An intuitionistic fuzzy graph's variation coefficient measure with application to selecting a reliable alliance partner.

Group decision-making (GDM) is crucial in various components of graph theory, management science, and operations research. In particular, in an intuitionistic fuzzy group decision-making problem, the experts communicate their preferences using intuitionistic fuzzy preference relations (IFPRs). This approach is a way that decision-makers rank or select the most desirable alternatives by gathering criteria-based information to estimate the best alternatives using a wider range of knowledge and experience. This article proposes a new statistical measure in a fuzzy environment when the data is ambiguous or unreliable to solve a decision-making problem. This study uses the variation coefficient measure combined with intuitionistic fuzzy graphs (IFG) and Laplacian energy (LE) to solve a GDM problem that utilizes intuitionistic fuzzy preference relations (IFPRs) to select a reliable alliance partner. Initially, the Laplacian energy determines the weight of individual standards, and the obtained weight average further estimates the overall criterion weight vector. We establish the authority criteria weights using the variation coefficient measure and then ultimately rank the alternatives for each criterion using the same measure. We examine four distinct companies Alpha, Beta, Delta, and Zeta to conduct a realistic GDM to choose which alliance partner would be ideal. We successfully implemented the suggested technique, determining that Alpha satisfies company standards and is ranked first among other companies. Moreover, this technique is useful for all kinds of Intuitionistic fuzzy group decision-making problems to select optimal ones.

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment.

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research's specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

Cattano Christov double diffusion model for third grade nanofluid flow over a stretching Riga plate with entropy generation analysis.

The current investigation delves into the convective heat and mass transfer characteristics of third-grade radiative nanofluid flow within a porous medium over a Riga plate configuration. The Riga plate structure incorporates magnets and electrodes strategically arranged on a plate surface. To enhance the accuracy of energy and concentration expressions within the third-grade fluid flow, the Cattano Christov Double Diffusion model is employed. Entropy generation analysis is conducted by applying the second law of thermodynamics, and Darcy's model is employed to characterize the behavior of a porous medium. Appropriate similarity transformations have been used to convert the partial differential equations monitoring the fluid flow model into dimensionless ordinary differential equations. The Galerkin weighted residual method is employed to resolve these equations numerically. The findings contain detailed explanations of how relevant factors affect the temperature field, concentration field, velocity field, entropy generation, and Bejan number, in addition to graphic representations of the results. The findings indicate that the medium's porosity and Brinkman number promote entropy generation. The Bejan number and entropy production is affected by the thermal radiation parameter, which first rises and then declines after a certain distance.

A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for interval-valued pythagorean fuzzy hypersoft set.

The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study.

Extension of correlation coefficient based TOPSIS technique for interval-valued Pythagorean fuzzy soft set: A case study in extract, transform, and load techniques.

Correlation is an essential statistical concept for analyzing two dissimilar variables' relationships. Although the correlation coefficient is a well-known indicator, it has not been applied to interval-valued Pythagorean fuzzy soft sets (IVPFSS) data. IVPFSS is a generalized form of interval-valued intuitionistic fuzzy soft sets and a refined extension of Pythagorean fuzzy soft sets. In this study, we propose the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFSS and examine their necessary properties. Based on the proposed correlation measures, we develop a prioritization technique for order preference by similarity to the ideal solution (TOPSIS). We use the Extract, Transform, and Load (ETL) software selection as an example to demonstrate the application of these measures and construct a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The method investigates the challenge of optimizing ETL software selection for business intelligence (BI). This study offers to illuminate the significance of using correlation measures to make decisions in uncertain and complex settings. The multi-attribute decision-making (MADM) approach is a powerful instrument with many applications. This expansion is predicted to conclude in a more reliable decision-making structure. Using a sensitivity analysis, we contributed empirical studies to determine the most significant decision processes. The proposed algorithm's productivity is more consistent than prevalent models in controlling the adequate conformations of the anticipated study. Therefore, this research is expected to contribute significantly to statistics and decision-making.

The hydrodyanamics of gravity-driven vessel drainage of third order fluid using perturbation method.

In this work, tank drainage phenomena for in-compressible and isothermal fluid having unsteady fluid flow for third order fluid is studied. Analytical solution of the proposed problem is obtained using perturbation method subject to proper boundary conditions. No-slip condition is used because of fluid will have zero velocity relative to a solid boundary. Object of this work is to find out the velocity profile, flow rate, time required to empty a tank (time efflux) and mathematical relation of time and depth of the tank. Influence of different parameter over velocity profile, effect of radius of the tank over depth, effect of radius of piper over flow rate and effect of depth over flow rate are examined graphically using mathematica. Velocity profile of this model is compared with newtonian fluid's while assuming epsilon as a zero using graph and table from which it is clear that third order fluid posses greater velocity then Newtonian fluid.

Heat transfer analysis of Maxwell tri-hybridized nanofluid through Riga wedge with fuzzy volume fraction.

This contribution aims to optimize nonlinear thermal flow for Darcy-Forchheimer Maxwell fuzzy [Formula: see text] tri-hybrid nanofluid flow across a Riga wedge in the context of boundary slip. Three types of nanomaterials, [Formula: see text] Cu and [Formula: see text] have been mixed into the basic fluid known as engine oil. Thermal properties with the effects of porous surface and nonlinear convection have been established for the particular combination [Formula: see text] Applying a set of appropriate variables, the set of equations that evaluated the energy and flow equations was transferred to the dimensionless form. For numerical computing, the MATLAB software's bvp4c function is used. The graphical display is used to demonstrate the influence of several influential parameters. It has been observed that flow rate decay with expansion in porosity parameter and nanoparticles volumetric fractions. In contrast, it rises with wedge angle, Grashof numbers, Darcy-Forchheimer, nonlinear Grashof numbers, and Maxwell fluid parameter. Thermal profiles increase with progress in the heat source, nanoparticles volumetric fractions, viscous dissipation, and nonlinear thermal radiation. The percentage increases in drag force for ternary hybrid nanofluid are 13.2 and 8.44 when the Modified Hartmann number takes input in the range [Formula: see text] and wedge angle parameters [Formula: see text]. For fuzzy analysis, dimensionless ODEs transformed into fuzzy differential equations and employed symmetrical triangular fuzzy numbers (TFNs). The TFN makes a triangular membership function (M.F.) that describes the fuzziness and comparison. This study compared nanofluids, hybrid nanofluids, and ternary nanofluids through triangular M.F. The boundary layer flow caused by a wedge surface plays a crucial role in heat exchanger systems and geothermal.

Laser-assisted plasma formation and ablation of Cu in a controlled environment.

In this paper, we explore the surface and mechanical alterations of Cu, as well as the parameters of laser-assisted plasma and ablation. The irradiation source is a Nd: YAG laser with a constant irradiance of 1.0 GW/cm2 (1064 nm, 55 mJ, 10 ns, 10 Hz). Physical parameters such as electron temperature (Te) and electron number density (ne), sputtering yield (yield), ablation depth (depth), surface morphology (morphology), and hardness (Vickers) of laser irradiated Cu are evaluated using instruments such as a Laser Induced Breakdown Spectrometer (LIBS), Quartz Crystal Microbalance (QCM), Optical Emission Microscope (OEM), Scanning Electron Microscope (SEM), and Vicker's hardness tester. These physical characteristics have been studied in relation to changes in pressure (from 10 torr to 100 torr) and the composition of two inert ambient gases (Argon and Neon). Pressures of Ar and Ne are found to enhance the emission intensities of spectral lines of Cu, Te, and ne, as well as the sputtering yield, crater depth, and hardness of laser ablated Cu, to a maximum at 60 torr, after which they decrease with subsequent increases in pressure up to 100 torr. Increases in pressure up to 60 torr are connected with plasma confinement effects and increased collisional frequency, whereas decreases in pressure between 60 and 100 torr are ascribed to shielding effects by the plasma plume. All numbers are also found to be greater in Ar compared to Ne. In Ar, laser-ablated Cu reaches a maximum of 15218 K, 1.83 × 1018 cm-3, 8.59 × 1015 atoms/pulse, 231 m, and 147 HV, whereas in Ne, it reaches a maximum of 12000 K, 1.75 × 1018 cm-3, 7.70 × 1015 atoms/pulse, 200 m, and 116 HV. Ar is more likely than Ne to develop surface features such as craters, distinct melting pools with elevating edges, flakes, cones, etc. It is also shown that there is a significant association between the outcomes, with an increase in Te and ne being responsible for a rise in sputtering yield, ablation depth, surface morphology, and surface hardness. These findings have potential uses in plasma spectroscopy for materials science and in industrial applications of Cu.

Underwater wireless sensor network-based multihop data transmission using hybrid cat cheetah optimization algorithm.

For the conservation and sustainable use of the oceanic environment, monitoring of underwater regions is ineluctable and is effectuated with the aid of an underwater wireless sensor network. It is accoutered with smart equipment, vehicles and sensors and utilized for the transmission of acquired data from the monitoring region and forwarded to the sink nodes (SN) where the data are retrieved. Moreover, data transmission from sensor nodes to SN is complicated by the aquatic environment's inherent complexities. To surpass those issues, the work in this article focusesto propose a Hybrid Cat Cheetah optimization algorithm (HC2OA) that purveys the energy efficient clustering based routing. The network is then partitioned into numerous clusters, each of which is led by a cluster head (CH) and comprised of many sub-clusters (CM). Based on the factors such as distance and residual energy the CH selection is optimized and collects data from the respective CMs and forwarded to the SN with a multi-hop transmission approach. The proposed HC2OA chooses the optimized multi-hop route from the CH to SN. Thus mitigates the complexities over multi-hop routing and CH selection. Simulations are effectuated in the NS2 simulator and analyzed the performance. The results of the study show that the proposed work has significant advantages over state-of-the-art works in terms of network lifetime, packet delivery ratio, and energy consumption. The energy consumption of the proposed work is 0.2 J with a packet delivery ratio is 95%.The network life time of proposed work, with respect to the coverage area around 14 km is approximately 60 h.

Computation of stagnation coating flow of electro-conductive ternary Williamson hybrid [Formula: see text] nanofluid with a Cattaneo-Christov heat flux model and magnetic induction.

Modern smart coating systems are increasingly exploiting functional materials which combine multiple features including rheology, electromagnetic properties and nanotechnological capabilities and provide a range of advantages in diverse operations including medical, energy and transport designs (aerospace, marine, automotive). The simulation of the industrial synthesis of these multi-faceted coatings (including stagnation flow deposition processes) requires advanced mathematical models which can address multiple effects simultaneously. Inspired by these requests, this study investigates the interconnected magnetohydrodynamic non-Newtonian movement and thermal transfer in the Hiemenz plane's stagnation flow. Additionally, it explores the application of a transverse static magnetic field to a ternary hybrid nanofluid coating through theoretical and numerical analysis. The base fluid (polymeric) considered is engine-oil (EO) doped with graphene [Formula: see text], gold [Formula: see text] and Cobalt oxide [Formula: see text] nanoparticles. The model includes the integration of non-linear radiation, heat source, convective wall heating, and magnetic induction effects. For non-Newtonian characteristics, the Williamson model is utilized, while the Rosseland diffusion flux model is used for radiative transfer. Additionally, a non-Fourier Cattaneo-Christov heat flux model is utilized to include thermal relaxation effects. The governing partial differential conservation equations for mass, momentum, energy and magnetic induction are rendered into a system of coupled self-similar and non-linear ordinary differential equations (ODEs) with boundary restrictions using appropriate scaling transformations. The dimensionless boundary value problem that arises is solved using the bvp4c built-in function in MATLAB software, which employs the fourth-order Runge-Kutta (RK-4) method. An extensive examination is conducted to evaluate the impact of essential control parameters on the velocity [Formula: see text], induced magnetic field stream function gradient [Formula: see text] and temperature [Formula: see text] is conducted. The relative performance of ternary, hybrid binary and unitary nanofluids for all transport characteristics is evaluated. The inclusion of verification of the MATLAB solutions with prior studies is incorporated. Fluid velocity is observed to be minimized for the ternary [Formula: see text]-[Formula: see text]-[Formula: see text] nanofluid whereas the velocity is maximized for the unitary cobalt oxide [Formula: see text] nanofluid with increasing magnetic parameter ([Formula: see text] Temperatures are elevated with increment in thermal radiation parameter (Rd). Streamlines are strongly modified in local regions with greater viscoelasticity i.e. higher Weissenberg number [Formula: see text]. Dimensionless skin friction is significantly greater for the ternary hybrid [Formula: see text]-[Formula: see text]-[Formula: see text] nanofluid compared with binary hybrid or unitary nanofluid cases.

MHD Free convection flows of Jeffrey fluid with Prabhakar-like fractional model subject to generalized thermal transport.

This article examines the effects of magnetohydrodynamics and heat absorption on an incompressible Jeffrey fluid' time-dependent free convection flow over an infinite, vertically heated plate with homogeneous heat flux. The constitutive equation for heat flow utilizes the Prabhakar-like fractional derivative. The Laplace transform technique obtains the precise solution for the momentum and thermal profiles. The typical case and well-known outcomes from the literature are retrieved as restraining cases. The graphical analysis of the impact of the flow and fractionalized parameters on the thermal and momentum profiles is presented. Additionally, a comparison is made between the ordinary model and the Prabhakar-like fractional model, which shows that the latter better captures the retention of the physical features of the problem. It is concluded that the Prabhakar-like fractional model is better suited for describing the memory effect of the thermal and momentum fields.

Convective heat and mass transfer rate on 3D Williamson nanofluid flow via linear stretching sheet with thermal radiation and heat absorption.

A mathematical analysis is communicated to the thermal radiation and heat absorption effects on 3D MHD Williamson nanoliquid (NFs) motion via stretching sheet. The convective heat and mass boundary conditions are taken in sheet when liquid is motion. As a novelty, the effects of thermal radiation, heat absorption and heat and mass convection are incorporated. The aim is to develop heat transfer. Williamson NFs are most important source of heat absorption, it having many significant applications in "energy generation, HT, aircraft, missiles, electronic cooling systems, gas turbines" etc. The suitable similarity transformations have been utilized for reduce basic governing P.D. E's into coupled nonlinear system of O.D. E's. Obtained O.D. Es are calculated by help of R-K-F ("Runge-Kutta-Fehlberg")4th order procedure with shooting technique in MATLAB programming. We noticed that, the skin friction coefficient is more effective in Williamson liquid motion when compared with NFs motion with higher numerical values of stretching ratio parameter, Williamson liquid motion is high when compared to NFs motion for large values of magnetic field. We compared with present results into previous results for various conditions. Finally, in the present result is good invention of previous results.

An interaction and feedback mechanism-based group decision-making for emergency medical supplies supplier selection using T-spherical fuzzy information.

Selecting a supplier for emergency medical supplies during disasters can be considered a typical multiple attribute group decision-making (MAGDM) problem. MAGDM is an intriguing common problem that is rife with ambiguity and uncertainty. It becomes much more challenging when governments and medical care enterprises adjust their priorities in response to the escalating problems and the effectiveness of the actions taken in different countries. As decision-making problems become increasingly complicated nowadays, a growing number of experts are likely to use T-spherical fuzzy sets (T-SFSs) rather than exact numbers. T-SFS is a novel extension of fuzzy sets that can fully convey ambiguous and complicated information in MAGDM. The objective of this paper is to propose a MAGDM methodology based on interaction and feedback mechanism (IFM) and T-SFS theory. In it, we first introduce T-SF partitioned Bonferroni mean (T-SFPBM) and T-SF weighted partitioned Bonferroni mean (T-SFWPBM) operators to fuse the evaluation information provided by experts. Then, an IFM is designed to achieve a consensus between multiple experts. In the meantime, we also find the weights of experts by using T-SF information. Furthermore, in light of the combination of IFM and T-SFWPBM operator, an MAGDM algorithm is designed. Finally, an example of supplier selection for emergency medical supplies is provided to demonstrate the viability of the suggested approach. The influence of parameters on decision results and comparative analysis with the existing methods confirmed the reliability and accuracy of the suggested approach.

Optimizing construction company selection using einstein weighted aggregation operators for q-rung orthopair fuzzy hypersoft set.

Infrastructure development and the economy heavily rely on the construction industry. However, decision-making in construction projects can be intricate and difficult due to conflicting standards and requirements. To address this challenge, the q-rung orthopair fuzzy soft set (q-ROFSS) has emerged as a useful tool incorporating fuzzy and uncertain contractions. In many cases, further characterization of attributes is necessary as their values are not mutually exclusive. The prevalent q-ROFSS structures cannot resolve this state. The q-rung orthopair fuzzy hypersoft sets (q-ROFHSS) is a leeway of q-ROFSS that use multi-parameter approximation functions to scare the scarcities of predominant fuzzy sets structures. The fundamental objective of this research is to introduce the Einstein weighted aggregation operators (AOs) for q-rung orthopair fuzzy hypersoft sets (q-ROFHSS), such as q-rung orthopair fuzzy hypersoft Einstein weighted average and geometric operators, and discuss their fundamental properties. Mathematical explanations of decision-making (DM) contractions is present to approve the rationality of the developed approach. Einstein AOs, based on predictions, carried an animated multi-criteria group decision (MCGDM) method with the most substantial significance with the prominent MCGDM structures. Moreover, we utilize our proposed MCGDM model to select the most suitable construction company for a given construction project. The proposed method is evaluated through a statistical analysis, which helps ensure the DM process's efficiency. This analysis demonstrates that the proposed method is more realistic and reliable than other DM approaches. Overall, the research provides valuable insights for decision-makers in the construction industry who seek to optimize their DM processes and improve the outcomes of their projects.

Influence of nanoparticles aggregation and Lorentz force on the dynamics of water-titanium dioxide nanoparticles on a rotating surface using finite element simulation.

This communication briefings the roles of Lorentz force and nanoparticles aggregation on the characteristics of water subject to Titanium dioxide rotating nanofluid flow toward a stretched surface. Due to upgrade the thermal transportation, the nanoparticles are incorporated, which are play significance role in modern technology, electronics, and heat exchangers. The primary objective of this communication is to observe the significance of nanoparticles aggregation to enhance the host fluid thermal conductivity. In order to model our work and investigate how aggregation characteristics affect the system's thermal conductivity, aggregation kinetics at the molecular level has been mathematically introduced. A dimensionless system of partial-differential equations is produced when the similarity transform is applied to a elaborated mathematical formulation. Thereafter, the numerical solution is obtained through a well-known computational finite element scheme via MATLAB environment. When the formulation of nanoparticle aggregation is taken into consideration, it is evident that although the magnitude of axial and transverse velocities is lower, the temperature distribution is enhanced by aggregation.

Nonlinearity in the relationship between COVID-19 cases and carbon damages: controlling financial development, green energy, and R&D expenditures for shared prosperity.

The world faces a high alert of coronavirus disease 2019 (COVID-19), leading to a million deaths and could become infected to reach a billion numbers. A sizeable amount of scholarly work has been available on different aspects of social-economic and environmental factors. At the same time, many of these studies found the linear (direct) causation between the stated factors. In many cases, the direct relationship is not apparent. The world is unsure about the possible determining factors of the COVID-19 pandemic, which need to be known through conducting nonlinearity (indirect) relationships, which caused the pandemic crisis. The study examined the nonlinear relationship between COVID-19 cases and carbon damages, managing financial development, renewable energy consumption, and innovative capability in a cross section of 65 countries. The results show that inbound foreign direct investment first increases and later decreases because of the increasing coronavirus cases. Further, the rise and fall in the research and development expenditures and population density exhibits increasing coronavirus cases across countries. The continued economic growth initial decreases later increase by adopting standardized operating procedures to contain coronavirus disease. The inter-temporal relationship shows that green energy source and carbon damages would likely influence the coronavirus cases with a variance of 17.127% and 5.440%, respectively, over a time horizon. The policymakers should be carefully designing sustainable healthcare policies, as the cost of carbon emissions leads to severe healthcare issues, which are likely to get exposed to contagious diseases, including COVID-19. The sustainable policy instruments, including renewable fuels in industrial production, advancement in cleaner production technologies, the imposition of carbon taxes on dirty production, and environmental certifications, are a few possible remedies that achieve healthcare sustainability agenda globally.

Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme.

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected machine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords "covid", "covid19", "coronavirus", "covid-19", "sarscov2", and "covid_19".

Financial development, oil resources, and environmental degradation in pandemic recession: to go down in flames.

The novel coronavirus disease-2019 (COVID-19) is a deadly disease that increases global healthcare sufferings. Further, it affects the financial and natural resource market simultaneously, as both are considered complementary goods. The volatility in the oil prices deteriorates the global financial market to substantiate the "financial resource (oil) curse" hypothesis primarily filled with earlier studies. In contrast, this study moved forward and extended the given relationship during the COVID-19 pandemic in a panel of 81 different countries. The study's main objective is to examine the volatility in the domestic credit provided to the private sector due to oil shocks and the COVID-19 pandemic across countries. The study is essential to assess the healthcare vulnerability in the COVID-19 pandemic, leading to the damage of financial stability, causing deterioration in the oil rents to affect the global sustainability agenda. The study employed statistical techniques to get sound inferences of the parameter estimates, including robust least squares regression, seemingly unrelated regression, and innovation accounting matrix to get a variable estimate at the level and inter-temporal framework. The results confirmed the U-shaped relationship between oil rents and financial development during the COVID-19 pandemic. Thus, it verifies the "financial resource (oil) curse" hypothesis at the initial stage of the COVID-19 pandemic. Later down, it supports the capital market when economies are resuming their economic activities and maintaining the SOPs to restrain coronavirus at a global scale. The qualitative assessment confirmed the negative effect of financial development and oil shocks on environmental quality during the pandemic crisis. The innovation accounting matrix shows that the COVID-19 pandemic will primarily be the main factor that intervenes in the relationship between oil rents and financial development, which proceed towards the "resource curse" hypothesis during the following years' time period. Therefore, the need for long-term economic policies is highly desirable to support the financial and resource market under the suggested guidelines of restraining coronavirus worldwide.

Does improvement in the environmental sustainability rating help to reduce the COVID-19 cases? Controlling financial development, price level and carbon damages.

The study's objective is to evaluate the impact of environmental sustainability rating, financial development, changes in the price level and carbon damages on the new COVID-19 cases in a cross-sectional panel of 17 countries. The study developed two broad models to analyse the relationship between the stated factors at the current level and forecast level. The results show that improvement in the environmental sustainability rating and financial efficiency reduces the COVID-19 cases, while continued economic growth and changes in price level likely to exacerbate the COVID-19 cases across countries. The forecast results suggest the U-shaped relationship between COVID-19 cases and carbon damages controlling financial development, price level and environmental sustainability rating. The variance decomposition analysis shows that carbon damages, environmental sustainability rating and price level changes will largely influence COVID-19 cases over the next year. The soundness of economic and ecological regulated policies would be helpful to contain coronavirus cases globally.