In this paper, we introduce the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the prioritization in the FMECA analysis applied in cyber-power grids. Classical FMECA assesses the risk level through the Risk Priority Number (RPN). The multiplication between three integer numbers computes this, called risk factors, representing the severity, occurrence, and detectability of each failure mode and are defined by a team of experts. The RPN does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language; this is the main FMECA shortcoming criticized in the literature. Our approach considers fuzzy variables defined by FMECA experts to represent the uncertainty associated with the human language and a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. To test our approach, we select a cyber-power grid previously analyzed by the authors using the classical FMECA. The results reveal our proposed fuzzy approach as promissory to represent the uncertainty associated with expert knowledge and to perform an accurate prioritization of failure modes in the context of electrical power systems.

Coal combustion in thermal power plants releases ash. Ash is reported to cause different adverse health hazards in humans and other organisms. Owing to the presence of radionuclides, it is also considered as a potential radiation hazard. In this study, based on the surface radiation measurements and relevant ancillary data, expected radiation risk zones were identified with regard to the human population residing near the Thermal Power Plant. With population density as the risk determining criteria, about 20% of the study area was at ‘High’ risk and another 20% of the study area was at ‘Low’ risk zone. The remaining 60% was under medium risk zone. Based on the findings remedial measures which may be adopted have been suggested.

the intermittency and variability of these permeated Distributed Generators (DGs) could critically cause many security and economy risks to distribution systems. This paper applied a certain mathematical distribution to imitate the output variability and uncertainty of DGs. And then, four risk indices EENS, PLC, EFLC and SI were established to reflect the system risk level of distribution system. For the certain mathematical distribution of the DGs' output power, a developed PEM (point estimate method)-based method was proposed to calculate these four system risk indices. In this developed PEM-based method, enumeration method was used to list the states of distribution systems, an improved PEM was presented to deal with the uncertainties of DGs and the value of load curtailment in distribution systems was calculated by an optimal power flow algorithm. Finally, the effectiveness and advantages of this proposed PEM-based method for distribution system assessment were verified by the tests of a modified IEEE 30-bus system. Simulation results have shown that this proposed PEM-based method has a high computational accuracy and highly reduced computational costs compared with other risk assessment methods and is very effective for risk assessments.

The Internet-of-Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state-of-the-art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.

Oil has become the object of global exploits and fierce competition among the major world powers as it is a key strategic non-renewable resource. Transnational petroleum investment is therefore an important mechanism available to countries and international corporations to control oil resources even though there are numerous inherent uncertainties and risks. A new risk assessment index system is proposed in this paper based on use of the Delphi expert scoring system and fuzzy comprehensive evaluation that aims to minimize the potential risks inherent to multinational petroleum investment. This approach encapsulates political, legal, socioeconomic, and infrastructural factors to develop a technical method that can be used for transnational petroleum investment risk assessment. An evaluation of oil investment risk within a case study area is also presented; results provide reference data that can be applied by national and international oil companies to mitigate risks of transnational oil investment.

Most of the municipalities in the Gulf region are facing performance related issues in their municipal solid waste management (MSWM) systems. They lack to possess a deliberate inter-municipality benchmarking processes. Instead of identifying the performance gaps for their key components (e.g., personnel productivity, operational reliability, etc.) and adopt proactive measures, the municipalities primarily rely on an efficient emergency response. A novel hierarchical modeling framework, based on deductive reasoning, is developed for performance assessment of MSWM systems. Fuzzy rule based modeling using Simulink-MATLAB was used for performance inferencing at different levels, i.e., component, sub-components, etc. The model is capable of handling the inherent uncertainties due to limited data and imprecise knowledge base. The model’s outcomes can exclusively assist the managers working at different levels of organizational hierarchy for effective decision-making. Performance of the key component, assists the senior management to assess the overall compliance level of performance objectives. Subsequently, operation management can hone in the sub-components to acquire useful information for intra-municipality performance management. While, individual indicators are useful for inter-municipality benchmarking. The model has been implemented on two municipalities operating in Qassim Region, Saudi Arabia. The results demonstrate the model’s pragmatism for continuous performance improvement of MSWM systems in the country and elsewhere.

The health of the landing gear retraction and extension hydraulic system may be assessed using fuzzy comprehensive evaluation (FCE), however the traditional FCE method depends solely on human assessment by specialists, which is excessively subjective. To address the issue of excessive human subjective variables in the assessment, an improved FCE model based on enhanced risk coefficient is provided, which includes four consideration indexes: failure probability, failure severity, failure detection difficulty, and failure repair difficulty. To reduce subjective human judgment errors entirely due to expert experience, the improved FCE takes into account the likelihood of failure using a statistical method, the severity of failure using a fault simulation analysis based on the LMS Imagine.Lab AMESim simulation platform, and the difficulty of fault detection and repair using the aircraft manufacturer's professional maintenance information. As part of the evaluation model, the range of health assessment values and accompanying treatment methods are included, making it easier to implement on a daily basis in aircraft maintenance. As a final step, the simulation is evaluated and the simulated faults are calculated.

Estimating the economic risks of climate shocks and climate stressors on spatially heterogeneous cities over time remain highly challenging. The purpose of this paper is to present a practical methodology to assess the economic risks of climate change in developing cities to inform spatially sensitive municipal climate response strategies. Building on a capital-based framework (CBF), spatially disaggregated baseline and future scenario scores for economic wealth and its exposure to climate change are developed for six different classes of capital and across 77 major suburbs in Cape Town, South Africa. Capital-at-risk was calculated by combining relative exposure and capital scores across different scenarios and with population impacted plotted against the major suburbs and the city’s 8 main planning districts. The economic risk assessment presented here provides a generic approach to assist investment planning and the implementation of adaptation options through an enhanced understanding of relative levels of capital endowment vis-à-vis relative levels of exposure to climate-related hazards over time. An informed climate response strategy in spatially heterogeneous cities need to include spatially sensitive estimates on capital-at-risk and populations disproportionally impacted by climate exposure over time. The economic risk assessment approach presented here helps in advancing to such a goal.

Human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore it is very crucial to recovering the footprints to identify the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defense, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints easily. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

Irish beef farms have experienced poor viability longitudinally, with officially acknowledged ‘crisis’ levels in 2013. In response, beef Producer Organisation (PO) legislation was introduced. Through the lens of international evidence in the literature about how POs function, this paper presents an analysis of Irish stakeholders’ views in the context of a public consultation process. While stakeholders indicate the need for individual POs to collaborate, little emphasis is placed on other factors necessary for success: collaboration with other chain actors; and market differentiation of products. Stakeholders identified primary threats as poor engagement on the part of both farmers and processors/purchasers.

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.

This article presents an estimation of the hypertension risk based on a dataset on 1007 individuals. The application of a Tobit Model shows that “Hypertension” is positively associated to “Age”, “BMI-Body Mass Index”, and “Heart Rate”. The data show that the element that has the greatest impact in determining inflation risk is “BMI-Body Mass Index”. An analysis was then carried out using the fuzzy c-Means algorithm optimized with the use of the Silhouette coefficient. The result shows that the optimal number of clusters is 9. A comparison was then made between eight different machine-learning algorithms for predicting the value of the Hypertension Risk. The best performing algorithm is the Gradient Boosted Trees Regression according to the analyzed dataset. The results show that there are 37 individuals who have a predicted hypertension value greater than 0.75, 35 individuals who have a predicted hypertension value between 0.5 and 0.75, while 227 individuals have a hypertension value between 0.0 and 0.5 units.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

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

The synchronous generator, as the main component of power systems, plays a key role in these system’s stability. Therefore, utilizing the most effective control strategy for modeling and control the synchronous generator results in the best outcomes in power systems’ performances. The advantage of using a powerful controller is to have the synchronous generator modeled and controlled as well as its main task i.e. stabilizing power systems. Since the synchronous generator is known as a complicated nonlinear system, modeling and control of it is a difficult task. This paper presents a sum of squares (SOS) approach to modeling and control the synchronous generator using polynomial fuzzy systems. This method as an efficacious control strategy has numerous superiorities to the well-known T–S fuzzy controller, due to the control framework is a polynomial fuzzy model, which is more general and effectual than the well-known T–S fuzzy model. In this case, a polynomial Lyapunov function is used for analyzing the stability of the polynomial fuzzy system. Then, the number of rules in a polynomial fuzzy model is less than in a T-S fuzzy model. Besides, derived stability conditions are represented in terms of the SOS approach, which can be numerically solved via the recently developed SOSTOOLS. This approach avoids the difficulty of solving LMI (Linear Matrix Inequality). The Effectiveness of the proposed control strategy is verified by using the third-part Matlab toolbox, SOSTOOLS.

Zoos are increasingly putting in place formalized animal welfare assessment programs to allow monitoring of welfare over time, as well as to aid in resource prioritization. These programs tend to rely on assessment tools that incorporate resource-based and observational animal- focused measures since it is rarely feasible to obtain measures of physiology in zoo-housed animals. A range of assessment tools are available which commonly have a basis in the Five Domains framework. A comprehensive review of the literature was conducted to bring together recent studies examining welfare assessment methods in zoo animals. A summary of these methods is provided with advantages and limitations of the approach es presented. We then highlight practical considerations with respect to implementation of these tools into practice, for example scoring schemes, weighting of criteria, and innate animal factors for consideration. It is concluded that would be value in standardizing guidelines for development of welfare assessment tools since zoo accreditation bodies rarely prescribe these. There is also a need to develop taxon or species- specific assessment tools to inform welfare management.

Portfolio weights solely based on risk avoid estimation error from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation and the Exponential Weighted Moving Average target matrices. By an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance, Inverse Volatility, Equal-risk-contribution and Maximum Diversification portfolios. We find out that the Identity and Variance Identity have very good statistical properties, being well-conditioned also in high-dimensional dataset. In addition, the these two models are the best target towards to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps reducing weights misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification, hence they benefit less from shrinkage.

The paper discusses how joint damage and deterioration affect the seismic response of existing reinforced concrete frames with sub-standard beam-column joints. The available simplified modeling techniques are critically reviewed to propose a robust, yet computationally efficient technique for simulating the nonlinear behavior of substandard beam-column joints. Improvements over the existing models include simulation of the cyclic deterioration of joint stiffness and strength as well as pinching in the hysteretic response, implemented considering a deteriorating hysteretic rule. A fibre-section forced-based inelastic beam-column element is developed; considering improved material models and fixed-end rotation due to bond failure, rebars-slip and inelastic extension, to simulate the deteriorating cyclic behavior of existing pre-cracked beam-column members. For the assessment of frames with substandard exterior beam-column joints, a nonlinear model for the exterior joint is developed and validated through a full-scale quasi-static cyclic test performed on a substandard T-joint connection. The proposed model allows considering structural performance in risk assessment while accounting for true inelastic mechanisms at the joints.

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

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.

We present a new method for assessing the strength of fuzzy rules with respect to a dataset, based on the measures of the greatest energy and smallest entropy of a fuzzy relation. Considering a fuzzy automaton (relation) in which A is the input fuzzy set and B the output fuzzy set, the fuzzy relation R1 with greatest energy provides information about the greatest strength of the input-output and the fuzzy relation R2 with the smallest entropy provides information about uncertainty of the relationship input-output. We consider a new index of the fuzziness of the input-output based over R1 and R2. In our method this index is calculated for each pair of input and output fuzzy sets in a fuzzy rule. A threshold value is set for choosing the most relevant fuzzy rules with respect to the data.

Wearable assistant devices play an important role in daily life for people with disabilities. Those who are hearing impaired may face dangers while walking or driving on the road. The major danger is their inability to hear warning sounds from cars or ambulances. Thus, the goal of this study is to develop a wearable assistant device for the hearing impaired to recognize emergency vehicle sirens on the road using edge computing. An EfficientNet-based fuzzy rank-based ensemble model was proposed to classify seven audio sounds, including human vocalizations and emergency vehicle sirens. This model was embedded in an Arduino Nano 33 BLE Sense development board. The audio files were respectively obtained from the CREMA-D dataset and Large Scale Audio dataset of emergency vehicle sirens on the road, with a total number of 8756 files. The seven audio sounds included neutral vocalization, anger vocalization, fear vocalization, happy vocalization, car horn sound, siren sound, and ambulance siren sound. The audio signal was converted into a spectrogram by the short-time Fourier transform as the feature. When one of the car horns, sirens, or ambulance sirens was detected, the wearable assistant device presented alarms through vibration and messages on the OLED panel. The performances of the EfficientNet-based fuzzy rank-based ensemble model in offline computing achieved an accuracy of 97.1%, precision of 97.79%, sensitivity of 96.8%, and specificity of 97.04%. In edge computing, the results were an accuracy of 95.2%, precision of 93.2%, sensitivity of 95.3%, and specificity of 95.1%. Thus, the proposed wearable assistant device has the potential benefit of helping the hearing impaired avoid traffic accidents.

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.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

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.

Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare services. However, ensuring secure authentication and key agreement mechanisms is essential to protect sensitive patient data and maintain the integrity of the system. The existing methods face limitations in terms of vulnerability to cyber attacks, scalability, and resource utilization. Furthermore, the integration of blockchain technology introduces new complexities that need to be addressed. This research proposes an optimized fuzzy logic approach combined with blockchain technology to address the authentication and key agreement challenges in digital healthcare systems. The proposed solution leverages the flexibility and adaptability of fuzzy logic algorithms to handle uncertainty and imprecision in authentication decisions. By employing fuzzy logic, the system can effectively minimize false positives and false negatives, enhancing the robustness against adversarial attacks. Moreover, the integration of blockchain technology provides a decentralized and tamper-proof infrastructure for securely storing and managing authentication and key agreement data. This ensures transparency and trust in the system, mitigating the risks of unauthorized access and data manipulation. The blockchain-based architecture also enables efficient resource utilization and scalability, allowing the system to handle authentication requests in a timely manner, even in large-scale digital healthcare environments.The proposed method is evaluated by using the NIST Special Database 302 and it shows superior performance compared to existing methods, with minimum False Rejection Rate (FRR), False Acceptance Rate (FAR), and response time. Moreover, the proposed method minimizes communication overhead during the authentication process and resists different cyber attacks including a Replay attack, Man-in-the-middle attack, Denial of Service (DoS) attack, and Impersonation attack. The proposed method achieves excellent performance in terms of security, efficiency, and resistance to various cyber-attacks, making it a promising approach for secure data sharing in P2P cloud environments.

In this paper, it is introduced the notion of r-fuzzy β-T_i, i = 0, 1, 2 separation axioms related to a fuzzy operator β on the initial set X which is a generalization of previous fuzzy separation axioms. An r-fuzzy α-connectedness related to a fuzzy operator α on the set X is introduced which is a generalization of many types of r-fuzzy connectedness. An r-fuzzy α-compactness related to a fuzzy operator α on the set X is introduced which is a generalization of many types of fuzzy compactness.

This research work deals hybrid control system based integrated Cuk converter fed brushless DC motor (BLDCM) for power factor correction. In this work, moth-flame optimization (MFO) and fuzzy logic controller (FLC) has been combined and moth –flame fuzzy logic controller (MFOFLC) has been proposed. Firstly, the BLDC motor modelling is composed with power factor correction (PFC) based integrated Cuk converter and BLDC speed is regulated using variable DC-Link inverter voltage which makes low switching operation with less switched losses. Here, with the use of switched inductor, the task and execution of proposed converter is redesigned. The DBR (diode bridge rectifier) trailed by proposed PFC based integrated Cuk converter operates in discontinuous inductor conduction mode(DICM) for achievement of better power factor.MFO is exhibited for gathering of dataset from the input voltage signal. At that point separated datasets is send to FLC to improve the updating function and minimization of torque ripple. However, our main objective is to assess adequacy of proposed method, the power factor is broke down. The execution of the proposed control methodology is executed in MATLAB/Simulink working platform and the display is assessed with the existing techniques.

A block-diagonal fuzzy neural network for short-term load forecasting is proposed. DBD-FELF consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed by use of Fuzzy C-Means clustering algorithm and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The electric load time-series of the Greek power system is examined, and hourly-based forecasting for the whole year is performed. The performance of DBD-FELF is tested via extensive experimental analysis and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted.

Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.

Digital-based differential amplifiers (DDA) are particularly suitable to low voltage digital integrated circuit technologies. This paper presents an exhaustive analysis of the digital-based analog amplifiers to take advantage of today's high-performance digital technologies and of computer-aided design (CAD) commonly employed to design integrated circuits. The operating principle and the main mathematical relations of the digital-based differential amplifiers are discussed along with an exhaustive explanation of its operating regions and of the corresponding power consumption. Finally, a detailed description of the design procedure of in UMC 180nm standard CMOS technology is shown.

The quadruple burden of diseases, early discharge from hospital and hospital at home have resulted in home-based care services becoming a requirement in South Africa. The home-based care services generate a significant amount of health care risk waste that is mismanaged. However, more attention is given to the health care risk waste generated in hospitals and clinics than to health care risk waste generated by home-based caregivers. Therefore, this study investigates the health care risk waste management practices by home-based caregivers. The study adopted a mixed research approach, qualitative and quantitative methods, using a literature review, interviews, and questionnaires as means of data collection. Results show that there are different types of health care risk waste generated as a result of different activities performed by home-based caregivers, but that the waste was found to be managed in an unsafe manner. The majority of households receiving home-based care did not have basic sanitation facilities such as toilets, running water and waste removal services, aggravating the issue of health care risk waste mismanagement. The study recommends a new policy framework that will lead to safe management practices of generated health care risk waste to be adopted by home-based caregivers.

Abstract. This article proposes new fuzzy controller to regulation the voltage, damp the oscillations and limit the overshoot behavior in single-machine infinite-bus power system. Two different independent MISO fuzzy controllers are designed. First controller, is voltage stabilizer and it composes of two adaptive fuzzy modules and proportional gain AVR. The voltage stabilizer is designed to regulate the terminal voltage, damp the voltage oscillation and limit the voltage overshoot to the industrial maximum allowable limit. The input signals of this stabilizer is are taken from the terminal voltage and the imposed voltage disturbance that can be read from any smart relay. The fuzzy output is used as feedback signal to control the AVR in order to achieve stabilized voltage equal to the reference voltage. The second fuzzy controller is designed with two input signals; “mechanical angle” and its derivative, and one output which is used as feedback signal to adapt the damping torque in order to damp the mechanical oscillation fast and with maximum overshoot not exceeding 10%, and hence damp the power angle oscillation that is important in the PSS improvement. MATLAB-SIMULINK was used for modelling and the test cases simulation. The results show very good performance for the proposed fuzzy controllers.

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area, because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

Various renewable energy sources such as wind power and photovoltaic (PV) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this paper, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability is investigated in an IEEE 16-machine 68-bus power system. Firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by the linearized state-space modeling. On this basis, converter-driven stability analyses are performed to reveal the modal resonance mechanisms of the interconnected power systems and the modal interaction phenomenon. Additionally, time-domain simulations are conducted to verify effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, an optimization strategy is further proposed by retuning the controller parameters of the HRES system. The overall results demonstrate the modal interaction effect between external AC power system and the HRES system and its various impacts on converter-driven stability.

This study presents a path planning method for a mobile robot to be effectively operated through a multi-objective decision-making problem. Specifically, the proposed Fuzzy analytic hierarchy process (FAHP) determines an optimal position as a sub-goal within the multi-objective boundary. The key features of the proposed FAHP are evaluating the candidates according to the fuzzified relative importance among objectives to select an optimal solution. In order to incorporate FAHP into path planning, an AHP framework is defined, which includes the highest level (goal), middle level (objectives), and the lowest level (alternatives). The distance to the target, robot’s rotation, and safety against collision between obstacles are considered as objective functions. Comparative results obtained from the artificial potential field and AHP/FAHP simulations show that FAHP is much preferable for the mobile robot’s path planning than typical AHP.

Manufacturing corporations has the acceptance of the Outsourcing Process (OP) to improve industrial activities as well as to archive the revenue objectives, and with this, Risk Analysis (RA) tools are constantly used to assure expected results. Failure Mode and Effect Analysis (FMEA) is one of preferred RA tools, moreover, it is proven that FMEA adds uncertainty because of the human participation at the RA, afterward it is demonstrated that Pythagorean Fuzzy Dimensional Analysis – FMEA – Value Stream Mapping (PFDA-FMEA-VSM) method removes the uncertainty in RA, likewise it aids to the stakeholders for decision making, giving more advantages improving the use of the resources on the project. This document exhibits a real case scenario in a manufacturing firm applying PFDA-FMEA-VSM method adapted for manufacturing OP. The application of PFDA-FMEA-VSM shows solid RA results, removing the human intervention uncertainty added to the risk ranking, gives advantages to the stakeholders for visualize the main risks in detailed diagram, as well as make easier to take better decisions on where to apply resources and mitigate risks during OP.

Product Design is getting nowadays new challenges on developing new products, since the industry is in a rush to introduce products into the marketplace, where customers demand products faster, cheaper, and free of failures. In the meantime, global companies are always trying to improve their Product Design process to get advantages over their competitors using proven tools like FEMA and mixing methodologies like Fuzzy theories with FMEA. Even today using all this tools and combination of methodologies there is a gap to address and it is required a robust risk analysis solving current issues in the electronic industry. This document aims to reveal a novel integrated method, where Failure Mode and Effect Analysis, Pythagorean Fuzzy Sets and Dimensional Analysis are cohesive into a model that minimize the uncertainty of the ranking and prioritization over the Failure Mode and Effect Analysis execution, helping to identify risks within an accurate grade over possible failures in the Product Design process. A real practical example is used to show the proposed method, where it is identified a robust methodology integration and solid and results using a sensitivity analysis.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

Task-Based Instruction (TBI) is a dynamic approach in tertiary language education that emphasizes the integration of authentic and meaningful tasks to enhance language learning outcomes. This study aims to explore the implementation of TBI in the Philippines, investigate its cognitive benefits and transformative nature, identify pedagogical considerations, and provide recommendations for optimization. The study employed a qualitative research design, with data collected from five private schools in Iligan City. A purposive sampling procedure was used to select 20 participants who engaged in interviews, focus group discussions, and thematic analysis. The results revealed that TBI positively influenced language proficiency, cognitive development, learner autonomy, intercultural competence, and pedagogical considerations. Participants reported enhanced language skills and increased confidence in real-life language use. They also highlighted the development of critical thinking, problem-solving, and decision-making skills through engaging in challenging tasks. TBI was found to empower learners by promoting autonomy, ownership, and self-directed learning. Additionally, it cultivated intercultural communicative competence and fostered appreciation for diverse perspectives. Pedagogical considerations included task design, clear instructions, scaffolding, and feedback, while recommendations focused on optimizing TBI, integrating technology-enhanced learning, and supporting professional development for educators. This research contributes to the growing body of literature on TBI in tertiary language education and underscores its potential for creating transformative learning experiences. The findings provide valuable insights for educators, curriculum designers, and policymakers in enhancing language education practices in the Philippines and beyond.

The aim of this paper is to determine a suitable definition for the concept of fuzzy Hilbert space. In order to achieve this, we firstly focused on various approaches from the already-existent literature. Then we considered another approach to the notion of fuzzy inner product and analysed its properties.

A new model for risk assessment of urban rainstorm disasters, based on information diffusion method and variable fuzzy sets (IDM-VFS) was proposed. In addition, an integrated index system of urban rainstorm risk was established. In the proposed model, IDM was employed to calculate the classification standards of urban rainstorm risk levels, then the VFS was adopted to assess the dangerousness, sensitivity, vulnerability and comprehensive risk of urban rainstorm disasters. In the present study, the urban rainstorm risk of Jiangsu province was evaluated with the proposed model. The results show that Wuxi, Changzhou, Nanjing and Suzhou have higher dangerousness, due to sustained rainfall and strong rainfall intensity in short duration; Wuxi, Changzhou and Nanjing have higher sensitivity because of lower disaster resistance ability; and Wuxi and Suzhou have higher vulnerability because these cities have higher potential losses in face of urban rainstorm disasters. The comprehensive risk zoning map of urban rainstorm shows apparent regional characteristics: the northwestern cities have lower risk than the southern cities. Moreover, most cities of the Jiangsu province are of the moderate urban rainstorm risk level. The results are consistent with the actual situation of Jiangsu province, and the study can provide some decision-making references for the urban rainstorm management.

Close to half (45.4%) of 2.3 million breast cancers (BC) diagnosed in 2020 were from Asia. While the burden of breast cancer has been examined on the level of broad geographic regions, literature on more in-depth coverage of the individual countries and subregions of the Asian continent is lacking. This review examines the breast cancer burden in 47 Asian countries. Breast cancer screening guidelines and risk-based screening initiatives are discussed.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

In the current study, the failure behavior of retrofitted steel structures was studied experimentally and theoretically with steel/CFRP double strap joints (DSJs) under quasi-static tensile loading. A series of DSJs with different bonding lengths are also considered and examined to experimentally assess the effective bond length. To predict the failure load values of the tested specimens, a new stress-based criterion, namely the point stress (PS) criterion is proposed. Although some theoretical predictive modelling for the strength between steel/CFRP joints under various loading conditions has been presented, in this work by using the new proposed approach, one can calculate rapidly and conveniently the failure loads of the steel/CFRP specimens. Furthermore, to assess the validity of the new proposed criterion, further experimental data on steel/CFRP DSJs available in the open literature are predicted using the PS criterion. Finally, it was found that a good agreement exists between the experimental results and the theoretical predictions based on the PS criterion.

More than fifty million people over the age of fifty have osteoporosis issues due to low bone mass, disease contribution, and not exercising continuously. Certain most important risk factors and causes of osteoporosis contain age, gender, hormones, visits to inexperienced doctors, and certain medical conditions. In this manuscript, we diagnosed certain frank operational laws under the availability of complex intuitionistic fuzzy (CIF) information. Furthermore, using the evaluated operational laws, we pioneered the theory of CIF frank weighted averaging (CIFFWA), CIF frank ordered weighted averaging (CIFFOWA), CIF frank hybrid averaging (CIFFHA), CIF frank weighted geometric (CIFFWG), CIF frank ordered weighted geometric (CIFFOWG), CIF frank hybrid geometric (CIFFHG) operators, and described their beneficial and valuable results with certain useful properties. Moreover, we aim to evaluate the most dangerous type of osteoporosis based on their symptoms, causes, and risk factors using diagnosed approaches. Finally, to enhance the worth of the evaluated operators, we compare the diagnosed result with certain prevailing results and illustrated their geometrical representation with the help of MATLAB.

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.

The city administration of China planned numerous metro projects and more metros can hardly avoid undercrossing a bridge. While metro shield construction when undercrossing a bridge (MSCUB) is frequently located in complicated natural and social context, which made the construction process more susceptible to safety accidents. Therefore, it is crucial to look into safety risk of MSCUB. The paper identified the safety risk factors during SSCUR by using literature review and experts’ evaluation, proposed a novel safety risk assessment model by integrating CFA and FER, and then selected a project case to test the validity of the suggested model. Research results show that (a) a safety risk factors list of MSCUB was identified, including 4 first-level safety risks and 37 second-level safety risk factors; (b) the proposed safety risk assessment model can be used to measure the risk values of the overall worksite safety risk, the first-level safety risks and the safety risk factors of MSCUB; (c) environment-type safety risk and personnel-type safety risk have higher risk values when shield construction undercrossing a bridge; (d) when compared to worker-type safety risk, manager-type safety risk is the higher risk value. The research can enrich the theoretic knowledge of MSCUB safety risk assessment and provide references to safety managers for conducting scientific and effective safety management on the construction site when subway undercrossing a river.

Multi-stakeholder based construction projects are subject to various risk factors due to dynamic business environments. These risks affect project activities which indirectly impact construction costs, resulting in delays and poor building quality. So, managing these project risks requires suitable risk mitigation strategies to evaluate and analyse their severity. Hence, risk evaluation and assessment of construction projects is a multi-criteria decision making (MCDM) problem. In present real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D number domain to handle three types of ambiguous evaluations, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D number based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster-Shafer (D-S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D number extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D number is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches (D-COPRAS, D-ARAS, D-MABAC, and D-TOPSIS). Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed for verify the robustness of the proposed method.

Many authors proposed analytical methods for solving fully fuzzy Sylvester matrix equation (FFSME) based on Vec-operator and Kronecker product. However, these methods are restricted to nonnegative fuzzy numbers and cannot be extended to FFSME with near-zero fuzzy numbers. The main intention of this paper is to develop a new numerical method for solving FFSME with near-zero trapezoidal fuzzy numbers that provides a wider scope of trapezoidal fully fuzzy Sylvester matrix equation (TrFFSME) in scientific applications. This numerical method can solve the trapezoidal fully fuzzy Sylvester matrix equation with arbitrary coefficients and find all possible finite arbitrary solutions for the system. In order to obtain all possible fuzzy solutions, the TrFFSME is transferred to a system of non-linear equations based on newly developed arithmetic fuzzy multiplication between trapezoidal fuzzy numbers. The fuzzy solutions to the TrFFSME are obtained by developing a new two-stage algorithm. To illustrate the proposed method numerical example is solved.

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each data with the data of its previous day in historical training time series to generate a new fluctuation trend time series(FTTS). Then, fuzzify the FTTS into fuzzy-fluctuation time series(FFTS) according to the up, equal or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of future is nonlinear, Particle Swarm Optimization (PSO) algorithm is employed to estimate the required parameters. Finally, use the acquired parameters to forecast the future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or such kinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

Background: It is necessary to apply a behavior-based safety (BBS) program to prevent at-risk behavior. An effective BBS program requires the implementation of not only behavioral definitions and a customized critical behavior checklist (CBC) but also observations of behavior, coupled with customized interventions at power plants. Method: In this study, a customized CBC and behavioral definition were developed through a review of five different sites that previously used a CBC. The rules of observation, flow, and target were established to initiate the observations. Customized interventions were selected to increase safe behaviors. CBC scoring was used to evaluate observed safe behaviors for three years. Recognized safe behaviors were evaluated with a questionnaire that included four items each for conformity and participation behaviors and were then analyzed through a factor analysis and a t-test. The questionnaires were conducted three months before and after the implementation of the BBS program. Results: The customized CBC, behavioral definition, and interventions were effective, such that observed safe behaviors and the levels of workers’ recognized safe behaviors increased. Conclusion: The application of the BBS program was found to increase the observed and recognized safe behaviors. Therefore, the program applied to this site can help increase safe behaviors at other identical or similar sites, as well as prevent an accident, which also corresponds with the results of prior studies.

A hybrid fuzzy MADM method is proposed in this paper for evaluating airline service quality. Fuzzy set theory is used since it helps in measuring the ambiguity of concepts associated with human being’s subjective judgment. After reviewing service quality evaluation models especially in airline industry, SSQAI model was adopted as a construct for evaluating airline service quality in Iran. Fuzzy DEMATEL was applied to determine the degree of influence and impact of criteria on each other and extract cause and effect relations between them that helped in ranking criteria based on degree of relationship. Then, ANP network map was constructed based on the relation map generated from Fuzzy DEMATEL analysis. Fuzzy ANP approach assisted in prioritizing criteria based on the need for improvement and enabled in a more accurate measurement in decision making process taking the advantage of using linguistic variables. Fuzzy DEMATEL results demonstrates that expertise, Problem-solving, and conduct has the most influence on other factors and in opposite Valence, Waiting Time, Comfort are the factors which get the most impact from other factors and according to Fuzzy ANP analysis Valence, Convenience, Problem-solving, and Safety&Security are the factors with most priorities that need improvement.

The paper explores the potential of a low-cost advanced video-based technique for the assessment of structural damage induced to buildings by seismic loading. A low-cost high-speed video camera was utilized for motion magnification (MM) processing of footages of a two-story reinforced concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e. in terms of modal parameters) and the structural deformations of the building in the MM videos. The results by MM were compared for method validation to damage assessment obtained by the analyses of conventional accelerometers and high-precision optical markers tracked by a passive 3D motion capture system. Also, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometers were also processed and analyzed by using several stationary and non-stationary techniques with the aim to analyze the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed MM-based procedure provided accurate estimate of the main modal frequency and the damage location through the analysis of modal shapes, which were confirmed by advanced analyses of accelerometric data.

Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data -such as municipality-level records of crop seeding- for mapping purposes implies facing a series of issues like data availability, quality, homogeneity etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using ``good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task.

Water quality assessment in pastoral of Tibetan Plateau, which is water sources for about 40% of world's population and the runoff-yield area of Yellow rivers, is very important. In this paper, Xiahe county which belongs to Tibetan Plateau pastoral was investigated. Six parameters(via, chloride, COD, ammonia nitrogen, nitrate, fluoride, sulfate) were selected to assess the water quality and health degree by using fuzzy comprehensive evaluation methods. The hydrochemical type in surface water was of HCO3--Mg2+-Ca2+ type. The cations and anions in surface water were mainly from weathering and dissolution of carbonate rock. Results showed that the water quality in all 69 sampling sites was all of class Ⅰ. The integrated health degree reached more than 0.85 and the health rate were 100%. Although ammonia nitrogen was regarded as the main contamination factor, but it had a little effect on the entire body of water. Overall, the surface water qualities of most samples in Xiahe County was good condition.

The aim of this 9th expository article is to conclude a study on domination in two fuzzy models, including t−norm fuzzy graphs and fuzzy graphs. All parts are twofold even if we don’t men- tion, directly. I.e., all results depicts some properties about fuzzy graph and t−norm fuzzy graph.

We generalize the concept of a fuzzy metric by introducing its approximating counterpart in order to make it more appropriate for the study of some problems related to words combinatorics. We establish close relations between fuzzy approximating metrics in case of special $t$-norms and approximating parametrized metrics, discuss some relations between fuzzy approximating metrics and fuzzy partial metrics as well as show some possible applications of approximating parametrized metrics in the problems of words combinatorics.

We introduce a new variation on the domination theme which we call vertex domination as reducing waste of time in transportation planning and optimization of transport routes. We determine the vertex domination number $\gamma_v$ for several classes of fuzzy graphs. The bounds is obtained for it. In fuzzy graphs, monotone decreasing property and monotone increasing property are introduced. We prove both of the vizing's conjecture and the Grarier-Khelladi's conjecture are monotone decreasing fuzzy graph property for vertex domination. We obtain Nordhaus-Gaddum (NG) type results for these parameters. The relationship between several classes of operations on fuzzy graphs with the vertex domination number of them is studied. Finally, we discuss about vertex dominating set of a fuzzy tree by using the bridges and $\alpha$-strong edges equivalence.

This Paper intends to introduce the notion of prime ideals of Intuitionistic fuzzy normed Rings and to establish basic properties related to it. It investigates these notions and shown anew Result using intuinistic fuzzy points and non membership function incorparating with t-norm and s-norm to show aome results of fuzzy prime ideal.

This work aimed to develop an easy-to-use smartphone-based electrochemical biosensor to quickly assess coffee blend's total polyphenol (Phs) content at industrial and individual level. The device is based on a commercial carbon-based screen-printed electrode (SPE) modified with multi-walled carbon nanotubes (CNTs) and gold nanoparticles (GNPs). At the same time, the biotransducer, Laccase from Trametes versicolor, TvLac, was immobilized on the sensor surface by using glutaraldehyde (GA) as cross-linking agent. The platform was electrochemically characterized to ascertain the influence of the SPE surface modification on its performance. The working electrode (WE) surface morphology characterization was obtained by scanning electron microscopy (SEM) and Fourier-transform infrared (FT-IR) imaging. All the measurements were carried out with a micro-potentiostat Sensit Smart by PalmSens connected to a smartphone. The developed biosensor provided a sensitivity of 0.12 μA/μM, a linear response ranging from 5 to 70 μM, and a lower detection limit (LOD) of 2.99 μM. Afterwards, the biosensor was tested for quantifying the total Phs content in coffee blends, evaluating the influence of both the variety and the roasting degree. The smartphone-based electrochemical biosensor performance has been validated though the Folin-Ciocâlteu standard method.

The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to be potential intrusions or treat. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behavior are hard to predict as there is no precise boundary differentiat-ing one from another. Here rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach based on rough set theory and intuitionistic fuzzy set theory is pro-posed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of softness properties both rough and fuzzy set theory to deal with uncer-tainty in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. The experimental results with a real world dataset and a syn-thetic dataset show that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29% respectively

Enzymatic polyethylene terephthalate (PET) recycling processes are gaining interest for their low environmental impact, use of mild conditions, and specificity. Furthermore, PET hydrolase enzymes are continuously being discovered and engineered. In this work, we studied a PET hydrolase (PET2), initially characterized as an alkaline thermostable lipase. PET2 was produced in a fusion form with a 6-histidine tag in the N-terminal. The PET2 activity on aromatic terephthalate and new indole-based polyesters was evaluated using polymers in powder form. Compared with IsPETase, an enzyme derived from Ideonella sakaiensis, PET2 showed a lower PET depolymerization yield. However, interestingly, PET2 gave significantly higher polybutylene terephthalate (PBT) and polyhexylene terephthalate (PHT) depolymerization yields. A clear preference was found for aromatic indole-derived polyesters over non-aromatic ones. No activity was detected on Akestra™, an amorphous copolyester with spiroacetal structures. Docking studies suggest that a narrower and more hydrophobic active site reduces its activity on PET but favours its interaction with PBT and PHT. Understanding the enzyme preferences of polymers will contribute to their effective use to depolymerize different types of polyesters.

Background: The aim of this study was to investigate the risk of obstructive sleep apnea (OSA) among ankylosing spondylitis (AS) patients in a nationwide population. Methods: We conducted a nationwide cohort study between 2003 and 2013 using the Taiwan National Health Insurance Research Database. The AS cohort included 2210 patients who were newly diagnosed between 2003 and 2013. Randomly selected non-AS controls were matched at a 1:4 ratio based on age, sex and index date. The endpoint of OSA was occurrence or the end of 2013. Cumulative incidences, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated after adjusting for age, gender, comorbidities and co-medications. Multivariate analyses were performed using Cox proportional hazards model. Due to violation of the proportionality assumption, landmark analysis was conducted to explore the risk of OSA during specific follow-up periods. Results: The adjusted HR (aHR) of OSA for the AS group was 2.826 (95% C.I. = 1.727–4.625) compared to the control group. On landmark analysis, aHR was 7.919 (95% C.I. = 3.169–19.792) for AS group 0–24 months from index date, and decreased to 1.816 (95% C.I. = 0.944–3.494) at ≥ 24 months from index date. On subgroup analyses increased risks of OSA in AS group compared to the control group were found for both males and females (aHRs were 4.533 (95% C.I. = 1.441–14.262) and 2.672 (95% C.I. = 1.522–4.692) for females and males, respectively). On age stratified analysis, there was significant risk only for the 40–59 age group with aHR of 3.913 (95% C.I. = 1.890–8.102). Conclusions: A higher risk of developing OSA was found among newly diagnosed AS cohort during the 12-year follow-up period, especially within 2 years after AS index date and in the 40–59 age group.

Background: This year has seen the emergence of two major crises, a significant increase in frequency and severity of hurricanes and the COVID-19 pandemic. However, little is known as to how each of these two events have impacted the other. A rapid qualitative assessment was conducted to determine the impact of the pandemic on preparedness and response to natural disasters and the impact of past experiences with natural disasters in responding to the pandemic. Methods: Semi-structured interviews were conducted with 26 representatives of 24 different community-based programs in southern Louisiana. Data were analyzed using procedures embedded in the Rapid Assessment Procedure – Informed Community Ethnography methodology, using techniques of immersion and crystallization and focused thematic analysis. Results: The pandemic has impacted the form and function of disaster preparedness, making it harder to plan for evacuations in event of a hurricane. Specific concerns included being able to see people in-person, providing food and other resources to residents who shelter in place, finding volunteers to assist in food distribution and other forms of disaster response, competing for funds to support disaster-related activities, developing new support infrastructures, and focusing on equity in disaster preparedness. However, several strengths based on disaster preparedness experience and capabilities were identified, including providing a framework for how to respond and adapt to COVID and integration of COVID response with their normal disaster preparedness activities. Conclusions: Although prior experience has enabled community-based organizations to respond to the pandemic, the pandemic is also creating new challenges to preparing for and responding to natural disasters.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

The concept of a Social License to Operate (SLO) has become increasingly important in the sustainability literature in recent years. Having its origins in the business discourse of the mining industry with respect to limiting opposition to mining projects, the notion of a social contract above and beyond legal requirements has since become applied across a number of different industries. Despite the concepts adoption confusion exists over the practices and outcomes of SLO, and particularly the nature of engagement. Given this situation it is surprising that not more attention to the role of marketing, and social marketing in particular, in operationalizing the concept. The paper discusses the potential of social marketing to contribute to SLO. Economic, political and social relations are complex in SLO and exchange is intricate in such relational environments. A community-based social marketing orientation is proposed as a means to improve exchange relations and enhance engagement. Seven models of SLO related social marketing models are discussed with community-based social marketing and adaptive co-marketing models being regarded as the most positive for the achievement of an SLO. Potential barriers to adoption of these approaches are noted.

In this paper, we employ fuzzy Sumudu transform for solving system of linear fuzzy differential equations with fuzzy constant coefficients. The system with fuzzy constant coefficients is interpreted under strongly generalized differentiability. For this purpose, new procedures for solving the system are proposed. A numerical example is carried out for solving system adapted from fuzzy radioactive decay model. Conclusion is drawn in the last section and some potential research directions are given.

Two well-known drawbacks in fuzzy clustering are the requirement of assign in advance the number of clusters and random initialization of cluster centers.; the quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of FCM using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.

There is an international concern for reducing energy consumption and protecting the environment in the construction industry. The energy efficiency of a building depends on the thermal resistance of concrete, and utilizing concrete products with elevated thermal resistance can enhance this aspect. This study analyzes the impact of perlite, as an addition, on the physico-mechanical properties of concrete, with the aim of identifying perlite concrete products that will respond to the improvement of the heat transfer resistance of the building envelope. The study examines the effect of replacing different amounts of concrete aggregates with perlite in percentages of 10%, 20%, 30%, and 100%. The results show that substituting sand with perlite improves the thermal performance of concrete, but its mechanical properties are negatively affected. The research carried out highlighted that perlite can be successfully introduced in the production of concrete blocks used for external non-bearing wall panels, the outstanding feature being the reduction of energy consumption in buildings. Overall, the paper identifies an efficient and environmentally friendly way of using perlite in the construction industry, enhancing the energy efficiency of buildings while minimizing the environmental footprint caused by the construction industry.

ABSTRACT Ethiopia is a developing country, where majority of the population lives in rural areas without access to electricity. 83% of the total population of the country use traditional biomass energy as a basic source of energy. In contrast, the country is endowed with sufficient renewable energy resources which can be used as a standalone electric energy supply system for electrifying remote areas of the country. These resources are mainly micro hydropower and wind which can be used individually or the best combination of one another. The application of hybrid renewable energy system has become an important alternative solution for rural electrification program. The Modeling and control of a hybrid PV-Wind-Hydro DG system is also addressed. Dynamic models for the major system components, namely, wind energy conversion system, PV energy conversion system, hydro, inverter, and overall fuzzy logic controller units are developed. Then, a simulation model for the proposed hybrid power system has been developed using MATLAB /Simulink environment. This is done by creating subsystem sets of the major dynamic component models and then assembling into a single aggregate model. The overall power management strategy for coordinating and/or controlling the different energy sources is also presented in the thesis work. Generally there are 800 households with total electric demand of 71.6KW.To satisfy this demand 52%, 35% and 13% is to be contributed from wind/hydro/solar respectively. To use the power economically fuzzy logic controller is used. The controller monitors the demand and the available sources and then switches to the appropriate power supply according to the written rules. Simulations have been carried out to verify the system dynamic performance using a practical load profile and weather data. The result shows that the overall power management strategy is effective and the load demand is balanced. To complete this work, a grid extension from the closest substation has been compared with hybrid system. Cost of the grid extension is estimated based on the data obtained from EEP office. This is done in order to compare the cost of the designed hybrid power system against the cost of grid extension. The result shows that breakeven grid extension distance to be 23.9km which indicates that grid extension is preferable.

The oil and gas sector has recognised structural integrity assessment of ageing platform for prospective life extension as a rising concern, particularly in encountering the randomness of the harsh ocean environments. This condition leads to uncertainty in wave-in-deck load estimates and a high load level being imposed on offshore structures. This emphasises the necessity of enhanced reliability, as failure might result in inaccessibility because of the uncertainties related to long-distance services, such as accuracy of predictions of loads and responses. Even though the established guidelines present a fundamental assessment, additionally, comprehensive rules are required. This paper performed a reliability analysis incorporating practical approaches that can more accurately represent time-dependent structural deterioration. The following two procedures have been adopted by a majority of significant oil and gas operators to monitor the safety and integrity of these structures: a) Ultimate Strength Assessment (USA) method and b) Reliability Design Assessment (ReDA) method. A comparison of these two reliability approaches was performed on selected ageing jacket structures in the region of the Malaysian sea. The comparative findings, namely, reserve strength ratio (RSR) at various years of the return period (RP) and ratio value for risk of failure regarding the probability of failure (POF), provided a check and balance in strengthening confidence in the results. The findings showed that the structural components might safely survive either using the USA and ReDA method in such conditions, as the reliability indexes were determined to be satisfactory compared to allowable values from ISO 19902 design specifications. Therefore, these evaluations were determined to control the risk level of the structure during the remaining of its lifetime and undertake cost-effective inspections or mitigation strategies when necessary.

Indonesia's government anticipates waste problems by managing them through a large number of waste reduction programs. From a governance perspective in Indonesia, it is known that actors from diverse governmental levels and across sectors are involved in waste management, and their involvement largely depends on their institutional goals and problems. Hence, they are expected to coherently collaborate together by developing and implementing sustainable instruments and resources for improving waste management problems. However, it is necessary to understand how supportive and restrictive the governance of waste reduction programs is to encourage successful trash reduction. This study is designed to evaluate and examine the application of Community-Based Solid Waste Management (CBSWM) in Makassar. In this research, The Governance Assessment Tool (GAT) was applied to analyze the governance context of the waste reduction programs in Makassar qualitatively. From the assessment, contexts can be described as either supportive or restrictive of the processes of implementing public policies on waste management. As a result of such an assessment, this study shed light on some opportunities to improve the governance of waste management implementation's impact on reducing waste in Makassar, South Sulawesi, Indonesia. The conclusion of this study depicts that the most important actors are affiliated with the local government and the community. Moreover, this study shows that the assessment of contextual governance is predominantly restrictive.

The term fuzzy refers to things that are unclear or vague. In the real world, we often encounter a situation where we cannot determine whether the state is true or false, and fuzzy logic allows for more flexible reasoning. This is a form of multivalued logic where the truth values ​​of variables can be any real number between 0 and 1, as opposed to Boolean logic where the logical values ​​can only be 0 or 1. Fuzzy logic is therefore a problem-solving technique used to evaluate all available information and thus make the best decisions. When applied to marketing, fuzzy logic allows treating customers in an individual and personalized way, instead of being fully identified within a particular market segment. Fuzzy marketing considers the degree to which a customer belongs to certain segments and subsequently allows them to be targeted with messages that engage them emotionally. To better understand the application and importance of fuzzy logic in marketing strategy, we developed a systematic review of the bibliometric literature (LRSB). It was possible to create a connection between these concepts, marketing and fuzzy logic, to increase the efforts of marketing professionals to achieve competitiveness in the unpredictable business environment.

In this paper, the concept of fuzzy locally convex spaces generated by a family of fuzzy seminorms is introduced. We prove that a Minkowski functional of zero neighborhoods generates a seminorm and finally deduce that the topology generated by the family of seminorms coincides with that of the Minkowski functional.

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of a an assigned output. Like first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the following outputs: mean temperature, max temperature and min temperature, all considered daily. Like second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, ARIMA and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples.

In this article, the concept of fuzzy $(\sigma,\mathcal{Z})$-contractive mapping has been introduced in fuzzy metric spaces which is an improvement over the corresponding concept recently introduced by Shukla et al. [Fuzzy Sets and system. 350 (2018) 85--94]. Thereafter, we utilized our newly introduced concept to prove some existence and uniqueness theorems in $\mathcal{M}$-complete fuzzy metric spaces. Our results extend and generalize the corresponding results of Shukla et al.. Moreover, an example is adopted to exhibit the utility of newly obtained results.

Fermatean fuzzy linguistic (FFL) set theory provides an efficient tool for modeling a higher level of uncertain and imprecise information, which cannot be represented using intuitionistic fuzzy linguistic (IFL)/Pythagorean fuzzy linguistic (PFL) sets. On the other hand, the linguistic scale function is the better way to consider the semantics of the linguistic terms during the evaluation process. In the present paper, we first define some new modified operational laws for Fermatean fuzzy linguistic numbers (FFLNs) based on linguistic scale function (LSF) to overcome the shortcomings of the existing operational laws and prove some important mathematical properties of them. Based on it, the work defines several new aggregation operators (AOs), namely, the FFL-weighted averaging (FFLWA) operator, the FFL-weighted geometric (FFLWG) operator, the FFL-ordered weighted averaging (FFLOWA) operator, the FFL-ordered weighted geometric (FFLOWG) operator, the FFL-hybrid averaging (FFLHA) operator and the FFL-hybrid geometric (FFLHG) operator under FFL environment. Several properties of these AOs are investigated in detail. Further, based on these operators, a multiple attribute group decision-making (MAGDM) approach with FFL information is developed. Finally, to illustrate the effectiveness of the present approach, a real-life supplier selection problem is presented where the evaluation information of the alternatives is given in terms of FFLNs.

In this study, we present the concept of interval-valued fuzzy soft point and then introduce the notions of neighborhood and quasi-neighbourhood of it in interval-valued fuzzy soft topological spaces. Separation axioms in interval-valued fuzzy soft topology, so-called $q$-$T_{i}$ for $ i=0,1,2,3,4 ,$ is introduced and some of its basic properties are also studied.

In this paper, improved matrix Reduction Method is proposed for the solution of fuzzy transportation problem in which all inputs are taken as fuzzy numbers. Since ranking fuzzy number is important tool in decision making, Fuzzy trapezoidal number is converting in to crisp set by using Mean techniques and solved by proposed method for fuzzy transportation problem. We give suitable numerical example for unbalanced and compare the optimal value with other techniques. The Result shows that the optimum profit of transportation problem using proposed technique under robust ranking method is better than the other method. Novelty: The numerical illustration demonstrates that the new projected method for managing the transportation problems on fuzzy algorithms.

Identifying productive, profitable and less risky cropping systems is important for sustaining farm-based livelihoods in the context of climatic uncertainties and market volatility in many developing nations. Reductionist field crop research identifies the best-bet solutions based on treatment replicates at a single point in time, which may not account for price instability under climatic uncertainties and volatile markets. Keeping this in mind, we estimated productivity, profitability and weather-related risk from eleven different rice-based cropping systems (eight existing and three potential systems) in the coastal region of West Bengal, India. Information on the crop management practices, yield and prices of the component crops of these eleven cropping systems, under ’best,’ ’normal’ and ‘worst’ situations (scenario), were collected through a ques-tionnaire survey on50 farms of Gosaba Block, West Bengal, India. Irrespective of the scenarios, the rice-lathyrus systems, followed by rice-onion and rice-lentil systems recorded the lowest rice-equivalent yields and system yields. However, the highest rice-equivalent yields and system yields were recorded for rice-chilli systems, followed by rice-tomato and rice-potato-green gram systems. Per hectare total paid-out cost (TPC) of rice-tomato systems was higher, followed by rice-chilli, rice-potato-green gram and rice-potato systems. However, irrespective of seasonal conditions (best, normal and worst), rice-chilli systems gave a higher net return followed by rice-tomato and rice-potato-green gram systems. The rice-fallow system recorded the lowest value for both parameters. Under the worst seasonal conditions, the rice-onion system gave a negative net return. Under all the scenarios, the highest B:C ratio was observed for rice-chilli, rice-tomato, rice-potato-green gram and rice-potato systems. The cumulative probability distribution of the rice-tomato system showed first-degree stochastic dominance over other systems and the rice-chilli system showed second-degree stochastic dominance over the rest of the systems. Only the rice-onion system had a small chance (< 1%) of a negative net return, while the rest of the cropping systems were highly likely to get a positive net return. Taking productivity, economics, and risk assessment of different rice-based systems into account, rice-vegetable systems, especially rice-tomato and rice-chilli among the existing systems and rice-potato-green gram system among the potential systems, can be recommended for sustainable intensification in these coastal saline tracts of the region. We also discuss additional socio-economic factors explored by in-depth in-terviews with the farmers, which might influence the adoption and upscaling of these cropping systems in the region.

Three kinds of Ni based alloy cladding coatings were prepared on 316L stainless steel at different power. The microstructure of the cladding layer was observed and analyzed by XRD, metallographic microscope and SEM. The hardness of the cladding layer was measured, and the wear resistance of the cladding layer was tested by friction instrument. The results show that the effect of laser cladding is good and the cladding layer has a good metallurgical bonding with the substrate. Different microstructures such as dendritic and equiaxed grains can be observed in the cladding layer. With the increase of laser power, more equiaxed and columnar dendrites can be observed. The phase composition of the cladding layer is mainly composed of γ - Ni solid solution and some intermetallic compounds such as Ni3B, Cr5B3 and Ni17Si3. The results of EDS show that there are some differences in the distribution of C and Si between dendrites. The hardness of the cladding layer is about 600 HV0.2, which is about three times of the substrate (~ 200 HV0.2). Through the analysis of the wear morphology, the substrate wear is serious, there are serious shedding, mainly adhesive wear and abrasive wear. However, the wear of cladding layer is slight, which is abrasive wear, and there are some grooves on the surface.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Recently, new advanced techniques have been proposed to improve the quality of smart grids that use intermittent renewable energy sources. One of the most effective techniques is active filtering, which utilizes a power electronic converter in the smart load. This technique has the potential to enhance the power quality of the supply voltage by minimizing current harmonics, which is crucial. In this article, we propose a new command intelligence system based on Fuzzy PI fractional (fuzzy FOPI) and GA to control the harmonic currents generated by an active filter powered by a wind system.

In fuzzy group decision making problems and fuzzy shortest path problems, the addition and subtraction are the basic problems. For the mixed normal fuzzy numbers and trapezoidal fuzzy numbers, the addition and subtraction operations are approximated by the normal fuzzy numbers in this paper. The behaviors of approximated normal fuzzy numbers are the same as those of the normal distributions from the viewpoint of probability. An application of the addition and subtraction operations of mixed fuzzy numbers to the fuzzy sample mean is also proposed.

This paper has a twofold objective. The first aim is to present a comprehensive bibliographical analysis of journal articles and book chapters on fuzzy-random option pricing (FROP) over the WoS and SCOPUS databases. It follows PRISMA criteria and takes special care of developments in continuous time. Thus, we described the principal findings about the research streams, outlets and authors of this topic and lets us suggest further research. The second contribution is motivated by the fact that the bibliographical revision has identified a lack of developments on equilibrium models on the yield curve. This question motivates extending Vasicek’s yield curve equilibrium model to introduce fuzziness in the parameters that govern interest rate movements (speed of reversion, equilibrium short-term interest rate, and volatility). Likewise, this paper develops an empirical application on the term structure of fixed income public bonds with the highest credit rating in the Euro area.

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a Fuzzy Inference System (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules and one output. The FIS is responsible to monitor the restoration results, grade their validity and choose the one that yields to a better grade. These grades are produced autonomously by analyzing results of Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 um wire probe are compared with the restorations. Main aims are therefore a) Provide unsupervised deblurring for device independent EB measurement; b) Improve the reliability of the process; c) Apply deblurring without knowing the probe size. These, further facilitates the deployment and manufacturing of EB probes and probe independent and accurate EB characterization. It also makes restoration of previously collected EB measurements easier, where the probe sizes are not known or recorded.

This study investigates the cyclic load application impact on fly ash-based geopolymer composites that are reinforced with a low amount of fibre reinforcement. For reinforcement purposes, PVA and steel fibres are used. For testing purposes, four geopolymer composite mixes were made, 3 of which had fibre reinforcement. Simultaneously specimens were tested for shrinkage, static load-induced creep, and cyclic load-induced creep. For static and cyclic creep testing, specimens were loaded with 20% of their strength. For cyclic creep testing, load application and release cycles were seven days long. When each cycle was introduced, the load was added in steps. In 5 minutes, by 25% steps of the necessary load, the specimens were loaded or unloaded. Only plain specimens show that static creep strains are within cyclic creep strains. For all the other specimens, the static load is higher than the cyclic load-induced creep amplitude. Also, 1% PVA fibre-reinforced specimens show the most elastic characteristics under cyclic load, and 1% steel fibre-reinforced specimens appear to be the most resistant to the cyclic load introduction.

Detecting, especially locating local degradations at an incipient stage, is very important for mission-critical high voltage rotating machines. One particular challenge of the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially made defects. These defects include turn-to-turn short, surface creepage, loose coils, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that the FDR can identify and locate both structure and insulation degradations of both shielded and unshielded objects with good sensitivity. This makes the FDR a comprehensive tool for fault diagnosis and aging assessment.

This paper presents an Application Based Active Learning (ABAL) methodology on Power Electronics (PE) and Electric Machines (EM) as a hybrid laboratory course for the undergraduate students to design and implement the real-world engineering problems. The ABAL is a type of active learning which is a branch of Learner-centered teaching (LCT). The DC/DC converter along with the speed control of DC separately excites the motor. In addition, a DC/AC converter is designed to control the speed of an induction motor. The results are then investigated on a hardware platform under the ABAL experimental methodology. This paper also discusses the problem identification selection of the equipment, circuit design, hardware mounting and critical analysis of the results acquired from the hybrid laboratory. The ABAL methodology was evaluated based on student satisfaction, feedback, grades and interest to solve the real-world problem rather than cramming the engineering concepts and fulfill so-called lab routine and tasks

Given a fuzzy normed space $ \left( X,N \right) $‎, ‎we will introduce the notion of fuzzy near best approximation within a relative distance $ \rho \geq 0 $‎. ‎Some basic properties are characterized and also many examples for illustration are presented‎.

The aim of this expository article is to present recent developments in the centuries-old discussion on the interrelations between several types of domination in graphs. However, the novelty even more prominent in the newly discovered simplified presentations of several older results. Domination can be seen as arising from real-world application and extracting classical results as first described by this article.The main part of this article, concerning a new domination and older one, is presented in a narrative that answers two classical questions: (i) To what extend must closing set be dominating? (ii) How strong is the assumption of domination of a closing set? In a addition, we give an overview of the results concerning domination. The problem asks how small can a subset of vertices be and contain no edges or, more generally how can small a subset of vertices be and contain other ones. Our work was as elegant as it was unexpected being a departure from the tried and true methods of this theory that had dominated the field for one fifth a century. This expository article covers all previous definitions. The inability of previous definitions in solving even one case of real-world problems due to the lack of simultaneous attentions to the worthy both of vertices and edges causing us to make the new one. The concept of domination in a variety of graphs models such as crisp, weighted and fuzzy, has been in a spotlight. We turn our attention to sets of vertices in a fuzzy graph that are so close to all vertices, in a variety of ways, and study minimum such sets and their cardinality. A natural way to introduce and motivate our subject is to view it as a real-world problem. In its most elementary form, we consider the problem of reducing waste of time in transport planning. Our goal here is to first describe the previous definitions and the results, and then to provide an overview of the flows ideas in their articles. The final outcome of this article is twofold: (i) Solving the problem of reducing waste of time in transport planning at static state; (ii) Solving and having a gentle discussions on problem of reducing waste of time in transport planning at dynamic state. Finally, we discuss the results concerning holding domination that are independent of fuzzy graphs. We close with a list of currently open problems related to this subject. Most of our exposition assumes only familiarity with basic linear algebra, polynomials, fuzzy graph theory and graph theory.

We propose a new Mamdani fuzzy rule-based system in which the fuzzy sets in the antecedents and consequents are assigned in a discrete set of points and approximated by using the extended inverse fuzzy transforms, whose components are calculated by verifying that the dataset is sufficiently dense with respect to the uniform fuzzy partition. We test our system in the problem of spatial analysis consisting in the evaluation of the liveability of residential housings in all the municipalities of the district of Naples (Italy). Comparisons are done with the results obtained by using trapezoidal fuzzy numbers in the fuzzy rules.

Investment management is a common process and practice used for achieving a desirable investment goal or outcome. Investment assessment should be carried out at various stages of project realization in accordance with capital investment volume. Investment risk management implies the effective control of all procedures and monitoring of risks in all phases of the investment project. Because of the reason that a single indicator in probability calculation of achieving optimal return from the investment does not exist, performing sufficiently reliable estimates of the quality of investments becomes a tedious task. There are many indicators, factors, and criteria required for consideration to reach the effective solution of the investment problem. Unfortunately, the systematic variation of economic situations in the marketplace stipulates the continuous and frequent changes of investment conditions and environment in which the investor should act and operate. Hence, the rules required for providing a reasonable quality of investment projects can be based only on investor’s management strategy and rely on investor’s intuition and practice. The importance of classification in investment management and decision making process is undeniable. The objects to be classified are described using assessments in accordance with various criteria which can be both quantitative and qualitative. With a competent formulation of the investment process, both methods are used in parallel. There exist various decision making approaches for the investment management, and simple additive weighting (SAW) is one of the well-known multi-criteria decision making (MCDM) methods aiming to provide an optimal decision for decision maker when solving various real-life problems, and particularly, investment problem. In this paper, fuzzy simple additive weighting (FSAW) method in group decision making is applied to undertake the capital investment expenditure for purchasing cars with the purpose of renting them to the public. The development of existing FSAW method is accomplished and this process involves the sensibility of outcomes to changes in the rate of fuzziness represented with decisions taken. Eventually, the degree of fuzziness involved in an analysis that directly attempts to model the immanent vagueness and imperfectness in particular precedency judgments made, is determined. A numerical example illustrates the importance and effectiveness of the suggested approach with the aim of ranking alternatives and hence, determining the most preferred alternative in MCDM problem.

The purpose of the study is to control the ratio of programs of different genres when forming the broadcast grid in order to increase and maintain the rating of the channel. In the multichannel environment, television rating control consists of selecting such content, ratings of which are completely restored after advertising. A hybrid approach combining the benefits of semantic training and fuzzy relational equations in simplification of the expert recommendation systems construction is proposed. The problem of retaining the television rating can be attributed to the problems of fuzzy resources control. The increase or decrease trends of the demand and supply are described by primary fuzzy relations. The rule-based solutions of fuzzy relational equations connect significance measures of the primary fuzzy terms. Rules refinement by solving fuzzy relational equations allows avoiding labor-intensive procedures for the generation and selection of expert rules. The solution set generation corresponds to the granulation of the television time, where each solution represents the time slot and the granulated rating of the content. In automated media planning, generation of the weekly TV program in the form of the granular solutions provides the decrease of the time needed for the programming of the channel broadcast grid.

Plant-based diets are increasingly popular for health as well as financial, ethical, and religious reasons. The medical literature clearly demonstrates that plant-based diets can be both nutritionally sufficient and medically beneficial. However, any person on an intentionally restrictive but poorly-informed diet may predispose themselves to clinically-relevant nutritional deficiencies. For persons on a poorly-informed plant-based diet, deficiencies are possible in both macronutrients (protein, essential fatty acids) and micronutrients (vitamin B12, iron, calcium, zinc and vitamin D). Practitioner evaluation of symptomatic patients on a plant-based diet requires special consideration of 7 key nutrient concerns for plant-based diets. This article translates these concerns into 7 practical questions that all practitioners can introduce into their patient assessments and clinical reasoning. Ideally, persons on plant-based diets should be able to answer these 7 questions. Each serves as a heuristic prompt for both clinician and patient attentiveness to a complete diet. As such, these 7 questions support increased patient nutrition knowledge and practitioner capacity to counsel, refer, and appropriately focus clinical resources.

Energy transition is a major structural change in the whole social system, and the energy system must be changed globally to replace fossil fuels. Hydropower is one of the largest renewable energy in the world. However the involuntary resettlees due to the construction of hydropower projects are suffering from being far away from their native land, losing the land cultivated for generations and the houses they live in, and losing the social relationship network based on geography and blood ties. Based on the system evaluation theory of reservoir resettlement and referring to relevant research findings, this paper constructs a comprehensive evaluation index framework for assessing the implementation effect of the PostRelocation Support (PReS) policy. The research region is located in Zhijin County, Bijie City, Guizhou Province, China. Accordingly, a combined method of a structural equation model and a fuzzy comprehensive evaluationary model is used in this paper to analyze the implementation of the PReS policy. The results show that the total score of implementing effects of the PReS policy is 4.4, with dramatic significance. The subindex scores of the resettlers’ family income, living conditions, and production conditions; the local economy; and social stability are 4.3, 4.6, 4.4, 4.6, and 4.3, respectively, with dramatic significance. This paper has analyzed and summarized the successful practices of implementing the PReS policy for reservoir resettlers in three dimensions: poverty alleviation, beautiful home construction and accelerating rural revitalization. Research shows that China's rural reservoir resettlers' PReS policy has been more effective in restoring the livelihoods of reservoir resettlers.