Informatica

Spatial Global Optimization branch and bound (B&B) methods aim at enclosing global minimum points in a guaranteed way with a certain accuracy. We extend simplicial B&B (sBB) concepts to polytopal B&B (pBB), with polytope subsets. The main challenges are: polytope division and extension of monotonicity tests theoretically and algorithmically. We compare the performance of interval B&B with linear constraints (iBBLC), sBB and pBB algorithms, to determine the most efficient B&B algorithm for different types of instances.

Traditional loss functions such as mean squared error (MSE) are widely employed, but they often struggle to capture the dynamic characteristics of high-dimensional nonlinear systems. To address this issue, we propose an improved loss function that integrates linear multistep methods, system-consistency constraints, and prediction-phase error control. This construction simultaneously improves training accuracy and long-term stability. Furthermore, the introduction of recursive loss and interpolation strategies brings the model closer to practical prediction scenarios, broadening its applicability. Numerical simulations demonstrate that this construction significantly outperforms both mean square error and existing custom loss functions in terms of performance.

A steganographic scheme based on perfect coverings of dichotomous shares with sparse observation windows is presented in this paper. The manipulations with pixels are based on the number of different colours in the sparse cells of the current observation window. The conditions for the existence of perfect coverings for different architectures of sparse observation windows are derived. The number and distribution of active cells in the current observation window contribute to the additional security of the proposed scheme. This paper also provides performance measures, statistical features, and demonstrates the robustness of the proposed steganographic scheme.

New generation battery technology investments play a key role in the transition process from fossil fuels to renewable energy. The main problem related to the subject is that decision makers experience uncertainty about which of these numerous criteria affecting investment performance are prioritized. The lack of comprehensive models in the literature for systematically prioritizing these criteria creates a significant gap. The aim of this study is to determine the priority strategies to increase the performance of new generation battery technology investments. In this context, an innovative decision-making model is developed by integrating multi-facet fuzzy sets, logarithmic least-squares and WASPAS techniques. This study makes a significant contribution to the literature by prioritizing the performance indicators of new generation battery technology investments via an innovative decision-making model. The development of multi-facet fuzzy sets in this study provides an important contribution to the literature. Moreover, dynamic decision-making opportunity is provided by redefining membership degrees with different parameter sets for each scenario. This provides the opportunity to make clearer decisions based on scenarios and dynamic evaluations in complex decision-making processes. The main findings of the study indicate that circularity and compatibility with existing manufacturing infrastructure are priorities in improving the performance of these projects.

Nowadays sustainability and transportation concepts have been incorporated by the authorities and engineers. The indicator of this situation is the introduction of hybrid vehicles into the market. For the consumers, the purchasing process of hybrid vehicles is not easy because of the many alternatives with different brands including different properties. This process is considered a multi criteria problem with multi alternatives. This paper aims to develop a solution methodology for this problem of a company. The proposed methodology integrates the Interval Valued Intuitionistic Fuzzy (IVIF) sets and two Multi Criteria Decision Making (MCDM) methods; Analytic Hierarchy Process (AHP) and the Multi Attributive Border Approximation Area Comparison (MABAC). With the help of IVIF sets, the fuzziness in the structures of the decision problem and decision-making process is overcome. The IVIF AHP evaluation has revealed the importance that consumers attach to the criteria. According to the IVIF AHP results, each of the criteria has a similar weight. According to the IVIF MABAC results, the ranking order of the hybrid vehicle alternatives is specified as A₁–A₂–A₃–A₅–A₄. The advantage of the integrated IVIF AHP and IVIF MABAC approach is that it helps in evaluating the most suitable alternatives when there is a disagreement about the relative suitability of the criteria and requires less numerical calculations. The results and the comparative analysis conducted in the study also support this situation.

Existing fuzzy inference systems are generally based on ordinary fuzzy sets, which do not let the second and third dimensions of the other fuzzy sets extensions to be employed. This paper suggests a decision-making approach by utilizing the fuzzy inference systems (FIS) based on spherical fuzzy sets (SFS). We prefer spherical fuzzy sets to consider the indecision degree together with membership and non-membership degrees in the proposed FIS. During the defuzzification of SF inference system, the indecision degree is distributed over membership and non-membership degree in balance regarding to indecision degree by using a special transformation function. By applying the proposed approach on FIS, it aims to cover hesitancies and uncertainties caused by insufficient assessments of the decision makers more effectively. The proposed decision-making approach is tested with a real-world application in the field of maintenance work order prioritization for scheduling. Finally, the result of the suggested approach based on SFS is compared with the risk assessment matrix technique (RAM) existing in the literature and Picture Fuzzy Inference Systems (PiFIS). It is observed that the proposed Spherical Fuzzy Inference System (SFIS) is more efficient than RAM and PiFIS methods.

Seismic hazard analysis plays a vital role in evaluating the potential earthquake risk in a given region. Northeast India is one of the most seismically active zones due to its tectonic positioning at the collision boundary of the Indian and Eurasian plates. This study aims to implement a comprehensive Seismic Hazard Assessment (SHA) framework using Fuzzy Multi-Criteria Decision Making (MCDM) techniques to improve the accuracy and reliability of Peak Ground Acceleration (PGA) estimates in Northeast India. The methodology integrates Trapezoidal Fuzzy Full Consistency Method (TrF-FUCOM) and Neutrosophic-TOPSIS under Single Valued Neutrosophic Set (SVNS) environment (Neutrosophic-TOPSIS), effectively addressing the limitations of traditional seismic hazard assessment methods, particularly in selecting and weighting Ground Motion Prediction Equations (GMPEs). An extensive earthquake catalogue covering the period from 1762 to 2024 has been analysed, and after declustering, fault zones have been delineated based on earthquake density along active faults. The analysis provides a detailed spatial distribution of Peak Ground Acceleration (PGA) across the region, with the highest PGA value reaching 1.43g using the Deterministic Seismic Hazard Assessment (DSHA) method. The findings of this study offer crucial insights for disaster preparedness, urban planning, and the design of earthquake-resistant infrastructure, helping to mitigate seismic risks and enhance the resilience of communities in Northeast India.

Quantum computing has come to stay in our lives. Companies are investing billions of dollars in it because of the potential benefits that it can achieve, providing promising applications in almost every business sector. Although quantum computing is evolving at an exponential rate, the development of tools, techniques, or frameworks for the evolution of current information systems towards quantum software systems is still proving to be a challenge. This research contributes to the evolution of current information systems towards hybrid information systems (combining the classical and quantum computing paradigm). We propose a software modernization process, by following model-driven engineering principles, adapted to the quantum paradigm, based on modified versions of standards for reverse engineering of classical, quantum software assets, and for the design of the target system. In particular, this paper focuses on the restructuring transformation from KDM to UML models, where KDM models have been generated from Q# code. This proposal has been validated through a case study involving 17 programmes. The results obtained show optimistic values regarding the complexity of the UML models generated, their expressiveness and scalability. The main implication of this research is that UML models can indeed help the software evolution of/toward hybrid information systems.

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.