Search Results (924)

This study addresses this gap by proposing a multilevel fuzzy evaluation model combined with an analytic hierarchy process (AHP) to quantify the greenness of furniture products across their entire lifecycle. Focusing on an office desk as a case study, we developed an indicator system encompassing environmental attributes, resource efficiency, energy consumption, economic costs, and quality performance. Weighting results revealed that environmental attributes (27.2%) and resource efficiency (27.2%) dominated the greenness evaluation, with material recycling rate (33.5%) and solid waste pollution (24.3%) as critical sub-indicators. The prototype achieved a moderate greenness score of 70.38/100, highlighting optimization potential in renewable material adoption (10% current rate) and modular design for disassembly. Mechanically recycled materials could reduce lifecycle emissions by 18–25% in key categories. The model demonstrates scalability for diverse furniture types and informs policy-making by prioritizing high-impact areas such as toxic material reduction and energy-efficient manufacturing, thus amplifying its global and interdisciplinary multiplier effects. Full article

Ventricular arrhythmias such as ventricular fibrillation (VF) and ventricular tachycardia (VT) are among the leading causes of sudden cardiac death worldwide, making their timely and accurate detection a critical task in modern cardiology. This study presents an advanced framework for the automatic detection of critical cardiac arrhythmias—specifically ventricular fibrillation (VF) and ventricular tachycardia (VT)—by integrating deep learning techniques with neuro-fuzzy systems. Electrocardiogram (ECG) signals from the MIT-BIH and AHA databases were preprocessed through denoising, alignment, and segmentation. Convolutional neural networks (CNNs) were employed for deep feature extraction, and the resulting features were used as input for various fuzzy classifiers, including Fuzzy ARTMAP and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Among these classifiers, ANFIS demonstrated the best overall performance. The combination of CNN-based feature extraction with ANFIS yielded the highest classification accuracy across multiple cardiac rhythm types. The classification performance metrics for each rhythm type were as follows: for Normal Sinus Rhythm, precision was 99.09%, sensitivity 98.70%, specificity 98.89%, and F1-score 98.89%. For VF, precision was 95.49%, sensitivity 96.69%, specificity 99.10%, and F1-score 96.09%. For VT, precision was 94.03%, sensitivity 94.26%, specificity 99.54%, and F1-score 94.14%. Finally, for Other Rhythms, precision was 97.74%, sensitivity 97.74%, specificity 99.40%, and F1-score 97.74%. These results demonstrate the strong generalization capability and precision of the proposed architecture, suggesting its potential applicability in real-time biomedical systems such as Automated External Defibrillators (AEDs), Implantable Cardioverter Defibrillators (ICDs), and advanced cardiac monitoring technologies. Full article

This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article

Missing data imputation is a critical preprocessing task that directly impacts the quality and reliability of data-driven analyses, yet many existing methods treat numerical and categorical data separately and lack the integration of advanced techniques. We suggest a novel imputation technique to overcome these restrictions that synergistically combines regression imputation using HistGradientBoostingRegressor and fuzzy rule-based systems and is enhanced by a tailored clustering process. This integrated approach effectively handles mixed data types and complex data structures using regression models to predict missing numerical values, fuzzy logic to incorporate expert knowledge and interpretability, and clustering to capture latent data patterns. Categorical variables are managed by mode imputation and label encoding. We evaluated the method on twelve tabular datasets with artificially introduced missingness, employing a comprehensive set of metrics focused on originally missing entries. The results demonstrate that our iterative imputer performs competitively with other established imputation techniques, achieving better and comparable error rates and accuracy. By combining statistical learning with fuzzy and clustering frameworks, the method achieves 15% lower Root Mean Square Error (RMSE), 10% lower Mean Absolute Error (MAE), and 80% higher precision in UCI datasets, thus offering a promising advance in data preprocessing in practical applications. Full article

To enhance the ability to identify and analyze the construction safety risks of prefabricated building projects, this paper explores the risk factors affecting the construction safety of prefabricated buildings from the perspective of the construction stage. Based on the WSR theory, this paper identifies risk-influencing factors from five dimensions: personnel, materials, management, technology, and environment, and constructs a safety risk assessment index system. This paper establishes a risk assessment model based on the G1-CRITIC method and cloud model. Firstly, it quantitatively analyzes the weights of the risk indicators for prefabricated building construction, and then evaluates the specific degree of impact of each indicator on the construction risk of this type of project. The research results show that the project is at the low-risk level, but there are still some potential risks in terms of material and technical factors, which require close attention and targeted management. The evaluation results obtained by applying this model are consistent with the current actual situation of prefabricated building construction, further demonstrating the applicability of this model. The risk assessment model proposed in this paper, by focusing on a specific type of risk, comprehensively incorporates the fuzziness and randomness of risk factors, thereby more effectively capturing the dynamic characteristics of risk evolution. This model can effectively evaluate the level of safety risk management and plays a positive role in reducing the incidence of engineering accidents. Furthermore, it also provides practical experience that can be drawn upon by risk managers of similar projects which holds significant theoretical value and practical guiding significance. Full article

To solve the problem of traditional engineering drilling rig propulsion systems being difficult to adapt to complex working conditions due to their bulky structure and poor load adaptability, this study proposes a new type of mechanical hydraulic composite electro-hydraulic proportional propulsion system. The system innovatively adopts a composite design of parallel hydraulic cylinders and movable pulley groups in mechanical structure, aiming to achieve system lightweighting through displacement multiplication effect. In terms of control strategy, a fuzzy adaptive PID controller based on position feedback was designed to improve the dynamic tracking performance and robustness of the system under nonlinear time-varying loads. The study established a multi physics domain mathematical model of the system and conducted joint simulation using AMESim and MATLAB/Simulink to deeply verify the overall performance of the proposed scheme. The simulation results show that the mechanical structure can stably achieve a 2:1 displacement multiplication effect, providing a feasible path for shortening the system size. Compared with traditional PID control, the proposed fuzzy adaptive PID control strategy significantly improves the positioning accuracy of the system. The maximum tracking errors of the master and slave hydraulic cylinders are reduced from 6.3 mm and 10.4 mm to 2.3 mm and 5.6 mm, respectively, and the accuracy is improved by 63.49% and 46.15%, providing theoretical support and technical reference for the design of engineering drilling rig propulsion control systems. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

As an energy-intensive industry, the aviation sector’s carbon emissions have drawn significant attention. Against the backdrop of the “dual carbon” goals, how to enhance the carbon emission efficiency of airlines has become an urgent issue to be addressed for both industry development and low-carbon targets. This paper constructs an evaluation system for the carbon emission efficiency of airlines and uses the SBM-DDF model under the global production possibility set, combined with the bootstrap-DEA method, to calculate the efficiency values. On this basis, the fuzzy-set qualitative comparative analysis method is employed to analyze the synergistic effects of multiple influencing factors in three dimensions: economic benefits, transportation benefits, and energy consumption on improving carbon emission efficiency. The research findings reveal that, first, a single influencing factor does not constitute a necessary condition for achieving high carbon emission efficiency; second, there are four combinations that enhance carbon emission efficiency: “load volume-driven type”, “scale revenue-driven type”, “high ticket price + technology-driven type”, and “passenger and cargo synergy mixed type”. These discoveries are of great significance for promoting the construction of a carbon emission efficiency system by Chinese airlines and achieving high-quality development in the aviation industry. Full article

This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief manual instruction. The effectiveness of the training methods was assessed using selected parameters obtained from driving simulator studies, including reaction times and system activation attempts. Given the large volume and nonlinear nature of the input data, a heuristic, expert-based approach was used to identify key evaluation criteria, structure the decision-making process, and define fuzzy rule sets and membership functions. This phase served as the foundation for the development of a fuzzy logic model in the MATLAB environment. The model processes inputs to generate a quantitative performance score. The results indicate that practical training (mean score = 4.0) demonstrates superior effectiveness compared to e-learning (3.09) and manual instruction (mean score = 3.01). The primary contribution of this work is a transparent, data-driven evaluation tool that overcomes the inherent subjectivity and bias of traditional trainer-based assessments. This model provides a standardised and reproducible approach for assessing driver competence, offering a significant advancement over purely qualitative, trainer-based assessments and supporting the development of more reliable certification processes. Full article

The adaptive distributed type-2 fuzzy dynamic event-triggered (DET) formation control problem of switched nonlinear multi-agent systems (SNMASs) with actuator faults is addressed in this study. Each agent has a switching subsystem and the switching method of each subsystem is heterogeneous. Interval type-2 fuzzy logic systems (T2FLSs) are adopted to handle uncertain nonlinearities. To conserve communication resources (UCRs), a novel distributed DET controller with an event triggering mechanism is proposed. Additionally, Zeno behavior is excluded. Then, the formation objective can be achieved with a designed common Lyapunov function (CLF). Finally, simulation results confirm the validity of the proposed scheme. Full article

In response to the increasing consumer interest in the health benefits of plant-based foods, in this study, fuzzy logic modeling (FLM) was used to optimize the lactic fermentation process of several buckwheat (Fagopyrum esculentum)-based substrates (B-bSs), which were bio-prospected for the development of pseudocereal-based fermented foodstuffs. The experimental methodology involved obtaining B-bSs, either green or roasted, under various milling conditions and subjecting them to two different types of thermal treatment. This experimental design allowed us to obtain a set of experimental data, based on which a fuzzy system was developed and calibrated. The main physicochemical characteristics (pH, total titratable acidity, dynamic viscosity, and color) and sensory attributes (appearance, color, aroma, taste, texture or mouthfeel, and overall acceptability) of B-bSs were evaluated. The fuzzy logic approach proved useful for monitoring the evolution of lactic fermentation and for the rapid and accurate identification of situations that require technological interventions, acting as a reliable tool for the ongoing optimization of fermentation processes. Our study’s results showed that the optimal technological variants identified using FLM corresponded to green buckwheat milled with a 0.12 mm gap disk and a hammer mill and subjected to ultrasonic water bath treatment. The hedonic descriptive sensory evaluation also validated this conclusion. Full article

Achieving high-level green technological innovation in heavily polluting enterprises is critical for advancing sustainable development, particularly in the context of both organizational and regional digitalization. This study adopts a configurational perspective grounded in the Technology–Organization–Environment (TOE) framework and integrates theoretical insights from resource orchestration, resource dependence, and IT capability theories. It investigates how different types of skilled talent, such as production, technical, sales, and managerial employees, contribute to green innovation under varying digital conditions. By applying fuzzy-set qualitative comparative analysis (fsQCA) to a sample of 96 publicly listed firms from China’s heavily polluting industries, this study identifies four distinct talent-based configurations that can lead to high levels of green innovation: production-centric, management-led, technical talent driven, and regionally enabled models. Each configuration reflects a specific system state in which a core group of skilled employees plays a leading role, supported by complementary functions, and shaped by the interaction between internal digital transformation and the external digital environment. This study contributes to the systems literature by elucidating the combinational roles of digital resources and talent deployment within the systemic TOE framework, and offers practical guidance for enterprises aiming to strategically utilize human capital to enhance green innovation performance amid ongoing digital transformations. Full article

Amid the increasing complexity of urban risks, urban parks not only serve ecological and recreational functions but are increasingly becoming a critical spatial foundation supporting public psychological resilience and social recovery. This study aims to systematically evaluate the daily adaptability of urban parks in the context of micro-risks. The research integrates the theories of “restorative environments,” environmental safety perception, urban resilience, and social ecology to construct a five-dimensional framework for perceived resilience, encompassing resilience, safety, sociability, controllability, and adaptability. Additionally, a dynamic feedback mechanism of perception–behavior–reperception is introduced. Methodologically, the study utilizes the Fuzzy Delphi Method (FDM) to identify 17 core indicators, constructs a causal structure and weighting system using DEMATEL-based ANP (DANP), and further employs the VIKOR model to simulate public preferences in a multi-criteria decision-making process. Taking three representative urban parks in Guangzhou as empirical case studies, the research identifies resilience and adaptability as key driving dimensions of the system. Factors such as environmental psychological resilience, functional diversity, and visual permeability show a significant path influence and priority intervention value. The empirical results further reveal significant spatial heterogeneity and group differences in the perceived resilience across ecological, neighborhood, and central park types, highlighting the importance of context-specific and user-adaptive strategies. The study finally proposes four optimization pathways, emphasizing the role of feedback mechanisms in enhancing urban park resilience and shaping “cognitive-friendly” spaces, providing a systematic modeling foundation and strategic reference for perception-driven urban public space optimization. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks. Full article