Search Results (671)

Excessive use of fossil fuel-powered vehicles is a major problem for the entire world today, because of which greenhouse gases are increasing day by day. As a result, climate change and global warming have grown to be serious problems that affect both the environment and life on Earth. However, the effective way of reducing greenhouse gases is to use electric vehicles for commuting. The assessment and selection of the best possible way of charging an electric vehicle is a convoluted decision-making challenge due to the presence of assorted contradictory criteria. Additionally, individual decision makers’ minds and insufficient data are obstacles to doing this. In this regard, interval-valued picture fuzzy sets have been considered as a compatible tool to handle vagueness. In this paper, a multi-attribute group decision-making problem with the bidirectional projection-based VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is considered where the weights are partially known. The objective weights of the attributes in this model are determined using the deviation-based approach. The compromised solution is also assessed using the VIKOR approach. Both the interval-valued image fuzzy Schweizer–Sklar power weighted geometric operator and the interval-valued picture fuzzy Schweizer–Sklar power weighted averaging operator are used in this process. Lastly, a numerical example showing the most suitable way to charge an electric vehicle is given to demonstrate the suggested methodology. To evaluate the robustness and efficacy of the suggested strategy, a comparative analysis with current techniques and a sensitivity analysis of the parameters are also carried out. Full article

Electric vehicles can substantially lower the overall carbon footprint of the transportation sector, and their batteries become key enablers of widespread electrification. Although high capacity and efficiency are essential for providing sufficient range and performance in electric vehicles, they can be compromised by the need to lower costs and environmental impacts and retain valuable materials. In the present work, multi-criteria decision analysis was adopted to assess the sustainability of different lithium-ion batteries. Life cycle carbon emissions and toxicity, material criticality, life cycle costs, specific energy, safety, and durability were considered in the analysis as key parameters of the transition to electric mobility. A subjective approach was chosen for the weight attribution of the criteria. Although certain alternatives, like lithium nickel cobalt manganese oxide (NCM) and lithium nickel cobalt aluminum oxide (NCA), outweigh others in specific energy, they lack in terms of safety, material preservation, and environmental impact. Addressing cost-related challenges is also important for making certain solutions competitive and largely accessible. Overall, while technical parameters are crucial for the development of lithium-ion batteries, it is equally important to consider the environmental burden, resource availability, and economic factors in the design process, alongside social aspects such as the ethical sourcing of materials to ensure their sustainability. Full article

The fundamental notions of the intuitionistic hesitant fuzzy set (IHFS) and rough set (RS) are general mathematical tools that may easily manage imprecise and uncertain information. The EDAS (Evaluation based on Distance from Average Solution) approach has an important role in decision-making (DM) problems, particularly in multi-attribute group decision-making (MAGDM) scenarios, where there are many conflicting criteria. This paper aims to introduce the IHFR-EDAS approach, which utilizes the IHF rough averaging aggregation operator. The aggregation operator is crucial for aggregating intuitionistic hesitant fuzzy numbers into a cohesive component. Additionally, we introduce the concepts of the IHF rough weighted averaging (IHFRWA) operator. For the proposed operator, a new accuracy function (AF) and score function (SF) are established. Subsequently, the suggested approach is used to show the IHFR-EDAS model for MAGDM and its stepwise procedure. In conclusion, a numerical example of the constructed model is demonstrated, and a general comparison between the investigated models and the current methods demonstrates that the investigated models are more feasible and efficient than the present methods. Full article

Currently, land drought events have become a frequent and serious global disaster. How to address these droughts has become a major issue for researchers. Traditional response strategies for land drought events have been determined by experts based on the severity levels of the events. However, these methods do not account for temporal variations or the specific risks of different areas. As a result, they overlooked the importance of spatio-temporal multi-scale strategies. This research proposes a multi-scale response strategy recommendation model for land drought events. The model integrates characteristics of drought-causing factors, disaster-prone environments, and hazard-bearing bodies using case-based reasoning (CBR). Additionally, the analytic hierarchy process (AHP) and entropy weighting methods (EWMs) are introduced to assign weights to the feature attributes. A case retrieval algorithm is developed based on the similarity of these attributes and the structural similarities of drought cases. The research further classifies emergency strategies into long-term and short-term approaches. Each approach has a corresponding correction algorithm. For short-term strategies, a correction algorithm based on differential evolutions is applied. For long-term strategies, a correction algorithm based on drought risk assessment is developed. The algorithm considers factors such as drought risk, vulnerability, and exposure. It facilitates multi-scale decision-making for drought events. The candidate case obtained using the correction algorithm shows an overall attribute similarity of 94.7% with the real case. The emergency response levels match between the two cases. However, the funding required in the candidate case is CNY 327 million less than the actual expenditure. Full article

Beginning with a symmetrical multiple-choice individual as the foundation, I develop a sociophysics model of decision-making. By simplifying the range of choices, the framework incorporates the complex Polytopic fuzzy model to capture nuanced dynamics. This approach enables a deeper analysis of decision-making processes within social systems. Decision-making problems commonly involve uncertainty and complexity, posing considerable challenges for organizations and individuals. Due to their structure and variable parameters, the Einstein t-norm (ETN) and t-conorm (ETCN) offer more elasticity than the algebraic t-norm (ATN) and t-conorm (ATCN). This flexibility makes them commonly effective and valuable in fuzzy multi-attribute decision-making (MADM) problems, where nuanced valuations are critical. Their application enhances the ability to model and analyze vagueness and uncertain information, eventually leading to more informed decision outcomes. The complex Polytopic fuzzy set (CPFS) improves the Polytopic fuzzy set (PFS) and complex fuzzy set (CPFS), allowing for a more precise valuation of attributes in complex (MADM) problems. This study aims to propose a MADM scheme using the ETN and ETCN within the framework of a complex Polytopic fuzzy environment. It begins by presenting the Einstein product and sum operations for complex Polytopic fuzzy numbers (CPFNs) and explores their necessary properties. This method enhances the accuracy and applicability of DM processes in ambiguous environments. Subsequently, three complex Polytopic fuzzy operators with known weighted vectors are developed: the complex Polytopic fuzzy Einstein weighted averaging (CPFEWA) operator, complex Polytopic fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Polytopic fuzzy Einstein hybrid averaging (CPFEHA) operator. Moreover, some substantial properties of the operators are studied. Finally, a method based on novel operators is planned, and a numerical example is provided to prove the practicality and effectiveness of the new proposed methods. Full article

The cockpit is evolving from passive, reactive interaction toward proactive, cognitive interaction, making precise predictions of driver intent a key factor in enhancing proactive interaction experiences. This paper introduces Cockpit-Llama, a novel language model specifically designed for predicting driver behavior intent. Cockpit-Llama predicts driver intent based on the relationship between current driver actions, historical interactions, and the states of the driver and cockpit environment, thereby supporting further proactive interaction decisions. To improve the accuracy and rationality of Cockpit-Llama’s predictions, we construct a new multi-attribute cockpit dataset that includes extensive historical interactions and multi-attribute states, such as driver emotional states, driving activity scenarios, vehicle motion states, body states and external environment, to support the fine-tuning of Cockpit-Llama. During fine-tuning, we adopt the Low-Rank Adaptation (LoRA) method to efficiently optimize the parameters of the Llama3-8b-Instruct model, significantly reducing training costs. Extensive experiments on the multi-attribute cockpit dataset demonstrate that Cockpit-Llama’s prediction performance surpasses other advanced methods, achieving BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L scores of 71.32, 80.01, 76.89, and 81.42, respectively, with relative improvements of 92.34%, 183.61%, 95.54%, and 201.27% compared to ChatGPT-4. This significantly enhances the reasoning and interpretative capabilities of intelligent cockpits. Full article

The paper presents an innovative method for tackling multi-attribute decision-making (MADM) problems within a hesitant fuzzy (HF) framework. Initially, the paper generalizes the Chi-square distance measure to the hesitant fuzzy context, defining the HF generalized Chi-square distance. Following this, the paper introduces the power average (P-A) operator and the power geometric (P-G) operator to refine the weights derived from Shannon entropy, taking into account the inter-attribute support. Leveraging the strengths of Aczel–Alsina operations and the power operation, the paper proposes the hesitant fuzzy Aczel–Alsina power weighted average (HFAAPWA) operator and the hesitant fuzzy Aczel–Alsina power weighted geometric (HFAAPWG) operator. Consequently, a hesitant fuzzy Aczel–Alsina power model is constructed. The applicability of this model is demonstrated through a case study examining the urban impacts of cyclonic storm Amphan, and the model’s superiority is highlighted through comparative analysis. Full article

The present research assesses the agri-environmental sustainability of the Baltic states, namely Lithuania, Latvia, and Estonia, while analyzing agricultural biodiversity, greenhouse gas emissions, land utilization, energy use, and water management. For the purpose of these evaluations and ranking, we employ the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), applied to a set of agri-environmental indicators (AES) collected from various sources, including Eurostat and similar databases. This knowledge is necessary to help policymakers or agricultural managers who are interested in developing more sustainable agriculture in the region. That is why, according to the findings, the highest AES value is attributed to Latvia, followed by Lithuania and Estonia. Conservation methods that were essential included High-Nature-Value farmland, bird populations in agricultural habitats, organic farming, and water. It emphasizes the use of Multi-Criteria Decision Making tools for combining both qualitative and quantitative data and form the general framework for sustainability assessment. Another goal of this research is to fill the gap in the literature regarding the lack of attention paid to agri-environmental sustainability in the Baltic area in general. This, therefore, suggests that assessing the strengths or weaknesses of these nation-states provides critical information that can inform the change of land management practices, nutrition practices in agriculture, and the ability of the natural world to adapt. The findings will be useful to governmental leaders and individuals involved in agriculture who need to find a balance between economic growth and conservation, as well as scholars working to improve the international measures for agri-environmental assessment. Full article

Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations. Full article

Adaptive design focuses on creating flexible products that meet evolving demands and enhance sustainability. However, evaluating adaptive design concepts poses significant challenges due to the dynamic nature of product features over time and the inherent uncertainty in decision-makers’ (DMs’) evaluations. Most traditional frameworks rely on static models that fail to capture the temporal evolution of attributes and often overlook decision-makers’ (DMs’) confidence levels, resulting in incomplete or unreliable evaluations. To bridge these gaps, we propose the m-polar linguistic Z-number (mLZN) to address these issues. This framework uses the dynamic representation capabilities of m-polar fuzzy sets (mFSs) and the symmetrical structure of linguistic Z-numbers (LZNs), which effectively integrate linguistic evaluations with corresponding confidence levels, providing a balanced and robust approach to handling uncertainty. This approach models design characteristics across multiple periods while accounting for DMs’ confidence levels. Based on this framework, we develop mLZN weighted and geometric aggregation operators, computation rules, and ranking methods to support dynamic multi-attribute group decision-making (MAGDM). The proposed framework’s effectiveness is demonstrated through a case study on adaptive furniture design for children, which showcases its ability to dynamically evaluate key attributes, including safety, ease of use, fun, and comfort. Furthermore, we validate its robustness and feasibility through comprehensive sensitivity and comparative analyses. Full article

This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this work proposes a novel Software-Defined Networking (SDN)-based framework for reliable satellite–terrestrial integration. The proposed framework leverages intelligent traffic steering and dynamic access network selection to optimise real-time communications. By addressing gaps in the literature with a distributed SDN control approach spanning terrestrial and space domains, the framework enhances resilience against disruptions, such as natural disasters, while maintaining low latency and jitter. Future research directions are outlined to refine the design and explore its application in 6G systems. Full article

Online product reviews provide valuable insights on user experiences and product qualities. However, issues such as information overload and the limited utilization of review features persist, particularly in personalized rankings for popular items like movies. To address these challenges—information overload in online reviews, limited review feature utilization, and personalized decision-making for high-demand products like movies—we introduce a personalized online decision-making framework that integrates a sentiment model for product attributes with an enhanced hesitant fuzzy scoring function. This framework incorporates the concept of symmetry in sentiment analysis. It employs feature words, sentiment terms, and modifiers to assess user sentiments within a hesitant fuzzy setting, utilizing symmetrical relationships between positive and negative sentiments. The improved fuzzy score function efficiently quantifies sentiment values for product features by considering the symmetrical balance of user opinions. Additionally, review quality assessment incorporates both content and reviewer characteristics, resulting in final attribute evaluations. An attribute weighting system, tailored to diverse product types, further captures product specifics and user inclinations, leveraging symmetry to balance varying user preferences. Validation through multi-genre movie sorting demonstrates the method’s capacity to handle review data across varied products and user tastes, offering a robust tool for enhancing online decision quality, especially for high-demand items. Full article

In recent years, Failure Mode and Effects Analysis (FMEA) has become an essential preventive tool widely applied across various fields. As a structured system analysis method, FMEA aids in identifying potential failure modes in product or process design, allowing for preventive measures to be taken in advance. However, traditional FMEA has certain limitations, as it does not comprehensively consider all potential failure factors. This study proposes an improved FMEA method that addresses these shortcomings by integrating it with a Multiple-Criteria Decision Making (MCDM) model, thereby enhancing the comprehensiveness of the assessment framework. Notably, this research introduces an economic risk factor—Expected Cost (EC)—to make the analysis results more aligned with real-world conditions. Additionally, to manage the uncertainty in expert opinions, this study applies Interval Type-2 Trapezoidal Fuzzy Numbers (IT2TFNs) and combines them with the Best-Worst Method (BWM) to calculate the weights of risk factors. Furthermore, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to explore the interrelationships between failure modes. Finally, the Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) method is used to rank risk factors, determining the priorities for improvement. This paper uses an air purifier as a case study to validate the effectiveness of the improved FMEA method, successfully addressing the shortcomings of traditional FMEA regarding uncertainty in expert opinions and the calculation of Risk Priority Numbers (RPNs). It provides a more practical and accurate risk assessment framework. Full article

Linguistic q-Rung orthopair fuzzy set is a new extension of the linguistic Pythagorean fuzzy set, which effectively represents the fuzzy and uncertain decision-making information based on qualitative modeling. However, its operational rules are unable to process pure linguistic exponential calculations, in which the exponents are represented using linguistic q-Rung orthopair fuzzy values and the bases are represented as linguistic terms or interval linguistic numbers. This greatly restricts its application in decision making under complex environments. As the complement of the existing linguistic q-Rung orthopair fuzzy operational rules, this paper defines linguistic q-Rung orthopair fuzzy calculation rules, including division, subtraction, and exponent operations. Based on theorem-based proofs, the relevant properties of the calculation rules have been analyzed, such as commutative law, distributive law, symmetry, and so on. Moreover, in order to facilitate the application of linguistic q-Rung orthopair fuzzy theory, this paper introduces the concept of dual linguistic q-Rung orthopair fuzzy value. Building on this foundation, a series of weighted aggregation operators for the calculations involving linguistic q-Rung orthopair fuzzy values and dual linguistic q-Rung orthopair fuzzy values have been designed. In conclusion, a novel pure linguistic multi criteria decision-making methodology is introduced in this work. The validity and utility of the proposed method are demonstrated via a real-world application in the decision process of energy resource exploitation. Full article

The massive usage of Internet services nowadays has led to a drastic increase in cyberattacks, including sophisticated techniques, so that Intrusion Detection Systems (IDSs) need to use AP technologies to enhance their effectiveness. However, this has resulted in a lack of interpretability and explainability from different applications that use AI predictions, making it hard to understand by cybersecurity operators why decisions were made. To address this, the concept of Explainable AI (XAI) has been introduced to make the AI’s decisions more understandable at both global and local levels. This not only boosts confidence in the AI but also aids in identifying different attributes commonly used in cyberattacks for the exploitation of flaws or vulnerabilities. This study proposes two developments: first, the creation and evaluation of machine learning models for an IDS with the objective to use Reinforcement Learning (RL) to classify malicious network traffic, and second, the development of a methodology to extract multi-level explanations from the RL model to identify, detect, and understand how different attributes affect uncertain types of attack categories. Full article