Informatica

As musculoskeletal illnesses continue to increase, practical computerised muscle modelling is crucial. This paper addresses this concern by proposing a mathematical model for a dynamic 3D geometrical surface representation of muscles using a Radial Basis Function (RBF) approximation technique. The objective is to obtain a smoother surface while minimising data use, contrasting it from classical polygonal (e.g. triangular) surface mesh models or volumetric (e.g. tetrahedral) mesh models. The paper uses RBF implicit surface description to describe static surface generation and dynamic surface deformations based on its spatial curvature preservation during the deformation. The novel method is tested on multiple data sets, and the experiments show promising results according to the introduced metrics.

In this paper, firstly, we propose two new GTHFNs-prioritized aggregation operators called generalized trapezoidal hesitant fuzzy number prioritized weighted average operator and generalized trapezoidal hesitant fuzzy number prioritized weighted geometric operator. Secondly, we investigate the fundamental properties of the operators in detail such as idempotency, boundedness and monotonicity. Thirdly, we propose a method based on the developed GTHF-numbers prioritized aggregation operators for solving an MADM problem with GTHF-numbers. Fourthly, we give a numerical example of the developed method. Finally, a comparative analysis is given with some existing methods in solving an MADM problem with GTHF-numbers.

Structural break detection is an important time series analysis task. It can be treated as a multi-objective optimization problem, in which we ought to find a time series segmentation such that time series theoretical models constructed on each segment are well-fitted and the segments are long enough to bear meaningful information. Metaheuristic optimization can help us solve this problem. This paper introduces a suite of new cost functions for the structural break detection task. We demonstrate that the new cost functions allow for achieving quantitatively better precision than the cost functions employed in the literature of this domain. We show particular advantages of each new cost function. Furthermore, the paper promotes the use of Particle Swarm Optimization (PSO) in the domain of structural break detection, which so far has relied on the Genetic Algorithm (GA). Our experiments show that PSO outperforms GA for many analysed time series examples. Last but not least, we introduce a non-trivial generalization of the top-performing state-of-the-art approach to the structural break detection problem based on the Minimum Description Length (MDL) rule with autoregressive (AR) model to MDL ARIMA (autoregressive integrated moving average) model.

Green communication is important for businesses to achieve customer satisfaction and gain a significant competitive advantage. Therefore, improving the indicators is very significant for increasing the green communication performance of businesses. However, these improvements cause cost increase for businesses. Hence, there is a significant need for a priority analysis on the variables that will affect the green communication performance of businesses to use the budget more effectively. The purpose of this study is to evaluate important indicators of effective green communication for the companies. For this purpose, a novel model is proposed that has mainly two different parts. In this process, the evaluations of three decision makers are taken into consideration. At the first stage, selected indicators are examined by using artificial intelligence-based sine trigonometric Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL). Secondly, emerging seven countries are ranked according to the performance of the green communication. In this context, artificial intelligence-based sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) technique is taken into consideration. Moreover, these countries are also ranked by using additive ratio assessment (ARAS) methodology to make a comparative evaluation. The main contribution of this study is that artificial intelligence methodology is integrated with the fuzzy decision-making model. Artificial intelligence methodology is considered to generate decision matrix. With the help of this situation, more appropriate calculations can be made. Proposing RATGOS methodology to the literature by the authors is another significant contribution of this proposed model. To overcome criticisms regarding the existing ranking decision-making techniques in the literature, RATGOS model is generated by making computations with geometrical mean. Owing to this issue, it can be possible to reach more effective solutions. The findings demonstrate that informativeness is the most crucial issue for the improvement of green communication performance of the companies. Meeting customer expectation is another important situation that should be taken into consideration in this manner. Considering these findings, it would be appropriate to establish sectoral standards and guidelines to provide information in green communication. Thanks to these standards, it is possible for companies to provide detailed and comprehensive information to their customers. The ranking results of both RATGOS and ARAS are the same that gives information about the consistency and coherency of the proposed model. The ranking results indicate that China and Russia are the most successful emerging countries with respect to the green communication performance.

The Hamiltonian cycle and path problems are fundamental in graph theory and useful in modelling real-life problems. Research in this area is directed toward designing better and better algorithms for general problems, but also toward defining new special cases for which exact polynomial-time algorithms exist. In the paper, such new classes of digraphs are proposed. The classes include, among others, quasi-adjoint graphs, which are a superclass of adjoints, directed line graphs, and graphs modelling a DNA sequencing problem.

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising.

One of the main trends for the monitoring and control of business processes is to implement these processes via private blockchain systems. These systems must ensure data privacy and verifiability for the entire network here denoted by ‘Net’. In addition, every business activity should be declared to a trusted third party (TTP), such as an Audit Authority (AA), for tax declaration and collection purposes.

The growing popularity of mobile and cloud computing raises new challenges related to energy efficiency. This work evaluates four various SQL and NoSQL database solutions in terms of energy efficiency. Namely, Cassandra, MongoDB, Redis, and MySQL are taken into consideration. This study measures energy efficiency of the chosen data storage solutions on a selected set of physical and virtual computing nodes by leveraging Intel RAPL (Running Average Power Limit) technology. Various database usage scenarios are considered in this evaluation including both local usage and remote offloading. Different workloads are benchmarked through the use of YCSB (Yahoo! Cloud Serving Benchmark) tool. Extensive experimental results show that (i) Redis and MongoDB are more efficient in energy consumption under most usage scenarios, (ii) remote offloading saves energy if the network latency is low and destination CPU is significantly more powerful, and (iii) computationally weaker CPUs may sometimes demonstrate higher energy efficiency in terms of J/ops. An energy efficiency measurement framework is proposed in order to evaluate and compare different database solutions based on the obtained experimental results.

Frequent gradual pattern extraction is an important problem in computer science widely studied by the data mining community. Such a pattern reflects a co-variation between attributes of a database. The applications of the extraction of the gradual patterns concern several fields, in particular, biology, finances, health and metrology. The algorithms for extracting these patterns are greedy in terms of memory and computational resources. This clearly poses the problem of improving their performance. This paper proposes a new approach for the extraction of gradual and frequent patterns based on the reduction of candidate generation and processing costs by exploiting frequent itemsets whose size is a power of two to generate all candidates. The analysis of the complexity, in terms of CPU time and memory usage, and the experiments show that the obtained algorithm outperforms the previous ones and confirms the interest of the proposed approach. It is sometimes at least 5 times faster than previous algorithms and requires at most half the memory.

This paper attempts to demystify the stability of CoCoSo ranking method via a comprehensive simulation experiment. In the experiment, matrices of different dimensions are generated via Python with fuzzy data. Stability is investigated via adequacy and partial adequacy tests. The test passes if the ranking order does not change even after changes are made to entities, and the partial pass signifies that the top ranked alternative remains intact. Results infer that CoCoSo method has better stability with respect to change of alternatives compared to criteria; and CoCoSo method shows better stability with respect to partial adequacy test for criteria.