conflict of InterestThe authors declare that they have no conflict of interest., (© Springer Nature Switzerland AG 2022.)

Hepatitis C is a prevalent disease in the world. Around 3 to 4 million new cases of Hepatitis C are reported every year across the globe. Effective, timely prediction of the disease can help people know about their Stage of Hepatitis C. To identify the Stage of disease, various noninvasive serum biochemical markers and clinical information of the patients have been used. Machine learning techniques have been an effective alternative tool for determining the Stage of this chronic disease of the liver to prevent biopsy side effects. In this study, an Intelligent Hepatitis C Stage Diagnosis System (IHSDS) empowered with machine learning is presented to predict the Stage of Hepatitis C in a human using Artificial Neural Network (ANN). The dataset obtained from the UCI machine learning repository contains 29 features, out of which the 19 most reverent are selected to conduct the study; 70% of the dataset is used for training and 30% for validation purposes. The precision value is compared with the proposed IHSDS with previously presented models. The proposed IHSDS has achieved 98.89% precision during training and 94.44% precision during validation., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this article., (Copyright © 2021 Muhammad Bilal Butt et al.)

Terminal heat stress causes irreversible damage to wheat crop productivity. It reduces the vegetative growth and flowering period that consequently declines the efficiency to capture available stem reserves (carbohydrates) in grains. Markers associated with thermotolerant traits ease in marker assisted selection (MAS) for crop improvement. It identifies the genomic regions associated with thermotolerant traits in wheat, but the scarcity of markers is the major hindrance in crop improvement. Therefore, 158 wheat genotypes were subjected to genotyping with 165 simple sequence repeat markers dispersed on three genomes (A, B and D). Allelic frequency and polymorphic information content values were highest on genome A (5.34 (14% greater than the lowest value at genome D) and 0.715 (3% greater than the lowest value at genome D)), chromosome 4 (5.40 (16% greater than the lowest value at chromosome 2) and 0.725 (5% greater than the lowest value at chromosome 6)) and marker xgwm44 (13.0 (84% greater than the lowest value at marker xbarc148 ) and 0.916 (46% greater than the lowest value at marker xbarc148 )). Bayesian based population structure discriminated the wheat genotypes into seven groups based on genetic similarity indicating their ancestral origin and geographical ecotype. Linkage disequilibrium pattern had highest significant (P < 0.001) linked loci pairs 732 on genome A at r 2  > 0.1 whereas, 58 on genome B at r 2  > 0.5. Linkage disequilibrium decay (P < 0.01 and r 2  > 0.1) had larger LD block (5-10 cM) on genome A. Highly significant MTAs (P < 0.000061) under heat stress conditions were identified for flag leaf area ( xwmc336 ), spikelet per spike ( xwmc553 ), grains per spike ( cxfa2147 , xwmc418 and xwmc121 ), biomass ( xbarc7 ) and grain yield ( xcfa2147 and xwmc671 ). The identified markers in this study could facilitate in MAS and gene pyramiding against heat stress in wheat., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier B.V. on behalf of King Saud University.)

As a new production factor, digitalization plays a vital role in society, economy, and the environment. Based on the expanded STIRPAT model, this paper empirically tests the impact of energy structure and digital economy on carbon emissions by panel data from 2011 to 2017 in 30 provinces of China. The results show that the energy structure mainly based on coal has a significant driving effect on carbon emissions. Compared with non-resource-based provinces, the increase of energy structure dominated by coal has a greater effect on carbon emission in resource-based provinces. It is worth noting that this kind of influence has a greater impact on the central region of China, followed by the western region and the eastern region. Besides, the digital economy has a significant moderating effect. With the development of digital economy, the impact of coal-based energy structure on carbon emissions is gradually decreasing. This effect is more significant in non-resource-based provinces and eastern China, but not significant in resource-based cities and central and western China., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

We assessed the influence of wealth and selected socio-demographic characteristics on antenatal care (ANC) utilization among 900 women within the reproductive ages across eight rural communities in Delta State, Southern part of Nigeria. Both descriptive and predictive analyses were undertaken. The characteristics of the women were presented with the use of simple proportion and frequency. The Chi-square test was used to examine the factors that were significantly associated with a minimum of eight antenatal care contacts. Multivariate logistic regression was used to examine factors that explained a minimum of eight antenatal contacts. Women on the highest wealth quintile were 2.6 times more likely to meet the recommended number of 8 ANC visits (aOR: 2.6, 95% CI: 0.63-3.86). This finding has an implication for the free maternal care policy currently in operation in Delta State. It implies that the policy has not succeeded in redressing problems of financial barriers in access to maternal care. The lesson, therefore, is that the policy of free maternal care is not enough, but if the government desires to maximize the dividend from the policy, then it must go beyond absolving pregnant women of ANC charges to provide other form of supports.

Background Persistent pulmonary hypertension of the newborn (PPHN) has been known for more than three decades, and lots of advancements have been made regarding its diagnosis and management. However, the exact causes of PPHN and the best treatment strategies remain debatable. This study aimed to compare the effectiveness of sildenafil and bosentan versus sildenafil and beraprost in the management of persistent pulmonary hypertension of the newborn (PPHN). Methodology This open-label, non-randomized, quasi-experimental study was conducted at the Department of Pediatric Cardiology and Neonatology, The Children's Hospital & The Institute of Child Health, Multan, Pakistan, from January 2021 to June 2021. We enrolled a total of 50 newborns (25 in each group) aged <10 days, gestational age above 34 weeks, who presented with respiratory distress and marked hypoxemia (PaO 2 < 50 mmHg) as per arterial blood gas (ABG) analysis and confirmed echocardiographic diagnosis of PPHN within 24 hours of admission. A total of 25 cases were given sildenafil and bosentan, while the other 25 cases were given sildenafil and beraprost. Echocardiographic examination was done again after 72 and 120 hours, and the findings were noted. Outcomes were measured in terms of the reduction in tricuspid regurgitation (TR), mortality, and duration of hospital stay. Results Of the 50 neonates, 27 (54.0%) were male. Overall, the mean age was calculated to be 3.54 ± 0.7 days. The mean gestational age was 35.0 ± 0.7 weeks. The mode of delivery was cesarean section among 35 (70.0%) neonates. A significantly higher reduction in tricuspid regurgitation after 72 and 120 hours following the initiation of the treatment was observed in the sildenafil plus bosentan group in comparison with the sildenafil plus beraprost group (p < 0.05). No statistically significant difference was observed in terms of the duration of hospitalization between both study groups (p = 0.1776). Conclusion The combination of sildenafil and bosentan was found to be more effective than sildenafil and beraprost in reducing tricuspid regurgitation after 72 hours, while they have comparable efficacy at 120 hours of treatment in the management of persistent pulmonary hypertension of the newborn., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, Adnan et al.)

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Muhammad Farrukh Khan et al.)

Elevated levels of doxycycline (DC) have been detected in the environment due to its extensive utilization as a veterinary antibiotic. Sorption-desorption behavior of DC in soil affects its transport, transformation, and availability in the environment. Thus, sorption-desorption behavior of DC was explored in three soils (S1, S2, and S3) after manure application with and without mesquite wood-waste-derived biochar (BC) pyrolyzed at 600 °C. Sorption batch trials demonstrated the highest DC sorption in soil S1 as compared to S2 and S3, either alone or in combination with manure or manure + BC. Chemical sorption and pore diffusion were involved in DC sorption, as indicated by the kinetic models. Soil S1 with manure + BC exhibited the highest Langmuir model predicted sorption capacity (18.930 mg g -1 ) compared with the other two soils. DC sorption capacity of soils was increased by 5.0-6.5-fold with the addition of manure, and 10-13-fold with BC application in a soil-manure system. In desorption trials, manure application resulted in 67%, 40%, and 41% increment in DC desorption in soil S1, S2, and S3, respectively, compared to the respective soils without manure application. In contrast, BC application reduced DC desorption by 73%, 66%, and 65%, in S1, S2, and S3, respectively, compared to the soils without any amendment. The highest DC sorption after BC application could be due to H bonding, π-π EDA interactions, and diffusion into the pores of BC. Hence, mesquite wood-waste-derived BC can effectively be used to enhance DC retention in contaminated soil to ensure a sustainable ecosystem.

Millions of human pathogenic viral particles are shed from infected individuals and introduce into wastewater, subsequently causing waterborne diseases worldwide. These viruses can be transmitted from wastewater to human beings via direct contact and/or ingestion/inhalation of aerosols. Even the advanced wastewater treatment technologies are unable to remove pathogenic viruses from wastewater completely, posing a serious health risk. Recently, coronavirus disease 2019 (COVID-19) has been urged globally due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has resulted in >4.1 million deaths until July 2021. A rapid human-to-human transmission, uncertainties in effective vaccines, non-specific medical treatments, and unclear symptoms compelled the world into complete lockdown, social distancing, air-travel suspension, and closure of educational institutions, subsequently damaging the global economy and trade. Although, few medical treatments, rapid detection tools, and vaccines have been developed so far to curb the spread of COVID-19; however, several uncertainties exist in their applicability. Further, the acceptance of vaccines among communities is lower owing to the fear of side effects such as blood-clotting and heart inflammation. SARS-CoV-2, an etiologic agent of COVID-19, has frequently been detected in wastewater, depicting a potential transmission risk to healthy individuals. Contrarily, the occurrence of SARS-CoV-2 in wastewater can be used as an early outbreak detection tool via water-based epidemiology. Therefore, the spread of SARS-CoV-2 through fecal-oral pathway can be reduced and any possible outbreak can be evaded by proper wastewater surveillance. In this review, wastewater recycling complications, potential health risks of COVID-19 emergence, and current epidemiological measures to control COVID-19 spread have been discussed. Moreover, the viability of SARS-CoV-2 in various environments and survival in wastewater has been reviewed. Additionally, the necessary actions (vaccination, face mask, social distancing, and hand sanitization) to limit the transmission of SARS-CoV-2 have been recommended. Therefore, wastewater surveillance can serve as a feasible, efficient, and reliable epidemiological measure to lessen the spread of COVID-19., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The development of a simple method to synthesize highly efficient and stable magnetic microsphere beads for sulfathiazole (STZ) removal from contaminated aqueous media was demonstrated in this study. Conocarpus ( Conocarpus erectus L.) tree waste (CW) derived biochar (BC) was modified to fabricate chitosan-BC (CBC) and magnetic CBC (CBC-Fe) microsphere beads. Proximate, chemical, and structural properties of the produced adsorbents were investigated. Kinetics, equilibrium, and pH adsorption batch trials were conducted to evaluate the effectiveness of the synthesized adsorbents for STZ removal. All adsorbents exhibited the highest STZ adsorption at pH 5.0. STZ adsorption kinetics data was best emulated using pseudo-second order and Elovich models. The equilibrium adsorption data was best emulated using Langmuir, Freundlich, Redlich-Peterson, and Temkin models. CBC-Fe demonstrated the highest Elovich, pseudo-second order, and power function rate constants, as well as the highest apparent diffusion rate constant. Additionally, Langmuir isotherm predicted maximum adsorption capacity was the highest for CBC-Fe (98.67 mg g -1 ), followed by CBC (56.54 mg g -1 ) and BC (48.63 mg g -1 ). CBC-Fe and CBC removed 74.5%-108.8% and 16.2%-25.6% more STZ, respectively, than that of pristine BC. π-π electron-donor-acceptor interactions and Lewis acid-base reactions were the main mechanisms for STZ removal; however, intraparticle diffusion and H-bonding further contributed in the adsorption process. The higher efficiency of CBC-Fe for STZ adsorption could be due to its magnetic properties as well as stronger and conducting microsphere beads, which degraded the STZ molecules through generation of HO • radicals., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since the developed world's economic prosperity has been heavily reliant on excessive fossil-based energy consumption, it has posed severe environmental quality challenges. This research attempts to revisit the relationship between income and anthropogenic emissions in the context of the environmental Kuznets curve (EKC) theory by considering electric power consumption, urbanization, and democratic accountability index in the Gulf Cooperation Council (GCC) region. It employs annual frequency panel data from 1990 through 2019 and three alternative advanced econometric estimation techniques. The main findings are as follows: Firstly, the EKC results for the whole sample strongly support the proposition of an inverse U-shaped connection between anthropogenic emissions and affluence in the long run. Secondly, the country-specific results confirm EKC only in Saudi Arabia and Bahrain, while the remaining countries demonstrate a U-shaped connection. Thirdly, the democratic accountability promoted anthropogenic emissions implying that it failed to contribute to environmental protection. It means that democratic setup in the GCC region performs poorly in accomplishing climate change mitigation and Sustainable Development Goals (SDGs). Fourthly, electric power consumption and urbanization impart positive and negative impacts on anthropogenic emission, respectively. These findings are found robust across the fully modified ordinary least square (FMOLS), bias-corrected LSDV (least squares dummy variable) (LSDV), and pooled mean group (PMG) estimators. Finally, Dumitrescu-Hurlin panel causality shows that (i) income and urbanization establish a two-way causality with the anthropogenic emissions. (ii) However, a unidirectional causal connection is revealed from electric power consumption and democratic accountability index to anthropogenic emissions. The findings suggest that the GCC region should prioritize environmental protection and SDGs across the political aims' recipe since it would direct the region on the path of climate change mitigation., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since climate change mitigation is the central debate of modern literature, the realization of carbon neutrality in response to diversified macroeconomic variables is the most crucial concern of international economies. However, the critical role of trade and renewable electricity output in export diversification-environmental nexus is missing. Therefore, this study investigates the combined influence of trade openness, exports diversification, and renewable electricity output on carbon dioxide emissions (CO 2 ) in China from 1989 through 2019. Applying novel time series econometric techniques robust to structural breaks, following new outcomes are obtained. Firstly, long-run equilibrium cointegration existed among the under-analysis variables. Secondly, export diversification and renewable electricity output are predicted to decelerate CO 2 , supporting carbon neutrality in the long run. Thirdly, trade openness and gross domestic product accelerated the CO 2 , delaying carbon neutrality accomplishment. Most importantly, significant structural break dummy interacting with trade openness implicated that during the post-2001 era, China's trade openness extensively deteriorated the environmental quality in the face of trade liberalization obtained after joining the World Trade Organization (WTO). Based on empirical results, export diversification and renewable electricity production policies should be mutually non-exclusive and closely coordinated. Further, to counter the carbon emission acceleration impact of trade openness, increasing the share of green tradable products is suggested. Finally, bilateral trade restructuring is recommended to realize the long-term dream of economic sustainability and carbon neutrality., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Since renewable energy has the paramount importance of achieving environmental sustainability, developing countries like Pakistan have been facing numerous renewable energy generation (REG) development challenges. Thus, the REG development in any country can be attributed to its crucial drivers as well as crucial barriers. This work aims to identify crucial influence factors of REG development by conducting multi-facet content analysis as well as partial least square-based structural equation modeling analysis on data collected through a questionnaire survey. It has been conducted based on academicians from four Pakistani universities as well as professionals from Islamabad Electric Supply Company. The key outcomes are as follows: First, through multi-facet content analysis, ten crucial influence factors are identified. Among those factors, lack of good governance, renewable energy adaptation, and governmental energy policies are revealed to be the crucial barriers to REG development, whereas endowed resources, power production approach, renewable energy demand, investment environment for renewable energy projects, economic returns of renewable energy projects, environmental effects, and public acceptance are found to be the crucial drivers of REG development. Based on relative significance, the lack of good governance is unveiled to be the most significant factor, while environmental effects is found to be the least significant one. Moreover, among the drivers of REG development, public acceptance is found to be the prime contributor. At the same time, among crucial barriers, lack of good governance has shown the largest contribution to REG development., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Developing countries are enthusiastically on the road to economic progress and economic openness, which is proved to be a silver bullet for them. However, it has put their environmental quality at stake. This study examines whether economic openness and electricity consumption matter for environmental deterioration by controlling for the influence of economic progress. For this, we have used time series frequency data of Pakistan from 1971 to 2016 and employed the state-of-the-art dynamic autoregressive distributed lag (ARDL) simulation model. The model has the advantage over traditional ARDL in determining the positive and negative environmental deterioration variations induced by economic openness, electricity consumption, and economic progress. The main findings are as follows: Firstly, electricity consumption in both long and short run positively and significantly influenced CO 2 emissions, while long-run influence exceeded that of short run. Secondly, economic progress validated an environmental Kuznets curve hypothesis and thus limited the environmental degradation. Thirdly, economic openness showed an insignificant influence on CO 2 emissions both in the long and short run. Based on research findings, it is suggested that economic progress and economic openness are not the direct culprits to deteriorate a developing country's environmental condition like Pakistan; rather, electricity consumption remained the key role player. Therefore, the transition from fossil-based electricity consumption to renewable energy consumption would provide a sustainable pathway towards achieving sustainable economic openness in the future., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Carbon dioxide emission and GHGs are associated with fossil fuels which have adverse effects on the environment. The key intention of this paper was to determine the asymmetric effect of CO 2 emission on expenditures, trade, FDI, and renewable energy consumption in Pakistan. An asymmetrical technique (nonlinear autoregressive distributed lag) was employed to validate the constructive and adverse relation among variables. Furthermore, the Granger causality test was also used to verify the unidirectional association amid variables. Study outcomes revealed that the adverse shocks of renewable energy consumption exposed expressively to upsurge CO 2 emission in the short-run dynamics. Conversely, constructive shocks of renewable energy consumption display an adversative association with CO 2 emission. Furthermore, the decreasing trend in foreign direct investment tends to impede the detrimental effects of CO 2 emission. Additionally, the variable expenditures also create the non-eco-friendly impacts and manifest the positive linkage through CO 2 emission. Trade possesses statistically insignificant linkage with environmental degradation. The results also disclose that positive as well as negative variations in the foreign direct investment expose to degrade the environmental eminence. Long-run results suggest the direct association between downward trend in renewable energy consumption and CO 2 emission signifying that the pollution level decreases, and the upward trend in renewable energy consumption, however, demonstrates insignificantly positive effects. The results also disclose that positive as well as negative variations in the FDI lead to degrade the CO 2 emission. Moreover, it is found that the expenditures soar the issue of pollution again in the long run. Finally, the consequence of trade on CO 2 emission is adverse, as the outcome suggests. In order to improve the environmental policies for sustainable growth, the study provides direction toward a sustainable environment by reducing carbon dioxide emission., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since the development of the service sector and renewable energy reduce fossil-based energy consumption which mitigates CO 2 emissions and this nexus provides a better understanding of the environmental sustainability. Considering the substantially increasing contribution of service sector and tremendous potential for renewable energy in ASEAN5 countries, leaning forward from ASEAN's energy and growth nexus, this study examines the impact of service sector contribution and renewable energy on the environmental quality of ASEAN5 using annual data from 1990 to 2018. The results of the fully modified ordinary least squared, dynamic ordinary least squared, and canonical co-integrating regressions depicted that the service sectors of Thailand, the Philippines, and Singapore augment CO 2 emissions; however, the service sectors of Malaysia and Indonesia could reduce CO 2 emissions. The increasing share of renewable energy can enhance environmental quality, but its magnitude varies in ASEAN5 economies; non-renewable energy, population, and economic development deteriorate the environment. Our results confirm the existence of environmental Kuznets curve in all the ASEAN5; the Gregory-Hansen test confirmed that results are robust. Finally, the Granger causality designated that economic development and non-renewable energy have a significant causal relationship with CO 2 emission of ASEAN5 countries. These findings suggest that the ASEAN5 economies need to optimize their economic structure for promoting sustainable development in the long run.Graphical abstract., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This work analyzed the long-run (LR) and short-run (SR) effects of renewable and non-renewable energy (RE and NRE) usage, economic development (ED), agricultural value-added (AVA), and forestry area (FA) on the environmental quality (EQ) in China spanning from 1990 to 2015. The autoregressive distributed lags (ARDL) bounds testing method and the Johansen cointegration approach are applied to produce empirical estimates. The empirical results of the ARDL and the fully modified ordinary least square (FMOLS) estimators established that renewable energy usage and forest area reduce CO 2 emissions and improve the environmental quality, while non-renewable energy consumption, economic development, and agricultural output increase the level of CO 2 emissions in China. The robustness of outcomes is checked through the Granger causality test, impulse response function (IRF), and variance decomposition method (VDM) suggesting that fossil fuel usage in the agriculture production process is mainly accountable for China's CO 2 emissions. These findings have inherent policy implications for the central and local Chinese government, which are exhibited in the "Conclusions" section., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this work., (Copyright © 2021 Rozi Bibi et al.)

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this work., (Copyright © 2021 Baha Ihnaini et al.)