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Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population's ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy model is presented for predicting the possibility of degradation due to salt crystallisation cycles. The formalization of the proposed model has been based on the multivariable approach which considers environmental data (such as temperature, solar radiation, wind speed, rain quantity, relative humidity), characteristic inflection points of specific salts and stone features derived from laboratory characterizations (including mechanical properties, porosity, and mineralogical composition). Modeling results have been compared with experimental data elaborations acquired by monitoring a semi-confined archaeological site situated in the city of Cagliari (Munatius Irenaus cubicle), revealing substantial alignment in the degradation kinetics trends. Moreover, the achieved outcomes show the remarkable capability to identify salt crystallisation phenomenon type (efflorescence or subflorescence). [ABSTRACT FROM AUTHOR]

The inspection of building components plays a crucial role in reducing maintenance costs throughout the building's life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls. Practical Applications: This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management. [ABSTRACT FROM AUTHOR]

A flood stands as one of the most common natural occurrences, often resulting in substantial financial losses to property and possessions, as well as affecting human lives adversely. Implementing measures to prevent such floods becomes crucial, offering inhabitants ample time to evacuate vulnerable areas before flood events occur. In addressing the flood issue, numerous scholars have put forth various solutions, such as the development of fuzzy system models and the establishment of suitable infrastructure. However, when applying a fuzzy system, it often results in a loss of interpretability of the fuzzy rules. To address this issue effectively, we propose to reframe the optimization problem by incorporating stage costs alongside the terminal cost. Results show the proposed model called hybrid fuzzy logic and neural networks (NNs) can mitigate the loss of interpretability. Results also show that the proposed method was employed in a flood early detection system aligned with integrating into Twitter social media. The proposed concepts are validated through case studies, showcasing their effectiveness in tasks such as XOR-classification problems. [ABSTRACT FROM AUTHOR]

This article examines the problems of optimizing the production process of stable gasoline based on models of the object, which is the target product of a primary oil refining installation, and proposes approaches to their effective solution. Currently, solving the problem of optimizing the main processes at a primary oil processing plant in the oil refining industry is one of the most pressing scientific and production problems, as it ensures the efficiency of further deep processing of the resulting petroleum products. In this regard, this study formulates a mathematical formulation of the problem of optimizing the production process of stable gasoline - the target product of the primary oil refining installation of the Atyrau Oil Refinery, characterized by uncertainty, and proposes a heuristic approach for its effective solution in a fuzzy environment. As a result of the research, mathematical models of complex objects characterized by ambiguity of some parameters, such as the stabilization column of a primary oil treatment plant, were developed, and on their basis a heuristic approach was developed that effectively optimizes the operation of the object in conditions of uncertainty. Models that determine the volumes of gasoline and gas from the stabilization column depending on the input operating parameters of the column are determined on the basis of experimental statistical data and the systematic application of the approach of sequential sequential connection of regressors and the least squares method. Fuzzy models that evaluate vaguely described quality indicators of stable gasoline are synthesized on the basis of expert assessment methods, fuzzy sets and modified methods of sequential inclusion of regressors, least squares. Based on the obtained models of the stabilization column, a heuristic algorithm has been developed for the effective optimization of the production process of stable gasoline under conditions of fuzzy conditions, based on a modification and combination of the principles of Pareto optimality and the ideal point for working in a fuzzy environment. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H ∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique. [ABSTRACT FROM AUTHOR]

The growth in the number of people suffering from eye diseases determines the relevance of research in the field of diagnosing retinal pathologies. Artificial intelligence models and algorithms based on measurements obtained via electrophysiological methods can significantly improve and speed up the analysis of results and diagnostics. We propose an approach to designing an artificial intelligent diagnosis system (AI diagnosis system) which includes an electrophysiological complex to collect objective information and an intelligent decision support system to justify the diagnosis. The task of diagnosing retinal diseases based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers—one classifier is based on a fuzzy model and a fuzzy rule base (RB-classifier) and one uses the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of algorithms in a multi-class classification on unbalanced data is assessed based on two indicators—MAUC (multi-class area under curve) and MMCC (multi-class Matthews correlation coefficient). Combining two algorithms in a decision support system provides more accurate and reliable pathology identification. The accuracy of diagnostics using the proposed AI diagnosis system is 5–8% higher than the accuracy of a system using only diagnostics based on electrophysical indicators. The AI diagnosis system differs from other systems of this class in that it is based on the processing of objective electrophysiological data and socio-demographic data about patients, as well as subjective information from the anamnesis, which ensures increased efficiency of medical decision-making. The system is tested using actual data about retinal diseases from the Russian Institute of Eye Diseases and its high efficiency is proven. Simulation experiments conducted in various scenario conditions with different combinations of factors ensured the identification of the main determinants (markers) for each diagnosis of retinal pathology. [ABSTRACT FROM AUTHOR]

Airborne magnetic particles may be harmful because of their composition, morphology, and association with potentially toxic elements that may be observed through relationships between magnetic parameters and pollution indices, such as the Tomlinson pollution load index (PLI). We present a fuzzy-based analysis of magnetic biomonitoring data from four Latin American cities, which allows us to construct a magnetic index of contamination (IMC). This IMC uses four magnetic parameters, i.e., magnetic susceptibility χ, saturation isothermal remanent magnetization SIRM, coercivity of remanence Hcr, and SIRM/χ, and proposes summarizing the information to assess an area based exclusively on magnetic parameters more easily. The fuzzy inference system membership functions are built from the standardization of the data to become independent of the values. The proposed IMC is calculated using the baseline values for each case study, similar to the PLI. The highest IMC values were obtained in sites close to industrial areas, and in contrast, the lowest ones were observed in residential areas far from avenues or highways. The linear regression model between modeled IMC and PLI data yielded robust correlations of R2 > 0.85. The IMC is proposed as a complementary tool for air particle pollution and is a cost-effective magnetic approach for monitoring areas. [ABSTRACT FROM AUTHOR]

Summary: In this article, the decentralized repetitive tracking controller design for fractional‐order large‐scale Takagi–Sugeno fuzzy system with time delays is developed. We mainly focus on the design of a decentralized repetitive tracking controller based on the Lyapunov stability theory, by which the addressed large‐scale system asymptotically stabilized with H∞$$ {H}_{\infty } $$ performance index. Further, the repetitive control with quantized signal is developed to ensure the good tracking performance with the presence of interconnected model and external disturbances. Specifically, a logarithmic quantizer is used to quantify the control signal which can reduce the data transmission rate in the network. Finally, a numerical example is presented to verify the effectiveness of the proposed controller design technique. [ABSTRACT FROM AUTHOR]

This article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

Floods have been considered the most common natural disaster worldwide in recent years. Flood hazard potential mapping is required for the management and mitigation of flood. Climatic change and the occupation of rivers and drainage have led to floods in arid areas. The objective of this study is to assess and zoning flood risk using the AHP-FUZZY hybrid approach model in the Dashti region of Bushehr Province. In this study, precipitation, elevation, slope, drainage density, land use, geology and soil parameters were used. These parameters have been constructed and classified in the ARC GIS Software; which were weighed according to the AHP method in AHP SOLVER software and the layers were fuzzy using the fuzzy model. Finally, by combining the AHP-FUZZY method, the flood risk assessment and zoning map were obtained.6.5% of the area is in the very high-risk range, and 27.9% is in the high-risk range. Analysis of the final map shows that Khormuj, Sana and Shonbeh are at greater risk of flooding. The combination of the AHP-FUZZY method in previous research has confirmed that this method has great capacity in flood risk assessment and zoning. Therefore, knowing the flood potential of the basin can be effective in formulating crisis management plans when faced with floods. [ABSTRACT FROM AUTHOR]

Growing older is a phenomenon that is associated with increasingly complex health situations as a result of the coexistence of several chronic diseases. As a result, there is a downward tendency in both older people and their caretakers’ quality of life, which frequently results in frailty. There are numerous solutions available to treat the issue, which primarily affects older people. The basic and most popular imaging method for predicting cognitive impairment is magnetic resonance imaging. Furthermore, few of the earlier models had a definite level of accuracy when diagnosing the condition. Further, there is a critical need to put in place a stronger, more reliable approach to precise prediction. When compared to other procedures, using magnetic resonance images to predict cognitive decline is the safest and most straightforward. The advanced concept for a better optimized strategy to predict cognitive impairment at an early stage is presented in this research. The hybrid krill herd and grey wolf optimization method is offered as a solution to address the challenges in locating the impacted area. In a short amount of time, a significant number of MRI images are analyzed, and the results show a more precise or higher rate of recognition. [ABSTRACT FROM AUTHOR]

Educators across different fields disseminate their knowledge and utilize digital technologies to improve student skills for their careers and sustainability. Students' skills that are improved are verified based on assessment and knowledge application over different circumstances. The article investigates the impact of the effective educator's knowledge assessment and their role in student skill development. The motivation for the research arises from the realization that teachers' knowledge and their capacity for transferring skills and information to students successfully play a significant role in the quality of education. The goal of the study is to develop a GA2M that has been verified and can be used by educators to improve their performance, enhance student's results, and eventually progress educational practices to use Fuzzy methods for reasoning and to include new rules for improving knowledge to bridge the knowledge gap between educators and students' skill growth. It requires a great effect by the educator to enhance their ability over successive performance improvement. This article analyzes the ability for better improvement using the proposed Guided Ability Assessment Model (GA2M). The proposed model discards the negative impact of the ability on students' skill deterioration. Besides, the ratio of skill improvement across various new abilities and exposures is analyzed using Fuzzy inference. This analysis frames the interference using knowledge rules required for different circumstances. These rules are framed using existing skill implications and problem-solving ability. This proposed model proposes new rules for development of various abilities of educators. Based on their current ability, further training process for the educator's skill development is prescribed. Therefore, the inference for fuzzification is performed for the positive impact on students' skill development. If the inference succeeds, the assessment is leveraged between skill guidance and ability. [ABSTRACT FROM AUTHOR]

Entering the coal phase-out era, major priorities of the Greek lignite mining industry are sustainable mine closure, land reclamation, and planning for long-term and sustainable land repurposing scenarios. A holistic and digitally enabled approach to water management could lead to effective strategic planning in the coal phase-out era. This research proposes an integrated approach to evaluate the hydrological profile of a large and complex open pit lignite mining area based on remote sensing data. This framework included processing of satellite images in different bands and an evaluated digital elevation model of an ≈170 km2 area within the Ptolemais Basin, northern Greece, that has extensive surface lignite mining. Several spectral (SMI, RIP, NDVI, NDWI, MNDWI) and topographic (SPI, TWI, LS) indices were investigated, providing details of spatial and temporal variations in the terrestrial water cycle. The analysis permitted the hydrological characteristics of the study area to be mapped with sufficient accuracy. Water bodies, low-flowing streams, water flow direction, vegetation cover, erodible areas, locations with water accumulation, and areas with high soil moisture were identified. A major additional outcome of this research was the construction of flood-susceptibility maps introducing several hydrological parameters in a fuzzy model. The results indicated several areas inside the mining area with high flood susceptibility. These results were subsequently validated through PPC flood event reports during heavy rainfalls and events reported in the National River Basin Management plans. Using high-resolution digital elevation models and remote sensing data processing to identify hazards, like flooding, could improve land use planning and water management strategies. [ABSTRACT FROM AUTHOR]

The "Art-Risk 3.0" research project has developed a new tool to address the challenges faced in maintaining and preserving cultural heritage. This tool aims to evaluate the functional operational age and life cycle of heritage buildings in the Arc de Berà area in Tarragona, Catalonia, Spain, with the goal of promoting preventive conservation through a multidisciplinary approach. The tool considers various factors that contribute to the condition of heritage buildings, including structural stability, material decay, environmental risks, and usage patterns. By employing a fuzzy model, it provides an assessment of the building's condition in historical sites. One of the key features of the Art-Risk tool is its ability to prioritize intervention among different case studies within a specific urban context. It generates three output results: vulnerability, risk, and functionality index. The vulnerability value indicates the level of vulnerability of a building, with lower values suggesting better structural stability and resilience against potential hazards. The risk value signifies the level of risk associated with a building, with lower values indicating a reduced likelihood of damage or deterioration. The functionality index reflects the operational condition and suitability of a building for its intended use, with higher values indicating better functionality and operational performance, by considering these diverse valuations, stakeholders and conservation plan managers can effectively establish priorities for intervention. Buildings with higher vulnerability, risk, or lower functionality index scores are given higher priority for intervention. This approach ensures that limited resources are allocated to the buildings that require immediate attention, maximizing the impact of conservation efforts. Art-Risk3.0 model incorporates 19 input variables, with five variables automatically assigned based on the building's geographic location. Users are required to provide valid geographical coordinates and the remaining 14 associated values to obtain an accurate assessment of the building's condition. [ABSTRACT FROM AUTHOR]

Experimental and modeling studies of the evolution of plate-like δ phase precipitates in Inconel 625 superalloy additively manufactured by the laser powder bed fusion process are performed. The maximum Feret diameter and the number of particles per unit area are used as parameters describing the size and distribution of the δ phase precipitates. On the basis of microstructural analysis and quantitative image analysis, the effect of time and temperature on the development of δ phase precipitates is determined. The distinct differences in the intensity of precipitation, growth, and coarsening of the δ phase precipitates during annealing at temperatures of 700 and 800 °C up to 2000 h are shown. The experimental results are compared with computational data obtained by thermodynamic modeling. Using the experimentally determined parameters of the δ phase precipitates in different variants of annealing, a fuzzy logic-based phase distribution model is designed. Since the quantity of available data was too small to train a model with the machine learning approach, expert knowledge is used to design the rules, while numerical data are used for its validation. Designed rules, as well as reasoning methodology are described. The proposed model is validated by comparing it with the experimental results. It can be used to predict the size and number density of the δ phase precipitates in the additively manufactured Inconel 625, subjected to long-term annealing at temperatures of 700–800 °C. Due to limited experimental data, the quality of assurance is not perfect, but warrants preliminary research. [ABSTRACT FROM AUTHOR]

Precipitation is one of the main elements of the Earth's hydro-climatic cycle and its variability depends on the complex and non-linear relationships between the climate system and environmental factors. Understanding these relationships and doing environmental planning based on them is difficult. Therefore, classifying data and dividing information into homogeneous and small categories can be helpful in this regard. In the present study, an attempt was made to prepare precipitation, altitude, slope, slope direction, and station density data for 3423 synoptic, climatological, and gauge stations in Iran in the 1961-2015 years’ period. These data were entered into fuzzy (FCM), self-organizing map neural network (SOM-ANN) models and precipitation-spatial zoning. The outputs of the two models were compared in terms of accuracy and efficiency. The results obtained from the output of the models have divided the rainfall conditions of Iran into four zones concerning environmental factors. Evaluations also showed that both models had high accuracy in classifying precipitation parameters; However, the fuzzy model has a relative advantage over the neural network model in the accuracy of results. [ABSTRACT FROM AUTHOR]

Diabetic is a metabolic syndrome which is characterized by hyperglycemia (high blood sugar) and impairs metabolism fat and protein. Common symptoms are frequent urination, increased appetite and increased thirst. If left untreated, diabetes can cause many health complications such as stroke, chronic kidney, cardiovascular disease, foot ulcers, nerve damage, and eye damage. Recent surveys have shown that the number of people affected by diabetes are growing rapidly, so early prediction of diabetes can solve many complications related to diabetes. Nowadays there are enough medical data available to do predictive analysis with the help of modern software and technologies. The aim of this paper is to provide more accurate predictions based on available medical data. The method proposed in this paper provides fuzzy solution for early prediction of diabetic disease using diabetes risk factors like pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree values and age. The experimental and visual results show the proposed method predicts early diabetes efficiently and accurately with accuracy 75.65%. [ABSTRACT FROM AUTHOR]

The prediction of time-series data is a challenging and complex issue. For many practical applications, network topology information and text information can play a perfect role in time-series prediction. This article takes stock data as an example by constructing a graph, connecting each stock's upstream and downstream industries, and obtaining useful text features and topological features to predict the stock time-series. Based on the time-series data features, text features, and the topological features of the stock industry chain of machine learning, we compared our prediction model with other fuzzy time-series prediction methods, which are only based on historical features. The experiment shows that our method is better than the other methods in terms of the performance of multiple stocks in each stock's time-series prediction. The experimental results show that the stock topology based on the industrial chain effectively improved time-series forecasting accuracy. [ABSTRACT FROM AUTHOR]

Time series classification is an increasingly attractive field with the appearance of new problems in an expanding digitalized world. Most of the proposals in the state-of-the-art have focused just on improving the results' performance, leaving interpretability on a secondary level. The available interpretable proposals do not provide competitive results, which is an issue to be addressed. This paper introduces a new fuzzy feature-based time series classification method, which joins the ability of time series features to capture essential information about the time series with Fuzzy logic. This proposal allows the fuzzy-based approach to incorporate global information about the behavior of time series in the membership calculation with the aim of improving the performance and interpretability of the results by using an interpretable classifier. The proposed method has been evaluated over the 112 state-of-the-art time series classification datasets from the UCR repository, and the results obtained show a better performance. Furthermore, the combination of time series features and fuzzy memberships has also increased the interpretability of final models. • PLAYTIME incorporates temporal information into fuzzy environments. • PLAYTIME combines fuzzy- and time-features to improve time series classification. • PLAYTIME improves interpretability and performance by using a tree-based classifier. • PLAYTIME is tested with 112 datasets of the UCR repository. [ABSTRACT FROM AUTHOR]

In actual distributed parameter systems (DPSs), each spatial point has nonlinear energy transfer with its local neighbor points. Also, this energy transfer is affected by the past states. However, these properties are often ignored by most of modeling methods, which causes these methods ineffective in modeling of DPSs. Aiming for this problem, a spatiotemporal kernel-local-embedding (STKLLE) Modeling approach is proposed here to reconstruct the nonlinear spatiotemporal dynamics of DPSs. First, in order to present the complex dynamics on space, a STKLLE strategy is developed to extract space basis functions (SBFs). On the one hand, this STKLLE method represents the energy transfer relation with its neighboring points and maintains this local relation in model. On the other hand, it considers the influence of the adjacent past states to the current state. Then, using T–S fuzzy algorithm, a temporal model is designed to represent the temporal dynamics of DPSs in each sampling period. Integrating these SBFs and the temporal fuzzy model, a spatiotemporal model is constructed to well present and predict the nonlinear spatiotemporal dynamics in DPSs. Through the actual experiment on Lithium-ion batteries and heating oven, the effectiveness of this proposed model is detailly verified, and quantitative comparisons with several data-driven modeling algorithms are further carried out to demonstrate the model efficiency. • A spatiotemporal KLLE method is designed to extract nonlinear relations of the local neighbor points and adjacent states. • A T-S fuzzy temporal model is developed to construct the temporal dynamics of DPSs. • A spatiotemporal model is developed to reconstruct the spatiotemporal dynamics of DPSs. • This proposed method is verified by theoretical analysis and experimental studies. [ABSTRACT FROM AUTHOR]

In this new round of power network development, the concept of coordinated and optimized operation of power network interconnection and smart grid "source network load" has gradually attracted attention. Based on the analysis of the impact of the flexible DC distribution network on the complex energy system with multiple data sources and large data volume under the big data platform, the coordinated dispatching of the source and load of the big data flexible DC distribution network is studied. Therefore, a source-load matching index that can evaluate the impact of different types of loads on the stability of the flexible DC grid is constructed and incorporated into the load recovery optimization model. First, use the scene reduction method to process all generated scenes; next, take the reduction technology and the scene generation method into account of the uncertainty caused by the prediction error and incorporate them into the multi-objective function optimization model; finally, the membership function of fuzzy numbers is used to model uncertainty. So as to construct a load recovery model that can coordinate the load recovery amount, importance and system dynamic response. The actual meaning of the matching index is explained through model solving and actual case analysis. [ABSTRACT FROM AUTHOR]

For many reasons, ship model interaction tests are performed in experimental towing tanks. This paper presents research on the hydrodynamic forces acting on a ship tied up at the solid berth, which is produced by other ships passing by using free-running ship models with much larger dimensions than those used in towing tanks. A passing ship model was controlled by a human operator - an experienced master. This enabled a study of the influence of the interaction impact on the course of the maneuver. The research was carried out at the Ship Handling Research and Training Centre in Iława. The ship model was moored alongside and equipped with multi-directional force sensors linking the ship model with a solid berth. Forces were measured as a function of the passing ship speed, side distance between both ships, ship sizes, and depth-to-draft ratio (H/T). Forces were measured in two planes: the longitudinal (surge) and the transversal (sway). A numerical database was processed and ordered according to the variables. The fuzzy model was created within a "Matlab" computing environment using a Sugeno-type self-learning neuron network model. The proposed Sugeno model was evaluated with other methods presented by Flory (2002), Seelig (2001), and PASS-MOOR by Wang (1975). The ultimate goal of this study was to simplify the method of predictive calculations for adjusting speed and distance when passing by the moored ship, which ensures compliance with safe port mooring requirements. [ABSTRACT FROM AUTHOR]

The aim of the article was to build a model based on fuzzy logic describing the level of sustainable development for the individual EU Member States in 2009--2019 and to conduct fuzzy simulations as a result of which scenarios for the prediction of sustainable development acknowledging financial variables (the share of environmental taxes, a deficit, a debt). The original research approach is expressed by including fuzzy models in the analysis of the level of sustainable development with the inclusion of financial variables. The Netherlands achieved the highest value (the highest level of sustainable development) (0.571). Slovenia (0.564) and Denmark (0.559) came second and third. The lowest average value (the lowest level of sustainable development) was recorded for Slovakia (0.423). After 10 years there are evident changes in the distribution of individual typological groups. In 2019, the countries belonging to the first and second typological groups were located primarily in the northern and central Europe. The scenario analysis showed that in the case of most countries, if the analyzed variables remained at the maximum or minimum level throughout the analyzed period, it would not significantly affect the assessment of their level of sustainable development, their ranking positions, and inclusion into typological groups. [ABSTRACT FROM AUTHOR]

The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin. [ABSTRACT FROM AUTHOR]

Training has great significance and should be an integral part of the daily routines of all elite athletes. Training allows the body to gradually develop strength and endurance, increase skill levels, and build trust, motivation, and ambition. The risk factors in sports training sessions include fragile or low self-confidence, Breakdowns in self-assurance, and high expectations are considered an important factors. In this paper, Dynamic Activity Adaptive Physical Fuzzy Model (DAAPFM) is proposed to become better athletes and meet their mental challenges. Mel does time-frequency analysis, and they tend to experience anxiety about performance. Fuzzy wrapping optimization analysis is integrated with DAAPFM to build confidence in the belief in one's ability to execute a task or win an event. The experimental results show that the data fusion approach can analyze the activities of athletes effectively. The simulation results give the average accuracy of the classification of sports activity 99.40% framework's efficiency to gradually develop strength and endurance, increase skill levels, and build trust, motivation, and ambition. [ABSTRACT FROM AUTHOR]

Operating Room (OR) management has been among the mainstream of hospital management research, as ORs are commonly considered as one of the most critical and expensive resources. The complicated connection and interplay between ORs and their upstream and downstream units has recently attracted research attention to focus more on allocating medical resources efficiently for the sake of a balanced coordination. As a critical step, surgical scheduling in the presence of uncertain surgery durations is pivotal but rather challenging since a patient cannot be hospitalized if a recovery bed will not be available to accommodate the admission. To tackle the challenge, we propose an overflow strategy that allows patients to be assigned to an undesignated department if the designated one is full. It has been proved that overflow strategy can successfully alleviate the imbalance of capacity utilization. However, some studies indicate that implementation of the overflow strategy exacerbates the readmission rate as well as the length of stay (LOS). To rigorously examine the overflow strategy and explore its optimal solution, we propose a Fuzzy model for surgical scheduling by explicitly considering downstream shortage, as well as the uncertainty of surgery duration and patient LOS. To solve the Fuzzy model, a hybrid algorithm (so-called GA-P) is developed, stemming from Genetic Algorithm (GA). Extensive numerical results demonstrate the plausible efficiency of the GA-P algorithm, especially for large-scale scheduling problems (e.g., comprehensive hospitals). Additionally, it is shown that the overflow cost plays a critical role in determining the efficiency of the overflow strategy; viz., benefits from the overflow strategy can be reduced as the overflow cost increases, and eventually almost vanishes when the cost becomes sufficiently large. Finally, the Fuzzy model is tested to be effective in terms of simplicity and reliability, yet without cannibalizing the patient admission rate. [ABSTRACT FROM AUTHOR]

• SARS-CoV2/nCoV has ∼89% genetic resemblance with SARS-CoV. • Initially, spreader proteins are identified in the Human-SARS-CoV PPIN, using SIS model. • A fuzzy affinity function is used to construct the level-1 of nCoV-Human PPIN. • Level-2 human spreaders of nCoV-Human PPIN are subsequently identified using the SIS model. • Human-nCoV PPIN is finally validated using six potential FDA-listed drugs for COVID-19. Novel coronavirus (SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein–protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered potential human targets for nCoV bait proteins. A gene-ontology-based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at a ∼99. 98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins. [ABSTRACT FROM AUTHOR]

This paper makes a direct contribution to the mathematical theory and practical implementation of interpretable deep fuzzy learning whilst addressing the model interpretability challenge posed by the typical non-interpretable nature of training data. A deep autoencoder, composed of a finite number of stochastic fuzzy filters, is learned using variational Bayes for an efficient representation of high-dimensional feature vectors at different levels of abstraction. The robustness of the deep model towards data outliers is achieved by incorporating heavy-tailed distributed noises in the inference mechanism. The proposed deep stochastic fuzzy autoencoder makes it possible to have an interpretable classification by means of an induced mapping from a non-interpretable feature space to an interpretable parameter space. The mapping induced by our autoencoder can be used to explain the classifier's predictions by interpreting the non-interpretable high-dimensional feature vectors. The experimental results obtained on various benchmark datasets (e.g., Freiburg Groceries, Caltech-101, Caltech-256, Adobe Panoramas, and MNIST) show that our proposed approach not only outperforms widely used machine learning methods in classification tasks but brings the added value of interpretability as a further advantage. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy, low efficiency and low stability of traditional methods and recent developments in advanced technology incite the industries to be in sync with modern technology. With respect to various available techniques, this paper designs a fuzzy comprehensive evaluation model of the manufacturing industry for transferring risk based on economic big-data analytics. The big-data analysis method is utilized to obtain the data source of fuzzy evaluation of the manufacturing industry to transfer risk using data as the basis of risk evaluation. Based on the risk factors, the proposed model establishes the risk index system of the manufacturing industry and uses the expert evaluation method to design the scoring method of the evaluation index system. To ensure the accuracy of the evaluation results, the manufacturing industry's fuzzy comprehensive model is established using the entropy weight method, and the expert evaluation results are modified accordingly. The experimental results show that the highest efficiency of the proposed method is 96%, the highest accuracy of the evaluation result is 75%. The evaluation result's stability is higher than the other existing methods, which fully verifies the effectiveness and can provide a reliable theoretical basis for enterprise risk evaluation research. [ABSTRACT FROM AUTHOR]

This article discusses $H_{\infty }$ fuzzy control for the nonlinear fourth-order parabolic equation with input delay via collocated actuator/sensor pairs. We suggest that the interval [0, 1] is divided into $M$ subdomains, where sensors provide spatially averaged/point discrete-time state measurements. The control design strategy is proposed based on output measurements. We derive constructive conditions ensuring that the resulting closed-loop system is internally exponentially stable and has $H_{\infty }$ performance by means of the Lyapunov approach. [ABSTRACT FROM AUTHOR]

In the presence of increasing demands for safety and efficiency of material handling systems, the development of advanced supervisory control, monitoring, data acquisition and diagnostic systems is involved, especially for large industrial cranes. The important part of such systems is the continuous monitoring of a crane load. The crane load monitoring system proposed in the paper is based on a fuzzy model that estimates a payload mass transferred by a crane based on measuring the crane girder deflection and trolley position. The model was identified using the fuzzy subtractive clustering and least mean square with the data collected during experiments carried out on the laboratory scaled overhead crane. [ABSTRACT FROM AUTHOR]

The precision agriculture and soil tillage are tasks which can be achieved by Automated Tractors (AT) through the integration of several servomechanisms. In order to reach a high autonomy under various work conditions of the AT, control laws' design and tuning are paramount. The aim of this work is to develop an angle position controller for steering systems applied to an AT. We introduce an automatic electronic steering system by using a fuzzy model, obtained through an Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm training. A proportional derivative (PD) controller is also tuned through a Takagi-Sugeno fuzzy model (T-S). Furthermore, several closed-loop stability tests were carried out, in numerical simulation and real-time implementation. The feasibility of our methodology is illustrated through the tracking of several angles' profiles in a real test scenario. For the tracking of a trajectory or several set-points, we have obtained mean errors about 0.6545 and 0.8651 degrees, respectively. Some mechatronic integrations to convert a conventional tractor into a basic Autonomous Agriculture Off-road Tractor (AAOT) are also shown. [ABSTRACT FROM AUTHOR]

The presence of formations containing gypsum layers could enhance the formation of gypsum karst forms. The thickness of the gypsum layers and the quality of the mentioned beds influence the formation of such landforms. The Maroon plain is located in the folded zone of the Zagros Mountains in Kohgiluyeh and Boyer-Ahmad Province. The existence of the Gachsaran Formation and the occurrence of the karst phenomena (among which sinkholes are the most significant) in this plain have a very destructive impact on the roads, facilities, farmlands, residential units, and the reservoir of the Maroon Dam located in the plain. However, this phenomenon is not well-known and well-studied. In the present study, the karst evolution zoning map was prepared using the fuzzy model. To produce this map, ten environmental factors including the slope, slope direction, geology, distance from the Knick line, distance from the flow line, distance from the river, elevation, land use, climate, and the NDVI Index were used. The results of this study demonstrated that 55.2% of the study area was situated in the zero-risk zone because it was situated out of the Gachsaran Formation realm and lacked the gypsum layer. Also, 11.2% of the area was in the high-risk zone, 18.5% in the medium-risk zone, and 15.1% in the low-risk zone. Field observations and studying the zoning map indicated that the main factors influencing the karst evolution of this area were slope and streamlines which were superimposed on the gypsum layers. [ABSTRACT FROM AUTHOR]

The H∞ stability analysis and interval type‐2 Takagi‐Sugeno (T‐S) fuzzy control is studied for a class of interval type‐2 T‐S fuzzy systems. Firstly, the interval type‐2 T‐S fuzzy model is employed to approximate the system plant. Secondly, the delay‐dependent interval type‐2 T‐S fuzzy dynamic output feedback controller is designed. Thirdly, two classes of stability conditions in terms of linear matrix inequalities (LMIs) are derived, and the closed‐loop system is asymptotically stable and prescribed H∞ performance is achieved. The delay‐dependent interval type‐2 T‐S fuzzy dynamic output feedback controller is designed to relax the control design conditions. The descriptor redundancy method is employed for avoiding the crossing vectors between the system gain matrices and controller gain matrices. The delay‐dependent Lyapunov functional with descriptor redundancy matrix is introduced, and the upper and lower membership functions information is considered. Some slack matrices are introduced via the upper and lower membership functions information to solve the obtained LMIs. Finally, simulation examples are performed to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In the present paper, a fuzzy model of assessment is proposed according the framework of fuzzy mathematical theory to estimate the complexity of rock fractures and geological structures. We established the fuzzy evaluation model and decided a trapezoidal membership function using the fuzzy mathematics. Dataset acquisition is integrated with an advanced technique of coupling multiscale fracture data obtained from both high-resolution satellite imagery and field data. Five parameters were selected, including qualitative and quantitative data from different scales, namely fractal dimension, fracture length, fracture intensity, the intersection angle between fractures and beddings and the fracture density. The weight of each factor is obtained with the principle that the relative value of the complexity index is larger and the weight is significant. This model was applied to the complexity assessment of the southwestern Tunisia. The rank of complexity of each sub-area in the study region is from strong to weak. The result shows that Jebal Orbata and Jebal Chamsi have the strongest complexity due to their position beside the 140 dextral strike-slip Gafsa fault. The east and central zone of the region of investigation has the weakest complexity. [ABSTRACT FROM AUTHOR]

Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data. [ABSTRACT FROM AUTHOR]

The change in the viscosity of a reaction mixture in the process of polystyrene synthesis and the possibility of controlling it in the course of polymerization have been studied. A laboratory reactor is used in this study, and the plots meeting our expectations are obtained for the change in the reactor stirrer rotation speed and output adjustable speed drive signal. An adequate fuzzy viscosity change model is created for use in the control system. [ABSTRACT FROM AUTHOR]

Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-Fuzzy Inference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models. [ABSTRACT FROM AUTHOR]

• Compact UAV leveraging Coandă effect for exceptional maneuverability. • Exploring theoretical study, mechatronic design, and manufacturing complexities of Coandă UAV. • First application of Fuzzy Logic Controller to Coandă-effect UAV, surpassing PID control. • Advantageous Sugeno model provides faster, more robust control in uncertain environments. • Systematic comparison shows FLC strategy outperforms PID approach by 35.23% in settling time. • Comprehensive experimentation in Matlab® and Simulink for microcontroller-based UAV. • Groundbreaking fusion of novel design and advanced control techniques for Coandă UAVs. • Addressing unique aerodynamic challenges, contributing significantly to UAV research. Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research. [ABSTRACT FROM AUTHOR]

In view of pregnancy at the uterine scar after cesarean section, there is an urgent need to find a conservative treatment mode with drugs that can improve the success rate of treatment, shorten the conservative treatment time, and cause little harm to the body. This research combines ultrasound intervention technology to construct a fuzzy model to comprehensively evaluate the clinical effects of traditional Chinese medicine combined with western medicine in the treatment of CSP. Moreover, this research combines the current application algorithms of machine vision to explain the relevant principles of ultrasound interventional vision. In addition, this research uses the background subtraction method to extract the target, and uses the machine vision algorithm to extract the ultrasonic detection target object. Finally, this research adjusts the control parameters according to the fuzzy control, and outputs the optimal control plan with BP network training. The results of this study show that the combination of Chinese and Western medicines in the treatment of uterine scars after cesarean section has the advantages of small gestational sacs, short pregnancy, and low blood β-HCG, and it has the advantages of less pain, economy, fewer side effects, and successfully preserves the uterus. Therefore, it is worthy of clinical application. [ABSTRACT FROM AUTHOR]

Music psychology can play an important role in the diagnosis and rehabilitation of mental health patients. Under the guidance of music psychology, this paper combines fuzzy models to process data, which effectively solves the problem that the number of categories in the fuzzy c-means algorithm needs to be manually given. The AFCC algorithm effectively combines the idea of semi-supervised clustering with the CA algorithm. Through two sets of must-link and cannot-link, this paper introduces the constraint penalty item into the objective function, which greatly improves the clustering accuracy. On this basis, this paper constructs a fuzzy model of psychological rehabilitation and diagnosis based on music psychology, designs experiments to verify the performance of this model, and conducts research results statistics from two aspects of diagnosis and rehabilitation. The research results show that the model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

The habitat development and establishment are directly related to the natural bedrock and geomorphologic complications. Due to topographical and geological conditions, the area under study has certain limitations in locating and developing human settlements. In order to reduce the hazards in the southeast of Khorasan Razavi province and modify plans for the development and establishment of human settlements, the feasibility of the area under study for residential development was investigated. After conducting library and field studies and selecting appropriate criteria, the fuzzy logic model was applied using the GIS software to identify areas for habitat development and establishment. In this study, to identify the potential areas for habitat development and establishment based on geomorphologic landforms, eleven parameters - including slope, slope direction, elevation, soil, land use, elevation, distance from fault, distance from river, distance from road, distance from settlement, and geomorphology - were used as independent variables in identifying geomorphological abilities and bottlenecks in the area. The results of zoning based on the fuzzy model showed that about 61% of the study area is located in very inappropriate and inappropriate classes, which means that geomorphological conditions in this part of the study area are unfavorable for the development and establishment of human settlements. About 14.58% of the study area has moderate conditions and about 24% of the study area shows favorable geomorphological conditions for construction activities and creation of new habitat areas. Alluvial fan units and alluvial plains are the most suitable sites for habitat development and establishment. [ABSTRACT FROM AUTHOR]

A nanofluid (Nf), which contains tungsten trioxide, WO3, nanoparticles as solid-particles distributed in liquid paraffin, is produced. A set-up is provided to experimentally measure the thermal conductivity (TC) of this mixture at various mass fractions and temperatures. Further, two universal approximators of fuzzy logic (FL) and artificial neural network (ANN) are presented to predict the TC of this mixture. The present work's novelty can be presented as: prepare a WO3/paraffin nanofluid, measure it's TC, and develop a novel statistical/numerical model of FL compared with ANN. As it can be seen, temperature and concentration effects positively on nanofluid TC. Moreover, ANN and fuzzy models show suitable precisions; however, the interpolation capability of the proposed fuzzy model outperforms ANN at non-trained inputs. [ABSTRACT FROM AUTHOR]

In agriculture with 4.0 technologies, developing a decision model with a 0.0 cost is attractive to small farmers. In water management, if this approach could be used to promote sustainability and optimization, it could become a pathway to reach the sustainable development goal in 2030. The core of this work is the development of a 4.0 mathematical model (based on fuzzy concepts) to verify the benefits of the production of lettuce irrigated with magnetically treated water at different replacement rates. This approach is achieved using computational 4.0 software and manual methods. The aim of mathematical modeling is to understand or explain a natural phenomenon associated with a given area of knowledge, and fuzzy-rule-based systems have been widely used in different types of in-depth research. [ABSTRACT FROM AUTHOR]

Supporting the Quality of Service (QoS) for broadcasting techniques in Vehicular Ad hoc NETetworks (VANETs) is a primordial concern. Qualitatively, QoS is an aspect reflecting the network performance, but quantitatively, it is a function of multiple parameters such as end-to-end delay, packet loss ratio and overhead. These parameters are changing over time and according to adopted protocols. Therefore, it is very difficult to estimate with a crisp value the system QoS. Besides, there is a lack of technical tools for modelling, measuring and comparing the level of QoS performed by different broadcasting protocols under different network constraints. This paper proposes FUZZYVAN-QoS a holistic model based on a fuzzy system simplifying this problem and going through discussing its dependency on different Vehicular Ad hoc NETtwork (VANET) services and applications types. Then, we use a case study; applied on multiple VANET broadcasting protocols, to illustrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In recent years, with the further breakthrough of artificial intelligence theory and technology, as well as the further expansion of the Internet scale, the recognition of human emotions and the necessity for satisfying human psychological needs in future artificial intelligence technology development tendencies have been highlighted, in addition to physical task accomplishment. Musical emotion classification is an important research topic in artificial intelligence. The key premise of realizing music emotion classification is to construct a musical emotion model that conforms to the characteristics of music emotion recognition. Currently, three types of music emotion classification models are available: discrete category, continuous dimensional, and music emotion-specific models. The pleasure-arousal music emotion fuzzy model, which includes a wide range of emotions compared with other models, is selected as the emotional classification system in this study to investigate the influencing factor for musical emotion classification. Two representative emotional attributes, i.e., speed and strength, are used as variables. Based on test experiments involving music and non-music majors combined with questionnaire results, the relationship between music properties and emotional changes under the pleasure-arousal model is revealed quantitatively. [ABSTRACT FROM AUTHOR]