In a collaborative forecasting task, experts may have unequal authority levels. However, this has rarely been considered reasonably in the existing fuzzy collaborative forecasting methods. In addition, experts may not be willing to discriminate their authority levels. To address these issues, an auto-weighting fuzzy weighted intersection (FWI) fuzzy collaborative intelligence approach is proposed in this study. In the proposed auto-weighting FWI fuzzy collaborative intelligence approach, experts’ authority levels are automatically and reasonably assigned based on their past forecasting performances. Subsequently, the auto-weighting FWI mechanism is established to aggregate experts’ fuzzy forecasts. The theoretical properties of the auto-weighting FWI mechanism have been discussed and compared with those of the existing fuzzy aggregation operators. After applying the auto-weighting FWI fuzzy collaborative intelligence approach to a case of forecasting the yield of a DRAM product from the literature, its advantages over several existing methods were clearly illustrated.

In this paper we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the possibility of polarization. Particularly, we are interested in dealing with the problem of taking into account the Polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more probably to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the JDJpol measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem.

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

Plant viral nanoparticles (VNPs) are attractive to researchers in nanomedicine due to their safety, easy production, resistance, and straightforward functionalization. In this paper, we developed and successfully purified a VNP derived from turnip mosaic virus (TuMV), a well-known plant pathogen, that exhibits high affinity for immunoglobulins G (IgG) thanks to its functionalization with the Z domain from staphylococcal protein A via gene fusion. We selected cetuximab as a model IgG to prove the versatility of this novel TuMV VNP by developing a fluorescent nanoplatform to mark tumoral cells of the line Cal33, from tongue squamous cell carcinoma. We observed that the fluorescent VNP-cetuximab bound selectively to Cal33 and were internalized by confocal microscopy, revealing the potential of this nanotool in cancer research.

Conventional radiation therapy uses white X-rays that consist of a mixture of X-ray waves with various energy levels. In contrast, a monochromatic X-ray (monoenergetic X-ray) has a single energy level. Irradiation of high Z elements with a synchrotron generated monochromatic X-ray with the energy at or higher than the K-edge energy of the element results in the production of the Auger electrons that cause DNA damage leading to cell killing. Delivery of high Z elements into cancer cells and tumor mass can be facilitated by the use of nanoparticles. Mesoporous silica nanoparticles (MSNs) have been shown to be effective in delivering high Z elements to cancer cells. A proof of principle experiment was reported that demonstrated the feasibility of this approach. This opens up a possibility to pursue the Auger cancer therapy by the combined use of MSNs loaded with high Z elements and monochromatic X-rays. Similar cancer therapies using other types of quantum beams such as neutron, proton and carbon ion beams can be envisioned.

Context and aim: Environmental awareness is the first step to climate change risk perceptions and environmental behaviour. However, this pathway is not always possible within different population segments and trust contexts. This study explores the conditional effect of trust on environmental behaviour in the Zed Generation in Albania. The consideration of the Zed generation due to two opposing traits, such as social courage and anti-social, make them an interesting topic to understand, especially for their importance in online communities. Method: The youth engagement environmental programme and the climate change risk perceptions framework are adopted in an input-output design. Environmental awareness (input) is the initiating factor, and climate change risk perception and community engagement as mediators of environmental behaviour(output). Individual and institutional trust is the moderating factor that inhibits/enhances engagement and environmental behaviour. Results: The moderation-mediation show that climate change risk perceptions did not mediate environmental behaviour. Environmental awareness directly affects it. However, low institutional trust inhibits the general trust that, in turn, lowers the impact of awareness in climate change perceptions and community engagement toward environmental protection. Practical recommendation: The application of the input-output model shows that the pathway that directly affects environmental behaviour among Zed Generation in Albania is not Climate change risk perception. In low-trust contexts, positive environmental behaviour can be generated through community engagement. Policymakers and educational programmes in low-middle-income countries might consider community engagement to mitigate trust issues and effectively deal with environmental problems with a global segment such as the Zed generation.

Sustainable Humanitarian logistics is gaining attention due to increased disasters. It involves various operations to support people's survival efforts. Key characteristics include unpredictable demand, sudden large demand volumes, high stakes in delivery timeliness, and limited resources. However, the complexity and unpredictability of disaster scenarios necessitate robust and adaptable performance measurement models to evaluate the effectiveness of sustainable humanitarian relief logistics (SHRL). This study proposes a novel performance measurement model for sustainable humanitarian logistics and supply chains that integrates the Fuzzy Delphi Method (FDM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) method. In this research, using 115 articles focused on sustainable humanitarian logistics and supply chains, crisis management, and humanitarian aid, this research identified 25 key performance indicators (KPIs) in the initial phase. Following the implementation of FDM, 19 KPIs were identified as crucial for further analysis. These KPIs were then divided into four distinct categories: "Quality," "Accountability," "Operational Excellence," and "Sustainability". Initially, FDM is utilized to reach a consensus among experts concerning KPIs for humanitarian logistics and supply chains. By incorporating the inherent uncertainty and vagueness in expert judgments, FDM refines the list of key performance indicators that reflect the real-life conditions and constraints in disaster operations. Finally, the fuzzy DEMATEL approach was used to analyze interrelationships among factors, identifying cause-and-effect behavior and ranking them, forming a robust theoretical framework. The study concludes with the development of a comprehensive performance measurement model that facilitates the strategic planning and operational assessment of humanitarian organization operations. The integration of FDM and fuzzy DEMATEL methods offers multiple perspectives for decision-making and helps stakeholders identify critical areas for improvement. Based on the acquired results, the KPIs attached to the Quality (P1) aspect of the proposed framework have gained significant importance and main cause in the cause-and-effect relationship which impacts and is helpful to improve the performance of humanitarian organizations in all phases of disaster management. The KPIs prompt delivery (D1), and delivery accuracy (D2) are more significant, while capacity building and training (D19) and delivery compliance (D15) are least significant in SHRL scenarios. This research is expected to support humanitarian organizations in enhancing their capabilities, thereby improving the effectiveness and efficiency of aid delivery in disaster-stricken areas.

In this paper, we study the convergence of new implicit iterations dealing with n-tupled fixed point results for nonlinear contractive-like mappings on W-hyperbolic metric spaces. Herein, we demonstrate that our newly implicit iteration schemes have faster rate of convergence than implicit S-iteration process, implicit Ishikawa and Mann type iteration processes. Furthermore, a numerical simulation to improve our theoretical results is obtained.

Intuitionistic fuzzy set, which can be represented using the triangular intuitionistic fuzzy number (TIFN), is a more generalized platform for expressing imprecise, incomplete and inconsistent information when solving multi-criteria decision-making problems, as well as for reflecting the evaluation information exactly in different dimensions. In this paper, the TIFN has been applied for solving some multi-criteria decision-making problems by developing a new triangular intuitionistic fuzzy geometric aggregation operator, that is the generalized triangular intuitionistic fuzzy ordered weighted geometric averaging (GTIFOWGA) operator, and defining some triangular intuitionistic fuzzy geometric aggregation operators including the triangular intuitionistic fuzzy weighted geometric averaging (TIFWGA) operator, the ordered weighted geometric averaging (TIFOWGA) operator and the hybrid geometric averaging (TIFHWGA) operator. Based on these operators, a new approach for solving multicriteria decision-making problems when the weight information is fixed has been proposed. Finally, the proposed method has been compared with some similar existing computational approaches by virtue of a numerical example to verify its feasibility and rationality.

This paper will present the torque control design of an AFPMSM, one stator, and one rotor, using an FLC and ANFIS in-wheels fed by a three-level T-type inverter. The Surgeon ambiguous inference file of the FLC controller is built by two input vectors, the stator current error and the derivative of the stator error. These input variables include five membership functions, Negative big (NB), Negative small (NS), Equal zero (ZE), Positive small (PS), and Positive big (PB). The FLC controller is implemented with a 5x5 matrix so that the output stator voltage of the controller is required. The ANFIS controller for the neural network-based feature set and the fuzzy system. The neural network develops the dataset on the stator current error (e) and error integral (∆e). Then, the generated dataset is fed to the fuzzy logic method, and the control rules are developed. This ANFIS controller is caused by the training and testing phases. Finally, the FLC and ANFIS torque controllers are compared with the PI controller. The correctness of the proposed control structured solution is demonstrated by the simulation results of MATLAB/SIMULINK.

The literature on substance use usually extracts conclusions from data with correlational methods. Our study shows the usefulness of complementing ordered logistic regression (OLR) and fuzzy set qualitative comparative analysis (fsQCA) to assess factors inducing cannabis consumption in a sample of 1,935 teenagers. OLR showed a significant influence of gender (odd ratio (OR) =0.383, p<0.0001), parental monitoring (OR=0.587, p=0.0201); religiousness (OR=0.476, p=0.006); parental tolerance to substance use (OR=42.01, p<0.0001) and having close peers that consume substances (OR=5.60, p<0.0001). FsQCA has allowed fitting linkages between factors from a complementary perspective. (1) Coverage (cov) and consistency (cons) attained by solutions explaining use (cons=0.808, cov=0.357) are clearly lower than by recipes of non-use (cons=0.952, cov=0.869) (2) The interaction of gender, a tolerant family to use and the attitude toward substances by peers is very consistent to explain cannabis use. (3) The most important recipe explaining resistance to cannabis is simply parental disagreement with substance consumption (cons=0.956, cov=0.861) (4) Factors as gender, religiosity, parental monitoring and age show also a relevant impact on attitude toward cannabis use. However, whereas some of them impact symmetrically on use and non-use this does not follow in factors such as parental monitoring or age.

In modern mathematics, many concepts and ideas are described in terms of category theory. From this viewpoint, it is desirable to analyze what can be determined if, instead of the basic category of sets, we consider a similar category of fuzzy sets. In this paper, we describe a natural fuzzy analog of the category of sets and functions, and we show that, in this category, fuzzy relations (a natural fuzzy analogue of functions) can be determined in category terms -- of course, modulo 1-1 mapping of the corresponding universe of discourse and 1-1 re-scaling of fuzzy degrees.

In this paper, we introduced a generalized ∆-implicit locally contractive condition and give some examples to support it and to show its significance in fixed point theory. We prove that the mappings satisfying generalized ∆-implicit locally contractive condition admits a common fixed point, where, the ordered multiplicative GM−metric space is chosen as underlying space. The obtained fixed point theorems generalize many earlier fixed point theorems on implicit locally contractive mappings. In addition, some nontrivial and interesting examples are provided to support our findings. To demonstrate the originality of our new main result, we apply it to show existence of solutions to a system of nonlinear -Volterra type- integral equations.

Z-ligustilide is a main active ingredient in the volatile oil of Angelica sinensis, a traditional Chinese medicine. Cisplatin is a commonly used chemotherapy drug for the treatment of lung cancer. Efficacy is often limited by the development of drug resistance after long-term treatment. Here, we investigated the effect of the combination of Z-ligustilide and cisplatin (Z-ligustilide+cisplatin) on resistance of cisplatin-resistant lung cancer cells and its action mechanism. Cell viability was analyzed by cell counting kit-8 (CCK-8) assay. Cell cycle and cell apoptosis were examined by flow cytometry assay. mRNA and protein levels of factors related to cell cycle and apoptosis were analyzed by real-time PCR and western blot. Liquid chromatography-mass spectrometry (LC-MS) analysis and RNA sequencing in A549, A549/DDP and A549/DDP cells treated with the Z-ligustilide+cisplatin were performed. The expression of PLPP1 was analyzed by the Cancer Genome Atlas (TCGA). The correlation between PLPP1 and prognosis was evaluated by immunohistochemistry and Kaplan-Meier (KM) plotter analysis. We found that Z-ligustilide+cisplatin could decrease the cell viability of cisplatin-resistant lung cancer cells. Z-ligustilide+cisplatin induced cell cycle arrest, and promoted cell apoptosis of cisplatin-resistant lung cancer cells. Metabolomics combined with transcriptomics revealed that Z-ligustilide+cisplatin inhibited phospholipid synthesis by upregulating the expression of PLPP1. Furthermore, PLPP1 expression was positively correlated with good prognosis. Knockdown of PLPP1 abolished the effects of Z-ligustilide+cisplatin on cell cycle and apoptosis. Z-ligustilide+cisplatin inhibited the activation of AKT by reducing the levels of PIP3 levels. Z-ligustilide+cisplatin induced cell cycle arrest and promoted cell apoptosis of cisplatin-resistant lung cancer cells by inhibiting PLPP1-mediated phospholipid synthesis.

Marek’s Disease (MD) has a significant impact on both the global poultry economy and animal welfare. The disease pathology can include neurological damage and tumour formation. Sexual dimorphism in immunity and known higher susceptibility of females to MD makes the chicken Z chromosome (GGZ) a particularly attractive target to study the chicken MD response. Previously, we used a Hy-Line F6 population from a full-sib advanced intercross line to map MD QTL regions (QTLRs) on all chicken autosomes. Here we mapped MD QTLRs on GGZ in the previously utilized F6 population with individual genotypes and phenotypes, and in eight elite commercial egg production lines with daughter-tested sires and selective DNA pooling (SDP). Four MD QTLRs were found from each analysis. Some of these QTLRs overlap regions from previous reports. All QTLRs were tested by indiviuals from the same eight lines used in the SDP and genotyped with markers located within and around the QTLRs. All QTLRs were confirmed. The results exemplify the complexity of MD resistance in chickens and the complex distribution of P-values and LD pattern and their effect on localization of the causative elements. Considering the fragments and interdigitated LD blocks while using LD to aid localization of causative elements, one must look beyond the insignificant markers, for possible distant markers and blocks in high LD with the significant block. The QTLRs found here may explain at least part of the gender differences in MD tolerance, and provide targets for mitigating the effects of MD.

To improve the voltage gain of step-up converters, cascaded technique is considered as a possible solution in this paper. By considering the concept of cascading two Z-source networks in a conventional boost converter, the converter takes the advantages of both impedance source and cascaded converters. However, by applying some modifications, the proposed converter provides high voltage gain while the voltage stress of switch and diodes are still low. Moreover, the low input current ripple of the converter makes it absolutely appropriate for photovoltaic applications in order to expand the lifetime of PV panels. After analyzing the operation principles of the proposed converter, simulation and experimental results of a 100W prototype are presented to verify the proposed converter performance.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprised of fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed wing aircraft systems. Here the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity techniquesto recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models will enable us to avoid common control design methods, such as gain scheduling, where stability issues can be extremely challenging.

We present a new unsupervised method aimed to obtain a partition of a complex urban system in homogenous urban areas, called urban contexts. The area of study is initially partitioned in microzones, homogeneous portion of the urban system, that are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental and socio-economic indicators need to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary to determine the type of urban context. We implement the set of spatial analysis processes necessary to calculate the indicators for microzone and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples - Italy); the reliability of out model is measured by comparing the results with the ones obtained by detailed analysis.

Objective: This paper aims to build an Effort Estimation Model for design, coding and testing Web Applications Based Fuzzy and Practical Models, which will help in optimizing the efforts in software development. Methods/Analysis: Soft computing approach is adopted and applied in the effort estimation and then compared with practical efforts in the development process with interpreting the historical data available for the existing functionalities. Findings: The effort estimation model presented in this paper focuses on the first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancement for a web application built on 3-tier architecture using Microsoft technologies. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. Novelty/Improvement: The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation tool would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data. And converts it into crisp value in the range [0…1] which helps to classify the complexity of the task and subtask in the design, coding and testing phases.

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

As one of the world's most populous countries, Indonesia is struggling with the composition of its people, the majority of whom are unemployed productive-age individuals with low education levels. Another issue is the high poverty rate, which is followed by issues of gender inequality. The government cannot solve these social issues on its own. There is a need for community support to solve these problems through sustainable social entrepreneurship activities. Using the Theory of Planned Behavior and Outcome Expectation, this study will determine the factors that influence the interest of individuals known as the Z and Millennial generation, aged 18 to 34 years old, in becoming social entrepreneurs. This study applied a quantitative approach with a judgmental sampling technique to a sample of Generation Z and Millennials who have ever participated in social activities but have not yet started their own business. The study gathered 210 respondents, who were then analyzed using the PLS-SEM method with multiple group analysis generation-based comparison. According to the findings, the Millennial Generation's Attitude Toward Behavior, Subjective Norms, and Outcome Expectation all had a positive impact on Social Entrepreneurial Intention. Meanwhile, Subjective Norms had no effect on Social Entrepreneurial Intention in the Z Generation. Only attitude toward behavior, perceived behavioral control, and outcome expectation have a positive effect on the development of social entrepreneurial intentions. Furthermore, the findings of this study confirm that subjective norms influence attitude toward behavior and Perceived Behavioral Control in both Generation Z and millennials.

model checking techniques are often used for the verification of software systems. Such techniques are accompanied with several advantages. However, state space explosion is one of the drawbacks to model checking. During recent years, several methods have been proposed based on evolutionary and meta-heuristic algorithms to solve this problem. In this paper, a hybrid approach is presented to cope with the SSE problem in model checking of systems modeled by GTS with an ample state space. Most of existence proposed methods that aim to verify systems are applied to detect deadlocks by graph transformations. The proposed approach is based on the fuzzy genetic algorithm and is designed to decline the safety property by verifying the reachability property and detecting deadlocks. In this solution, the state space of the system is searched by a fuzzy genetic algorithm to find the state in which the specified property is refuted/verified. To implement and evaluate the suggested approach, GROOVE is used as a powerful designing and model checking toolset in GTS. The experimental results indicate that the presented hybrid fuzzy method improves speed and performance by comparing other techniques

Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but also soil dryness caused by temperature increases and associated long-term hydrological changes. Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and agricultural drought trends was carried out to characterize century-scale (1902 – 2016) changes in droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought trends. There is on average 1% - 6% annual increase in dryness with the lower estimate based on precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area between 37.5° E – 42.5° E denotes a climate hot-spot. Precipitation declines in Belg along the Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher have the potential to exacerbate transboundary water conflicts and further threaten the food security of the region.

The purpose of this research article is to provide a comprehensive method that allows the evaluation of the public transportation in their different transport lines that offer in Ciudad Juárez, Chihuahua. This study presents a description of the public transport system as part of the literature review that describes an appropriate model based on the more outstanding publications about urban mobility and public transportation for passengers’ as well as success cases published which serves as a starting point to check the actual state of the public transportation system based on the Pythagorean Fuzzy CODAS to analyze and evaluate the alternatives through criteria that defines the general performance. The integration of these methods provides an adequate methodology for decision-making concerning urban planning and mobility to detect and improve the performance of criteria not considered within sustainable urban mobility plans.

Reinforcement learning (RL) is an extensively applied control method for the purpose of designing intelligent control systems to achieve high accuracy as well as better performance. In the present article, the PID controller is considered as the main control strategy for brushless DC (BLDC) motor speed control. For better performance, the fuzzy Q-learning (FQL) method as a reinforcement learning approach is proposed to adjust the PID coefficients. A comparison with the adaptive PID (APID) controller is also performed for the superiority of the proposed method, and the findings demonstrate the reduction of the error of the proposed method and elimination of the overshoot for controlling the motor speed. MATLAB/SIMULINK has been used for modeling, simulation, and control design of the BLDC motor.

Cyclic olefin copolymer (COC) is a novel type of thermoplastic polymer gaining the attention of the scientific community in electronic, optoelectronic, biomedicine and packaging applications. Despite the benefits in the use of COC like undoubted optical transparency, chemical stability, good water-vapor barrier and biocompatibility, its original hydrophobicity restricts its wider applicability and optimization of its performances. Presently, we report on the optical, and morphological properties of the films of COC covered with Ti in selected areas. The layer of Ti on COC was deposited by pulsed lased deposition processing. The Ti/COC film was characterized by UV-Vis spectroscopy indicating that its transmittance in the visible region decreased by about 20% with respect to the pristine polymer. The quality of the deposited Ti was assessed with the morphology by scanning electron (SEM) and atomic force microscopies (AFM). The modification of the wettability was observed by the sessile drop method indicating a reduction of the native hydrophilicity.

Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritize their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. It might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Visual Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of 28 reinforced concrete buildings from the Bingöl earthquake in Turkey.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

This addendum contains clarifications and a sharpening of some of the results on the VIT transform framework developed in [1]. The focus is on the right-coefficient and left- coefficient forms of the transform, the extraction of a first-order term from a left polynomial fraction, and the application to linear time-varying systems.

At present, aeroponic control is mainly regulated according to the environmental factors of aeroponics, such as the temperature, humidity, light intensity, carbon dioxide concentration, pH, and electrical conductivity (EC) of the nutrient solution. There is a lack of knowledge available in the literature for an aeroponic control system for plant growth information as the primary feedback signal. Therefore, a fuzzy logic-controlled aeroponics system was developed for the growth of lettuce as a feedback signal. After designing, the effectiveness test of system was performed with experiment having three treatments T1 (180C), T2 (220C) and without control (CK). The results show an increasing trend in shoot length, root length, and shoot area over time in all treatments. In terms of shoot area, the maximum of 345 cm² was observed in the T1 treatment, while the CK treatment had a minimum of 241.8 cm². Statistical analysis indicated that the T1 and T2 treatments were not significantly different, but both were significantly different from the CK treatment. The study demonstrates the effectiveness of the fuzzy logic control system and an 18°C nutrient solution temperature, resulting in improved lettuce growth and reduced root-to-shoot ratio. The findings suggest the potential of using fuzzy logic control in aeroponics for optimized cultivation outcomes.

During the smelting process of AOD furnace, the unbalanced reaction of material will lead to the occurrence of splashing. It will not only damage the smelting equipment, but also seriously injure the personnel. In this study, first, the information of liquid level, audio information, and vibration information are detected by multiple sensors respectively. Then, the fused information is used to forecast the splashing. Finally, the multitasking fuzzy controller is used to suppress splashing. The results show that the method of forecasting and suppressing splashing can accurately forecast and achieve rapid suppression. Thus, the efficiency of smelting can be improved.

The global textile sector, pivotal to economic development, significantly contributes to environmental degradation, prompting a shift towards sustainable raw material sources. Embracing circular economy principles, this industry is exploring the valorization of agricultural by-products. This study introduces an innovative approach to repurposing mango waste into valuable textile fibers, a byproduct of the fruit processing industry, predominantly generated during juice and jam production. The focus is on the lignocellulosic constituents of mango waste, specifically the peel, seed, and fibrous material, traditionally considered as refuse. Previous research has predominantly utilized mango peel in pectin extraction for food applications, whereas the potential of seed and fibrous content remains underexplored. This investigation endeavors to transform these fibrous by-products into high-value materials suitable for textile manufacturing. The process involved washing, drying, and comprehensive physicochemical characterization of mango fibers, emphasizing lignin quantification, revealing a composition of 12.2% lignin. An experimental design was employed to elucidate the impact of variables such as liquid-to-solid ratio, reaction duration, and sodium hydroxide concentration on lignin dissolution through alkaline hydrolysis. Optimal conditions—10% NaOH concentration, 30 mL/g liquid-to-solid ratio over six hours—achieved an 83% reduction in lignin content. The resulting fibers exhibited properties akin to conventional natural fibers, including a favorable aspect ratio indicative of their suitability for textile applications. A subsequent softening process using propylene glycol enhanced the fibers' spinability, producing a durable, textured fabric ideal for eco-conscious fashion applications, including bags, footwear, and accessories. This research demonstrates the feasibility of converting mango waste into a textile-worthy material and underscores the importance of waste revalorization in promoting sustainability within the circular economy framework.

A simple mathematical interaction function, with the limit value w = e2π/e, derived as a basis for modelling emergent properties of nanoparticles, nanodrops and liquids, has been further developed. A correlation between physical systems of uniform particles and fundamental properties of natural numbers leads to a model, which acts as a bridge between properties of macroscopic systems and fundamental physical constants. The four fundamental coupling constants, and the masses of fundamental elementary particles, including the Higgs and Z bosons, are derived by using this interaction function. A proposal for the mass of the electron neutrino is given. Finally, the nuclear binding interaction is modeled with the interaction function. Only the experimental values of the Planck constant h and the speed of light c are needed for the model.

As a new approach, for a nonzero normed algebra $A$, we will define some different classes of algebra fuzzy norms on $A$ generated by homomorphisms and continuous homomorphisms. Also as a source of examples and counterexamples in the field of fuzzy normed algebras, separate continuity of the elements of each class are investigated.

Contract violations, uncontracted load, and the alternating nature of electricity are the key causes of the frequency irregularity and Unscheduled Interchange (UI) / Deviation Settlement pricing in a restructured organized smart grid. These variations cause the frequency to fluctuate. The topic of frequency variation is discussed in this paper under various market circumstances. To correct frequency discrepancies under various market conditions, a fleet of electrical vehicles (EV) is offered as a distributed energy storage (DES). A bidirectional charger with a PI controller is suggested for the EV bidirectional power transfer. It is suggested to use a fuzzy logic (FL) controller to modify the proportional-integral (PI) controller gains. It has been noted that utilizing such a new approach lowers the UI prices.

This paper analyzes the representation behaviors of a comparison measure between two compared fuzzy sets. Three types of restrictions on two fuzzy sets are considered in this paper: the two disjoint union fuzzy sets, the two disjoint fuzzy sets and the two general fuzzy sets. Differences exist among the numbers of possible representations of a comparison measure for the three types of fuzzy sets restrictions. The value of comparison measure is constant for the two disjoint union fuzzy sets. There are 42 candidate representations of a comparison measure for the two disjoint fuzzy sets. Of which 13 candidate representations with one or two terms can be used to easily calculate and compare a comparison measure.

In many application problems, F-transform algorithms are very efficient. In F-transform techniques, we replace the original signal or image with a finite number of weighted averages. The use of weighted average can be naturally explained, e.g., by the fact that this is what we get anyway when we measure the signal. However, most successful applications of F-transform have an additional not-so-easy-to-explain feature: the partition requirement, that the sum of all the related weighting functions is a constant. In this paper, we show that this seemingly difficult-to-explain requirement can also be naturally explained in signal-measuring terms: namely, this requirement can be derived from the natural desire to have all the signal values at different moments of time estimated with the same accuracy.

In the history of high-performance polymer composite parts with continuous in-plane fibre reinforcement, the comparatively moderate structural-mechanical properties in their out-of- plane-z-direction and the limited load carrying capability between individual reinforcement layers within the laminated structures have always been one of the greatest challenges. Plenty of research work has been carried out to improve load transfer capability as well as fracture toughness for crack opening events between the reinforcement layers by means of out-of-plane fibre reinforcement implementation, for example by sewing or z-pinning technologies. However, the results have not become established across a wide range of applications so far, because the technologies used have led to considerable losses of in-plane strength properties. In this paper first results are presented for a new method of a low fraction z-fibre reinforcement by means of very thin diameter laser drilled holes filled with short carbon fibres, demonstrating potential to improve Mode II fracture toughness.

We demonstrate how a rigorous mathematical approach can be used to extract a current CMB temperature of 2.7276 ±0.0723 K from the 580 type Ia supernova data points in the Union2 database. Using redshift formula: z = Sqrt(Rh/R_t) − 1, incorporated into our new CMB redshift prediction redshift formula, we find that the above current CMB temperature value is a remarkably good match for this particular data set. In addition, we can alternatively start out with the measured current CMB temperature and extract the Hubble constant from all 580 supernovae. We then get an estimated Hubble constant of H0 = 66.8711+0.0019 km/s/Mpc when using the CMB temperature from the recent Dhal et al study [1] in combination with redshifts from all 580 supernovae. Furthermore, we get an estimated Hubble constant value of H_0 = 66.8943+0.0287 km/s/Mpc when using the CMB temperature measured by Fixsen [2]. In summary, our simple model essentially perfectly matches all 580 type Ia supernova redshifts while extracting H_0 or T_0 values. The Hubble constant we extract by this method fits very well with the Hubble constant extracted from the CMB by other studies. We believe that our new approach likely solves the Hubble tension problem, at least inside R_h = ct cosmology.

The duality is one of the most interesting properties of the Laplace and Fourier transforms associated to the integer order derivative. Here, we will generalize it for fractional derivatives and extend the results to the Mellin, Z and discrete-time Fourier transforms. The scale and nabla derivatives are used.

Formalin-Fixed Paraffin Embedded (FFPE) tissues are a valuable resource in studying different markers and mechanistic molecules (protein, DNA and RNA) in order to understand the etiology of different cancers as well as many other diseases. Degradation and modification of RNA is the major challenge in utilizing FFPE tissue samples in medical research. Recently, non-protein coding transcripts long non-coding RNAs (lncRNAs), have gained significant attention due to their important biological actions and potential involvement in cancer. There is no validated method except qRTPCR or RNAseq to evaluate and study lncRNA expression. We have standardized and are reporting a sensitive Z probe based in situ hybridization method to identify, localize and quantitate lncRNA in FFPE tissues. This assay is sensitive to single transcript and localizes lncRNA in individual cells within tumor. We have characterized a tumor suppressor lncRNA-NRON (non coding repressor of NFAT), which is scarcely expressed, a moderately expressed oncogeneic lncRNA UCA1 (urothelial cancer associated 1), and a highly studied and expressed lncRNA MALAT1 (metastasis associated lung adenocarcinoma transcript1) in different cancers. High MALAT1 staining was found in colorectal, breast and pancreatic cancer. MALAT1 expression increased with the progression of the stage in colorectal cancer and invasiveness in breast cancer.

This paper provides a comprehensive analysis of food security in Sudan by adopting a unique approach of Hesitant Fuzzy Entropy (HFE) analysis to discern the key determinants influencing food security levels and aligning the findings with the Integrated Food Security Phase Classification (IPC) framework. The research utilized a dataset comprising 32 crucial variables related to food security assessment. Employing entropy values, weight coefficients, heatmaps, and scatterplots, the analysis aimed to unravel the intricate distribution of food security levels among Sudanese individuals in our study area. The study reveals that approximately 26% of the Sudanese population in our study region is poised to experience food security, with an additional 24% categorized as marginally food secure. Moreover, an estimated 35% are expected to confront moderate food insecurity, while 15% are projected to face severe food insecurity between January and June 2024. Anticipations for the period from January 2024 to June 2024 project more than 15 million individuals to encounter IPC Phase 3 and fall under moderately or severely food insecure levels. These findings emphasize the urgent need for both short-term humanitarian aid and long-term strategies to address Sudan's deepening food insecurity crisis.

Quantitative and qualitative fuzzy measures have been proposed to hesitant fuzzy sets (HFSs) from different points. However, few of the existing HFSs fuzzy measures refer to the grey relational analysis (GRA) theory. Actually, the GRA theory is very useful in the fuzzy measure domain, which has been developed for such the intuitionistic fuzzy sets. Therefore, in this paper, we apply the GRA theory to the HFSs and interval-valued hesitant fuzzy sets (IVHFS) domain. We propose the HFSs grey relational degree, HFSs slope grey relational degree, HFSs synthetic grey relational degree and IVHFSs grey relational degree, which describe the closeness, the variation tendency and both the closeness and variation tendency of HFSs and closeness of IVHFSs, respectively, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs and IVHFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection with HFSs information and emergency management evaluation with IVHFSs information, we obtain the most desirable decision results, and compared with the previous methods, the validity, effectiveness and accuracy of the proposed grey relational degree for HFSs and IVHFSs are demonstrated in detail.

Disruptive technologies (DT) have featured prominently in almost every human activity since the advent of computerization. The likely effects of DT on economic processes and human professions have and continue to generate fears and debates which spurred this investigation. To break away from the traditional approach the operational breakeven theory and the discriminant analysis techniques of Altman’s Z-score, and Enyi’s Relative Solvency Ratio were used to examine the relationship between firms’ market-induced-survival-ratio (MISR) and the disruptive technology gains index (DTGI) of seventy-three firms drawn from Nigeria and India. Descriptive and inferential statistics were used to analyze the data generated. The results showed that a sizeable number of firms has profited from the introduction of disruptive technologies with MISR and DTGI returning a 10% significant relationship while others are still struggling to measure up to the requirements of disruptive technologies in their chosen economic fields. The implication of this is that businesses must brace up and embrace digital transformation if they must stay afloat in this era of disruptive technologies. This study recommends a revolutionary approach to digital transformation in view of the fast pace of global integration while managers and business owners should adopt more pragmatic approach in appraising the operations and finances of a firm for effective results and timely responses to potential business challenges.

The idea of strong and weak adjacencies between vertices has been generalized into fuzzy graphs and intuitionistic fuzzy graphs (IFGs) and it is an important part of making decisions. However, one or two membership degrees are not always sufficient for making decisions on real-world problems that need an answer of types “yes, neutral, and no". Consequently, in previous work, we generalized the concept into picture fuzzy graphs (PFGs) where each element in the PFG has membership, neutral, and non-membership degrees. Moreover, we constructed the notion of the coloring of PFGs based on strong and weak adjacencies between vertices. In this paper, we investigate some properties of the chromatic number of PFGs based on the concept of strong and weak adjacencies between vertices. According to the properties, we construct an algorithm to find the chromatic number of PFGs. The algorithm is usefull when we work with large PFGs. Further, we improve the method to implement the PFG’s coloring for determining traffic signal phasing at an intersection. A case study has also been done to evaluate the method.

To avoid loss of information and incorrect ranking, this paper proposes a method for ranking generalized fuzzy numbers, which guarantees both horizontal and vertical values are important parameters affecting the final ranking score. In this method, the normalized height coefficient is introduced to evaluate the influence of the height of fuzzy numbers on the final ranking score. The higher the normalized height coefficient of a fuzzy number is, the higher its ranking. The left area and the right area are presented to calculate the impact of vertical value on the final ranking score. The left area is considered the benefit area. The right area is considered the cost area. The fuzzy number is preferred if the benefit area is larger and the cost area is smaller. The proposed method can be employed to rank both normal and non-normal fuzzy numbers without normalization or height minimization. Numerical examples and comparison with other methods highlight the feasibility and robustness of the proposed method, which can overcome the shortcomings of some existing methods and can support decision-makers to select the best alternative.

The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.

MicroRNAs are used as biomarkers for classification of cancer subtypes since certain miRNAs are differentially expressed in normal and patient samples. Moreover, miRNAs target mRNAs and can heavily influence Gene Expressions. Thus, deregulation of miRNAs is linked to various disorders. Thus, miRNAs can be used for prognosis and developing personalized health solutions for patients. Given the importance of miRNAs, there has been substantial work done in the field. In this paper, recent works in the field of using miRNAs expressions of patients were considered. A total of 20 papers were surveyed which utilized feature selection ensembles, fuzzy logic as well as deep learning. 10 papers have been reported which offer insight into how miRNAs can be utilized for subtype-specific or generalized cancer diagnosis.

The growing importance of sustainability in organizational success, particularly in the pharma-ceutical industry, underscores the need for leveraging technologies like blockchain to enhance sustainability indicators across environmental, social, and economic pillars. This study aims to identify and understand the challenges hindering the adoption of blockchain in the pharmaceuti-cal sector for improving sustainability performance, addressing two research questions: the spe-cific challenges faced by blockchain adoption in this context and the interdependencies among these challenges. Employing a two-step approach, the study compiles challenges through a litera-ture review, refines them via expert opinions, and establishes their interrelationships using methodologies like Fuzzy Interpretive Structural Modeling (FISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). The research contributes to unraveling the complex relationships and dependencies within the system, providing a structured framework for improved decision-making and strategic planning. It fills a literature gap by being the first attempt to outline driving and dependent factors related to the challenges of adopting blockchain technology for sustainability enhancement in the pharmaceutical sector, offering insights that can significantly impact brand image, company perception, and consumer value.

Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine-learning (ML) have been evaluated as a computer-aided alternative. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a novel data-driven fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this novel data-driven FIS for such application. The novel FIS provided the smallest error (0.86), indicating a promising alternative tool for real-time and highly accurate coagulant dosing in drinking water treatment.

This study aims to elucidate a soft computing approach for quantitative assessment of the scoring grade or rubrics for students in an outcome based education system. The intended approach resorts to a fuzzy membership based assessment of the different parameters of the scoring system, thereby yielding a novel and humanly assessment technique. The selection of the membership functions is based on the human behavior so as to make a realistic representation of the scoring strategy. The novelty of the proposed strategy lies in assigning fuzzy membership based weighted scores instead of simply assigning score bands to rubric categories, as is performed in normal rubrics based assessment. Comparative results demonstrated on a case study of Indian education scenario reveal the effectiveness of the proposed strategy over other fuzzy membership and normal rubrics based assessment procedures.

More than 110 countries including 500 cities worldwide have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, the heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision-makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution based Multi-Criteria Decision Making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the final ranking. The robustness of the proposed method is verified by 3 sensitive analyses from different aspects. Air to water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide the decision-makers apply this ranking guideline with their practical feasible situations.

The aim of this paper is the evaluation of the success of graduating with distinction in higher education (SGDHE) using the fuzzy DEMATEL method. The observation has been done using cause and effect criteria. 11 cause and 14 effect clusters have been used in this study. The study result of this work shows that all the effects are connected to the given causes and a cause-effect graph has been generated for each connection. This proposed approach is demonstrated with the empirical case of Dire Dawa University students in Dire Dawa Ethiopia.

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

In this paper, we scrutinize the axiomatic system of effect algebras which is given by D. J. Foulis and M.K. Bennett in the paper Effect Algebras and Unsharp Quantum Logics. We prove that this axiomatic system consists of independent axioms. To do this, we construct some models to indicate the indepence of each axiom. Therefore none of these axioms can be reduced when constructing any effect algebra. As a result, any algebra is an effect algebra if and only if it verifies (E1)-(E4) axioms.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

Abstract: — management of the Continuous Stirred Tank Reactor (CSTR) operation has proven to be a difficult undertaking due to the complex and ever-changing nature of its behavior, which is both nonlinear and dynamic, which is further compounded by its instability. Attempts have been made to use Proportional-Integral-Derivative (PID) controllers for such processes, but the performance is often poor due to the inherent limitations of the controllers. In contrast, heuristic algorithms have been employed to tune the controllers, but these methods can only provide optimal performance for a restricted range of parameter variations. This paper presents a novel approach that involves using a conventional gain scheduling PID controller in combination with a fuzzy gain scheduling (FGS) controller method to calibrate set-point weighted PID controllers for the CSTR process. The paper outlines the design of the controllers and evaluates their performance by subjecting them to varying input signals within the given bandwidth.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

In the context of economic development and international economic integration, the logistics industry in Vietnam is developing to meet the market demand for the transportation of goods; thus, many logistics enterprises have been formulated and grown up in recent years. This research aims to measure the efficiency of logistics enterprises and recommend a feasible solution to improve their future performance by integrating the Super Slacked-based Measure model (Super-SBM) in Data Envelopment Analysis and Fuzzy Analytic Hierarchy Processes (Fuzzy AHP) in multi-criteria decision-making. The super-SBM model was utilized to conduct the efficiency scores of logistics enterprises from 2016 to 2022 based on calculating the ratio between input and output variables; the empirical result determined each enterprise's effectiveness and ineffectiveness. Next, the Fuzzy AHP method evaluated and ranked criteria that directly impacted the operational process of logistics enterprises based on experts' opinions; the examined result suggested a feasible direction to improve their business efficiency in the future. The proposed hybrid models are a helpful solution for efficiency determination and development direction for logistics enterprises. An overall picture of the logistics enterprises was also drawn to describe their operational business process.

Recently, the medical image processing is extensively used in several areas. In earlier detection and treatment of these diseases is very helpful to find out the abnormality issues in that image. Here there are number of methods available for segmentation to detect the lung nodule of computer tomography (CT) image. The main result of this paper, the earlier detection of lung nodules using Pre-processing techniques of top-hat transform, median and adaptive bilateral filter was compared both filtering methods and proved the adaptive bilateral filter is suitable method for CT images. The proposed segmentation technique uses novel strip method and the image is split into number of strips 3, 4, 5 and 6. A marker- watershed method based on PSO and Fuzzy C-mean Clustering method was proposed method. Firstly, the input image was reduced noise reduction and smoothing and the filter image is using strips method and then the image is segmented by marker watershed method. Secondly, the enhanced PSO technique was used to locate the better accurate value of the clustering centers of Fuzzy C-mean Clustering. Final stage, with the accurate value of centers and the enhanced target function and the small region of the segmented object was clustered by Fuzzy C-mean. In segmentation algorithm presented in this paper gives 95% of accuracy rate to detect lung nodules when strip count is 5.

This research work explains the practical realization of hybrid solar wind based standalone power system with maximum power point tracker (MPPT) to produce electrical power in rural places (residential applications). The solar inspired Ant Colony Optimization (ACO) based MPPT algorithm is employed for the purpose of fast and accurate tracking power from solar and wind system. Fuzzy Logic Control (FLC) inverter controlling strategy is adopted in this presented work compared to classical PI control. Moreover, single Cuk converter is operated as impedance power adapter to execute MPPT functioning. Satisfactory practical results have been realized using dSPACE (DS1104) platform that justify the superiority of proposed algorithms designed under various operating situations.

Machine Learning (ML) has proved its capabilities in different scientific and industrial fields, but it needs to be further investigated in the construction industry for practical utilization. One of the use cases of ML is delegating the structural calculation process. In this study, to discuss ML’s capabilities in performing the work of a structural designer, calculations of concrete sections based on ACI (American Concrete Institute) as a case study were selected. At first, all manual design steps and standard considerations for a concrete beam section were coded parametrically in MATLAB. After comparing with structural design references to prove the accuracy of codes in calculating shear and bending capacities, the parametric results were used as initial data (Look−up table) for training the ML operators. Regarding different types of ML techniques and as a comparison between them, in the next steps, all essential codes for Fuzzy Logic (FL), Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were coded in the same platform. The performance of the three coded ML operators to replace (delegate) standard calculations compared to direct calculations was individually investigated and displayed through parametric examples. After initial examples, the influences of the number of parameters and size of the Look−up table on the accuracy of each operator were discussed. The study concluded that although all three operators can delegate the standard calculation, the precision of the results differs considerably. In case of the desirable size of the Look−up table, ANFIS operators can represent the standard calculations with a different number of parameters and entirely high precision.

In this paper, experimental results about the performance of a Takagi-Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration for the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. The Takagi-Sugeno Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near from the upright position of the EV3BRS's body. The Parallel Distributed Compensation (PDC) approach is used to the design of the TSFC. The Linear Matrix Inequalities (LMIs) approach is used to get the feedback gains for every local linear controller guaranteeing global asymptotically stability, via a more relaxed stability condition, of the overall fuzzy control system. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry.

This study presents a vibration control using actively tunable vibration absorbers (ATVA) to suppress vibration of a thin plate. The ATVA’s is made of a sandwich hollow structure embedded with the electrorheological fluid (ERF). ERF is considered to be one of the most important smart fluids and it is suitable to be embedded in a smart structure due to its controllable viscosity property. ERF’s apparent viscosity can be controlled in response to the electric field and the change is reversible in 10 microseconds. Therefore, the physical properties of the ERF-embedded smart structure, such as the stiffness and damping coefficients, can be changed in response to the applied electric field. A mathematical model is difficult to be obtained to describe the exact characteristics of the ERF embedded ATVA because of the nonlinearity of ERF’s viscosity. Therefore, a fuzzy modeling and experimental validations of ERF-based ATVA from stationary random vibrations of thin plates are presented in this study. Because Type-2 fuzzy sets generalize Type-1 fuzzy sets so that more modelling uncertainties can be handled, a semi-active vibration controller is proposed based on Type-2 fuzzy sets. To investigate the different performances by using different types of fuzzy controllers, the experimental measurements employing type-1 fuzzy and interval type-2 fuzzy controllers are implemented by the Compact RIO embedded system. The fuzzy modeling framework and solution methods presented in this work can be used for design, performance analysis, and optimization of ATVA from stationary random vibration of thin plates.

This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

This paper aims to introduce an overview of several aspects of the so-called Artificial Intelligence, their potential impacts on economic and social dimen- sions, and suggestions for possible approaches of investiment based upon effective and mature techniques. In this fashion, it is important to address from educational and academic issues to industrial densities and profiles, rel- atively to a given region, country or continent. Even etymological adequacy and psychological consequences of the denomination ”Artificial Intelligence” need some reflection, and suggestions for a lucid replacement are presented. In addition, suggestions about how can specific firms choose the right type of technique in order to improve profit and organizational efficiency. After all, what are the main transformations needed to amplify gains and structural improvements from the use of higher level technological mecha- nisms? Which connections can be established between the pillars of evolu- tionary economics and this field of knowledge? Which institutional contexts are able to benefit from AI tools, inducing constructive externalities to firms in terms of education, technical and scientific skills upgrading, so as to reach higher levels of employment in the long term and limit unemployment in the short one? Which branches of the so-called Artificial Intelligence are best suited to which types of activities? This work aims to contribute in the search for answers to these questions.

Chromatographic separation is a critical technique in the manufacturing processes of both chemical products and biopharmaceuticals. Its principle relies on exploiting the differences in distribution between the stationary and mobile phases to achieve the separation of mixtures. The precision of substance concentration during separation directly impacts the quality and usability of the final product. Therefore, the development of an effective and precise separation control technique has long been a vital concern in the field of chromatographic separation control. Currently, the employment of Simulated Moving Bed (SMB) technology for chromatographic separation, which allows for continuous feed, has been recognized as a cutting-edge technique. SMB is a continuous process technology that enhances the efficiency of adsorbents within the bed. This sequential multi-column SMB technology not only increases production capacity but also reduces the consumption of solvents and water. It is acknowledged as one of the cleanest manufacturing technologies in the biopharmaceutical industry. However, multi-column SMB involves various variables such as flow rates in multiple sections and valve switching times, rendering its system control highly complex. Unlike traditional control objectives that aim to minimize control output errors, the control objective of SMB is to achieve a specific proportion of substance concentration. Consequently, designing and implementing traditional control theories for SMB is challenging, leading to SMB systems largely being controlled based on simple PLC controls. Moreover, these controls are often applicable only to single or a few-column SMB setups, limiting the effectiveness of high-capacity applications. To achieve effective control of SMB, this study employs an adjustable intelligent fuzzy controller with a structure like an approximate neural network (NN) for SMB control research. Simulation results demonstrate that the intelligent controller effectively achieves desirable control outcomes for the SMB system.

In this paper, we introduce an innovative approach to distinguish Gliosarcoma (GSM) from Glioblastoma (GBM). Our method combines causal fuzzy logic rules with the Big Bird architecture, a Transformer-based Deep Learning algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach harnesses the complete dataset in tandem with our causal fuzzy Big Bird architecture. Additionally, we benchmark our results not only against previous Gliosarcoma/Glioblastoma studies but also against GPT-2 for a comprehensive evaluation.

For a nonzero normed linear space X‎, ‎we will define some different classes of fuzzy norms on X generated by linear and bounded linear functionals‎. ‎Also separate continuity of the elements of each class are investigated‎. ‎The aim of this research is to introduce a source of examples and counterexamples in the field of fuzzy normed spaces‎.

This paper presents a novel approach for individual design perception modeling using the YUKI-trained Fuzzy Inference System. The study focuses on understanding how individuals perceive design based on personality traits, particularly openness to experience. The proposed YUKI algorithm optimizes the FCM clustering algorithm, enhancing its ability to handle uncertain and imprecise data. The YUKI-trained FIS generates several Sugeno-type FIS models to predict design perception, to minimize the Root Mean Squared Error between the model prediction and the actual design perception of participants. The results demonstrate that the YUKI-trained FIS offers more accurate predictions compared to the traditional FCM-trained FIS, and the RMSE values for individual design perceptions fall within a satisfactory range of 0.84 to 1.32. The YUKI-trained FIS proves effective in clustering individuals based on their level of openness, providing insights into how personality traits influence design perception.

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system based on the opinion of a neurology expert to classify relapsing-remitting multiple sclerosis patients: high, medium, and low responder to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated to interferon-beta response is proposed for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results shows that the fuzzy system presents a 100% efficiency compared with an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model is evaluated, and a 0.8 testing accuracy is achieved. Hence, a pipeline model including data standardization, data compression, and a learning algorithm, can be a useful tool for getting reliable predictions about the response to interferon-beta.

Accelerators play a critical role in fostering innovative ecosystems and nurturing startups. The evaluation and selection of startups, particularly technology-based startups, for acceleration programs are essential in accelerator economy and management. Assessment of startups requires consideration of numerical and qualitative criteria such as sales, prior startup experience, demand validation, and product maturity. Startups must be ranked based on the varying importance of criteria, which can be identified as a fuzzy multi-criteria decision-making (MCDM) problem. MCDM methods have proven effective in managing complex problems. However, the use of MCDM techniques in startup selection and evaluation of criteria interrelationships from the accelerator perspective is yet to be researched. This study proposes a hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model to rank startups and examine the interrelationships between factors. The final preference values are fuzzy numbers, making a fuzzy ranking method necessary for decision-making. An extension of ranking fuzzy numbers using a spread area-based relative maximizing and minimizing set is suggested to improve the flexibility of existing ranking MCDM methods. Algorithms and formulas are derived and a comparison demonstrates the merits of the proposed method. Finally, a numerical experiment is designed to address the viability of the hybrid DEMATEL-ANP-based fuzzy PROMETHEE II model.

The estimation of the efficiency of road transport vehicles remains a significant problem for contemporary transport companies, as the technological process is influenced by numerous stochastic impacts, such as demand stochasticity, road conditions uncertainty, transport market fluctuations, etc. To consider the uncertainty related to the estimation of the vehicles’ fleet efficiency, we propose a fuzzy-logic approach, where the efficiency of a given vehicle is described by a membership function. The efficiency of the whole fleet and its rational structure in that case can be evaluated as a fuzzy set. To demonstrate the developed approach, we depict a case study of using cargo vehicles for deliveries of agricultural products in the Republic of Kazakhstan. The numeric results are presented for the selected models of vehicles that a transport company uses to service a set of clients located in Northern Kazakhstan.

We demonstrate, in the context of Loop Quantum Gravity, the Quantum Holographic Principle, according to which the area of the boundary surface enclosing a region of space encodes a qubit per Planck unit. To this aim, we introduce fermion fields in the bulk, whose boundary surface is the two-dimensional sphere. The doubling of the fermionic degrees of freedom and the use of the Bogoliubov transformations lead to pairs of spin network’s edges piercing the boundary surface with double punctures, giving rise to pixels of area encoding a qubit. The proof is also valid in the case of a fuzzy sphere.

The most important issue in integrated next generation wireless systems (NGWS) by fourth generation networks which consists of various wireless architectures extending from cellular networks to satellite networks is to allow everyone around the world to connect seamlessly to applications anywhere at any time through the best network. Heterogeneous networks have created many challenges such as mobility management, handoff, resource management. For always best connectivity the selection of parameters plays an important role in the decision of vertical handoff, some of parameters are depend upon MT and some are depend upon the network conditions . In this paper we designed Intelligent Vertical Handoff based on Fuzzy Logic Decision model ( IVH-FL), IVH-FL model which has five parameters for vertical handoff decision : Received Signal Strength (RSS), Available bandwidth (B), Users Preference (UP), Mobile Speed (SM) and Power Consumption (PC), with the help of Fuzzy Logic tool box and concept of fuzzy linguistic variables. The results confirmed improvement performance , and reduced the number of unnecessary handover .

The paper presents design methodology for the production process of a new product from the point of view of the assembly operations technology criterion (Design for Assembly - DFA) in the conditions of high-volume production. Mentioned are DFA methods and techniques used in the implementation of a new product. Author presents a new method to assess design for manufacturability based on fuzzy variables based on fuzzy variables. An example was given to illustrate the proposed course of action

Type-2 fuzzy sets were introduced by L. Zadeh aiming at modelling some settings in which fuzzy sets (usually called type-1 fuzzy sets) are not sufficient to reflect certain uncertainty degrees - loosely speaking, they are fuzzy sets whose membership degrees are ordinary fuzzy sets. On the other hand, fiber bundles are topological entities of extreme importance in Mathematics itself and many other scientific areas, like Physics (General Relativity, Field Theory etc.), finance modelling, and statistical inference. The present work introduces a conceptual link between the two ideas and conjectures about the potential mutual benefits that can be obtained from this viewpoint.As an objective and usable product of the presented ideas, it is described a framework for defining type-2 fuzzy hedges, proper to operate on interval type-2 fuzzy sets.

Satellites are subject to various severe vibration during different phases of flight. The concept of satellite smart adapter is proposed in this study to achieve active vibration control of launch vehicle on satellite. The satellite smart adapter has 18 active struts in which the middle section of each strut is made of piezoelectric stack actuator. Comprehensive conceptual design of the satellite smart adapter is presented to indicate the design parameters, requirements and philosophy applied which are based on the reliability and durability criterions to ensure successful functionality of the proposed system. The coupled electromechanical virtual work equation for the piezoelectric stack actuator in each active strut is drived by applying D'Alembert's principle. Modal analysis is performed to characterize the inherent properties of the smart adapter and extraction of a mathematical model of the system. Active vibration control analysis was conducted using fuzzy logic control with triangular membership functions and acceleration feedback. The control results conclude that the proposed satellite smart adapter configuration which benefits from piezoelectric stack actuator as elements of its 18 active struts has high strength and shows excellent robustness and effectiveness in vibration suppression of launch vehicle on satellite.

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

Natural events such as floods, fires, tsunamis, earthquakes and others have nowadays caused serious damage to human beings and nature. The precise detection of these natural events and especially the earthquake has nowadays become the focus of many computer and geoscientific researchers. Computer science and machine learning algorithms have revolutionized early detection and prediction of these events. Hence, a fuzzy method has been initially used in this article to enhance the authenticity of data based on application of effective variables and then combination of neural network algorithms of the MLP perceptron and radial network of RBF in form of a collective learning system in order to more accurately identify seismic events on a small scale. It was observed after simulating the proposed method that the proposed method has significantly improved based on actual error and root-mean-square error (RMSE) criteria compared to basic methods.

This paper presents a) the novel hybrid learning method to train the type-1 non-singleton interval type-3 (IT3) fuzzy logic systems (IT3 NSFLS-1) and b) the novel method named enhanced Wagner-Hagras (EWH) IT3 NSFLS-1 fuzzy systems which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the back propagation (BP) method to train the antecedent parameters. This proposal dynamically changes the parameters of only the level alpha 0 minimizing some criterion function as new information becomes available to each level alpha k. The antecedent sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the EWH IT3 NSFLS-1 (OLS-BP) system. The algorithm was tested in a hot strip mill facility to predict the transfer bar surface temperature showing its superior capability to obtain the industrial pyrometer’s knowledge uncertainty for tuning and its better performance when compared with IT2 SFLS, IT2 NSFLS-1, GT2 SFLS, GT2 NSFLS-1, IT3 SFLS, and IT3 NSFLS-1 trained with the BP-BP algorithm.

The airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) problem is critical for ramp work safety management, because AGHS equipment malfunctions affect airport ramp work safety. Appropriate vendor selection can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. The AGHSEVS problem is a time-consuming and complex process that requires professional knowledge and experience to make judgments. Specifically, AGHSEVS is a multi-criteria decision-making (MCDM) problem. Previous research has seldom integrated MCDM methods with linear and goal programming to solve the AGHSEVS problem. The objective of this study was to develop a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We test the proposed approach on an AGHS company in Taiwan.

This paper presents the mathematical modeling for designing a 10[kW] DC-AC inverter system from 48 [V] DC to 120 V ACrms, considering its non-linear operation, allowing to obtain a detailed simulation to assess the proposed fuzzy PI control (with anti-windup action) and its optimal operation. The performance assessment of the converter was carried out with the discrete treatment of the voltage and current signals using spectral analysis, filtering considerations, and evaluation of limit cycle technique to determine the system's stability. The main results showed that THD at the filter output (transformer and capacitor) was around 0.0322% and 0.0632% at nominal load in open loop and closed loop, respectively. The inverter system presented more sensitivity in the current signal with a ripple at 120 [Hz] of 2.02% and at 5000 [Hz] of 10.62% (at nominal load). However, when the system operates in a closed loop, the current ripple at 120 [Hz] is 2.38% and at 5000 [Hz] is 10.62%.

This paper aims to investigate impact of employees’ gender on OCB as per the employees’ perception in Jordanian governmental hospitals. A convenient sample of 126 employees working in the three main governmental hospitals in north of Jordan has been taken for the purpose of this study. The collected data includes linguistic terms that suffer from uncertainty which, in turn, cannot be dealt with traditional numerical values. The result prove that gender impact on OCB has shown statistically significant differences at (α=0.05) as far as altruism, courtesy, and civic virtue are concerned; and this variable stands in favor of males with the total score of 0.011%. Similarly, as far as the effect of age factor on OCB is concerned, there have been statistically significant differences at (α=0.05) in relation to courtesy, sportsmanship, and civic virtue with the total score of 0.27%. Finally, the results provide a baseline data for further studies which may contribute more significant in the field of OCB.

Abstract: An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion, involving the decomposition of the organic matter in two or more substrates by bacteria and archaea, in the absence of oxygen. 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, with Monod kinetics involving two substrates and an intermediate (M2SI model), 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 results simulated by M2SI were used in neural network modeling, firstly employing a “generic” network applicable to all 8 scenarios. A very good fit was obtained (R²>0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition). The parameters of the M2SI model for the 8 different conditions were also mapped using a neural network, as a function of the organic material composition. A fit with R²>0.99 was obtained using 25 neurons. In the case of the fuzzy logic, RMSE 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).

The bridge crane is often used in a complex environment, often subject to interference of all loads. Some uncertainties factors often have an inevitable impact on its swing. So the force situation of the bridge crane during a working cycle is analyzed, and a three-dimensional dynamic mathematical model of the bridge crane is built. Through the simulation analysis of the model under the action of driving force and wind load, the change law of the swing angle of the bridge crane is studied. Then, the fuzzy control theory is used to determine the control parameter in the anti sway control process. The position, swing angle deviation and deviation rate of the bridge crane are taken as the input, and the parameter correction is obtained after the fuzzification by using the center of gravity method, the anti sway fuzzy control system of the bridge crane is designed and simulated. The research results show that the fuzzy PID anti sway controller can not only improve the adaptability of the control system, but also overcome the large overshoot, quickly restrain the swing, and effectively realize the anti sway function of the bridge crane.

Hamacher operation which is generalization of the Algebraic and Einstein operation, can widely provide a large number of arithmetical operation with respect to uncertainty information, and Heronian mean can deal with correlations of the input arguments or different criteria and don’t make calculation redundancy, meanwhile, the normal intuitionistic fuzzy numbers (NIFNs) can depict distinctively normal distribution information in practical decision making. In this paper, a multi-criteria group decision-making (MCGDM) problem is researched under the NIFNs environment, and a new MCGDM approach is introduced on the basis of the Hamacher operation. Firstly, according to Hamacher t-conorm and t-norm, some operational laws of NIFNs are presented. Secondly, it is noticed that Heronian mean can’t only once take into account mutual relation between attribute values once, but also consider the correlation between input argument and itself. Therefore, we develop some operators and study their properties in order to aggregate normal intuitionistic fuzzy numbers information, these operators include Hamacher Heronian mean (NIFHHM), Hamacher weighted Heronian mean (NIFHWHM), Hamacher geometric Heronian mean (NIFHGHM) and Hamacher weighted geometric Heronian mean (NIFHWGHM). Furthermore, we apply the proposed operators to the MCGDM problem and present a new method. The main characteristics of this new method involve that: (1) it is suitable to make decision under the normal intuitionistic fuzzy numbers environment and more reliable and reasonable to aggregate the normal distribution information. (2) it utilizes Hamacher operation which can provide more reliable and flexible decision-making results and offer an effective and powerful mathematic tool for the MAGDM under uncertainty. (3) it uses the Heronian mean operator which can considers relationships between the input arguments or the attributes and don’t brings subsequently about redundancy. Lastly, an application is given for showing the feasibility and effectiveness of the presented method in this paper.

In this work, a series of compounds in the CaO-½Er2O3-CoOz ternary oxide system were synthesized in air at 885℃ using high temperature solid-phase synthesis method. Phase boundary of each solid solution region in the CaO-½Er2O3-CoOz system was determined by X-ray powder diffraction technique. The phase diagram of the CaO-½Er2O3-CoOz system at 885℃ includes three series of ternary oxide solid solutions : (Ca3-xErx)Co4O9-z (0≤x≤0.9), (Ca3-xErx)Co2O6-z (0≤x≤1.25) and (Er1-xCax)CoO3-z (0≤x≤0.33). Four three-phase regions and five solid solution tie-line regions were obtained. The structure of perovskite solid solution (Ca3-xErx)Co2O6-z has been analyzed by Rietveld refinements. With the increase of Er content, the cell parameters of (Ca3-xErx)Co2O6-z exhibit an decreasing trend in a and b directions and an increasing trend in c direction.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

Instead of the conventional 0 and 1 values, bipolar reasoning uses -1, 0, +1 to describe double-sided judgements in which neutral elements are halfway between positive and negative evaluations (e.g., “uncertain” lies between “impossible” and “totally sure”). We discuss the state-of-the-art in bipolar logics and recall two medieval forerunners, i.e., William of Ockham and Nicholas of Autrecourt, who embodied a bipolar mode of thought that is eminently modern. Starting from the trivial observation that “once a wheat sheaf is sealed and tied up, the packed down straws display the same orientation”, we work up a new theory of the bipolar nature of networks, suggesting that orthodromic (i.e., feedforward, bottom-up) projections might be functionally coupled with antidromic (i.e., feedback, top-down) projections via the mathematical apparatus of presheaves/globular sets. When an entrained oscillation such as a neuronal spike propagates from A to B, changes in B might lead to changes in A, providing unexpected antidromic effects. Our account points towards the methodological feasibility of novel neural networks in which message feedback is guaranteed by backpropagation mechanisms endowed in the same feedforward circuits. Bottom-up/top-down transmission at various coarse-grained network levels provides fresh insights in far-flung scientific fields such as object persistence, memory reinforcement, visual recognition, Bayesian inferential circuits and multidimensional activity of the brain. Implying that axonal stimulation by external sources might backpropagate and modify neuronal electric oscillations, our theory also suggests testable previsions concerning the optimal location of transcranial magnetic stimulation’s coils in patients affected by drug-resistant epilepsy.

Our method for density elimination is generalized to the non-commutative substructural logic GpsUL∗. Then the standard completeness of HpsUL∗follows as a lemma by virtue of previous workbyMetcalfeandMontagna. This result shows that HpsUL∗ is the logic of pseudo-uninorms and their residua and answered the question posed by Prof. Metcalfe, Olivetti, Gabbay and Tsinakis.

In the current work, the stable queueuing system's Generalised Z-Entropy (GZE) of Rényian formalism is examined in its fractal dimension. Notably, fractal dimension’s resulting behaviour that corresponds to the GZE parameters is examined through numerical tests. This research makes a substantial generalization in the literature by fusing fractal geometry and information theory to shed light on how entropy and complexity interact. More fundamentally, the significant role of fractal dimension to advance Big Data Analytics(BDAs) is highlighted. Closing remarks combined with open problems and the next phase of research are provided.

The airline sector is critical in today's globalized society, supporting the efficient movement of people and products worldwide. Airlines are continuously looking for methods to enhance their operational performance to remain competitive in the face of increased competition. This study focuses on the application of Multi-Criteria Decision Making (MCDM) techniques to improve low-cost carrier (LCC) also known or low-cost (budget) airlines' ground operational performance. MCDM techniques have gained considerable attention in recent years to address complex decision problems with multiple conflicting goals. This research aims to bridge this gap by proposing a comprehensive framework that combines MCDM techniques to enhance airline operational strategies to increase performance. The study utilizes qualitative and quantitative data, drawing on previously published materials on MCDM techniques in the aviation sector. It utilizes fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. A case study is conducted to evaluate the ground operational performance of three airline companies based in Türkiye, considering five main criteria and 18 sub-criteria. The findings of this research will contribute to a comprehensive understanding of operational performance in the airline sector. It also highlights the implications and limitations of the research and suggests future research directions.

: In this article, the authors propose an algorithmic approach to building a model of the dynamics of economic and, in particular, innovation processes. The approach under consideration is based on a complex algorithm that includes (1) decomposition of the time series into components using singular spectrum analysis; (2) recognition of the optimal component model based on fuzzy rules, and (3) creation of statistical models of individual components with their combination. It is shown that this approach corresponds to the high uncertainty characteristic of the tasks of the dynamics of innovation processes. The proposed algorithm makes it possible to create effective models that can be used both for analysis and for predicting the future states of the processes under study. The advantage of this algorithm is the possibility to expand the base of rules and components used for modeling. This is an important condition for improving the algorithm and its applicability for solving a wide range of problems.