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The purpose of this article is to identify problems and opportunities for improving the formation of financial statements (FS) in accordance with International Standards, which is the most important instrument for international economic integration.

In carrying out the research, we used the FS of the modern organizations, prepared in accordance with International Standards (IASs and IFRSs), posted on the official websites of these organizations. At the same time, we researched the FS of both Russian and foreign organizations in order to draw conclusions about the problems of preparing FS in accordance with International Standards, which are typical for most modern organizations in different countries. When conducting research, we used methods such as comparison, analysis and synthesis.

We identified the main problems that arise in practice when preparing FS in accordance with International Standards, which are typical for both Russian and foreign organizations. We also analysed the project of the IASB (the organization that develops these standards) to improve the requirements for the preparation of FS in accordance with International Standards. Based on the analysis carried out, we identified the main problems arising from the planned requirements of this project. As a result of the research carried out, we made proposals for solving the identified problems of the formation of FS in accordance with International Standards.

Our proposals can be used to improve the regulations of International Standards for the formation of FS, and can also be applied in the practical work of modern organizations.

Purpose – The purpose of this research is to investigate the relationship of lead (Pb) and zinc (Zn) contents in sediment of Faunus ater (F. ater) population density and to analyze the relationship between Pb and Zn accumulation in F. ater with F. ater density in Reuleng River, Leupung, Aceh Besar.

Design/Methodology/Approach – Sampling was conducted in November 2016 until January 2017. Density of F. ater was analyzed by density formula while its relationship to Pb and Zn in sediments and F. ater was conducted by correlation analysis method.

Findings – The results showed that correlation between Pb and Zn in sediments and in F. ater varies at each locations on every month of sampling. Pb and Zn content in sediments found a fluctuating relationship in each month of sampling with density of F. ater. Correlation of Pb content in sediments with F. ater density showed a medium correlation in January 2017 with r-value = 0.665. Zn in sediment has a very strong correlation to F. ater density in November 2016 with r-value = 0.891. Pb in F. ater has a medium correlation to F. ater density in January 2017 with r-value = 0.436. Furthermore, accumulation of Zn in F. ater to its density does show some apparent correlation in each month of sampling.

Research Limitation/Implications – This research gives information about the relationship of Pb and Zn contents in sediment to density of F. ater and to analyze correlation of Pb and Zn in F. ater to density of F. ater in Reuleng River, Leupung, Aceh Besar district.

Originality/Value – This is the first time research is conducted about on the correlation between lead and zinc to obtain the density of F. ater.

This chapter adopts value at risk (VaR) to analyze the hedge timing issue. Suppose that a producer, at a give time, recognizes the possible need of a futures contract for risk reduction purpose. Should the producer trade in the futures market immediately or should he wait? Conditions are characterized under which delaying the hedge decision is preferred as it produces a smaller VaR. For an efficient futures market, it appears that the producer is better off delaying the hedge decision as long as possible. However, strong backwardation promotes early hedging.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

This study aims to explore the pandemic's opportunities for enhancing the environmental practices of the food and beverages green supply chains and its effect on the supply chains' viability by exploring the relationship between fear and uncertainty of COVID-19, food and beverages green supply chain management (F&B-GSCM) and supply chains’ viability based on the two dimensions (robustness and resilience) and examine the moderating effect of innovative technology adoption like big data analysis (BDA) capabilities and blockchain technologies (BCT) on this relationship.

This study adopted partial least squares structural equation modeling (PLS-SEM) on a sample of 362 F&B small and medium enterprises (SMEs)’ managers in the Egyptian market for data analysis and hypothesis testing.

The empirical results show that the fear and uncertainty of the pandemic have a significant positive effect on green supply chain management (GSCM). Also, BDA moderates the relationship between fear and uncertainty of COVID-19 and GSCM. However, BCT do not moderate that relationship. Similarly, GSCM positively affects supply chain viability dimensions (robustness and resilience). In addition, F&B-GSCM significantly mediates the relationship between fear and uncertainty of COVID-19 and supply chain viability dimensions (robustness and resilience).

Food and beverages (F&B) managers could develop a consistent strategy for applying BCT and BDA to provide clear information and focus on their procedures to meet their stakeholders' needs during COVID-19. Governments and managers should develop a consistent strategy to apply food and beverages supply chains (F&B SCs)' green practices to achieve F&B SCs' resilience and robustness, especially during the pandemic.

The Egyptian F&B SCs have been linked directly with many European countries as a main source of many basic food and agriculture products, which have been affected lately by the pandemic. Based on the “social-cognitive,” “stakeholder” and “resource-based view” theories, this study sheds light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of F&B-GSCM, SC resilience, SC robustness and innovative technologies back into the light, which helps in solving F&B SC issues and helps to achieve their viability.

Permanent magnet synchronous spherical motors can have wide use in robotics and industrial automation. They enable three-DOF omnidirectional motion of their rotor. They are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. Unlike conventional synchronous motors, permanent magnet synchronous spherical motors consist of a fixed inner shell, which is the stator, and a rotating outer shell, which is the rotor. Their dynamic model is multivariable and strongly nonlinear. The treatment of the associated control problem is important.

In this paper, the multivariable dynamic model of permanent magnet synchronous spherical motors is analysed, and a nonlinear optimal (H-infinity) control method is developed for it. Differential flatness properties are proven for the spherical motors’ state-space model. Next, the motors’ state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a time-varying operating point, which is defined by the present value of the motors’ state vector and by the last sampled value of the control input vector. For the approximately linearized model of the permanent magnet synchronous spherical motors, a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains, an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. Finally, the performance of the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops.

Due to the nonlinear and multivariable structure of the state-space model of spherical motors, the solution of the associated nonlinear control problem is a nontrivial task. In this paper, a novel nonlinear optimal (H-infinity) control approach is proposed for the dynamic model of permanent magnet synchronous spherical motors. The method is based on approximate linearization of the motor’s state-space model with the use of first-order Taylor series expansion and the computation of the associated Jacobian matrices. Furthermore, the paper has introduced a different solution to the nonlinear control problem of the permanent magnet synchronous spherical motor, which is based on flatness-based control implemented in successive loops.

The presented control approaches do not exhibit any limitations, but on the contrary, they have specific advantages. In comparison to global linearization-based control schemes (such as Lie-algebra-based control), they do not make use of complicated changes of state variables (diffeomorphisms) and transformations of the system's state-space description. The computed control inputs are applied directly to the initial nonlinear state-space model of the permanent magnet spherical motor without the intervention of inverse transformations and thus without coming against the risk of singularities.

The motion control problem of spherical motors is nontrivial because of the complicated nonlinear and multivariable dynamics of these electric machines. So far, there have been several attempts to apply nonlinear feedback control to permanent magnet-synchronous spherical motors. However, due to the model’s complexity, few results exist about the associated nonlinear optimal control problem. The proposed nonlinear control methods for permanent magnet synchronous spherical motors make more efficient, precise and reliable the use of such motors in robotics, electric traction and several automation systems.

The treated research topic is central for robotic and industrial automation. Permanent magnet synchronous spherical motors are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. The solution of the control problem for the nonlinear dynamic model of permanent magnet synchronous spherical motors has many industrial applications and therefore contributes to economic growth and development.

The proposed nonlinear optimal control method is novel compared to past attempts to solve the optimal control problem for nonlinear dynamical systems. Unlike past approaches, in the new nonlinear optimal control method, linearization is performed around a temporary operating point, which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector and not at points that belong to the desirable trajectory (setpoints). Besides, the Riccati equation which is used for computing the feedback gains of the controller is new, and so is the global stability proof for this control method. Compared to nonlinear model predictive control, which is a popular approach for treating the optimal control problem in industry, the new nonlinear optimal (H-infinity) control scheme is of proven global stability, and the convergence of its iterative search for the optimum does not depend on initial conditions and trials with multiple sets of controller parameters. It is also noteworthy that the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems which can be transformed into the linear parameter varying form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes, which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions. Furthermore, the second control method proposed in this paper, which is flatness-based control in successive loops, is also novel and demonstrates substantial contribution to nonlinear control for robotics and industrial automation.

This study aims to present the cost and revenue efficiency evaluation models in data envelopment analysis in the presence of fuzzy inputs, outputs and their prices that the prices are also fuzzy. This study applies the proposed approach in the energy sector of the oil industry.

This study proposes a value-based technology according to fuzzy input-cost and revenue-output data, and based on this technology, the authors propose an approach to calculate fuzzy cost and revenue efficiency based on a directional distance function approach. These papers incorporated a decision-maker’s (DM) a priori knowledge into the fuzzy cost (revenue) efficiency analysis.

This study shows that the proposed approach obtains the components of fuzzy numbers corresponding to fuzzy cost efficiency scores in the interval [0, 1] corresponding to each of the decision-making units (DMUs). The models presented in this paper satisfies the most important properties: translation invariance, translation invariance, handle with negative data. The proposed approach obtains the fuzzy efficient targets corresponding to each DMU.

In the proposed approach, by selecting the appropriate direction vector in the model, we can incorporate preference information of the DM in the process of evaluating fuzzy cost or revenue efficiency and this shows the efficiency of the method and the advantages of the proposed model in a fully fuzzy environment.

The purpose of this study is to analyze the transportation of heat and mass in three-dimensional (3D) shear rate-dependent viscous fluid. Thermal enhancement plays a significant role in industrial and engineering applications. For this, the authors dispersed trihybrid nanoparticles into the fluid to enhance the working fluid’s thermal enhancement.

The finite element method is a numerical scheme and is powerful in achieving convergent and grid-independent solutions compared with other numerical techniques. This method was initially assigned to structural problems. However, it is equally successful for computational fluid dynamics problems.

Wall shear stress has shown an increasing behavior as the intensity of the magnetic field is increased. Simulations have predicted that Ohmic heat in the case of trihybrid nanofluid (MoS₂–Al₂O₃–Cu/C₂H₆O₂) has the greatest value in comparison with mono and hybrid nanofluids. The most significant influence of chemical reaction on the concentration in tri-nanofluid is noted. This observation is pointed out for both types of chemical reaction (destructive or generative) parameters.

Through a literature survey, the authors analyzed that no one has yet to work on a 3D magnetohydrodynamics Carreau–Yasuda trihybrid nanofluid over a stretched sheet for improving heat and mass transfer over hybrid nanofluids. Herein, molybdenum disulfide (MoS₂), aluminum oxide (Al₂O₃) and copper (Cu) nanoparticles are mixed in ethylene glycol (C₂H₆O₂) to study the thermal enhancement and mass transport of their corresponding resultant mono (Cu/C₂H₆O₂), hybrid (Al₂O₃–Cu/C₂H₆O₂) and trihybrid (MoS₂–Al₂O₃–Cu/C₂H₆O₂) nanofluids.