Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

3D molecular structural modeling and characterization of indium phosphide via irregularity topological indices.

Indium phosphide (InP) is a binary semiconductor composed of indium and phosphorus. It has a zinc blende crystal structure, which is a type of cubic lattice structure. This lattice is composed of indium and phosphorus atoms arranged in a lattice of cube-shaped cells, with each cell containing four indium atoms and four phosphorus atoms. This lattice structure is the same for all materials with a zinc blende crystal structure and is the most common type of lattice structure in semiconductors. Indium phosphide (InP) has several chemical applications. It is commonly used as a dopant in the production of semiconductors, where it helps control the electrical properties of the material. InP is also utilized in the synthesis various indium-containing compounds, which can have applications in catalysts and chemical reactions. Additionally, InP nanoparticles have been investigated for their potential use in biomedical imaging and drug delivery systems. The topological characterization of 3D molecular structures can be performed via graph theory. In graph theory, the connections between atoms are represented as edges and the atoms themselves are represented as nodes. Furthermore, graph theory can be used to calculate the topological descriptors of the molecule such as the degree-based and reverse degree-based irregularity toplogical indices. These descriptors can be used to compare the topology of different molecules. This paper deals with the modeling and topological characterization of indium phosphide ( InP ) via degree-based and reverse irregularity indices. The 3D crystal structure of the InP is topologically modeled via partition of the edges, and derived closed form expressions for its irregularity indices. Our obtained results will be surely be helpful in investigating the QSPR/QSAR analysis as well as understanding the deep irregular behavior of the indium phosphide ( InP ) .

Numerical investigation of heat transfer and fluid flow characteristics of ternary nanofluids through convergent and divergent channels.

The characteristics of nanomaterials have garnered significant attention in recent research on natural and forced convection. This study focuses on the forced convection characteristics of ternary nanofluids within convergent and divergent channels. The ternary nanofluid comprises titanium oxide (TiO2), zinc oxide (ZnO), and silver suspended in water, which serves as the base fluid. Using COMSOL Multiphysics 6.0, a reliable software for finite element analysis, numerical simulations were conducted for steady and incompressible two-dimensional flow. Reynolds numbers varying from 100 to 800 were employed to investigate forced convection. Additionally, we explored aspect ratios (channel height divided by the height of the convergent or divergent section) of -0.4, -0.2, 0, 0.2, and 0.4. Our findings revealed that only at aspect ratio a = 0.4 did the average outlet temperature increase as the Reynolds number rose, while other aspect ratios exhibited decreasing average temperatures with declining Reynolds numbers. Moreover, as the Reynolds number increased from 100 to 800 and the total volume fraction of the ternary nanofluids ranged from 0.003 to 0.15, there was a significant 100% enhancement in the average Nusselt number. For clarity, this article briefly presents essential information, such as the study's numerical nature, fluid properties (constant-property fluid), and the methodology (COMSOL Multiphysics 6.0, finite element analysis). Key conclusions are highlighted to enable readers to grasp the main outcomes at a glance. These details are also adequately covered in the manuscript to facilitate a comprehensive understanding of the research. The utilization of this emerging phenomenon holds immense potential in various applications, ranging from the development of highly efficient heat exchangers to the optimization of thermal energy systems. This phenomenon can be harnessed in scenarios in which effective cost management in thermal production is a critical consideration.

Three dimensional study for entropy optimization in nanofluid flow through a compliant curved duct: A drug delivery and therapy application.

This research explores the three-dimensional characteristics of nanofluid dynamics within curved ducts, in contrast to earlier studies that mainly focus on two-dimensional flow. By using this ground-breaking method, we can capture a more accurate depiction of fluid behavior that complies with the intricate duct design. In this study, we investigate the three dimensional flow and entropic analysis of peristaltic nanofluid flows in a flexible curved duct, comparing the effects of silver and copper nanoparticles. To obtain accurate results, we assume physical constraints such as long wavelength and low Reynolds number and used a perturbation technique through NDSolve commands for finding exact solutions of the obtained differential equations. A comprehensive error analysis is provided through residual error table and figures to estimate a suitable range of the physical factors. Our findings indicate that the velocity of the nanofluid is directly proportional to the elasticity of the walls, while the mass per unit volume inversely affects velocity. We show that reducing the aspect ratio of the duct rectangular section can decrease entropy generation by raising magnitudes of damping force exerted by to the flexible walls of the enclosure. Additionally, using a larger height of the channel than the breadth can reduce stream boluses. The practical implications of this study extend beyond turbines and endoscopy to biomedical processes such as drug delivery and microfluidic systems.

Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function.

The LINEX loss function, which climbs exponentially with one-half of zero and virtually linearly on either side of zero, is employed to analyze parameter analysis and prediction problems. It can be used to solve both underestimation and overestimation issues. This paper explained the Bayesian estimation of mean, Gamma distribution, and Poisson process. First, an improved estimator for µ 2 is provided (which employs a variation coefficient). Under the LINEX loss function, a better estimator for the square root of the median is also derived, and an enhanced estimation for the average mean in such a negatively exponential function. Second, giving a gamma distribution as a prior and a likelihood function as posterior yields a gamma distribution. The LINEX method can be used to estimate an estimator λ B L ^ using posterior distribution. After obtaining λ B L ^ , the hazard function h B L ^ and D B L ^ the function of survival estimators are used. Third, the challenge of sequentially predicting the intensity variable of a uniform Poisson process with a linear exponentially (LINEX) loss function and a constant cost of production time is investigated using a Bayesian model. The APO rule is offered as an approximation pointwise optimal rule. LINEX is the loss function used. A variety of prior distributions have already been studied, and Bayesian estimation methods have been evaluated against squared error loss function estimation methods. Finally, compare the results of Maximum Likelihood Estimation (MLE) and LINEX estimation to determine which technique is appropriate for such information by identifying the lowest Mean Square Error (MSE). The displaced estimation method under the LINEX loss function was also examined in this research, and an improved estimation was proposed.

Impact of pangolin bootleg market on the dynamics of COVID-19 model.

In this paper we consider ant-eating pangolin as a possible source of the novel corona virus (COVID-19) and propose a new mathematical model describing the dynamics of COVID-19 pandemic. Our new model is based on the hypotheses that the pangolin and human populations are divided into measurable partitions and also incorporates pangolin bootleg market or reservoir. First we study the important mathematical properties like existence, boundedness and positivity of solution of the proposed model. After finding the threshold quantity for the underlying model, the possible stationary states are explored. We exploit linearization as well as Lyapanuv function theory to exhibit local stability analysis of the model in terms of the threshold quantity. We then discuss the global stability analyses of the newly introduced model and found conditions for its stability in terms of the basic reproduction number. It is also shown that for certain values of R 0 , our model exhibits a backward bifurcation. Numerical simulations are performed to verify and support our analytical findings.

Sensitivity analysis and optimal control of COVID-19 dynamics based on SEIQR model.

It is of great curiosity to observe the effects of prevention methods and the magnitudes of the outbreak including epidemic prediction, at the onset of an epidemic. To deal with COVID-19 Pandemic, an SEIQR model has been designed. Analytical study of the model consists of the calculation of the basic reproduction number and the constant level of disease absent and disease present equilibrium. The model also explores number of cases and the predicted outcomes are in line with the cases registered. By parameters calibration, new cases in Pakistan are also predicted. The number of patients at the current level and the permanent level of COVID-19 cases are also calculated analytically and through simulations. The future situation has also been discussed, which could happen if precautionary restrictions are adopted.

Numerical study of fractional order COVID-19 pandemic transmission model in context of ABO blood group.

The worldwide association of health (WHO) has stated that COVID-19 (the novel coronavirus disease-2019) as a pandemic. Here, the common SEIR model is generalized in order to show the dynamics of COVID-19 transmission taking into account the ABO blood group of the infected people. Fractional order Caputo derivative are used in the proposed model. Our study is guided by the results that have been obtained by Chen J, Fan H, Zhang L, et al. from three unique medical clinics in Wuhan and Shenzhen, China. In this study, the feasibility region of the proposed model are calculated plus the points of equilibrium. Also, the equilibrium points stability is examined. A unique solution existence for the proposed paradigm is proved via utilizing the fixed point theory with regards to Caputo fractional derivative. Numerical experiments of the proposed paradigm is done and we show its sensitivity to the fractional order.

Mathematical analysis of COVID-19 via new mathematical model.

We develop a new mathematical model by including the resistive class together with quarantine class and use it to investigate the transmission dynamics of the novel corona virus disease (COVID-19). Our developed model consists of four compartments, namely the susceptible class, S ( t ) , the healthy (resistive) class, H ( t ) , the infected class, I ( t ) and the quarantine class, Q ( t ) . We derive basic properties like, boundedness and positivity, of our proposed model in a biologically feasible region. To discuss the local as well as the global behaviour of the possible equilibria of the model, we compute the threshold quantity. The linearization and Lyapunov function theory are used to derive conditions for the stability analysis of the possible equilibrium states. We present numerical simulations to support our investigations. The simulations are compared with the available real data for Wuhan city in China, where the infection was initially originated.

Theoretical and numerical analysis of novel COVID-19 via fractional order mathematical model.

In the work, author's presents a very significant and important issues related to the health of mankind's. Which is extremely important to realize the complex dynamic of inflected disease. With the help of Caputo fractional derivative, We capture the epidemiological system for the transmission of Novel Coronavirus-19 Infectious Disease (nCOVID-19). We constructed the model in four compartments susceptible, exposed, infected and recovered. We obtained the conditions for existence and Ulam's type stability for proposed system by using the tools of non-linear analysis. The author's thoroughly discussed the local and global asymptotical stabilities of underling model upon the disease free, endemic equilibrium and reproductive number. We used the techniques of Laplace Adomian decomposition method for the approximate solution of consider system. Furthermore, author's interpret the dynamics of proposed system graphically via Mathematica, from which we observed that disease can be either controlled to a large extent or eliminate, if transmission rate is reduced and increase the rate of treatment.

A Hybrid Semantic Knowledge Integration and Sharing Approach for Distributed Smart Environments.

Distributed systems provide smart functionality to everyday objects with the help of wireless sensors using the internet. Since the last decade, the industry is struggling to develop efficient and intelligent protocols to integrate a huge number of smart objects in distributed computing environments. However, the main challenge for smart and distributed system designers lies in the integration of a large number of heterogeneous components for faster, cheaper, and more efficient functionalities. To deal with this issue, practitioners are using edge computing along with server and desktop technology for the development of smart applications by using Service-Oriented Architecture (SOA) where every smart object offers its functionality as a service, enabling other objects to interact with them dynamically. In order to make such a system, researchers have considered context-awareness and Quality of Service (QoS) attributes of device services. However, context modeling is a complicated task since it could include everything around the applications. Moreover, it is also important to consider non-functional interactions that may have an impact on the behavior of the complete system. In this regard, various research dimensions are explored. However, rich context-aware modeling, QoS, user priorities, grouping, and value type direction along with uncertainty are not considered properly while modeling of incomplete or partial domain knowledge during ontology engineering, resulting in low accuracy of results. In this paper, we present a semantic and logic-based formal framework (hybrid) to find the best service among many candidate services by considering the limitations of existing frameworks. Experimental results of the proposed framework show the improvement of the discovered results.