Search Results (78)

Evolving fuzzy systems (EFS) offer an incremental learning, making them promising for fault diagnosis (FD) in industrial processes, where unknown faults and changing operation conditions are common. The evolving fuzzy structure enables incremental rule adaptation while maintaining interpretability and reduced computational complexity compared with deep learning approaches. However, the performance of EFS depends heavily on the preprocessing of input data. This study evaluates eight preprocessing strategies for EFS applied to the Tennessee Eastman benchmark process. A one-vs-rest EFS architecture was implemented for ten representative faults (IDV1, IDV2, IDV4, IDV5, IDV6, IDV7, IDV8, IDV10, IDV13 and IDV14) in order to make a comparison with other FD techniques. This approach uses seven variables selected by using the least angle regression. Preprocessing methods were applied to highlight fault signatures. Using the Daubechies-4 in the preprocessing achieved the best overall F1-score (73.68%) with a sensitivity of 97.37%, outperforming the no-preprocessing baseline (F1 = 70.67%). Per-fault analysis showed high performance for faults IDV6, IDV7, and IDV14, while IDV1, IDV2, IDV5, and IDV8 exhibited high sensitivity but lower specificity. These findings indicate that wavelet preprocessing significantly enhances EFS for FD, and that the choice of wavelet should be guided by application priorities: Daubechies-4 is recommended for maximum detection and fewer false alarms. The obtained results demonstrate that wavelet preprocessing substantially improves classification robustness and fault discrimination compared with the non-preprocessed baseline. Full article

Estimation of distribution algorithms (EDAs) are optimization methods that search for explicit probabilistic models which are used to sample promising candidate solutions. The optimization process consists of a sequence of incremental updates to an initial probabilistic model, which is then used to sample candidate solutions for the problem to be solved. EDAs use an explicit probability distribution encoded by a Bayesian network, a multivariate normal distribution, or another model class. Although some EDAs optimize the structure of the probabilistic graph model, the distribution type at each node is typically fixed; for continuous variables, the nodes generally encode normal distributions. The current paper proposes M-EDA (mixture-based estimation of distribution algorithm)—an EDA variant based on genetic algorithms which aims to identify an optimal type of probabilistic model, encoding mixtures of probability distributions and their corresponding parameters. M-EDA optimizes such mixtures in order to fit complex landscapes. These mixtures are encoded in the genetic algorithm (GA) through heterogeneous variable-length chromosomes. M-EDA was tested on several numerical optimization problems used widely in the literature on genetic algorithms and reached near-optimal solutions. It also demonstrated multimodal optimization capabilities. Finally, M-EDA was also tested on the instance selection (IS) problem, obtaining a substantial reduction in the number of selected instances, and outperforming most of the competing techniques—in accuracy on balanced datasets, and in instance-reduction rate on imbalanced or high-dimensional ones. Full article

This paper presents a surrogate-based uncertainty quantification (UQ) framework for coupled structural–acoustic systems subject to material and geometric variability. The proposed methodology integrates the Finite Element Method (FEM) with two metamodeling techniques—the Quadratic Response Surface (QRS) and Kriging—and Monte Carlo Simulations (MCS), to efficiently characterize the probabilistic behavior of the acoustic response. Two accuracy metrics (cross-validation error and prediction error) are used to validate the surrogate models. Numerical experiments demonstrate that the Kriging metamodel trained with 30 Latin Hypercube Sampling (LHS) points achieves superior predictive accuracy, with a Relative Maximum Error of 4.125 × 10⁻⁷. Monte Carlo Simulations conducted via the Kriging surrogate reduce the computational cost by more than six orders of magnitude compared to direct FEM-based MCS, while maintaining high accuracy. The proposed framework is validated on a rectangular cavity coupled with two flexible aluminum plates, and provides an efficient and accurate tool for vibro-acoustic UQ in complex engineering systems. Full article

Against the backdrop of global sustainable development and ecological civilization construction, tourism ecological security at cultural landscape heritage sites faces both opportunities and challenges. This study constructs a cultural landscape heritage tourism ecological security (CLHTES) evaluation system based on the Driver–Pressure–State–Impact–Response (DPSIR) framework. It dynamically assesses CLHTES in the Yangtze River Delta Integrated Demonstration Zone (YRDIDZ) from 2014 to 2023 using the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and linear stretching transformation, identifies obstacle factors with the obstacle degree model, and predicts CLHTES trends for 2024–2030 using a radial basis function (RBF) neural network. Results show that: (1) The CLHTES index in the YRDIDZ presented a three-stage fluctuating upward trend during 2014–2023, with medium-clustered security levels and divergent evolution across the DPSIR criteria layers; (2) CLHTES obstacles feature a multi-level differentiated structure, with rising barriers in D and P layers, the R layer as the future core obstacle, and high-frequency barriers concentrated in cultural and social indicators; (3) Under the assumption of structural continuity in current trajectories, the conditional trend projection suggests that the CLHTES index of the YRDIDZ may sustain a general upward tendency during 2024–2030, with a possibility of approaching Level VII after 2028; however, these projections should be interpreted as exploratory and scenario-like rather than as robust forecasts, given the short annual series and the absence of exogenous disturbance variables. This study explores tourism-ecology interactions from a social-ecological complex system perspective, supporting synergistic tourism development and ecological protection of cultural landscape heritage. Full article

A polyvinyl chloride (PVC)-based tissue-mimicking polymer phantom was developed and mechanically characterised to replicate stiffness ranges relevant to breast elastography and to provide a controlled platform for evaluating shear wave elastography (SWE) measurements. SWE provides quantitative stiffness information that complements B-mode ultrasound in breast imaging. However, measurement variability related to operator technique and tissue continues to limit confidence in clinical interpretation. This study evaluates the reproducibility of SWE using custom-fabricated PVC-based breast phantoms with mechanically defined stiffness properties. Two PVC-based breast phantoms with identical geometry and different background stiffnesses were scanned using a single ultrasound system under a fixed SWE protocol. Each phantom contained four embedded inclusions representing clinically relevant stiffness categories. Six breast imagers independently acquired repeated SWE measurements in transverse and longitudinal planes, blinded to lesion identity and ground truth. Inter-operator reproducibility was assessed using intraclass correlation coefficients, and was high across both phantom backgrounds, with low intra-operator variability following quality assurance exclusion of one dataset due to sampling error. Measurement variability was lowest for solid inclusions and increased for the cyst-like inclusion in the stiffer background. SWE measurements consistently preserved the relative stiffness ordering of inclusions, although absolute values differed systematically from mechanically derived ground-truth stiffness. These findings demonstrate that PVC-based polymer phantoms provide a stable and reproducible platform for evaluating SWE measurement behaviour under controlled conditions. By isolating operator and acquisition effects from biological variability, this polymer-based framework supports methodological standardisation and structured operator training in breast elastography. Full article

Integrated blisk–shaft rotors represent a critical advancement in aero-engine design, offering enhanced structural integrity and weight reduction. However, their complex dynamic behavior under inherent material uncertainties poses significant challenges for reliable vibration prediction. This study presents a novel stochastic uncertainty analysis framework combining reduced-order finite element modeling and artificial neural networks (ANNs) to efficiently and accurately quantify the modal variability of integrated blisk–shaft rotors. A high-fidelity finite element model is first developed, followed by the construction and validation of a reduced-order model (ROM) to substantially decrease computational costs while preserving modal accuracy. Material parameter uncertainties are introduced, and corresponding natural frequencies are computed using the ROM. Subsequently, an ANN surrogate model is trained to capture the nonlinear mapping between uncertain input parameters and modal frequencies, enabling rapid prediction across the stochastic parameter space. The proposed approach is employed to perform comprehensive uncertainty propagation and global sensitivity analyses, identifying the dominant parameters influencing each modal frequency. Results demonstrate that the combined ROM-ANN methodology achieves high predictive accuracy with significantly reduced computational effort, offering an effective tool for uncertainty-aware dynamic analysis and design optimization of integrated blisk–shaft rotors. This work advances the integration of machine learning techniques with classical structural dynamics for robust aero-engine rotor design under uncertainty. Full article

Manufacturing industries are increasingly applying sustainable closed-loop supply chains (CLSCs) to meet economic, environmental, and societal goals. The increasing complexity and interdependence associated with the sustainability CLSCs make them highly vulnerable to disruption risks that threaten continuity and sustainability. However, prior studies fall short of guiding how disruption risks in sustainable CLSCs can be systematically prioritized under uncertainty in a stable and decision-relevant manner. To fill this literature void, this study develops a hybrid of the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) method and the genetic algorithm (GA) technique to prioritize disruption risks under uncertainty. Triangular fuzzy numbers are used to capture the imprecision of 13 experts from industry and academia, whereas the GA technique used aimed to improve stability and reduce the variability commonly observed in conventional fuzzy multi-criteria decision-making methods. The method is validated through a real-world case study, identifying supplier disruption risk, route disruption risk, and industrial accidents as the most critical risks. Moreover, sensitivity analysis is conducted to validate the robustness of GA-based Fuzzy-TOPSIS, demonstrating its superior stability and reliability compared to the classical Fuzzy-TOPSIS method in uncertain environments. The novelty of this study lies in embedding a GA-driven approach within the fuzzy-TOPSIS structure to explicitly address ranking instability under uncertainty in sustainable CLSCs. The study provides significant theoretical contributions by enhancing multi-attribute decision-making regarding disruption risk in sustainable CLSC literature, as well as practical insights for decision-makers to efficiently allocate resources by focusing mitigation investments on consistently high-priority risks instead of low-priority ones. Full article

The present study evaluates the performance of hotel companies in Romania using Data Envelopment Analysis (DEA) integrated with a hybrid weighted TOPSIS model (Technique for Order Preference by Similarity to the Ideal Solution). This approach captures both technical efficiency and multidimensional competitiveness. The DEA included an output-oriented Variable Returns to Scale (VRS) model (with four inputs and one output). It was followed by TOPSIS aggregation with hybrid entropy weights to obtain a composite performance index. The research used cross-sectional financial data for 2023, specific to hotels in Romania, and allowed interpretation across five territorial categories based on predominant relief. The results show that the 852 analyzed hotels have a relatively homogeneous structure and moderate variations in performance scores. At the same time, top-performing units are strongly concentrated in economically or touristically dynamic counties. The integrated DEA–TOPSIS results indicate that high-performing hotels tend to cluster spatially, with plain counties hosting the largest number of hotels at the national level and also a substantial share of high-performance hotels relative to major urban centers; thus, their performance structure is not uniform but strongly polarized. In contrast, the other geographical areas show pronounced clustering, with top hotels concentrated around consolidated leisure destinations, such as Brașov, Sibiu, Constanța, and Prahova. Overall, research using the DEA–TOPSIS method highlights significant spatial disparities that influence both managerial decision-making and regional development policies, affecting the long-term sustainable performance and competitiveness of the Romanian hotel sector. Full article

The correct estimation of the population variance plays a vital role in the sampling procedure in surveys, especially when simple random sampling techniques are used. In this work, we propose a new generalized statistical inference in order to estimate the population variance using auxiliary information. We can use the relationship between the study variable and the auxiliary variable to construct a novel generalized class of estimators that is better performing in terms of minimum mean squared error (MSE) and has a higher percentage of relative efficiency than the traditional estimators. The proposed methodology is based on the existing methods of inference with the introduction of modifications to cover the known population parameters of additional auxiliary variables, like the mean, the coefficient of variation, skewness, or kurtosis. Theoretical properties such as bias and mean squared error are obtained with regard to the first-order approximation. The performance of the proposed class of estimators is checked by comparing with that of the classical variance estimators in different population conditions based on real-life data sets and a simulation study. The numerical findings have indicated that the suggested class of estimators is more effective compared to classical methods, especially in cases where there is a very high linear correlation between the auxiliary and the study variables. Also, the estimators are robust, as confirmed using various sample sizes and population structures. The research has made a significant contribution to the development of statistical procedures in survey sampling because the practical and efficient tools provided in the study were useful in estimating the variance. The results have been of great importance when applied by researchers and practitioners active in large-scale surveys. Subsequently, in the case of efficient utilization of auxiliary information, it is feasible to have more accurate and cost-effective statistical inference. Full article

Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking stability within TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Seven widely used normalization procedures are analyzed regarding mathematical properties, sensitivity to extreme values, treatment of benefit and cost criteria, and rank reversal. Normalization is treated as a source of uncertainty in MCDA outcomes, as different schemes can produce divergent rankings under identical decision settings. Shannon entropy is employed as a descriptive measure of information dispersion and structural uncertainty, capturing the heterogeneity and discriminatory potential of criteria rather than serving as a weighting mechanism. An illustrative experiment with ten alternatives and four criteria (two high-entropy, two low-entropy) demonstrates how entropy mediates normalization effects. Seven normalization schemes are examined, including vector, max, linear Sum, and max–min procedures. For vector, max, and linear sum, cost-type criteria are treated using either linear inversion or reciprocal transformation, whereas max–min is implemented as a single method. This design separates the choice of normalization form from the choice of cost-criteria transformation, allowing a cleaner identification of their respective contributions to ranking variability. The analysis shows that normalization choice alone can cause substantial differences in preference values and rankings. High-entropy criteria tend to yield stable rankings, whereas low-entropy criteria amplify sensitivity, especially with extreme or cost-type data. These findings position entropy as a key mediator linking data structure with normalization-induced ranking variability and highlight the need to consider entropy explicitly when selecting normalization procedures. Finally, a practical entropy-based method for choosing normalization techniques is introduced to enhance methodological transparency and ranking robustness in MCDA. Full article

The increasing generation of biomass-derived waste has accelerated the development of sustainable strategies for its valorization into functional materials. Activated carbon (AC), due to its high surface area, tunable porosity, and chemical versatility, has emerged as a key product for applications in adsorption, catalysis, energy storage, and biosensing, among others. Recent studies have highlighted the importance of symmetry and asymmetry in determining the structural and functional performance of AC. Symmetric architectures, typically generated via templating methods, yield ordered pore networks, whereas asymmetric structures, commonly produced through direct chemical activation or heteroatom doping, exhibit hierarchical porosity and heterogeneous surface functionalities. This work critically examines the fundamentals of symmetry and asymmetry in AC materials, as well as their influence on design and use. It discusses synthesis strategies, characterization techniques, and recent approaches that enable the rational engineering of carbon structures. Application-specific case studies are presented, along with current challenges related to feedstock variability, scalability, and regulatory integration. By highlighting the interplay between structural order and functional diversity, this work provides a conceptual framework for guiding future research in the development on symmetrical and asymmetrical carbonaceous materials for sustainable waste valorization. Full article

Modern smart cities face increasing pressure to invest in sustainable and reliable energy systems while navigating uncertainties arising from fluctuating market conditions, evolving technology landscapes, and diverse expert opinions. Traditional multi-criteria decision-making (MCDM) approaches often fail to fully represent these uncertainties as they typically rely on crisp inputs, lack temporal memory, and do not explicitly account for stochastic variability. To address these limitations, this study introduces a novel Stochastic Fractional Fuzzy Tensor (SFFT)-based Group Decision-Making framework. The proposed approach integrates three dimensions of uncertainty within a unified mathematical structure: fuzzy representation of subjective expert assessments, fractional temporal operators (Caputo derivative, $\alpha =0.85$) to model the influence of historical evaluations, and stochastic diffusion terms ($\sigma =0.05$) to capture real-world volatility. A complete decision algorithm is developed and applied to a realistic smart city renewable energy selection problem involving six alternatives and six criteria evaluated by three experts. The SFFT-based evaluation identified Geothermal Energy as the optimal choice with a score of 0.798, followed by Offshore Wind (0.722) and Waste-to-Hydrogen (0.713). Comparative evaluation against benchmark MCDM methods—TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), and WSM (Weighted Sum Model)—demonstrates that the SFFT approach yields more robust and stable rankings, particularly under uncertainty and model perturbations. Extensive sensitivity analysis confirms high resilience of the top-ranked alternative, with Geothermal retaining the first position in 82.4% of 5000 Monte Carlo simulations under simultaneous variations in weights, memory parameter ($\alpha \in [0.25,0.95]$), and noise intensity ($\sigma \in [0.01,0.10]$). This research provides a realistic, mathematically grounded, and decision-maker-friendly tool for strategic planning in uncertain, dynamic urban environments, with strong potential for deployment in wider engineering, management, and policy applications. Full article

Bamboo rhizomes, the belowground stems of bamboo, play a crucial role in ecosystem functioning and material cycling; however, they have long been regarded as forest residues, receiving limited research attention. This review systematically summarizes current knowledge on the anatomical structure, chemical composition, physical and mechanical properties, and applications of bamboo rhizomes, thereby highlighting their potential for high–value utilization. Based on existing studies, a three-tier framework of rhizome characteristics is proposed: (1) age–driven changes, including lignin deposition, cellulose distribution, and cell wall development; (2) interspecific differences in chemical and anatomical traits, which modulate mechanical performance and durability; and (3) functional differentiation between rhizomes and culms, reflecting adaptation to belowground environments. Within this framework, the structural, chemical, and physicomechanical properties of bamboo rhizomes exhibit tight coupling, thus providing theoretical guidance for species selection, harvesting strategies, and processing. Moreover, bamboo rhizomes have been applied in handicrafts, agricultural organic fertilizers, and composite materials, and they show emerging potential in high-friction functional materials and bio–based composites. Nevertheless, systematic investigations remain limited, particularly regarding structure–property relationships, interspecific performance variability, and optimized processing technologies. Therefore, future research should focus on multidimensional characterization, elucidation of structure–property coupling mechanisms, and development of high–value processing techniques, in order to promote the transformation of bamboo rhizomes into value–added products, thereby supporting green bamboo industry development and the “Bamboo Instead of Plastic” initiative. Full article

Poultry waste composting is a necessary technique for agricultural farm sustainability. Composting is a dynamic process influenced by multiple variables. Humidity and temperature play fundamental roles in analyzing its different phases according to the environment and composting technique. Current developments for monitoring these variables include automation via intelligent Internet of Things (IoT)-based sensor networks for variable tracking. These advancements serve as efficient tools for modeling that facilitate the simulation and prediction of composting process variables to improve system efficiency. Therefore, this paper presents the dynamic modeling of composting via forced aeration processes in high-mountain climates, with the intent of estimating biomass temperature dynamics in different phases using system identification techniques. To this end, four dynamic model estimation structures are employed: transfer function (TF), state space (SS), process (P), and Hammerstein–Wiener (HW). The and model quality, fitting results, and standard error metrics of the different models found in each phase are assessed through residual analysis from each structure by validation with real system data. Our results show that the second-order underdamped multiple-input–single-output (MISO) process model with added noise demonstrates the best fit and validation performance. Full article

Thermal spraying technique has potential in manufacturing economic, profitable thermoelectric coatings. In order to characterize the electrical performance of thermoelectric coatings more conveniently, a simple setup for thermoelectric power factor of thermoelectric coatings is designed and developed. The indigenously designed setup is simple and low-cost. The compact structure makes it easy to cooperate with existing heating furnace, allowing a fast measurement in a variable temperature range. The differential method and the off-axis four-point geometry are used in Seebeck coefficient and electrical resistivity measurement, respectively. The Spring-load unit and other details of construction of the setup are described specifically. The Seebeck coefficient of the plasma-sprayed higher manganese silicide (HMS) coating was measured to be approximately 132.35 μV/K at 150 °C, with measurements showing high linearity (R² > 0.99). The setup demonstrated reliable electrical resistivity results for Cr20Ni80 alloy, closely matching published values (1.16 × 10⁻⁶ Ω·m vs. 1.10 × 10⁻⁶ Ω·m). HMS coating was also characterized from 50 °C to 500 °C to validate the setup on thermoelectric performance characterization across a wide temperature range. These results confirm the reliability of the developed setup. Full article