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With the development of renewable energy power systems, electric vehicles, as an important carrier of green transportation, are gradually having an impact on the power grid load curve due to their charging behavior. However, the significant influx of electric vehicles (EVs) and distributed power sources has led to multiple uncertainties, increasing the difficulty in making grid scheduling decisions. Traditional robust scheduling strategies tend to be overly conservative, resulting in poor economic performance. Therefore, this paper proposes a robust and economic dispatch strategy for park power grids based on the information gap decision theory (IGDT). Firstly, based on the probabilistic characteristics of the spatial and temporal distribution of EVs charging, the Monte Carlo method is used to generate typical electricity usage scenarios for EVs. Simultaneously, an economic dispatch model for the park power grid is established with the objective of minimizing operating costs. Taking into account the uncertainty of renewable energy output, simulation analysis is conducted through the IGDT model. Finally, through the verification of the improved IEEE-33 node test system and comparison with other methods, the proposed approach in this paper can reduce decision conservatism and effectively reconcile the contradiction. Through analysis, the proposed method in this paper can reduce the total operational cost of the system by up to 3.2%, with a computational efficiency of only 8.9% of the traditional stochastic optimization time. [ABSTRACT FROM AUTHOR]

The traffic volume and maintenance demand on Taiwan's provincial highways have been steadily increasing. One of the most challenging issues in maintenance is determining the optimal timing and allocation of funds to avoid duplicative investments and maximize resource utilization. Currently, provincial highway maintenance units rely heavily on manual processes and paper-based records, using experiential methods to formulate maintenance strategies and conduct maintenance operations. This indicates a lack of objective maintenance strategies and pavement management systems in these units. This study aims to address this gap by integrating domestic and international literature on pavement maintenance decision-making. Existing approaches typically fall into two categories: "Pavement Indicator Rating" and "Pavement Maintenance Prioritization". However, there has not been research integrating these methods for decision-making. Therefore, this research integrates these two approaches to establish a comprehensive maintenance strategy for Taiwan's provincial highways. The Analytic Hierarchy Process (AHP) is employed as the decision-making theory, involving expert interviews to calculate maintenance weights for different pavement maintenance indicators. The results show that the pothole count, International Roughness Index (IRI), and Pavement Condition Index (PCI) are the three most critical maintenance indicators. The first phase of the maintenance strategy uses the "Pavement Indicator Rating" to directly assess the pothole count, IRI, and PCI to categorize pavement sections as "maintenance sections" or "observation sections". The second phase employs "Pavement Maintenance Prioritization", integrating maintenance weights for each indicator to calculate maintenance scores. This phase prioritizes maintenance activities based on the results of the first phase's rating for "maintenance sections". Additionally, a provincial highway pavement management system is proposed to implement these strategies, enhancing maintenance management efficiency and ensuring the overall quality and longevity of provincial highway maintenance efforts. [ABSTRACT FROM AUTHOR]

This document discusses the importance of decision-making theories and methodologies for water, energy, and food security. It highlights nine selected papers that contribute to this field of research. The papers cover various topics such as the efficiency of the water-energy-food system, hydrological modeling, nutrient pollution in water, river regulation decisions, water governance, water allocation, water pollution management, integrated management of water resources, and water conservation behavior. These papers provide valuable insights and recommendations for environmental scientists, water resource managers, industry experts, and national authorities. [Extracted from the article]

Featured Application: Optimal operation for renewable energy integrated smart microgrid under uncertainty. Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park's peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids. [ABSTRACT FROM AUTHOR]

Renovating old neighborhoods for the benefit of people has become increasingly important in urban renewal. Nevertheless, old neighborhood renovations are currently considered a group decision-making issue under public participation, involving diverse decision-making subjects. Conflicts within a group are a common problem during group decision-making. In this paper, conflict is examined in the decision-making process for renovating old neighborhoods and novel ideas are provided for quantifying conflict. Public participation in old neighborhood renovations is assessed using conflict degree calculations in group decision-making. Based on the preferences of decision-making experts, a MIP DEA–DA (Mixed Integer Programming Data Envelopment Analysis–Discriminant Analysis) based partial binary tree cyclic clustering model is constructed for clustering experts, and an aggregated group conflict indicator and an aggregated conflict vector are computed, allowing for the quantification of conflict during the renovation process of the old neighborhood based on actual situations. Results indicate that there is primarily a conflict between the benefits of decision-making subject interests and the professionalism of decision-making renovations. This paper contributes to improving public participation, promoting the application of group decision-making theory in old neighborhood renovation, reducing conflict between decision-makers, and speeding up urban renewal. [ABSTRACT FROM AUTHOR]

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius. [ABSTRACT FROM AUTHOR]

This paper proposes a linear consensus protocol QuickBFT based on Vague sets and multi-attribute decision-making methods. QuickBFT simplifies the communication process based on the HotStuff protocol, reduces the four-stage communication to three-stage communication, and reduces the consensus delay. Furthermore, we introduce the Vague set and multi-attribute decision-making theory into the consensus protocol and propose a new leader node selection algorithm, which can prevent Byzantine nodes from becoming leader nodes, thereby improving the protocol performance when the leader node is attacked. Experimental results show that the throughput of QuickBFT is slightly higher than that of the HotStuff protocol without Byzantine nodes, and the consensus delay is reduced by 20%. In the presence of Byzantine nodes, the throughput of QuickBFT is increased by 80% compared with the HotStuff protocol, and the consensus delay is reduced by 60%. [ABSTRACT FROM AUTHOR]

Simple Summary: Cells continually sense and receive signals from the environment and respond accordingly. Due to biological noise, however, the response is not always as expected. Such a response can induce a different cell fate and may disrupt some cellular functions. In the presence of noise, cells may either mistakenly perceive non-existent signals and act accordingly, or may ignore the actual signals and do nothing. We label these two as false alarm and signal miss events, respectively. In this paper, we consider an important signaling system with one input and two outputs to show how the likelihood of false alarm and signal miss events can be computed, using the experimentally measured joint response of the two outputs of the signaling system. The two system outputs are the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2), whereas the system input is the tumor necrosis factor (TNF). These molecules are highly involved in essential processes such as cell survival, cell death, and viral replication. The introduced methodology and the measured false alarm and miss probabilities using experimental data can model complex cellular decision-making processes and provide insight into how they may contribute to the development of some pathological conditions. A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions. [ABSTRACT FROM AUTHOR]

In this study, ten questions measuring distributive justice on classical Likert and neutrosophic Likert scales consisting of two subdimensions—distributive and procedural justice—were used. Participants responded to the same questions for both the classical Likert and neutrosophic Likert scales within a single survey, with the neutrosophic method applied, for the first time, to the questions included in the scale. The neutrosophic scale responses were answered in percentages to resemble natural language, and the answers received for each question were reduced to the range [−1, 1] to grade the agreement approach through a score function used in neutrosophic decision-making theory. In this study, the neutrosophic scale, a scaling method with strong theoretical foundations, was compared with the traditional Likert scale. The results of the statistical analyses (exploratory factor analysis, reliability analysis, neural network analysis, correlation analysis, paired samples t-test, and one-way and two-way ANOVAs) and evaluations of the scales were compared to measure organizational justice within a single study. In this article, the symmetric and non-symmetric properties of statistical analysis that are specific to this paper in addition to general symmetric and non-symmetry properties are discussed. These symmetric and non-symmetric features are conceptualized according to the features on which each statistical analysis focuses. Finally, although this study presents a new area of research in the social sciences, we believe that the neutrosophic Likert scale and survey approach will contribute to collecting detailed and sensitive information on many topics, such as economics, health, audience perceptions, advertising responses, and product, market, and service purchase research, through the use of score functions. [ABSTRACT FROM AUTHOR]

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency. [ABSTRACT FROM AUTHOR]

The uncertainty of intuitionistic fuzzy numbers (IFNs) is further enhanced by the existence of the degree of hesitation (DH). The shortcomings of existing researches are mainly reflected in the following situations: when comparing IFNs, the comparison rules of IFNs are difficult to apply to the comparison of any two IFNs, or the relevant methods do not fully consider the uncertainty expressed by DH. Thus, the rationality of the decision results needs to be improved. On the other hand, multi-attribute decision making (DADM) based on IFNs is often not objective due to the need to determine the attribute weight. Moreover, the strict condition of attribute aggregation of classical dominance relation makes it a method that fails considering the practical application. Aiming at the comparison problem of IFNs, this paper takes probability conversion as the starting point and proposes an IFN comparison method based on the area method, which can better deal with the comparison problem of "either superior or inferior" IFNs. In addition, aiming at the MADM problem of an intuitionistic fuzzy information system, we propose an intuitionistic fuzzy probabilistic dominance relation model and construct the MADM method under the probabilistic dominance relation. The series properties of IFNs and probabilistic dominance relation were summarized and proved, which theoretically ensured the scientificity and rigor of the method. The results show that the comparison and ranking method of IFNs proposed in this paper can be applied to the comparison of any two IFNs, and the dominance degree of IFNs is presented in the form of probability, which is more flexible and practical than the classical method. The probabilistic dominance relation method based on IFNs avoids the problem of determining attribute weights subjectively or objectively, and the decision maker can reflect decision preference by adjusting decision parameters to better match the actual problem. The application of this model to a campus express site evaluation further verifies the feasibility of the proposed method and the rationality of the results. In addition, various extension problems of the model and method proposed in this paper are discussed, which pave the way for future related research. This paper constructs a complete decision-making framework through theoretical analysis and application from practical problems, which provides a reference for enriching and improving uncertain decision-making theory and the MADM method. [ABSTRACT FROM AUTHOR]

Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM--an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision--theory, experiments, and applications). [ABSTRACT FROM AUTHOR]

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment. [ABSTRACT FROM AUTHOR]

On taking the intermediate value theorem (IVT) and its converse as a point of departure, this paper connects the intermediate value property (IVP) to the continuity postulate typically assumed in mathematical economics, and to the solvability axiom typically assumed in mathematical psychology. This connection takes the form of four portmanteau theorems, two for functions and the other two for binary relations, that give a synthetic and novel overview of the subject. In supplementation, the paper also surveys the antecedent literature both on the IVT itself, as well as its applications in economic and decision theory. The work underscores how a humble theorem, when viewed in a broad historical frame, bears the weight of many far-reaching consequences; and testifies to a point of view that the apparently complicated can sometimes be under-girded by a most basic and simple execution. [ABSTRACT FROM AUTHOR]

The concept of the hybrid structure, as an extension of both soft sets and fuzzy sets, has gained significant attention in various mathematical and decision-making domains. In this paper, we delve into the realm of hemirings and investigate the properties of hybrid h-bi-ideals, including prime, strongly prime, semiprime, irreducible, and strongly irreducible ones. By employing these hybrid h-bi-ideals, we provide insightful characterizations of h-hemiregular and h-intra-hemiregular hemirings, offering a deeper understanding of their algebraic structures. Beyond theoretical implications, we demonstrate the practical value of hybrid structures and decision-making theory in handling real-world problems under imprecise environments. Using the proposed decision-making algorithm based on hybrid structures, we have successfully addressed a significant real-world problem, showcasing the efficacy of this approach in providing robust solutions. [ABSTRACT FROM AUTHOR]

The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emotional alternative attractiveness. The objective of this paper is the comparison of two multistep operational algorithms of the intelligent network: one based on discrete dynamics and the other on continuous dynamics. By means of numerical analysis, it is shown that, depending on the network parameters, the characteristic probabilities for continuous and discrete operations can exhibit either close or drastically different behavior. Thus, depending on which algorithm is employed, either discrete or continuous, theoretical predictions can be rather different, which does not allow for a uniquely defined description of practical problems. This finding is important for understanding which of the algorithms is more appropriate for the correct analysis of decision-making tasks. A discussion is given, revealing that the discrete operation seems to be more realistic for describing intelligent networks as well as affective artificial intelligence. [ABSTRACT FROM AUTHOR]

Concerning the development of computational tools and solutions as a decision-making aid, this paper presents the results of the waspasWEB project, which strives to provide decision-makers with a readily accessible mechanism to employ the weighted aggregated sum product assessment (WASPAS) method. The social contribution of the project encompasses the development of a user-friendly and publicly accessible internet tool, as well as a package launched on the Comprehensive R Archive Network (CRAN) to serve the community of users of the R language. The use of operational research methodologies is crucial to justify decisions, and this effort seeks to advance the adoption of such methodologies, offering managers, researchers, and the general public an intuitive and easily accessible multi-criteria decision-making tool. In this way, we present the technical specifications, usability, and interactivity of the user with the computational platform, being validated its viability through a hypothetical case study. At the end of the research, it exposes the limitations and feasibility of the proposed computational model along with future research. [ABSTRACT FROM AUTHOR]

As an important branch of modern decision-making theory, multi-attribute decision-making (MADM) plays an important role in various fields. Classic MADM methods can provide a ranking of alternatives, and decision-makers need to evaluate the level subjectively based on the ranking results. Because of the limitation of knowledge, this is likely to lead to potential individual losses. Three-way decision (3WD) theory has good classification ability. Therefore, this paper proposes a dynamic hybrid multi-attribute 3WD (MA3WD) model. First, a new scheme for constructing loss functions is proposed from the perspective of gray relational analysis (GRA), which is an accurate and objective way to describe the relationship between loss functions and attribute values. Then, conditional probabilities are determined by employing the gray relational analysis technique for order preference by similarity to the ideal solution (GRA-TOPSIS). With these discussions, a GRA-based hybrid MA3WD model for a single period is proposed by considering multi-source information. Furthermore, by extending the single-period scenario to a multi-period one, we construct a dynamic hybrid MA3WD model, which can obtain the final three-way decision rules as well as the results of each period and each attribute. Finally, the proposed method is applied to the case of performance evaluation of elderly-care services to demonstrate the effectiveness of the method, and comparative analyses are given to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The theoretical literature on risk aversion and Expected Utility Theory is extensive; however, the analysis of this behaviour with natural experiments could be more comprehensive. In this paper, we use data from 120 episodes of the Portuguese version of the TV game show The Price is Right, namely from The Wheel game, to explore risk aversion as well as the impact of gender in decision-making. The Wheel game has straightforward rules and huge expected payoffs. All contestants have access to the same information and distributions of uncertainty, making it a unique field laboratory to conduct an experimental test of rational decision theory. The objective is to infer the risk aversion levels of decision-makers from their choice to turn the wheel and the influence of gender on risk attitudes. There is a widespread view that women are more risk-averse than men. However, we could not reject the hypothesis that women and men have the same level of risk aversion. Nevertheless, we have evidence that contestants are more risk-averse than risk-seeking. The omission bias, loss aversion and regret can explain that behaviour. [ABSTRACT FROM AUTHOR]

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms. [ABSTRACT FROM AUTHOR]

Emergency evacuation simulation is significant for architectural design and emergency plan implementation. To explore the influence of evacuees' physical behavior and evacuees' decisions on the evacuation process, as well as to address the problems of traditional emergency evacuation models with insufficient detail of the situation (realism), low reusability, poor operability, and lack of subsequent scalability, this paper first analyzed pedestrian characteristics in emergencies. To describe pedestrian decision-making in an emergent evacuation situation, a multi-agent design based on decision theory was proposed, solving the multi-agent decision-making problem in an emergency evacuation environment by the A* algorithm. Then the designed multi-agent was embedded into the social force model by AnyLogic software. Finally, the model reproduces the pedestrian evacuation process in an emergency evacuation situation on the built platform, depicting three kinds of typical behaviors: pedestrian partnering, obstacle avoidance, and exit competition. In addition, this study also analyzed a large student apartment building by example and proposed corresponding optimization solutions to improve its evacuation capacity through simulation results. [ABSTRACT FROM AUTHOR]

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China's power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China's power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs' perception of "gain" and "loss" with market risks in China's two-tier market environment, the prospect theory and the marine predator's algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators' participation in electricity trading. [ABSTRACT FROM AUTHOR]

In this paper, we study an independent Bernoulli two-armed bandit with unknown parameters ρ and λ , where ρ and λ have a pair of priori distributions such that d R (ρ) = C R ρ r 0 (1 − ρ) r 0 ′ d μ (ρ) , d L (λ) = C L λ l 0 (1 − λ) l 0 ′ d μ (λ) and μ is an arbitrary positive measure on [ 0 , 1 ] . Berry proposed the conjecture that, given a pair of priori distributions (R , L) of parameters ρ and λ , the arm with R is the current optimal choice if r 0 + r 0 ′ < l 0 + l 0 ′ and the expectation of ρ is not less than that of λ. We give an easily verifiable equivalent form of Berry's conjecture and use it to prove that Berry's conjecture holds when R and L are two-point distributions as well as when R and L are beta distributions and the number of trials N ≤ r 0 r 0 ′ + 1 . [ABSTRACT FROM AUTHOR]

IoT technology and drones are indeed a step towards modernization. Everything from field monitoring to pest identification is being conducted through these technologies. In this paper, we consider the issue of smart pest detection and management of cotton plants which is an important crop for an agricultural country. We proposed an IoT framework to detect insects through motion detection sensors and then receive an automatic response using drones based targeted spray. In our proposed method, we also explored the use of drones to improve field surveillance and then proposed a predictive algorithm for a pest detection response system using a decision-making theory. To validate the working behavior of our framework, we have included the simulation results of the tested scenarios in the cup-carbon IoT simulator. The purpose of our work is to modernize pest management so that farmers can not only attain higher profits but can also increase the quantity and quality of their crops. [ABSTRACT FROM AUTHOR]

The aim of is paper is to address the problem of identifying critical factors in the analysis of non-coal mine explosion accidents as well as to improve the rationality and accuracy of the risk analysis results. Hence, we developed a risk identification method for non-coal mine explosion accidents, combining the Systems-Theoretic Accident Model and Process (STAMP) and the Rank-order Centroid (ROC) method based on the Poset decision-making theory. The proposed method was applied to identify risk in engineering cases. Findings showed that four main dangerous events (out of twelve identified ones) were the primary culprits of related accidents, which were the events "Blasters without licenses and illegal operation" at the basic level, the event of "the confusion about the safety management system of non-coal mine companies" at the control level, and the event of "the failure about the emergency management departments" and "public security departments" at the supervision level. The approximate values of the average rank of the four events are 11.56, 10.4, 4.33, 4.33. The results of risk identification of non-coal mine explosion accidents based on Poset were consistent with the results obtained by the method used in the case study. This study extends the methods for identifying risks of non-coal mine explosion accidents and facilitates the formulation of effective preventive measures. [ABSTRACT FROM AUTHOR]

We propose a quantum-mechanical model that represents a human system of beliefs as the quantised energy levels of a physical system. This model represents a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry of opinion radicalisation. In particular, the model demonstrates the phenomena of pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings on opinion polarisation via social media exposure. Advancing this model, we establish a robust framework that integrates elements from physics, psychology, behavioural science, decision-making theory, and philosophy. We also emphasise the inherent advantages of the quantum approach over traditional models, suggesting a number of new directions for future research work on quantum-mechanical models of human cognition and decision-making. [ABSTRACT FROM AUTHOR]

Revealed preference is one of the most influential ideas in economics. It is, however, not clear how it can be generally applied in cases where agents' choices depend on arbitrary changes in the decision environment. In this paper, we propose a generalization of the classic rational choice theory that allows for such framing effects. Frames are modeled as different presentations (e.g., visual or conceptual) of the alternatives that may affect choice. Our main premise is that framing effects are neutral (i.e., independent of labeling the alternatives). An agent exhibiting these neutral framing effects who is otherwise rational, is called impressionable rational. We show that our theory encompasses many familiar behavioral models such as status-quo bias, satisficing, present bias, framing effects resulting from indecisiveness, certain forms of limited attention, categorization bias, and the salience theory of choice, as well as hybrid models. Moreover, in all these models, sufficiently rich choice data allow our theory to identify the "correct" underlying preferences without invoking each specific cognitive process. Additionally, we introduce a falsifiable axiom that completely characterizes the behavior of agents who are impressionable rational. [ABSTRACT FROM AUTHOR]

High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification. [ABSTRACT FROM AUTHOR]

One of the most interesting applications of Structural Health Monitoring (SHM) is the possibility of providing real-time information on the conditions of civil infrastructures during and following disastrous events, thus supporting decision-makers in prompt emergency operations. The Bayesian decision theory provides a rigorous framework to quantify the benefit of SHM through the Value of Information (VoI) accounting for different sources of uncertainties. This decision theory is based on utility considerations, or, in other words, it is based on risk. Instead, decision-making in emergency management is often based on engineering judgment and heuristic approaches. The goal of this paper is to investigate the impact of different decision scenarios on the VoI. To this aim, a general framework to quantify the benefit of SHM information in emergency management is applied to different decision scenarios concerning bridges under scour and seismic hazards. Results indicate that the considered decision scenario might tremendously affect the results of a VoI analysis. Specifically, the benefit of SHM information could be underestimated when considering non-realistic scenarios, e.g., those based on risk-based decision-making, which are not adopted in practice. Besides, SHM information is particularly valuable when it prevents the selection of suboptimal emergency management actions. [ABSTRACT FROM AUTHOR]

Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection. [ABSTRACT FROM AUTHOR]

This paper investigates the robustness of the conventional mean-variance (MV) optimization model by making two adjustments within the MV formulation. First, the portfolio selection based on a behavioral decision-making theory that encapsulates the MV statistics and investors psychology. The second aspect involves capturing the portfolio asset dependence structure through copula. Using the behavioral MV (BMV) and the copula behavioral MV (CBMV), the results show that stocks with lower behavioral scores outperform counterpart portfolios with higher behavioral scores. On the other hand, in the Forex market, the reverse is observed for the BMV approach, while the CBMV remains consistent. [ABSTRACT FROM AUTHOR]

Completeness is one of the basic assumptions about the rational preference relation in classical decision theory. Strongly and weakly consistent preferences are presented by abandoning the completeness of the rational preference relation. Some expansion and contraction conditions are proposed and the relationships between these conditions of rationality are discussed. The relationships between the conditions of rationality and boundedly rational choice behavior based on strongly and weakly consistent preferences are analyzed and discussed. Furthermore, an example about the choices of chocolates with interval ordinal numbers is given to explain some of the main conclusions in this paper. The results can be used as references for the study of boundedly rational decisions. [ABSTRACT FROM AUTHOR]

The point estimation problems that emerge in Bayesian predictive inference are concerned with random quantities which depend on both observable and non-observable variables. Intuition suggests splitting such problems into two phases, the former relying on estimation of the random parameter of the model, the latter concerning estimation of the original quantity from the distinguished element of the statistical model obtained by plug-in of the estimated parameter in the place of the random parameter. This paper discusses both phases within a decision theoretic framework. As a main result, a non-standard loss function on the space of parameters, given in terms of a Wasserstein distance, is proposed to carry out the first phase. Finally, the asymptotic efficiency of the entire procedure is discussed. [ABSTRACT FROM AUTHOR]

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. Multi-attribute decision-making (MADM) technique is a very effective and well-known tool to investigate fuzzy information more effectively. However, the selection of houses cannot be carried out by utilizing symmetry information, because enterprises does not have complete information, so asymmetric information should be used when selecting enterprises. Hamy mean (HM) operator is a feasible tool to handle strategic decision-making problems because it can capture the order between the finite input terms. Additionally, the complex interval-valued q-rung orthopair fuzzy (CIVq-ROF) setting is a broadly flexible and massively dominant technique to operate problematic and awkward data in actual life problems. The major contribution of this analysis is how to aggregate the collection of alternatives into a singleton set, for this we analyzed the technique of CIVq-ROF Hamy mean (CIVq-ROFHM) operator and CIVq-ROF weighted Hamy mean (Cq-ROFWHM) operator and some well-known results are deliberated. Keeping the advantages of the parameters in HM operators, we discussed the specific cases of the invented operators. To investigate the decision-making problems based on CIVq-ROF information, we suggested the following multi-attribute decision-making (MADM) technique to determine the beneficial term from the finite group of alternatives with the help of evaluating several examples. This manuscript showed how to make decisions when there is asymmetric information about enterprises. Finally, based on the evaluating examples, we try to discover the sensitive analysis and supremacy of the invented operators to find the flexibility and dominancy of the diagnosed approaches. [ABSTRACT FROM AUTHOR]

The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model's training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field. [ABSTRACT FROM AUTHOR]

Nikk Effingham and Huw Price argue that in certain cases of Newcomb problems involving time travel and foreknowledge, being given information about the future makes it rational to choose as an evidential decision theorist would choose. Although the cases they consider have some intuitive pull, and so appear to aid in answering the question of what it is rational to do, we argue that their respective positions are not compelling. Newcomb problems are structured such that whichever way one chooses, one might be led by one's preferred decision theory to miss out on some riches (riches which others obtain whilst employing their preferred decision theory). According to the novel aesthetic diagnosis we shall offer of the Newcomb dialectic, missing out in this way does not render one irrational but, rather, subject to being seen as absurd. This is a different kind of cost but not one that undermines one's rationality. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous–categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks. [ABSTRACT FROM AUTHOR]

Currently, high levels of output stochasticity in renewable energy and inefficient electrolyzer operation plague IESs when combined with hydrogen energy. To address the aforementioned issues, an IGDT-based economic scheduling strategy for integrated energy systems is put forth. Firstly, this strategy establishes an IES consisting of coupled electricity, heat, hydrogen, and gas taking the hydrogen production electrolyzer's thermal energy utilization into account. Second, to minimize the system's overall operating costs, a deterministic scheduling model of the IES is built by taking into account the stepped carbon trading mechanism and the integrated demand response. Lastly, an optimal dispatch model is built using the information gap decision theory under the two strategies of risk aversion and risk seeking, taking into account the uncertainty of renewable energy generation. CPLEX is the solver used to solve the proposed model. After taking into account the effective use of thermal energy from the electrolyzer and loads demand response, the results show that the system carbon emission is reduced by 2597.68 kg and the operating cost is lowered by 44.65%. The IES scheduling model based on IGDT can effectively manage costs while maintaining system risk control, all while accommodating decision-makers' varying risk preferences. This study can provide a useful reference for the research related to the scheduling of the IES low-carbon economy. [ABSTRACT FROM AUTHOR]

Attacks launched from IoT networks can cause significant damage to critical network systems and services. IoT networks may contain a large volume of devices. Protecting these devices from being abused to launch traffic amplification attacks is critical. The manufacturer usage description (MUD) architecture uses pre-defined stateless access control rules to allow or block specific network traffic without stateful communication inspection. This can lead to false negative filtering of malicious traffic, as the MUD architecture does not include the monitoring of communication states to determine which connections to allow through. This study presents a novel solution, the enhanced profiling assurance (EPA) architecture. It incorporates both stateless and stateful communication inspection, a unique approach that enhances the detection effectiveness of the MUD architecture. EPA contains layered intrusion detection and prevention systems to monitor stateful and stateless communication. It adopts three-way decision theory with three outcomes: allow, deny, and uncertain. Packets that are marked as uncertain must be continuously monitored to determine access permission. Our analysis, conducted with two network scenarios, demonstrates the superiority of the EPA over the MUD architecture in detecting malicious activities. [ABSTRACT FROM AUTHOR]

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information. [ABSTRACT FROM AUTHOR]

The key to whether elderly individuals in the community can enjoy their later years peacefully lies in the service capabilities of community comprehensive elderly care service facilities (CCECSF) under the Public-Private Partnership (PPP) mode. To maintain a high level of service capability in community comprehensive elderly care service facilities under PPP mode, scientific evaluation of the effectiveness of these facilities is equally crucial. This article first constructs a post-occupancy evaluation index system of community comprehensive elderly care service facilities under PPP mode that includes three evaluation attributes and fifteen evaluation criteria based on the Chinese culture and lifestyle habits. Regarding the issue of direct users being unable to directly participate in evaluations, the uncertainty in evaluation information, and the volatility of evaluation results, a multi-stage post-occupancy evaluation model is constructed based on probabilistic linguistic term set, TOPSIS model and multi-stage decision theory. The above post-occupancy evaluation index system and evaluation model together constitute a multi-stage post-occupancy evaluation framework for community comprehensive elderly care service facilities under PPP mode. The outcomes of the case study indicate that the post-occupancy evaluation index system can offer a scientifically guided approach for evaluating the service level of community comprehensive elderly care service facilities under the PPP mode; meanwhile, the multi-stage evaluation model can enable direct user participation in the post-evaluation of facility usage and improve the robustness and reduce the fluctuation of the evaluation results, so as to improve the scientificity of the evaluation results. [ABSTRACT FROM AUTHOR]

In a previous paper, the problem of how the preventive maintenance organization for the k-out-of-n: F system could be used, in order to maximize system availability, was considered. The current paper continues these investigations using a different optimization criterion. The proposed approach is based on decision making theory for regenerative processes. We propose a general procedure for comparing different preventive maintenance strategies based on the ordered statistics distributions, aiming to choose the best one with respect to cost-type criterion. The lifetime distributions of system units are usually unknown and only one or two of their moments are available. For this reason, we pay special attention to the sensitivity analysis of decision making about preventive maintenance, taking into account the shape of the system unit lifetime distributions. A numerical study of two examples based on a real-world system illustrates the results of the proposed approach. [ABSTRACT FROM AUTHOR]

We review some general aspects about the Black–Scholes equation, which is used for predicting the fair price of an option inside the stock market. Our analysis includes the symmetry properties of the equation and its solutions. We use the Hamiltonian formulation for this purpose. Taking into account that the volatility inside the Black–Scholes equation is a parameter, we then introduce the Merton–Garman equation, where the volatility is stochastic, and then it can be perceived as a field. We then show how the Black–Scholes equation and the Merton–Garman one are locally equivalent by imposing a gauge symmetry under changes in the prices over the Black–Scholes equation. This demonstrates that the stochastic volatility emerges naturally from symmetry arguments. Finally, we analyze the role of the volatility on the decisions taken by the holders of the options when they use the solution of the Black–Scholes equation as a tool for making investment decisions. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI). [ABSTRACT FROM AUTHOR]

The conflicts that arise between natural resources consumption and the desire to preserve them make the multicriteria decision theory necessary. Brazil, one of the 10 largest timber producers globally, uses optimization models that represent the growth of forests integrated with decision support systems. Brazilian forest plantation managers often face conflicts when continuously seeking efficiency gains (higher productivity at lower costs) and efficacy (higher profits with minimum social and environmental impacts). Managers of leading producing countries on timber, pulp, and fiberboard constantly interact to fine-tune industry processing demands vis-a-vis the demands of highly productive fast-growing forest plantations. The decision process in such cases seeks a compromise that accommodates short-term industry productivity optimization and long-term forestry production capacity. This paper aims to apply a forest management decision support system (FMDSS) to a case study that represents the challenges that industrial plantations in Brazil usually face. A vertically integrated pulp company situation was simulated to provide a real scenario. In this scenario, forest managers tend to shorten the rotations due to Brazil's usually high-interest rates; meanwhile, industrial managers tend to ask for longer ones due to the positive correlation between age and wood density. Romero®, a Forest Management Decision Support System, developed by following the multi-criteria decision theory, was used to process the case study. Expressly, the hypothesis that mill managers initially have, that older ages rotation could improve mill production, was not confirmed. Moreover, mill managers lean towards changes in the short term, while the case study shows that changes in rotation size and genetic material take time, and decisions have to be made involving both interests: forest and mill managers. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and "epidemics, pandemics, and disasters" (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITSs) are an important development for society. C-ITSs enhance road safety, improve traffic efficiency, and promote sustainable transportation through interconnected and intelligent communication between vehicles, infrastructure, and traffic-management systems. Many real-world implementations still consider traditional Public Key Infrastructures (PKI) as the underlying trust model and security control. However, there are challenges with the PKI-based security control from a scalability and revocation perspective. Lately, certificateless cryptography has gained research attention, also in conjunction with C-ITSs, making it a new type of security control to be considered. In this study, we use certificateless cryptography as a candidate to investigate factors affecting decisions (not) to adopt new types of security controls, and study its current gaps, key challenges and possible enablers which can influence the industry. We provide a qualitative study with industry specialists in C-ITSs, combined with a literature analysis of the current state of research in certificateless cryptographic in C-ITS. It was found that only 53% of the current certificateless cryptography literature for C-ITSs in 2022–2023 provide laboratory testing of the protocols, and 0% have testing in real-world settings. However, the trend of research output in the field has been increasing linearly since 2016 with more than eight times as many articles in 2022 compared to 2016. Based on our analysis, using a five-phased Innovation-Decision Model, we found that key reasons affecting adoption are: availability of proof-of-concepts, knowledge beyond current best practices, and a strong buy-in from both stakeholders and standardization bodies. [ABSTRACT FROM AUTHOR]

The theory of three-way decision has been widely utilized across various disciplines and fields as an efficient method for both knowledge reasoning and decision making. However, the application of the three-way decision theory to partially labeled data has received relatively less attention. In this study, we propose a semi-supervised co-training model based on the three-way decision and pseudo labels. We first present a simple yet effective method for producing two views by assigning pseudo labels to unlabeled data, based on which a heuristic attribute reduction algorithm is developed. The three-way decision is then combined with the concept of entropy to form co-decision rules for classifying unlabeled data into useful, uncertain, or useless samples. Finally, some useful samples are iteratively selected to improve the performance of the co-decision model. The experimental results on UCI datasets demonstrate that the proposed model outperforms other semi-supervised models, exhibiting its potential for partially labeled data. [ABSTRACT FROM AUTHOR]

Digital education is very important and valuable because it is a subpart of artificial intelligence, which is used in many real-life problems. Digital education is the modern utilization of digital techniques and tools during online purchasing, teaching, research, and learning and is often referred to as technology-enhanced learning or e-learning programs. Further, similarity measures (SM) and complex fuzzy (CF) logic are two different ideas that play a very valuable and dominant role in the environment of fuzzy decision theory. In this manuscript, we concentrate on utilizing different types of dice SM (D-SM) and generalized dice SM (GD-SM) in the environment of a CF set (CFS), called CF dice SM (CFD-SM), CF weighted dice SM (CFWD-SM), CF generalized dice SM (CFGD-SM), and CF weighted generalized dice SM (CFWGD-SM), and also derived associated outcomes. Furthermore, to evaluate or state the supremacy and effectiveness of the derived measures, we aim to evaluate the application of artificial intelligence in digital education under the consideration of derived measures for CF information and try to verify them with the help of several examples. Finally, with the help of examples, we illustrate the comparison between the presented and existing measures to show the supremacy and feasibility of the derived measures. [ABSTRACT FROM AUTHOR]

As an essential part of modern intelligent decision-making science, multi-attribute decision-making problems can effectively select and rank all candidate schemes under multiple indicators. Because of the complexity of the real environment and the uncertainty of the decision-making problem, interval numbers are often used to represent the evaluation information of the object. The existing methods of the multi-attribute decision-making problems rarely use the object set but give the decision results by selection or ranking, which often have strong subjectivity. We propose a ranking method from an acceleration viewpoint based on the three-way decision model to solve the interval-valued multi-attribute decision-making problem. A distance measure of two objects is a measure that describes the relationship between objects. Therefore, the fuzzy dominance distance is introduced to express order relations among objects. First, we present a method to compare any two interval numbers, which converts interval numbers into connection numbers according to the characteristics of interval numbers in multi-attribute decision-making problems. Second, the three-way decision theory is introduced to divide the object set into high, medium, and low dominance regions for the speed and rationality of decision-making. Finally, the multi-attribute decision-making problems can be simplified into the problem of selection in three regions by ranking the objects of the selected region. Unlike traditional methods, the experiments demonstrate that our proposed method has the lowest cost. Our method is shown to be efficient and can obtain comparable results. [ABSTRACT FROM AUTHOR]