Search Results (18)

This paper evaluates botanical gardens in terms of their potential for tourist valorization, aiming to identify the garden with the highest tourist appeal and integration opportunities within the tourist market. Based on a literature review and established attractiveness criteria, a methodological framework using multi-criteria decision-making was developed to compare and rank the botanical gardens. The empirical part of the study focuses on botanical gardens in Split–Dalmatia County, including six gardens evaluated across nine criteria. Eight local tourism experts assessed the importance of these criteria and the gardens’ performance. The fuzzy SiWeC (SImple WEight Calculation) method was used to determine the importance of each criterion. The fuzzy TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) was used to measure the potential of botanical gardens. The main results obtained with this approach showed that the most important criteria are C4—Visitor content and C3—Biodiversity conservation. The Botanical Garden of Primary School Ostrog has the greatest potential, followed by the Botanical Garden Split. All observed botanical gardens have excellent tourist potential, with minimal differences in ranking among them. These findings demonstrate that botanical gardens play a key role in diversifying the tourist offer, reducing seasonality, and increasing the overall attractiveness of destinations. They also contribute to raising environmental awareness and emphasizing the importance of nature conservation and sustainable development, aligning with the increasing tourist interest in natural and ecologically responsible experiences. This study offers practical insights, as the results can assist garden management, tourism communities, and policymakers in developing and promoting strategies. Additionally, the framework proposed can be applied in other regional and international contexts. Full article

The development of technology has influenced changes in agricultural production. Farmers are increasingly using modern devices and machinery that provide valuable information, and to manage this information effectively, it is necessary to use specialized applications. This research aims to evaluate various applications and determine which one is most suitable for small- and medium-sized farmers to adopt in precision agriculture. This research employed expert decision-making to determine the importance of criteria and evaluate applications using linguistic values. Due to the presence of uncertainty in decision-making, an interval type-2 fuzzy (IT2F) set was used, which addresses this problem through the support of a membership function. This approach allows for the display of uncertainty and imprecision using an interval rather than a single exact value. This enables a more flexible and realistic representation of ratings, leading to more confident decision-making. These membership functions are formed in such a way that there is symmetry around the central linguistic value. To use this approach, the SiWeC (simple weight calculation) and CORASO (compromise ranking from alternative solutions) methods were adapted. The results of the IT2F SiWeC method revealed that the most important criteria for experts are data accuracy, efficiency, and simplicity. The results of the IT2F CORASO method displayed that the A3 application delivers the best results, confirmed by additional analyses. This research has indicated that digital tools, in the form of applications, can be effectively used in small- and medium-scale precision agriculture production. Full article

This study presents a machine learning-enhanced optimization framework for proton exchange membrane fuel cell (PEMFC), designed to address critical challenges in dynamic load adaptation and thermal management for automotive applications. A high-fidelity model of a 65-cell stack (45 V, 133.5 A, 6 kW) is developed in MATLAB/Simulink, integrating four core subsystems: PID-controlled fuel delivery, humidity-regulated air supply, an electrochemical-thermal stack model (incorporating Nernst voltage and activation, ohmic, and concentration losses), and a 97.2–efficient SiC MOSFET-based DC/DC boost converter. The framework employs the NSGA-II algorithm to optimize key operational parameters—membrane hydration (λ = 12–14), cathode stoichiometry (λO₂ = 1.5–3.0), and cooling flow rate (0.5–2.0 L/min)—to balance efficiency, voltage stability, and dynamic performance. The optimized model achieves a 38% reduction in model-data discrepancies (RMSE < 5.3%) compared to experimental data from the Toyota Mirai, and demonstrates a 22% improvement in dynamic response, recovering from 0 to 100% load steps within 50 ms with a voltage deviation of less than 0.15 V. Peak performance includes 77.5% oxygen utilization at 250 L/min air flow (1.1236 V/cell) and 99.89% hydrogen utilization at a nominal voltage of 48.3 V, yielding a peak power of 8112 W at 55% stack efficiency. Furthermore, fuzzy-PID control of fuel ramping (50–85 L/min in 3.5 s) and thermal management (ΔT < 1.5 °C via 1.0–1.5 L/min cooling) reduces computational overhead by 29% in the resulting digital twin platform. The framework demonstrates compliance with ISO 14687-2 and SAE J2574 standards, offering a scalable and efficient solution for next-generation fuel cell electric vehicle (FCEV) aligned with global decarbonization targets, including the EU’s 2035 CO₂ neutrality mandate. Full article

The specificity of the business of agro-food companies is that their products have little or no impact on the environment. However, environmental pollution of these products is caused by the use of packaging. Therefore, it is necessary to apply the principles of the circular economy in the business of companies. Applying green packaging that has little or no impact on the environment helps in preserving the environment. Companies usually purchase packaging from suppliers and therefore, it is necessary to choose the right supplier from which to purchase green packaging to support the implementation of the circular economy. The aim of this research is to select a green packaging supplier for company X in order to influence the development of a circular economy in the company’s business. Based on this, the following research question is considered in this paper: how can the selection of a green packaging supplier influence the implementation of a circular economy at company X? The research covers ten criteria used in this selection, with which eight suppliers were observed. Because every decision-making process in the economy is characterized by risk and insecurity that affects the uncertainty in decision-making, an intuitionistic fuzzy set (IFS) was used. Determining the importance of weights was performed directly based on the ratings of the decision-maker (DM) and the steps of the SiWeC (Simple Weight Calculation) method, as well as using the Entropy method. The compromise results of these methods showed that the most important criteria for assessing the life cycle of packaging are transparency and ethics in business. The ranking of suppliers was carried out using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and its results showed that supplier 5 is the first choice for establishing long-term cooperation in the procurement of green packaging. Full article

Cottonseeds, rich in high-quality protein and fatty acids, represent a vital plant-derived feedstuff and edible oil resource. To systematically investigate genetic variation patterns in nutritional quality and screen superior germplasm, this study analyzed 26 nutritional quality traits and 8 fiber traits across 259 upland cotton (Gossypium hirsutum L.) accessions using multivariate statistical approaches. Results revealed significant genetic diversity in cottonseed nutritional profiles, with coefficients of variation ranging from 3.42% to 26.37%. Moreover, with advancements in breeding periods, the contents of protein, amino acids, and the proportion of unsaturated fatty acids (UFAs) increased, while oil content and C16:0 levels decreased. Correlation analyses identified significant positive associations (p < 0.05) between proteins, amino acids, UFAs, and most fiber traits, except for seed index (SI), fiber micronaire (FM), and fiber elongation (FE). Through a principal component analysis–fuzzy membership function (PCA-FMF) model, 13 elite accessions (F > 0.75) with high protein content, high UFA proportion, and excellent fiber quality were identified. These findings provide both data-driven foundations and practical germplasm resources for value-added utilization of cottonseed and coordinated breeding for dual-quality traits of nutrition and fiber. Full article

The increasing deployment of renewable energy sources is reshaping power systems and presenting new challenges for the integration of distributed generation and energy storage. Power converters have become essential to manage energy flows, coordinate storage systems, and maintain grid stability. This study presents a literature review following the PRISMA 2020 methodology, covering 71 peer-reviewed articles published between 2014 and 2024. The analysis organizes current research into five main areas: converter topologies, storage integration, grid interaction, advanced control strategies, and renewable energy applications. Recent developments include progress in multilevel and bidirectional converter designs, the use of wide-bandgap semiconductors (SiC, GaN), and the application of advanced control techniques such as model predictive control, fuzzy logic, and reinforcement learning. However, several challenges remain unresolved, including the lack of standardized validation protocols, limited implementation of modular and scalable converter solutions, and insufficient integration of hybrid storage technologies such as hydrogen and second-life batteries. Future efforts should focus on developing interoperable control platforms, extending field validation studies, and incorporating digital twins and AI-based supervisory systems to improve the reliability, efficiency, and scalability of converter-based energy storage solutions under high renewable energy scenarios. Full article

A comprehensive roadmap for advancing Electric Micromobility (EMM) systems addressing the fragmented and scarce information available in the field is defined as a transformative solution for urban transportation, targeting short-distance trips with compact, lightweight vehicles under 350 kg and maximum speeds of 45 km/h, such as bicycles, e-scooters, and skateboards, which offer flexible, eco-friendly alternatives to traditional transportation, easing congestion and promoting sustainable urban mobility ecosystems. This review aims to guide researchers by consolidating key technical insights and offering a foundation for future exploration in this domain. It examines critical components of EMM systems, including electric motors, batteries, power converters, and control strategies. Likewise, a comparative analysis of electric motors, such as PMSM, BLDC, SRM, and IM, highlights their unique advantages for micromobility applications. Battery technologies, including Lithium Iron Phosphate, Nickel Manganese Cobalt, Nickel-Cadmium, Sodium-Sulfur, Lithium-Ion and Sodium-Ion, are evaluated with a focus on energy density, efficiency, and environmental impact. The study delves deeply into power converters, emphasizing their critical role in optimizing energy flow and improving system performance. Furthermore, control techniques like PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed to enhance safety, efficiency, and adaptability in diverse EMM scenarios by using cutting-edge semiconductor devices like Silicon Carbide (SiC) and Gallium Nitride (GaN) in well-known configurations, such as buck, boost, buck–boost, and bidirectional converters to ensure great efficiency, reduce energy losses, and ensure compact and reliable designs. Ultimately, this review not only addresses existing gaps in the literature but also provides a guide for researchers, outlining future research directions to foster innovation and contribute to the development of sustainable, efficient, and environmentally friendly urban transportation systems. Full article

More and more investments are being made in energy conversion projects from renewable energy sources (RESs), and a large number of investors are entering this sector. The focus of this study is the decision-making by the investor BD Green Energy in the Brčko District of Bosnia and Herzegovina. In order to choose the RES system that would realize this investment in the most efficient way, expert decision-making based on the fuzzy–rough approach and the Bonferroni mean operator was used. Determining the importance of the criteria was conducted using the fuzzy–rough SiWeC (simple weight calculation) method. The results of this method showed that all used criteria have similar importance for the investor. RES system selection was conducted using the fuzzy–rough CoCoSo (combined compromise solution) method. The results of this method showed that investing in photovoltaic (PV) energy is the best for the investor. This research provided guidance on how investors should make investment decisions in RES systems with incomplete information and uncertainty in the decision-making process. Full article

In the present work, predictive modelling and optimization with the adaptive network based fuzzy inference system (ANFIS) modelling of the mechanical properties of laser-coated NB/SiC/Ni welds was studied based on the Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties were taken as the output of the ANFIS. The microstructure of welds was analysed using scanning electron microscopy with an energy-dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi’s design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified. Full article

An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab (v. 6.1.1), considering the Monod two-substrate with an intermediate (M2SI) kinetic model proposed by Rakmak et al. (Rakmak, N.; Noynoo, L.; Jijai, S.; Siripatana, C. Lecture Notes in Applied Mathematics and Applied Science in Engineering. Melaka, Malaysia, p. 11–20, 2019), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi–Sugeno approach available in the ANFIS package of Matlab. The biomethane production data simulated using Scilab were considered in neural network modeling and validation, firstly employing a “generic” network applicable to all eight scenarios, providing a very good fit (R² > 0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition, again by using validation data generated by the M2SI model). The parameters of the M2SI model for the eight different conditions were also mapped using a neural network, as a function of the organic material composition, providing a fit with R² > 0.99 when using 25 neurons. In the case of fuzzy logic, an RMSE (Root Mean Squared Error) of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks). Full article

The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise. Full article

Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic approach. Hence, to enhance the accuracy and eliminate the uncertainties involved, artificial intelligence (AI) was used in the present research. The main aim of this study is to propose a high-performance machine learning (ML) model to determine the factor of safety (FOS) of gravity retaining walls against overturning. The projected methodology included a novel hybrid machine learning model that merged with an adaptive neuro-fuzzy inference system (ANFIS) and meta-heuristic optimization techniques (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FFA) and grey wolf optimization (GWO)). In this research, four hybrid models, namely ANFIS-PSO, ANFIS-FFA, ANFIS-GA and ANFIS-GWO, were created to estimate the factor of safety against overturning. The proposed hybrid models were evaluated on two distinct datasets (training 70% and testing 30%) with three input combinations, namely cohesion (c), unit weight of soil (Υ) and angle of shearing resistance (φ). To access the prediction power of different hybrid models, various statistical parameters such as R², AdjR², VAF, WI, LMI, a-20 index, PI, KGE, RMSE, SI, MAE, NMBE and MBE were computed for training (TR) and testing (TS) datasets. The overall performance of the models indicated that ANFIS-PSO provided better results among all four models. The reliability index was computed using the first-order second-moment (FOSM) method for all models, and the probability of failure was also computed. A Williams plot was drawn to check the applicability domain of the hybrid model and to check the influence of different input parameters on the prediction of the factor of safety, and the Gini index was also computed. Full article

Despite their excellent performance, two-dimension nanomaterials have certain limitations in improving the performance of cement-based materials due to their poor dispersity in the alkaline environment. This paper has synthesized a new two-dimension stacked GO-SiO₂ (GOS) hybrid through the sol-gel method. Nano-SiO₂ is coated on the surface of GO with wrinkling characteristics, and the atomic ratio of C, O, and Si in GOS is 1:1.69:0.57. The paper discusses the impacts on the spreading, Marsh cone flow time, rheological properties, mechanical properties, and microstructure of cement-based materials for the GOS at different mixing quantities. Furthermore, with the same mixing quantity of 0.01%, the influences on the dispersity, flow properties, rheological parameters, and mechanical properties of GOS and graphene oxide (GO) are compared. Lastly, fuzzy matrix analysis has been adopted to analyze the comprehensive performance of cement-based materials containing GOS. The research results indicate that, compared with the reference sample, the spreading for the GOS cement mortar with 0.01% mixing quantity was reduced by 4.76%, the yield shear stress increased by 37.43%, and the equivalent plastic viscosity was elevated by 2.62%. In terms of the 28 d cement pastes, the compressive and flexural strength were boosted by 27.17% and 42.86%, respectively. According to the optical observation, GOS shows better dispersion stability in the saturated calcium hydroxide solution and simulated pore solution than GO. Compared with the cement-based materials with the same mixing quantity (0.01%), GOS has higher spreading, lower shear yield stress, and higher compressive and flexural strength than GO. Finally, according to the results of fuzzy matrix analysis, when the concentration of GOS is 0.01%, it presents a more excellent comprehensive performance with the highest score. Among the performance indicators, the most significant improvement was in the flexural properties of cement-based materials, which increased from 8.6 MPa to 12.3 MPa on the 28 d. Full article

In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company. Full article

Integrated devices incorporating MEMS (microelectromechanical systems) with IC (integrated circuit) components have been becoming increasingly important in the era of IoT (Internet of Things). In this study, a hybrid fuzzy MCDM (multi-criteria decision making) model was proposed to effectively evaluate alternative technologies that incorporate MEMS with IC components. This model, composed of the fuzzy AHP (analytic hierarchy process) and fuzzy VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods, solves the decision problem of how best to rank MEMS and IC integration technologies in a fuzzy environment. The six important criteria and the major five alternative technologies associated with our research themes were explored through literature review and expert investigations. The priority weights of criteria were derived using fuzzy AHP. After that, fuzzy VIKOR was deployed to rank alternatives. The empirical results show that development schedule and manufacturing capability are the two most important criteria and 3D (three-dimensional) SiP (system-in-package) and monolithic SoC (system-on-chip) are the top two favored technologies. The proposed fuzzy decision model could serve as a reference for the future strategic evaluation and selection of MEMS and IC integration technologies. Full article