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Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local feedback and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic that fuses model-based and learning-based approaches and can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast. [ABSTRACT FROM AUTHOR]

This paper presents an offline optimization method designed for use with industrial robots in environments with static obstacles. It is particularly useful in industry where stability and predictability are crucial to meeting expected timelines in automated guided vehicle operations. The main methodological contribution of this work lies in the integral process used to define an effective fitness function that guides the optimization method in the search for optimal solutions. This cost function plays a critical role in the effectiveness of the trajectory tracking algorithm by quantifying path quality and allowing comparisons between solutions. The design of this fitness function poses challenges including accuracy, suitability, minimization of path length, and avoiding or reducing collisions. To achieve the optimization objectives and address some issues such as sensitivity to parameter scaling and the risk of premature convergence, different approaches can be used. This work proposes to incorporate constraints into the fitness function, adjust the optimization parameters to reflect the conditions of the problem, and design a fitness function based on prior knowledge and an accurate representation of the goals. The three relevant contributions for the planning and optimization of routes of automated guided vehicle in industrial environments are the following. Firstly, the development of a mathematical model of trajectories based on Frenet curves that considers the static occupancy map of the environment. Second, an optimization strategy to generate optimal safe paths. Finally, a fitness function that guides the optimization method towards optimal solutions considering the sensitivity to scaling and resolution of the parameters. This study presents an exhaustive analysis of the different fitness functions obtained, each one evaluated based on key metrics such as the length of the trajectory, the average and minimum distance to the occupancy map, and the number of collisions along the path. The results show that the obtained cost function successfully avoids collisions with the environment in all scenarios and consistently remains the fitness function with the largest average distance to obstacles, at least 50% higher than other functions used in this study. [ABSTRACT FROM AUTHOR]

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local feedback and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic that fuses model-based and learning-based approaches and can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast. [ABSTRACT FROM AUTHOR]

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance. This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed. An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week. [ABSTRACT FROM AUTHOR]

Since the start of COVID-19, a fair amount of work has been undertaken by scholars around the world to model its progression. It became clear from the start of pandemic that its progression is affected by various factors within different communities. Subsequently, the necessary means and the range of measures used to effectively control the virus would vary from place to place. And we have been witness to different approaches adopted around the world to maintain the virus under check both in the short term and the long term. So, in this unexpected situation, it is a great challenge for the world health organization (WHO) to save the lives of COVID-19 patients. For this, several mathematical models have been made for better understanding the coronavirus contagion. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. Thus, in this unexpected situation, fuzzy sets (FSs) are considered due to their inherent capability to deal with uncertainty. Fuzzy sets (FSs) theory has the ability to manage uncertain situations. Thus, the goal of this research is to present newly mathematical methods based on complex Pythagorean fuzzy sets (CPyFSs) and their operators, namely complex Pythagorean fuzzy Einstein weighted geometric operator, and induced complex Pythagorean fuzzy Einstein hybrid geometric operator to reduce the spreading rate of COVID-19. At the end of the paper an illustrative example is constructed to show the effectiveness, reliability of the new techniques. [ABSTRACT FROM AUTHOR]

The recycling of the used products collected by the manufacturing companies for recycling from their customers and their inclusion in the production system for remanufacturing has become an essential topic regarding sustainable production systems. Using parts and equipment in the remanufacturing system, especially in electronic products and products in the automotive sector, is preferred because of the opportunities to reduce costs and increase energy and workforce efficiency. In this article, we analyzed the management of a hybrid production system that processes new or returned materials in the manufacturing system. An intuitionistic fuzzy linear mathematical model is developed to demonstrate financial benefit and make decisions about using used parts in remanufacturing systems. Our study contributes to the literature by first adapting the magnitude function to the hybrid manufacturing and remanufacturing process optimization problem. Also, the innovative scope of the paper is expanded by applying IVITFN to the remanufacturing process optimization problem for the first time using comparative analysis. We consider relevant costs, the uncertainty about the number of returned products, demand, unit costs and unit production times, and capacity limitation in determining the optimal operation condition. The proposed model results show smaller cost values against other models in the literature. [ABSTRACT FROM AUTHOR]

The second-hand vessel market has quite different dynamics than the market of the new-building vessel, and highly complicated and conflicting criteria and many uncertainties affect the ship selection process. Therefore, it is required to use a robust mathematical model to solve these kinds of decision-making problems. For this purpose, this paper presents an extended version of WASPAS (Weighted Aggregated Sum Product ASsessment) techniques with the help of T2NN based on the Bonferroni function (T2NN WASPAS'B). The three main focal points of the proposed approach are (i) setting the influential criteria to select the appropriate Ro-Ro vessel in the second-hand vessel market; and (ii) presenting a flexible group decision-making approach, which is proper to real decision-making problems. (iii) detecting the interrelations among criteria and eliminating the negative impacts of undesirable and excessive values in input variables on the results. Practical use of the proposed approach is demonstrated to select the appropriate Ro-Ro vessel in the second-hand market. The analysis results show that the most effective and determinative factor is Trailer Lane length, and the most effective alternative is GREIFSWALD. Besides, the consistency and validity of the obtained results have been verified with the help of a stability and robustness check. The results prove that the proposed novel T2NN WASPAS'B model is robust, powerful, and reliable for making rational and realistic decisions. • The current paper presents the T2NN WASPAS'B approach strengthen with the help of T2NN based on the Bonferroni function. • The proposed model is applied to solve second-hand Ro-Ro vessel selection problems, which are highly complex and include uncertainties. • It has identified the selection criteria for second-hand Ro-Ro ships for the first time. • It presents a flexible decision-making approach, which is proper for real decision-making problems. • It demonstrates interrelations among factors to make it more understandable in complex situations. [ABSTRACT FROM AUTHOR]

Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models. [ABSTRACT FROM AUTHOR]