Fuzzy Logic Modeling Approach Research Articles

Fe3O4 supported UiO-66 (Zr) metal-organic framework for removal of drug contaminants from water: fuzzy logic modeling approach.

The increase in production and consumption of pharmaceuticals and personal care products causes environmental problems. In this study, naproxen and clofibric acid adsorption were studied using Fe3O4-supported UiO-66 (Zr) metal-organic framework (Mag-UiO-66). The adsorption processes were carried out in batch mode at pH value 3.0. The optimum adsorbent quantities, equilibrium periods, pseudo-first-order (PFO), pseudo-second-order (PSO), and intra-particles diffusion kinetic models were calculated. Non-linear Langmuir, Freundlich, Dubinin-Radushkevich (D-R), and Sips isotherm equations were applied to experimental data. Thermodynamic analyses of naproxen and clofibric acid adsorption were also carried out in this study. The Langmuir isotherm qm values were found as 14.15mg/g for naproxen at 308K and 41.87mg/g for clofibric acid at 298K. Both of the adsorption processes were exothermic. MISO (multi-input single-output) fuzzy logic models for removal of both naproxen and clofibric acid adsorptions were designed based on the experimental data to estimate the removal uptake values. It is noteworthy that the results obtained through designed fuzzy logic models matched well with the experimental data and the findings of this study emphasize the validity of designed fuzzy logic models.

Sensory evaluation of microwave-ultrasound treated bottle gourd juice using hybrid features of fuzzy logic and proportional odd modelling approach.

In the present investigation, sensory evaluation of bottle gourd juice (BGJ) samples, obtained from microwave-ultrasound based combined treatment was performed. The raw (sample-1) and conventionally treated (sample-2) alongside microwave-ultrasound treated (sample-3) were considered for the assessment of sensory evaluation. An innovative approach of hybrid fuzzy logic and proportional odd modelling (FL-POM) was implemented for the analysis of the sensory scores. The similarity values for the juice samples and their quality attributes were resolved from the results obtained by fuzzy logic. These values were considered as input for hybridization with the POM approach. The assessed coefficients obtained from the results of POM were considered for the ranking of the samples and quality traits. The ranking of the BGJ samples was observed in the order of sample-1 > sample-3 > sample-2, and their related quality attributes ranked in the order color > taste > aroma > mouth feel. The microwave-ultrasound treated BGJ evinced as the best sample in comparison to the raw one.

A Fuzzy-Based Model to Predict the Spatio-Temporal Performance of the Dolichogenidea gelechiidivoris Natural Enemy against Tuta absoluta under Climate Change.

Simple SummaryTuta absoluta is an invasive pest threatening the productivity of the tomato crop. Dolichogenidea gelechiidivoris was imported and released as a natural enemy of Tuta absoluta. Mapping the efficacy of Dolichogenidea gelechiidivoris can improve its use as a control agent against Tuta absoluta. The Dolichogenidea gelechiidivoris efficacy map provides a tool for its targeted deployment as a Tuta absoluta natural enemy.The South American tomato pinworm, Tuta absoluta, causes up to 100% tomato crop losses. As Tuta absoluta is non-native to African agroecologies and lacks efficient resident natural enemies, the microgastrine koinobiont solitary oligophagous larval endoparasitoid, Dolichogenidea gelechiidivoris (Marsh) (Syn.: Apanteles gelechiidivoris Marsh) (Hymenoptera: Braconidae) was released for classical biological control. This study elucidates the current and future spatio-temporal performance of D. gelechiidivoris against T. absoluta in tomato cropping systems using a fuzzy logic modelling approach. Specifically, the study considers the presence of the host and the host crop, as well as the parasitoid reproductive capacity, as key variables. Results show that the fuzzy algorithm predicted the performance of the parasitoid (in terms of net reproductive rate (R0)), with a low root mean square error (RMSE) value (<0.90) and a considerably high R2 coefficient (=0.98), accurately predicting the parasitoid performance over time and space. Under the current climatic scenario, the parasitoid is predicted to perform well in all regions throughout the year, except for the coastal region. Under the future climatic scenario, the performance of the parasitoid is projected to improve in all regions throughout the year. Overall, the model sheds light on the varying performance of the parasitoid across different regions of Kenya, and in different seasons, under both current and future climatic scenarios.

Application of Fuzzy Logic and Fractal Modeling Approach for Groundwater Potential Mapping in Semi-Arid Akka Basin, Southeast Morocco

Groundwater potential delineation in the Akka basin, southwest Morocco, has been determined through the combination of geospatial techniques and geological data. The geometric average and expected value are two multi-criteria approaches used to integrate a set of factors–data for which the weights of each factor are assigned using the fuzzy logic function, which transforms values of factors influencing groundwater presence in a range of [0, 1]. The efficiency factors used in this study are the lineament density, node density, drainage density, distance from rivers, distance from lineament, permeability, slope, topographic witness index, plan curvature, and profile curvature. Thereafter, the groundwater potential map was generated in a GIS environment. To assess and compare the efficiency of the two models, the well data existing in the basin were used to choose the most efficient model. For that reason, the prediction area (P–A) graph, the normalized density (Nd), and its weight (We) were applied to estimate the capacity of each model to predict the target area. The analysis shows that the expected value model (Nd = 1.86 and We = 0.62) is more efficient than the geometric average model (Nd = 0.96 and We = −0.04). The results of the expected value model can be used in the future planning and management of water resources in the Akka basin.

A fuzzy logic modelling approach on psychological data

Meeting basic psychological needs could be difficult to maintain in current pandemic times, mainly due to preventive measures involving social distancing or full quarantine, which seem to play a very important role in well-being. The theory of basic psychological needs is a sub-theory of human motivation theory known as the theory of self-determination. This theory argues that meeting the needs of autonomy, relatedness and competence is crucial for motivation, optimal development, efficient functioning and health. Several research, examining the effects of basic psychological needs on well-being, concluded that changes in meeting the three needs had a significant effect on well-being. Because perceived stress plays a vital role in daily life, several coping strategies have been shown to effectively manage stress and reduce its negative consequences. In this study, coping mechanisms refer to both cognitive and behavioral efforts to alleviate or overcome stressful situations, especially when an automatic response is not readily available. The present study aims to examine a predictive model of competence need satisfaction based on adaptive coping mechanisms: active coping and positive reframing, on a convenience sampling of 403 Romanian respondents. Results show that 3% of the variance in competence need satisfaction is explained by active coping and positive reframing. In this work, we have used fuzzy logic modelling on our psychological data to deal with the imprecision and vagueness inherent in input data and build a more reliable model for estimating psychological variables relations. Implications and conclusions are discussed.

CONSUMER PREFERENCE STUDY ON COMBINED ULTRASOUND AND SODIUM HYPOCHLORITE TREATED FRESHCUT KIWIFRUITS COATED WITH CHITOSAN USING THE FUZZY LOGIC APPROACH

Fresh cut kiwi fruits are gaining popularity among consumers due to increased nutrition, health, and convenience. In this study ultrasound treated fresh cut kiwifruit slices were coated with different concentrations (0.6, 0.8, and 1%) of chitosan. Sensory evaluation was conducted in linguistic terms for the kiwifruit slices with a panel of 15 well-trained judges to understand the consumer preference. The linguistic approach was analysed and decoded using the fuzzy logic modeling approach to find the best sample and quality attribute responsible for consumer preference. The slices with the highest defuzzified scores were obtained for the 1% chitosan-coated sample. Ranking of kiwifruit slices was based on defuzzified scores was S1&gt;S2&gt;S3&gt;S4, while ranking of the quality attributes was smell&gt;taste&gt;color&gt;texture. Therefore, the fuzzy logic modeling could be a practical approach for finding the consumer preference of fresh cut kiwifruits, thus increasing product marketability.

Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.

Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach

The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world.

Fuzzy Logic-Based Predictive Model for the Risk of Sexually Transmitted Diseases (STD) in Nigeria

This study developed a classification model for monitoring the risk of sexually transmitted diseases (STDs) among females using information about non-invasive risk factors. Structured interview with physicians was done in order to identify the risk factors that are associated with the risk of STDs in Nigeria. The model was simulated using the fuzzy logic toolbox accessible in the MATLAB® R2015a Software. The results showed that nine non-invasive risk factors were associated with the risk of STDs among female patients in Nigeria. Two, three, and four triangular membership functions were appropriate for the formulation of the linguistic variables of the factors while the target risk was formulated using four triangular membership functions for the linguistic variables namely no risk, low risk, moderate risk, and high risk. The study concluded that the fuzzy logic model approach was adequate for predicting the risk of STDs based on the knowledge of the risk factors.

A Fuzzy Logic Approach to Preventive Conservation of Cultural Heritage Churches in Popayán, Colombia

ABSTRACT From a decision-maker’s perspective, the management of cultural heritage is a challenging task because of different objectives pursued, the public/private nature of heritage constructions studied, the wide variety of associated values (artistic, historical, cultural, economic) and diverse interests of different stakeholders. Careful consideration of environmental, social and economic factors is crucial in order to predict the functional service life of heritage constructions. This study outlines a new fuzzy system approach based on expert knowledge, which focuses on the serviceability of heritage buildings using a Mamdani fuzzy model. The method input considers 10 intrinsic variables directly related to the vulnerability of buildings and 9 external hazards, classified according to static-structural features and environmental hazards, which are considered useful for defining emergency programs in historical centres in South America (Colombia). The automation of inspection programs similar to the one described here may reduce the consumption of natural resources and benefit a more rational management of future maintenance actions. In general terms, this type of fuzzy logic model approach is designed to serve as an indicator for the future evolution of the functionality of buildings. This research contributes to the study of new service life models applied to heritage buildings in South America.

AUTOMATING BACKHOE OPERATION USING FUZZY LOGIC

During the past decade, automation has significantly increased in all forms of engineering. With the fast-paced research and development in automation, it is expected that automation will be commonplace and many operations will be performed using robots. Automation of heavy construction equipment is in its early stages. Attempts are persistently made to push the frontiers of science and technology to automate heavy equipment but, limited progress has been made. This research presents a fuzzy logic modeling approach for moving a back-hoe bucket from a fill point to a dump point. The fuzzy logic model is designed to accept all feasible points formed by boom and stick movements in 3D space. Inputs to the model are coordinates of fill and dump spots, as well as stick and boom dimensions. The model provides output signals to automate the bucket movement by swinging the bucket, lowering (or raising) boom, and stretching (or retracting) stick. The paper also includes three site scenarios to demonstrate the performance of the model. The three scenarios are varied from a very basic to a very complex maneuver. All three scenarios are tested for error, and the errors are within acceptable limits.

Adaptive command-filtered fuzzy backstepping control for linear induction motor with unknown end effect

In this paper, the problem of the speed control is considered for linear induction motor with the end effects, uncertain system parameters, and external load disturbance. Firstly, the proposed controller is based on backstepping method, and the adaptive laws are designed.The fuzzy logic modeling approach is used to deal with the unknown nonlinear functions and uncertain parameters. Secondly, a constrained command filter is employed to overcome the problem of differential expansion and controller saturation in the backstepping method, and a filter’s compensating signal is used to mitigate the error caused by the constrained command filter. Thirdly, the projection operator introduced in the adaptive laws guarantees boundedness of the estimated functions and robustness of the designed controller in the presence of time-varying disturbances and uncertainties. Thus, the proposed control controller can ensure that all response signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified by simulation and comparison with command-filtered backstepping controller.

Capturing the stakeholders' managerial competency risks of mega infrastructure projects: a fuzzy logic modelling approach

This paper develops a fuzzy-logic model to aggregate the numerous managerial risks driven by the various project stakeholders at different project stages. The model accounts for the various perceptions of assessors involved in the risk evaluation process. The proposed model provides means for performing scenario analysis at an early project procurement stage to manage macro risks. Four managerial processes or competencies were considered as critical success factors for infrastructure projects. These are communication, coordination, knowledge sharing and decision-making. A Sugeno fuzzy logic model was calibrated using three input variables representing the importance of the various projects stakeholder groups, the criticalness level of the management process as perceived by the various groups at the various project stages, and the effectiveness level of the management competency. The model was validated using surveys of various stakeholder groups of a mega project.

Capturing the stakeholders' managerial competency risks of mega infrastructure projects: a fuzzy logic modelling approach

Capturing the stakeholders' managerial competency risks of mega infrastructure projects: a fuzzy logic modelling approach

State-of-charge observers for lead-acid storage units used in autonomous solar applications

The article provides practical means to monitor batteries that work in the so-called float cycling regime. The main interest of the case study is destined for autonomous solar applications. The problem of the state-of-charge estimation of lead-acid storage unit is studied. Two approaches are considered and compared: the sliding mode technique and the fuzzy logic modelling. Based on the grounds of observers design theory, it was shown that the sliding mode technique exhibits robustness properties in the face of parameters imprecision and unknown non-linear behaviour. The fuzzy logic modelling approach allows designing the monitoring scheme using the reduced number of available information. An evolutionary programming technique is applied to adjust the parameters of the fuzzy logic resolution scheme. The results are compared with reference state-of-charge measurements taken from an industrial battery charge regulator. The developed models are potentially extensible to other types of storage units.

A new method to detect dissolved gases in transformer oil using NIR-IR spectroscopy

Dissolved gas analysis (DGA) is widely accepted as an effective technique to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are generated as a result of insulation oil decomposition, while carbon monoxide and carbon dioxide are generated as a result of insulation paper decomposition. Laboratory-based gas chromatography (GC) in accordance to ASTM D3612 standard is globally accepted as a reliable technique to quantify dissolved gases in transformer oil samples. However, this technique incurs running cost and requires an expert to conduct the test. Furthermore, due to the complexity of the equipment, measurement is only performed in a laboratory environment and takes long time to get the results since the extraction of the oil sample from the transformer. To overcome these issues, various online DGA techniques have been developed and currently used by industry. However, these techniques are still considered in development stage with some limitations that have to be addressed to fully accept its results. This paper proposes a new method to estimate the concentration of various dissolved gases in transformer oil using near infrared-to-infrared (NIR-IR) spectroscopy that can be conducted instantly onsite with relatively inexpensive equipment. The proposed measuring technique does not call for expert person to conduct the measurements and has the potential to be implemented online. Results show a good correlation between oil spectral response and the concentration of each dissolved gas in the oil at particular wavelength range. Fuzzy logic model approach is employed to model this correlation and estimate the concentration of each dissolved gas.

Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

Perceived impact of offshore aquaculture area on small-scale fisheries: A fuzzy logic model approach

Recently an action plan has been put in place off southeastern Portugal, consisting in an offshore aquaculture area off the Armona sandy barrier island, Armona Pilot Production Aquaculture Area (APPAA). The infrastructure was created after the initiative of the Portuguese Government aiming to stimulate local employment opportunities related to seafood production. The APPAA aims to improve resilience of finfish and shellfish production for the future. However, the delimited infrastructure is located nearby some fishery-dependent communities. Therefore, such proximity may cause friction with some fishermen due to the limitations post APPAA development (e.g. may feel their jobs are threatened). In this study, we queried the reasoning rules elicited by local fishing communities and their perceived impact of the APPAA implementation. In that scope, a fuzzy logic expert system approach was used to investigate the interaction between three input variables (namely, ‘availability of fishable area’, ‘navigational disturbance’, and ‘catch variation’) and the output variable (i.e., overall ‘fishing community satisfaction’). The results from the fuzzy logic expert system showed that ‘catch variation’ was the input that most affected ‘fishing community satisfaction’ and seemed to be the one that suffered most changes. The results also show that, for the analyzed years, where the catch was higher, the degree of satisfaction tended to follow the trend, independently of the fishing community. The other two input variables were more conditioned by governmental arrangement (‘availability of fishable area’) and by small-scale fishermen reaction (‘navigational disturbance’). The fuzzy logic expert system proved to be a valuable tool, facilitating the analysis of governance arrangements, particularly those dealing with the interaction between the fisheries–offshore aquaculture system as a whole.

Fuzzy modeling reveals a dynamic self-sustaining network of the GLI transcription factors controlling important metabolic regulators in adult mouse hepatocytes.

The GLI transcription factors, GLI1, GLI2, and GLI3, transduce Hedgehog and non-hedgehog signals and are involved in regulating development and tumorgenesis. Surprisingly, they were recently found to modulate important functions of mature liver. However, less is known about their mutual interactions and possible target genes in mature hepatocytes. To get a deeper insight into these interactions cultured mouse hepatocytes were transfected with siRNAs against each GLI factor. RNA was extracted at different times and the expression levels of the genes of interest were determined by quantitative real-time PCR. The time-dependent data were analysed by a fuzzy logic-based modelling approach. The results indicated that the GLI factors constitute an interconnected network. GLI2 inhibited GLI1 expression and was coupled with GLI3 by a positive feedback loop. The regulatory activity between GLI1 and GLI3 was more complex switching between a positive and a negative feedback loop depending on whether the level of GLI2 is low or high, respectively. Generally, this network structure enables a dynamic behaviour. When GLI2 is low, it may keep GLI1 and GLI3 activity balanced favouring the appropriate modulation of transcription factors like the Ppars and Srebp1. When GLI2 is high, it may prevent an uncontrolled amplification that may lead to cancer. In conclusion, the three GLI factors in mature hepatocytes form an interactive transcriptional network that is involved in the control of target genes associated with metabolic zonation as well as with lipid and drug metabolism. Its structure in mature cells seems different from embryonic cells.

Data-derived modeling characterizes plasticity of MAPK signaling in melanoma.

The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma.