Informatica

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.

This study proposes a novel method called the “Integrative Reference Point Approach (IRPA)” as an alternative method to existing MCDM methods. The basis of the newly proposed method is the satisfaction function and the reference set approach. Three different applications are performed to verify the validity of the proposed method from the perspective of optimal alternative rankings and sensitivity to changes in criteria weights. All results of comparative and sensitivity analyses show that the novel method is moderately sensitive to changes in criteria weights and compatible with other methods.

The Autonomous Vehicle (AV) industry is constantly growing, thus analysing its perspectives is essential. However, for this analysis a sophisticated approach is necessary which considers the ambiguity of decision-makers, and different objectives and criteria related to stakeholders. In this paper a new model is proposed based on Decomposed Fuzzy Sets and the Best-Worst Method to deal with possible non-reciprocity of pairwise comparisons and different preferences of stakeholders in the AV industry. The main advantage of the model is that it is capable of considering optimistic and pessimistic attitudes along with the different objectives and criteria of the involved groups. The results show that users require short travel time, while operators, manufacturers and legislators expect mainly the increase of revenues from the AV implementation. Among the most important criteria, our analysis indicates the need of regulatory and safety issues are the most essential obstacles of expanding the AV industry. The new model can also be applied for evaluating the perspectives of other emerging technologies and industrial sectors.

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.

In this paper, we focus on the problem of whether efficient Lithuanian large language models (LLMs) can be achieved from Llama2 LLMs, which lack Lithuanian-specific components. Although the Llama2 architecture was previously successfully utilised to derive various regional LLMs, we propose and describe the first open Llama2 LLMs for the Lithuanian language (7 and 13 billion parameter versions), an accompanying question/answer (Q/A) dataset, and translations of popular language understanding benchmarks (Arc, Belebele, Hellaswag, MMLU, TruthfulQA, and Winogrande), which contribute to the standardisation of Lithuanian LLM evaluation. We empirically evaluate the proposed models by investigating their perplexity and performance in the translated language understanding benchmarks. The perplexity experiments show that it decreases consistently during pretraining, reflecting enhanced next-token prediction capabilities. Benchmarking the proposed LLMs against language understanding tasks reveals that high-quality pretraining datasets may be essential to achieve models that perform efficiently on these benchmarks. Comparison of the proposed LLMs with the latest open multilingual LLM shows that our model with 13 billion parameters is ranked 4th of 8 models in tasks such as Arc, Hellaswag, and Winogrande, but is generally outperformed in other tasks. These benchmarks allow us to hypothesise that from recent LLMs more efficient Lithuanian language models can be derived in the future. The complete realisations of the LLMs and other contributed components are available in the accompanying open repository https://huggingface.co/neurotechnology.

For more than two decades, Lithuanian speech recognition has been researched solely in Lithuania due to the need for deep knowledge of Lithuanian. AI advancements now allow high-quality speech-to-text systems to be built without native knowledge, given sufficient annotated data is available. This study evaluated as many as 18 Lithuanian speech transcribers using a small piece of recording; 7 best ones were selected and evaluated using extensive data. The top system achieved a WER of 5.1% for Lithuanian words, with three others showing 8.7–9.2%. For other word-size tokens, such as numbers, speech disfluencies, abbreviations, foreign words, a classification adapted to the Lithuanian language was proposed. Different processing strategies for tokens of these classes were examined and it was assessed which transcribers tend to follow which strategies.

Electricity demand estimation is vital for the optimal design and operation of microgrids, especially in isolated, unelectrified, or partially electrified areas where demand patterns evolve with electricity adoption. This study proposes a causal model for electricity demand estimation that explicitly considers the electrification process along with key factors such as hour, month, weekday/weekend distinction, temperature, and humidity, effectively capturing both temporal and environmental demand patterns.

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.

This paper introduces a novel approach that bridges the floating-point (FP) format, widely utilized in diverse fields for data representation, with the μ-law companding quantizer, proposing a method for designing and linearizing the μ-law companding quantizer to yield a piecewise uniform quantizer tailored to the FP format. A key outcome of the paper is a closed-form approximate expression for closely and efficiently evaluating the FP format’s performance for data with the Laplacian distribution. This expression offers generality across various bit rates and data variances, markedly reducing the computational complexity of FP performance evaluation compared to prior methods reliant on summation of a large number of terms. By facilitating the evaluation of FP format performance, this research substantially aids in the selection of the optimal bit rates, crucial for digital representation quality, dynamic range, computational overhead, and energy efficiency. The numerical calculations spanning a wide range of data variances provided for some commonly used FP versions with an 8-bit exponent have demonstrated that the proposed closed-form expression closely approximates FP format performance.

Sustainable practices are essential for long-term societal development, minimizing environmental impacts while promoting the efficient use of resources. Multi-criteria decision-making (MCDM) approaches can play a vital role in assessing and prioritizing sustainability solutions by considering diverse economic, social, and environmental factors. This study proposes a multi-criteria group decision-making approach based on the Objective Pairwise Adjusted Ratio Analysis (OPARA) method in a fuzzy environment and presents its application for the assessment of sustainable agriculture solutions. In the proposed approach, the evaluation criteria weights are determined by combining subjective weights from experts and objective weights obtained from the MEREC (Method Based on the Removal Effects of Criteria) method. The Relative Preference Relation (RPR) approach is employed for ranking fuzzy numbers and final evaluation. Sensitivity analysis and comparison with other methods are conducted to assess the robustness and validity of the proposed approach. The results demonstrate the effectiveness of the proposed approach in evaluating solutions. Based on the final evaluation from the case study, the most important criteria are “Availability and quality of water”, “Focus on immediate economic returns”, and “Financial incentives and access to credit”, while the most suitable solutions for advancing sustainable agriculture are “Financial and credit support”, “Education and enhancement of farmers’ knowledge”, and “Enhancement of research and development”.