Informatica

The Multi-Objective Mixed-Integer Programming (MOMIP) problem is one of the most challenging. To derive its Pareto optimal solutions one can use the well-known Chebyshev scalarization and Mixed-Integer Programming (MIP) solvers. However, for a large-scale instance of the MOMIP problem, its scalarization may not be solved to optimality, even by state-of-the-art optimization packages, within the time limit imposed on optimization. If a MIP solver cannot derive the optimal solution within the assumed time limit, it provides the optimality gap, which gauges the quality of the approximate solution. However, for the MOMIP case, no information is provided on the lower and upper bounds of the components of the Pareto optimal outcome. For the MOMIP problem with two and three objective functions, an algorithm is proposed to provide the so-called interval representation of the Pareto optimal outcome designated by the weighting vector when there is a time limit on solving the Chebyshev scalarization. Such interval representations can be used to navigate on the Pareto front. The results of several numerical experiments on selected large-scale instances of the multi-objective multidimensional 0–1 knapsack problem illustrate the proposed approach. The limitations and possible enhancements of the proposed method are also discussed.

Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence.

This article offers a comprehensive overview of the results obtained through numerical methods in solving the minimal surface equation, along with exploring the applications of minimal surfaces in science, technology, and architecture. The content is enriched with practical examples highlighting the diverse applications of minimal surfaces.

The significance of earth observation data spans diverse fields and domains, driving the need for efficient management. Nevertheless, the exponential increase in data volume brings new challenges that complicate processing and storing data. This article proposes an optimized multi-modular service for earth observation data management in response to these challenges. The suggested approach focuses on choosing the optimal configurations for the storage and processing layers to improve the performance and cost-effectiveness of managing data. By employing the recommended optimized strategies, earth observation data can be managed more effectively, resulting in fast data processing and reduced costs.

The extensions of ordinary fuzzy sets are problematic because they require decimal numbers for membership, non-membership and indecision degrees of an element from the experts, which cannot be easily determined. This will be more difficult when three or more digits’ membership degrees have to be assigned. Instead, proportional relations between the degrees of parameters of a fuzzy set extension will make it easier to determine the membership, non-membership, and indecision degrees. The objective of this paper is to present a simple but effective technique for determining these degrees with several decimal digits and to enable the expert to assign more stable values when asked at different time points. Some proportion-based models for the fuzzy sets extensions, intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and spherical fuzzy sets are proposed, including their arithmetic operations and aggregation operators. Proportional fuzzy sets require only the proportional relations between the parameters of the extensions of fuzzy sets. Their contribution is that these models will ease the use of fuzzy set extensions with the data better representing expert judgments. The imprecise definition of proportions is also incorporated into the given models. The application and comparative analyses result in that proportional fuzzy sets are easily applied to any problem and produce valid outcomes. Furthermore, proportional fuzzy sets clearly showed the role of the degree of indecision in the ranking of alternatives in binomial and trinomial fuzzy sets. In the considered car selection problem, it has been observed that there are minor changes in the ordering of intuitionistic and spherical fuzzy sets.

Derivative-free DIRECT-type global optimization algorithms are increasingly favoured for their simplicity and effectiveness in addressing real-world optimization challenges. This review examines their practical applications through a systematic analysis of scientific journals and computational studies. In particular, significant challenges in reproducibility have been identified with practical problems. To address this, we conducted an experimental study using practical problems from reputable CEC libraries, comparing DIRECT-type techniques against their state-of-the-art counterparts. Therefore, this study sheds light on current gaps, opportunities, and future prospects for advanced research in this domain, laying the foundation for replicating and expanding the research findings presented herein.

The interval-valued intuitionistic fuzzy sets (IVIFSs), based on the intuitionistic fuzzy sets (IFSs), combine the classical decision method and its research and application is attracting attention. After a comparative analysis, it becomes clear that multiple classical methods with IVIFSs’ information have been applied to many practical issues. In this paper, we extended the classical EDAS method based on the Cumulative Prospect Theory (CPT) considering the decision experts (DEs)’ psychological factors under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, an original EDAS method, based on the CPT under IVIFSs (IVIF-CPT-EDAS) method, is created for multiple-attribute group decision making (MAGDM) issues. Meanwhile, the information entropy method is used to evaluate the attribute weight. Finally, a numerical example for Green Technology Venture Capital (GTVC) project selection is given, some comparisons are used to illustrate the advantages of the IVIF-CPT-EDAS method and a sensitivity analysis is applied to prove the effectiveness and stability of this new method.

Conventional parking lots struggle to meet demand, prompting the rise of Fully Automated Parking Systems (FAPS), offering eco-friendly alternatives with advanced technology. However, operational challenges persist, especially in planning and scheduling. Real-time responsiveness necessitates dispatching rules and heuristics. This study comprehensively explores FAPS operational dynamics, assessing various rule combinations’ impact on customer wait times and system utilization. Utilizing a six-month MATLAB simulation, results favour the Nearest-Available-Slot (NAS) allocation rule coupled with First-Come-First-Served (FCFS) sequencing, emphasizing allocation’s pivotal role in system efficiency. Future research will refine allocation strategies to further optimize FAPS operational performance.

Energy conservation and emission reduction are important policies vigorously promoted in China. With the continuous popularization of the concept of green transportation, electric vehicles have become a green transportation tool with good development prospects, greatly reducing the pressure on the environment and resources caused by rapid economic growth. The development status of electric vehicles has a significant impact on urban energy security, environmental protection, and sustainable development in China. With the widespread application of new energy vehicles, charging piles have become an important auxiliary infrastructure necessary for the development of electric vehicles. They have significant social and economic benefits, so it is imperative to build electric vehicle charging piles. There are many factors to consider in the scientific layout of electric vehicle charging stations, and the location selection problem of electric vehicle charging stations is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Combined Compromise Solution (CoCoSo) technique and CRITIC technique have been utilized to deal with MAGDM issues. Spherical fuzzy sets (SFSs) can uncover the uncertainty and fuzziness in MAGDM more effectively and deeply. In this paper, on basis of CoCoSo technique, a novel spherical fuzzy number CoCoSo (SFN-CoCoSo) technique based on spherical fuzzy number cosine similarity measure (SFNCSM) and spherical fuzzy number Euclidean distance (SFNED) is conducted for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC technique is extended to SFSs to acquire the attribute weights based on the SFNCSM and SFNED. Finally, the SFN-CoCoSo technique is utilized for location selection problem of electric vehicle charging stations to prove practicability of the developed technique and compare the SFN-CoCoSo technique with existing techniques to further demonstrate its superiority.

Signcryption integrates both signature and encryption schemes into single scheme to ensure both content unforgeability (authentication) and message confidentiality while reducing computational complexity. Typically, both signers (senders) and decrypters (receivers) in a signcryption scheme belong to the same public-key systems. When signers and decrypters in a signcryption scheme belong to heterogeneous public-key systems, this scheme is called a hybrid signcryption scheme which provides more elastic usage than typical signcryption schemes. In recent years, a new kind of attack, named side-channel attack, allows adversaries to learn a portion of the secret keys used in cryptographic algorithms. To resist such an attack, leakage-resilient cryptography has been widely discussed and studied while a large number of leakage-resilient schemes have been proposed. Also, numerous hybrid signcryption schemes under heterogeneous public-key systems were proposed, but none of them possesses leakage-resilient property. In this paper, we propose the first hybrid signcryption scheme with leakage resilience, called leakage-resilient hybrid signcryption scheme, in heterogeneous public-key systems (LR-HSC-HPKS). Security proofs are demonstrated to show that the proposed scheme provides both authentication and confidentiality against two types of adversaries in heterogeneous public-key systems.