Fuzzy multicomponent stress-strength reliability in presence of partially accelerated life testing under generalized progressive hybrid censoring scheme subject to inverse Weibull model

It typically takes a lot of time to monitor life-testing experiments on a product or material. Units can be tested under harsher conditions than usual, known as accelerated life tests to shorten the testing period. This study's goal is to investigate the issue of partially accelerated life testing that use generalized progressive hybrid censored samples to estimate the stress-strength reliability in the multicomponent case. Also, the fuzziness of the model is considered that gives more sensitive and accurate analyses about the underlying system. Maximum likelihood estimation method under the inverse Weibull distribution and using the generalized progressively hybrid censoring scheme is introduced to obtain an estimator for the fuzzy multicomponent stress-strength reliability. Also, an asymptotic confidence interval is deduced to examine the reliability of the fuzzy multicomponent stress-strength. Simulation study is conducted using maximum likelihood estimates and confidence intervals for the fuzzy multicomponent stress-strength reliability for different values of the parameters and different schemes. A real data application representing the data for the failure times for a certain software model is introduced to obtain the fuzzy multicomponent stress-strength reliability for different schemes.•The fuzzy multicomponent stress-strength reliability is investigated under partially accelerated life testing and the generalized progressively hybrid censored scheme.•An algorithm is introduced to simulate data for the censoring scheme.•A real data application is presented to obtain the fuzzy multicomponent stress-strength reliability at different schemes.

Fuzzy-logic technique for gold mineralization prospecting using Landsat 9 OLI processing and fieldwork data in the Bibemi goldfield, north Cameroon

Identifying potential hydrothermal alteration areas is indeed an essential method for mineral exploration. In this research, we developed an algorithm for the delineation of alteration mineral deposits related to gold mineralization in the Bibemi region using set of criteria derived from Landsat 9 OLI data using false colour composites, band ratios, principal component analysis, spectral angle mapper, and fuzzy-logic overlay methods. The methods used showed iron-oxides, ferrous, and hydroxyl-bearing and carbonate mineral properties related to gold mineralization. The fuzzy overlay map identified regions depending on their mineralization prospective, serving as foundation for prospective mineral deposit evaluation investigation, which was produced by the merging of band ratios and PC's alteration markers labelled very good, and excellent and encompasses 0.8-0.9, 0.9-1.0 respectively. The identified regions fit gold mineralization zones based on their potential as proven by prior and field research. In addition, lineaments analysis showed the presence of three main structural direction impacting the Bibemi region (N-S, NNE-SSW, and ESE-WNW to SSE-NNW), when merged with identified rock formations permits the possible deposition of mineral deposits. The innovative aspect of this research is the integration and processing of Landsat 9 OLI and fieldwork data, which allows for the identification of potentially mineralized rock formations and defining exploration targets.

A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns

BACKGROUND

Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians' traffic behavior pattern by fuzzy clustering algorithm and assess the factors related to higher-risk traffic behavior of pedestrians. Study Design: This study is a secondary methodological study based on the data from a cross-sectional study.

METHODS

The fuzzy c-means (FCM), as a machine learning clustering method, was conducted to identify the pattern of traffic behaviors by collecting data from 600 pedestrians in Urmia, Iran via "the Pedestrian Behavior Questionnaire" (PBQ) and using 5 domains of PBQ. Multiple logistic regression was fitted to identify risk factors of traffic behaviors.

RESULTS

Results revealed two clusters consisting of lower-risk and higher-risk behaviors. The majority of pedestrians (64.33%) were in the lower-risk cluster. Subjects≤33 years old (Odds ratio [OR]=1.92, P<0.001), subjects with≤6 years of education (OR=1.74, P=0.010), males (OR=1.90, P=0.001), unmarried pedestrians (OR=3.61, P=0.007), and users of public transportation (OR=2.01, P=0.002) were more likely to have higher-risk traffic behavior.

CONCLUSION

We identified traffic behavior patterns of Urmia pedestrians with lower-risk and higher-risk behaviors via FCM. The findings from this study would be helpful for policymakers to promote safety measures and train pedestrians.

A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu

COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies.

Multi-objective optimisation of engine characteristics of an RCCI diesel engine powered with Jatropha/1-pentanol blend: a Taguchi-fuzzy approach

Researchers are examining the possibilities for alternative fuel research as a fossil fuel replacement in light of global energy insecurity and other urgent challenges like global warming, severe emissions, and growing industrialization. This research uses 1-pentanol as a low reactivity fuel and Jatropha biodiesel as a high reactivity fuel to explore the reactivity-controlled compression ignition engine characteristics. A water-cooled single-cylinder engine is used in an experiment with varied loads of 25%, 50%, and 75% at a constant speed of 2000 rpm to examine the effects of operational parameters (i.e., (23 bTDC, 25 bTDC, and 27 bTDC) and (400 bar, 500 bar, and 600 bar)). The fuzzy-based Taguchi approach predicts operational parameters, including fuel injection time, fuel injection pressure, and engine load. Utilizing this ideal model, one may increase brake thermal efficiency and braking power while minimizing unburned hydrocarbon and nitrogen oxide emissions. An L20 orthogonal array is used to analyze the effects of various variables on an engine running on B20/1-pentanol fuel, including engine load, fuel injection timing, and fuel injection pressure. Multiple models are generated and verified with the use of experimental findings. Compared to other operating parameters, for reducing oxides of nitrogen, hydrocarbons, and brake-specific energy consumption maximally, engine load of 75%, FIP of 400 bar, and FIT of 23 bTDC are optimal based on the greatest MPCI value of 0.802.

Fuzzy Soft Skills Assessment through Active Learning Sessions

This research aims to present a Fuzzy Expert System with psychologist expertise that seeks to assist professors, researchers and educational institutions in assessing the level of incorporation of students' Soft Skills while attending Active Learning sessions. The difficulties encountered by higher education institutions, researchers and professors in evaluating subjective and behavioral components, such as Soft Skills, was one of the problems that motivated the undertaking of this research. The theoretical framework on which this work is based includes the development and evaluation of Soft Skills in students, some concepts and characteristics about Active Learning and the main attributes and properties of Fuzzy Logic. This research is of an exploratory applied nature, a qualitative and quantitative approach is proposed, in which the methodological triangulation between the bibliographic analysis, the case study and the modeling and implementation of the expert system called Fuzzy Soft Skills Assessment was used to achieve the objective proposed.

Enhanced rainfall prediction performance via hybrid empirical-singular-wavelet-fuzzy approaches

Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objective is to develop a predictive model to enhance daily rainfall prediction accuracy with an extended time horizon. In the literature, various methods for the prediction of daily rainfall data for short lead times are presented. However, due to the complex and random nature of rainfall, in general, they yield inaccurate prediction results. Generically, rainfall predictive models require many physical meteorological variables and consist of challenging mathematical processes that require high computational power. Furthermore, due to the nonlinear and chaotic nature of rainfall, observed raw data typically has to be decomposed into its trend cycle, seasonality, and stochastic components before being fed into the predictive model. The present study proposes a novel singular spectrum analysis (SSA)-based approach for decomposing observed raw data into its hierarchically energetic pertinent features. To this end, in addition to the stand-alone fuzzy logic model, preprocessing methods SSA, empirical mode decomposition (EMD), and commonly used discrete wavelet transform (DWT) are incorporated into the fuzzy models which are named as hybrid SSA-fuzzy, EMD-fuzzy, W-fuzzy models, respectively. In this study, fuzzy, hybrid SSA-fuzzy, EMD-fuzzy, and W-fuzzy models are developed to enhance the daily rainfall prediction accuracy and improve the prediction time span up to 3 days via three (3) stations' data in Turkey. The proposed SSA-fuzzy model is compared with fuzzy, hybrid EMD-fuzzy, and widely used hybrid W-fuzzy models in predicting daily rainfall in three distinctive locations up to a 3-day time horizon. Improved accuracy in predicting daily rainfall is provided by the SSA-fuzzy, W-fuzzy, and EMD-fuzzy models compared to the stand-alone fuzzy model based on mean square error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) model assessment metrics. Specifically, the advocated SSA-fuzzy model is found to be superior in accuracy to hybrid EMD-fuzzy and W-fuzzy models in predicting daily rainfall for all time spans. The results reveal that, with its easy-to-use features, the advocated SSA-fuzzy modeling tool in this study is a promising principled method for its possible future implementations not only in hydrological studies but in water resources and hydraulics engineering and all scientific disciplines where future state space prediction of a vague nature and stochastic dynamical system is important.

Fuzzy model for quantitative assessment of the epidemic risk of African Swine Fever within Australia

African Swine Fever (ASF) has spread rapidly across different continents since 2007 and caused huge biosecurity threats and economic losses. Establishing an effective risk assessment model is of great importance for ASF prevention, especially for those ASF-free countries such as Australia. With a vast territory and an economy heavily relying on primary industry, Australia faces a threat from the spread of ASF. Although ordinary quarantine measures have been well-performed throughout Australia, there is still a need to develop an effective risk assessment model to understand the spread of ASF due to the strong transmission ability of ASF. In this paper, via a comprehensive literature review, and analyzing the transmission factors of ASF, we provide a fuzzy model to assess the epidemic risk of Australian states and territories, under the assumption that ASF has entered Australia. As demonstrated in this work, although the pandemic risk of ASF in Australia is relatively low, there is a risk of irregular and scattered outbreaks, with Victoria (VIC) and New South Wales (NSW) - Australia Capital Territory (NSW-ACT) showed the highest risk. The reliability of this model was also systematically tested by a conjoint analysis model. To our knowledge, this is the first study to comprehensively analyze the ASF epidemic risk in a country using fuzzy modeling. This work can provide an understanding of the risk ASF transmission within Australia based on the fuzzy modeling, the same methodology can also provide insights and useful information for the establishment of fuzzy models to perform the ASF risk assessment for other countries.

An integrated of fuzzy-WASPAS and E-FMEA methods for environmental risk assessment: a case study of petrochemical industry, Iran

Due to the scope and volume of activities, the petrochemical industry has a high potential for risk to humans and the environment. This study aimed to evaluate the environmental risks caused by the ammonia and urea production process. To screen the risks identified in the follow-up phase, the process hazard analysis (PHA) was used. The environmental aspects were also assessed using environmental failure mode and effects analysis (EFMEA). The most significant environmental aspect with a Risk Priority Number (RPN) of 100 was related to CO₂ emissions from the disposal tower. To rank the final aspects, the criteria "severity," "probability of occurrence," "probability of detection," and the "extent of contamination" were first weighed by the fuzzy Shannon entropy method. Then, each aspect was prioritized based on the mentioned criteria and using fuzzy Weighted Aggregated Sum Product Assessment (WASPAS). According to this technique, among the 24 environmental aspects, the highest score (with a value of 0.702) was given to CO₂ emissions from the disposal tower. Finally, suggestions were made to mitigate the risks.

Fuzzy PROMETHEE model for public transport mode choice analysis

The importance of public transportation service quality research is significantly increasing in recent years, it is the key to understanding and analyzing passengers' preferences. Different approaches are utilized to explore users' preferences however, dominantly these apply merely subjective scoring of the attributes and alternatives of the mobility. In this paper, we design a specific model for public transportation mode choice which is capable of integrating subjective scoring with scoring by objective measures such as distance or time. Owing to this purpose, we combine the outranking Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) as a method to evaluate passengers' preferences for tangible and intangible criteria with the fuzzy theory, and the Graphical Analysis for Interactive Aid (GAIA) plane to visualize the interactions between attributes as well as to test the robustness of the results via sensitivity analysis. The contribution of this paper is the constructed integrative method that is less subjective than the well-known models but also keeps the freedom of individual evaluators in expressing their preferences. Moreover, another significant issue of mode choice analysis is the group consideration, which is also refined in the new methodology by taking into account not only the mean of group preferences but also their range. A common characteristic of public surveys, the possible vague responses of the layman pattern is solved with the fuzzy approach to reduce the risk of uncertain scoring. The proposed model acts as a great base for the fuzzy inference system that can facilitate mode choice for passengers within a changing environment. The efficiency of the new methodology is demonstrated through a real-world case study of Budapest city, the obtained results are supporting underground mode service quality and highlighting its impact on citizens' behavior in favor of public transport.

Appraisal of groundwater pollution risk by combining the fuzzy AHP and DRASTIC method in the Burdur Saline Lake Basin, SW Turkey

To ensure sustainable groundwater management, water resources must be protected in terms of quantity and quality. In this context, it is important to reveal the potential of existing groundwater resources to be affected by environmental and/or geogenic pollutants. In the present study, groundwater vulnerability assessment was performed using DRASTIC model by fuzzy AHP and GIS integration for Burdur Saline Lake basin, SW Turkey. In addition, validation and sensitivity analyses were performed in the study. Weight and rating values assigned for DRASTIC parameters and sub-parameters were determined by fuzzy AHP method, and these values were used in GIS analyses. In the validation analysis of the obtained groundwater vulnerability map, NO₃ ion was considered since the agricultural activities are intense in the study area. In addition, sensitivity analyses were performed to determine the most effective parameter. According to the obtained results, 26.43% of the study area was determined as the highly vulnerable areas, while 11.44 and 62.13% were moderate vulnerable and low vulnerable, respectively. Impact of the vadose zone, depth to water table, and net recharge parameters were determined as more effective than other parameters. The nitrate concentrations of the groundwater in the region confirm the vulnerability map obtained by the study. Therefore, it is recommended to take realistic and urgent protection measures aimed at sustainable use of groundwater in highly vulnerable areas.

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy person's sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. With constant rise in the COVID-19 cases, there has been a constant rise in the need of efficient and safe ways to detect an infected individual. With the cases multiplying constantly, the current detecting devices like RT-PCR and fast testing kits have become short in supply. An effectual Covid-19 detection model using devised hybrid Honey Badger Optimization-based Deep Neuro Fuzzy Network (HBO-DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed hybrid HBO algorithm. Accordingly, the developed Hybrid HBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed Hybrid HBO-based DNFN is outpaced than other existing approaches in terms of testing accuracy, sensitivity and specificity of "0.9176, 0.9218 and 0. 9219". All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. When k-fold value is 9, sensitivity of existing techniques and developed JHBO-based DNFN is 0.8982, 0.8816, 0.8938, and 0.9207. The sensitivity of developed approach is improved by means of gaussian filtering model. The specificity of DCNN is 0.9125, BI-AT-GRU is 0.8926, and XGBoost is 0.9014, while developed JHBO-based DNFN is 0.9219 in k-fold value 9.

Action selection in risk assessment with fuzzy Fine-Kinney-based AHP-TOPSIS approach: a case study in gas plant

In this study, the hazards occurring in a medium-sized gas filling facility were defined, and the risk scores of these hazards were determined by the expert team according to the Fine-Kinney risk analysis method. However, since the same risk significance score is obtained in different combinations of scale values in the classical Fine-Kinney risk analysis method and the characteristics/constraints of the company applied in the risk analysis are not taken into account, the hazards were evaluated using fuzzy Fine-Kinney risk analysis, and the most critical hazards were determined. Action plans are defined for critical hazards determined as a result of fuzzy Fine-Kinney risk analysis. Among the actions that require company resources, the action selection was performed with the TOPSIS method, taking into account their relationship with the hazards by integrating the weights, which was calculated with the AHP method, of affected groups. The effect of operating constraints is included in the last step of the study to calculate the final weights. Calculating the results by including the risk-affected groups and company constraints and ranking the actions reveals that the study is an original, objective, and applicable study.

A survey of image encryption for healthcare applications

Recently, medical image encryption has attracted many researchers because of security issues in the communication process. The recent COVID-19 has highlighted the fact that medical images are consistently created and disseminated online, leading to a need for protection from unauthorised utilisation. This paper intends to review the various medical image encryption approaches along with their merits and limitations. It includes a survey, a brief introduction, and the most utilised interesting applications of image encryption. Then, the contributions of reviewed approaches are summarised and compared regarding different technical perspectives. Lastly, we highlight the recent challenges along with several directions of potential research that could fill the gaps in these domains for researchers and developers.

A facilitating framework for a developing country to adopt smart waste management in the context of circular economy

To achieve higher standards of sustainability, the waste management sector now requires the incorporation of circular economy (CE) principles. However, an easy transition toward the particular goal would require the use of smart waste technologies. To achieve the aforementioned goal, this study aims to provide a facilitating framework for the adoption of smart waste management in the context of CE for Pakistan. To help Pakistan transition toward the new paradigm, a total of 16 critical facilitators are evaluated based on five distinctive criteria using a novel fuzzy hybrid multi-criteria decision-making (MCDM) approach. The hybrid MCDM approach includes fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) for allocating weights to the determined criteria; whereas, the fuzzy VIšekriterijumsko kompromisno rangiranje (VIKOR) approach is used to rank the critical facilitators adopted from the secondary literature. The fuzzy approach in both cases is to deal with any kind of uncertainty during the data collection process. Based on the achieved results, the study suggests that before the application of smart waste technologies in the country, Pakistan should first focus on devising regulations that effectively address the mismanagement of waste produced in the country. Also, the industries in the country need to become more responsible and should adopt environmental management systems that foster waste minimization. Lastly, the country in the third phase should focus on the wide application of digitalization both in the streams of ICT and IoT, for collecting, sharing, and receiving waste data. The study further provides policy recommendations to the respective stakeholders that will help the country achieve zero-waste CE.

Lessons learned from the COVID-19 pandemic in planning the future energy systems of developing countries using an integrated MCDM approach in the off-grid areas of Bangladesh

The COVID-19 epidemic is impeding energy development in developing countries and exacerbating the problems associated with energy planning in off-grid locations. To address such complicated decision-making issues and consider scenarios during this long-lasting pandemic, this study proposes a novel integrated MCDM (Multi-Criteria Decision Making) approach using the Delphi based FO-BWM (Fuzzy Optimistic Best-Worst Method), IDOCRIW (Integrated Determination of Objective Criteria Weights) and the Aggregated Weighting Method integrated with the CoCoSo method under different normalization methods based on a case study of the off-grid areas in Bangladesh. The results of Delphi analysis showed that a total of five criteria were agreed upon by the expert panel. After integrating 5 normalization methods with CoCoSo and using three weighting methods separately, a total of 15 MCDM models were constructed. Finally, the 8 sorted MCDM models demonstrate that Solar Home System (SHS) and Mini-Grid systems need to be prioritized, and the criterion Opportunity of Local Funding (OLF) is essential for choosing between SHS and Mini-Grid systems. Sensitivity analysis showed that the proposed method is effective for easing the dilemmas of energy planning in off-grid areas and provides useful insight to address the impacts of future pandemics on energy planning.

Evaluation of green lean production in textile industry: a hybrid fuzzy decision-making framework

Textile industry is an old and effective industry in Iran. However, due to its age and high energy consumption, this industry has low profitability and entrepreneurship. One of the most important problems of the weaving industry is the issue of waste regarding manpower, materials, machinery, and especially energy consumption. Another problem is environmental pollution. In this paper, using a multiple decision-making model for ranking and selecting criteria and sub-criteria, which is presented using the step-wise weight assessment ratio analysis (SWARA) and also by examining several industrial plants on weaving, the final ranking was performed using the fuzzy COPRAS method. According to the final result and using the opinions of experts and reviewing the studied cases, the environmental criterion was more important than other criteria, and also according to the existing sub-criteria, the amount of CO2 production and pH in the process of completion and washing and the types of pollution in the effluent and sewage were more important than other sub-criteria. Also, among the alternatives, company 5 is evaluated as the best alternative.

Fuzzy-based vulnerability assessment of coupled social-ecological systems to multiple environmental hazards and climate change

Climate change and combining related parameters of environmental hazards have left a considerable challenge in assessing social-ecological vulnerability. Here we integrated a fuzzy-based approach in the vulnerability assessment of mangrove social-ecological systems combining environmental parameters, socio-economic, and vegetative components from exposure dimensions, sensitivity and adaptive capacity along the northern coasts of the Persian Gulf and the Gulf of Oman for the first time. This study aims to provide critical information for habitat-scale management strategies and adaptation plans by assessing the vulnerability of mangrove social-ecological systems. This study provides a methodology framework that consists of five steps. Step 1: We combined the fuzzy weighted maps of seven environmental hazards, including tidal range, maximum wind speeds, drought magnitude, maximum temperatures, extreme storm surge, sea-level rise, significant wave height, and social vulnerability. This map combination determined that the computed exposure index is from 1.07 to 4.32 across the study areas, with an increasing trend from the coasts of the Persian Gulf to the Gulf of Oman. Step 2: We integrated the fuzzy weighted maps of four sensitivity variables, including area change, health change, seaward edge retreat, and production potential change. The findings show that the sensitivity index is from 1.40 to 2.64 across the study areas, increasing the trend from the Persian Gulf coast to the Gulf of Oman. Step 3: Besides, we combined the fuzzy weighted maps of three adaptive capacity variables, including the availability of migration areas, recruitment, and local communities' participation in restoration projects and education programs. The result showed that the index value across the study areas varies between 0.087 and 2.38, decreasing the trend from the Persian Gulf coast to the Gulf of Oman. Step 4: Implementing fuzzy hierarchical analysis process to determine the relative weight of variables corresponding to exposure, sensitivity and adaptive capacity. Step 5: The integration of exposure, sensitivity and adaptive capacity and the vulnerability index maps in the study areas showed variation from 0.25 to 5.92, with the vulnerability of mangroves from the west coast of the Persian Gulf (Nayband) decreasing towards Khamir, then increasing to the eastern coasts of the Gulf of Oman (Jask and Gwadar). Overall, the results indicate the importance of the proposed approach to the vulnerability of mangroves at the habitat scale along a coastal area and across environmental gradients of climatic, maritime and socio-economic variables. This study validated the findings based on the ground truth measurements, and high-resolution satellite data incorporated the Consistency Rate (CR) in the Fuzzy Analytic Hierarchy Process (FAHP). The overall accuracy of all classified remote sensing images and maps consistently exceeded 90%, and the CR of the 25 completed questionnaires was <0.1. Finally, this study indicates differences in vulnerability of various habitats, leading to focus conservation completion and rehabilitation and climate change adaptation planning to support the Sustainable Development Goal (SDG)-13 implementation.

Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC.

RESULTS

In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines.

CONCLUSION

Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories

Among natural disasters, flood is increasingly recognized as a serious worldwide concern that causes the most damages in parts of agriculture, fishery, housing, and infrastructure and strongly affects economic and social activities. Universally, there is a requirement to increase our conception of flood vulnerability and to outstretch methods and tools to assess it. Spatial analysis of flood vulnerability is part of non-structural measures to prevent and reduce flood destructive effects. Hence, the current study proposes a methodology for assessing the flood vulnerability in the area of watershed in a severely flooded area of Iran (i.e., Kashkan Watershed). First interdependency analysis among criteria (including population density (PD), livestock density (LD), percentage of farmers and ranchers (PFR), distance to industrial and mining areas (DTIM), distance to tourist and cultural heritage areas (DTTCH), land use, distance to residential areas (DTRe), distance to road (DTR), and distance to stream (DTS)) was conducted using the decision-making trial and evaluation laboratory (DEMATEL) method. Hence, the cause and effect factors and their interaction levels in the whole network were investigated. Then, using the interdependency relationships among criteria, a network structure from flood vulnerability factors to determine their importance of factors was constructed, and the analytical network process (ANP) was applied. Finally, with the aim to overcome ambiguity, reduce uncertainty, and keep the data variability, an appropriate fuzzy membership function was applied to each layer by analyzing the relationship of each layer with flood vulnerability. Importance analysis indicated that land use (0.197), DTS (0.181), PD (0.180), DTRe (0.140), and DTR (0.138) were the most important variables. The flood vulnerability map produced by the integrated method of DEMATEL-ANP-fuzzy showed that about 19.2% of the region has a high to very high flood vulnerability.

An integrated approach for modeling uncertainty in human health risk assessment

A human health risk assessment (HHRA) will not remain simple and straightforward when it involves multiple uncertain input variables. Uncertainties in HHRA result from the unavailability and subjectivity of input variables. Though several studies have performed HHRA, the quantification of uncertainty in HHRA under a situation of data scarcity and the simultaneous application of random and non-random input variables have rarely been reported. The present study proposes an integrated hybrid health risk modeling framework involving the concurrent treatment of random and non-random input variables and estimating the uncertainties linked to the input variables in HHRA. The proposed framework presents the flexibility to classify the input variables into fuzzy and probabilistic categories, based on their data availability and provenience nature. The framework is demonstrated over the Turbhe sanitary landfill in Navi Mumbai, India, where the fate and transport of heavy metals in leachate are investigated through LandSim modeling. The present study considers the LandSim-simulated heavy metal concentration and body weight as a random variable and water intake, exposure duration, frequency, bioavailability, and average time as fuzzy variables. Further, the uncertainties in the non-carcinogenic human health risk have been quantified using Monte Carlo simulations, followed by a comprehensive multivariate sensitivity analysis of the proposed framework. High health risk at Turbhe is estimated for the male and female population. This study presents the first effort to quantify the non-carcinogenic human health risks from leachate-contaminated groundwater considering the health risk input variables as non-deterministic. The proposed framework is generic and applicable to any landfill site and will remain unaltered when integrated health risk assessment and uncertainty assessment are performed for the landfill.

Fuzzy case-based reasoning approach for finding COVID-19 patients priority in hospitals at source shortage period

In this research article, we introduced an algorithm to evaluate COVID-19 patients admission in hospitals at source shortage period. Many researchers have expressed their conclusions from different perspectives on various factors such as spatial changes, climate risks, preparedness, blood type, age and comorbidities that may be contributing to COVID-19 mortality rate. However, as the number of people coming to the hospital for COVID-19 treatment increases, the mortality rate is likely to increase due to the lack of medical facilities. In order to provide medical assistance in this situation, we need to consider not only the extent of the disease impact, but also other important factors. No method has yet been proposed to calculate the priority of patients taking into account all the factors. We have provided a solution to this in this research article. Based on eight key factors, we provide a way to determine priorities. In order to achieve the effectiveness and practicability of the proposed method, we studied individuals with different results on all factors. The sigmoid function helps to easily construct factors at different levels. In addition, the cobweb solution model allows us to see the potential of our proposed algorithm very clearly. Using the method we introduced, it is easier to sort high-risk individuals to low-risk individuals. This will make it easier to deal with problems that arise when the number of patients in hospitals continues to increase. It can reduce the mortality of COVID-19 patients. Medical professionals can be very helpful in making the best decisions.

Digital security vulnerabilities and threats implications for financial institutions deploying digital technology platforms and application: FMEA and FTOPSIS analysis

Digital disruptions have led to the integration of applications, platforms, and infrastructure. They assist in business operations, promoting open digital collaborations, and perhaps even the integration of the Internet of Things (IoTs), Big Data Analytics, and Cloud Computing to support data sourcing, data analytics, and storage synchronously on a single platform. Notwithstanding the benefits derived from digital technology integration (including IoTs, Big Data Analytics, and Cloud Computing), digital vulnerabilities and threats have become a more significant concern for users. We addressed these challenges from an information systems perspective and have noted that more research is needed identifying potential vulnerabilities and threats affecting the integration of IoTs, BDA and CC for data management. We conducted a step-by-step analysis of the potential vulnerabilities and threats affecting the integration of IoTs, Big Data Analytics, and Cloud Computing for data management. We combined multi-dimensional analysis, Failure Mode Effect Analysis, and Fuzzy Technique for Order of Preference by Similarity for Ideal Solution to evaluate and rank the potential vulnerabilities and threats. We surveyed 234 security experts from the banking industry with adequate knowledge in IoTs, Big Data Analytics, and Cloud Computing. Based on the closeness of the coefficients, we determined that insufficient use of backup electric generators, firewall protection failures, and no information security audits are high-ranking vulnerabilities and threats affecting integration. This study is an extension of discussions on the integration of digital applications and platforms for data management and the pervasive vulnerabilities and threats arising from that. A detailed review and classification of these threats and vulnerabilities are vital for sustaining businesses' digital integration.

B-Map: a fuzzy-based model to detect foreign objects in a brain

To cope up with the medical complications, scientists and physicians rely more on digitized historical evidence. It helps them to identify the disease and to develop new drugs and strategies. The authors have designed a model called B-Map. It can detect and segmenting any foreign object in the brain using fuzzy rules. The model can detect objects such as cancer and brain tumor. The proposed work aims at designing a classifier. The classifier would help to detect all possible foreign objects using one application. B-Map has been compared with benchmark algorithms such as K-means and ANN. It was found that the proposed model performs significantly better than the current techniques. Original patients' sample reports are taken from various medical laboratories. The figure numbers are retained as in the paper. The proposed model is able to find the edges and segment different types of foreign objects or one can say unexpected developments. Figure 12 shows the outer edges of a section of a brain MRI. The patient's MRI very clearly shows Hydrocephalus. The same is segmented and shown in Fig. 13. Figure 14 shows a segment of benign development and 15 shows a cancerous development which are again successfully segmented by the proposed model. The data on which testing is done is clinical data of the original patients. As the patient's details and data cannot be shared the author's cannot upload the data in the repository. As soon as the research completes, a benchmark dataset will be created and uploaded in public domain so that researchers can access it.

Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

Breast cancer becomes the second major cause of death among women cancer patients worldwide. Based on research conducted in 2019, there are approximately 250,000 women across the United States diagnosed with invasive breast cancer each year. The prevention of breast cancer remains a challenge in the current world as the growth of breast cancer cells is a multistep process that involves multiple cell types. Early diagnosis and detection of breast cancer are among the greatest approaches to preventing cancer from spreading and increasing the survival rate. For more accurate and fast detection of breast cancer disease, automatic diagnostic methods are applied to conduct the breast cancer diagnosis. This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. This study aims to resolve the limitation of an existing method, ID3 algorithm that unable to classify the continuous-valued data and increase the classification accuracy of the decision tree. FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. FUZZYDBD method, an automatic fuzzy database definition method, would be used to design the fuzzy database for fuzzification of data in the FID3 algorithm. It was used to generate a predefined fuzzy database before the generation of the fuzzy rule base. The fuzzified dataset was applied in FID3 algorithm, which is the fuzzy version of the ID3 algorithm. The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. This study also analysed the results using three breast cancer datasets: WBCD (Original), WDBC (Diagnostic) and Coimbra. Furthermore, the comparison of FID3 algorithm with the existing methods is conducted to verify the proposed method's capability and performance. This study identified that the combination of FID3 algorithm with FUZZYDBD method is reliable, robust and managed to perform well in breast cancer classification.

Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (±7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (±2) compared to 26 (±1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients.

Extracting Mutual Interaction Rules Using Fuzzy Structured Agent-based Model of Tumor-Immune System Interactions

Background:

There are many studies to investigate the effects of each interacting component of tumor-immune system interactions. In all these studies, the distinct effect of each component was investigated. As the interaction of tumor-immune system has feedback and is complex, the alternation of each component may affect other components indirectly.

Objective:

Because of the complexities of tumor-immune system interactions, it is important to determine the mutual behavior of such components. We need a careful observation to extract these mutual interactions. Achieving these observations using experiments is costly and time-consuming.

Material and Methods:

In this experimental and based on mathematical modeling study, to achieve these observations, we presented a fuzzy structured agent-based model of tumor-immune system interactions. In this study, we consider the confronting of the effector cells of the adaptive immune system in the presence of the cytokines of interleukin-2 (IL-2) and transforming growth factor-beta (TGF-β) as a fuzzy structured model. Using the experimental data of murine models of B16F10 cell line of melanoma cancer cells, we optimized the parameters of the model.

Results:

Using the output of this model, we determined the rules which could occur. As we optimized the parameters of the model using escape state of the tumor and then the rules which we obtained, are the rules of tumor escape.

Conclusion:

The results showed that using fuzzy structured agent-based model, we are able to show different output of the tumor-immune system interactions, which are caused by the stochastic behavior of each cell. But different output of the model just follow the predetermined behavior, and using this behavior, we can achieve the rules of interactions.

An exploration of psychological symptom-based phenotyping of adult cochlear implant users with and without tinnitus using a machine learning approach

The identification of phenotypes within populations with troublesome tinnitus is an important step towards individualizing tinnitus treatments to achieve optimal outcomes. However, previous application of clustering algorithms has called into question the existence of distinct tinnitus-related phenotypes. In this study, we attempted to characterize patients' symptom-based phenotypes as subpopulations in a Gaussian mixture model (GMM), and subsequently performed a comparison with tinnitus reporting. We were able to effectively evaluate the statistical models using cross-validation to establish the number of phenotypes in the cohort, or a lack thereof. We examined a cohort of adult cochlear implant (CI) users, a patient group for which a relation between psychological symptoms (anxiety, depression, or insomnia) and trouble tinnitus has previously been shown. Accordingly, individual item scores on the Hospital Anxiety and Depression Scale (HADS; 14 items) and the Insomnia Severity Index (ISI; 7 items) were selected as features for training the GMM. The resulting model indicated four symptom-based subpopulations, some primarily linked to one major symptom (e.g., anxiety), and others linked to varying severity across all three symptoms. The presence of tinnitus was self-reported and tinnitus-related handicap was characterized using the Tinnitus Handicap Inventory. Specific symptom profiles were found to be significantly associated with CI users' tinnitus characteristics. GMMs are a promising machine learning tool for identifying psychological symptom-based phenotypes, which may be relevant to determining appropriate tinnitus treatment.

Characteristic analysis and fuzzy simulation of falls-from-height mechanics, and case studies

In this paper, methods for scientifically inferring the causes of the falls-from-height accidents, that is, the initial fall postures, and reconstructing the fall accident are presented. For this purpose, the general types of fall were subdivided into eight, and the characteristics of each fall type were analyzed. An initial posture estimation tool based on Fuzzy method was developed utilizing the massive amount of quantitative database that was constructed by repeating the simulation program for all types of falls. In addition, the initial conditions for each type were standardized with the experimental results and simulation data to reconstruct the fall behavior using the estimated fall accident type. The results of four carefully selected case studies were presented to verify the reliability and practicality of the developed fall analysis program and the reconstruction method.

Automatic classification of gait patterns in children with cerebral palsy using fuzzy clustering method

BACKGROUND

Subjective classification of gait pattern in children with cerebral palsy depends on the assessor's experience, while mathematical methods produce virtual groups with no clinical interpretation.

METHODS

In a retrospective study, gait data from 66 children (132 limbs) with a mean age of 9.6 (SD 3.7) years with cerebral palsy and no history of surgery or botulinum toxin injection were reviewed. The gait pattern of each limb was classified in four groups according to Rodda using three methods: 1) a team of experts subjectively assigning a gait pattern, 2) using the plantarflexor-knee extension couple index introduced by Sangeux et al., and 3) employing a fuzzy algorithm to translate the experiences of experts into objective rules and execute a clustering tool. To define fuzzy repeated-measures, 75% of the members in each group were used, and the remaining were used for validation. Eight parameters were objectively extracted from kinematic data for each group and compared using repeated measure ANOVA and post-hoc analysis was performed. Finally, the results of the clustering of the latter two methods were compared to the subjective method.

FINDINGS

The plantarflexor-knee extension couple index achieved 86% accuracy while the fuzzy system yielded a 98% accuracy. The most substantial errors occurred between jump and apparent in both methods.

INTERPRETATION

The presented method is a fast, reliable, and objective fuzzy clustering system to classify gait patterns in cerebral palsy, which produces clinically-relevant results. It can provide a universal common language for researchers.

Planar Cell Polarity Effector Proteins Inturned and Fuzzy Form a Rab23 GEF Complex

A subset of Rab GTPases have been implicated in cilium formation in cultured mammalian cells [1-6]. Rab11 and Rab8, together with their GDP-GTP exchange factors (GEFs), TRAPP-II and Rabin8, promote recruitment of the ciliary vesicle to the mother centriole and its subsequent maturation, docking, and fusion with the cell surface [2-5]. Rab23 has been linked to cilium formation and membrane trafficking at mature cilia [1, 7, 8]; however, the identity of the GEF pathway activating Rab23, a member of the Rab7 subfamily of Rabs, remains unclear. Longin-domain-containing complexes have been shown to act as GEFs for Rab7 subfamily GTPases [9-12]. Here, we show that Inturned and Fuzzy, proteins previously implicated as planar cell polarity (PCP) effectors and in developmentally regulated cilium formation [13, 14], contain multiple longin domains characteristic of the Mon1-Ccz1 family of Rab7 GEFs and form a specific Rab23 GEF complex. In flies, loss of Rab23 function gave rise to defects in planar-polarized trichome formation consistent with this biochemical relationship. In cultured human and mouse cells, Inturned and Fuzzy localized to the basal body and proximal region of cilia, and cilium formation was compromised by depletion of either Inturned or Fuzzy. Cilium formation arrested after docking of the ciliary vesicle to the mother centriole but prior to axoneme elongation and fusion of the ciliary vesicle and plasma membrane. These findings extend the family of longin domain GEFs and define a molecular activity linking Rab23-regulated membrane traffic to cilia and planar cell polarity.

Wastewater treatment in electrocoagulation systems: investigation of the impact of temperature using a fuzzy logic control algorithm

In an electrocoagulation process, controlling various parameters, such as temperature, pH, and conductivity, increases the performance of the process. In this study, fuzzy logic algorithms were used to control the temperature of the electrocoagulation system for the removal of pollutants from textile wastewater. In the experimental part, we used a reactor with a cooling jacket to control the temperature, and the flow rate of the cooling water was a variable that we could manipulate. Also, we investigated the use of a single-variable fuzzy control process and a multivariable fuzzy control process to control the dynamic behavior of the system. In the single variable control process, the effect of temperature was investigated at chosen original temperatures, i.e., 17.2 °C, 20 °C, and 23 °C. In the multivariable control, the temperature-pH and temperature-conductivity pairs were controlled separately in different processes. When the removal efficiencies were determined for the two control approaches, it was observed that the temperature-pH control process outperformed the temperature-conductivity control process, and its removal efficiencies for COD, color, and turbidity were about 74.6%, 85.2%, and 91.0%, respectively. Thus, the results obtained from this study will be useful for other investigators in the field. Graphical abstract .

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with distributed higher-order activation and match functions and a dual vigilance mechanism. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype cluster representations, retrieve arbitrarily-shaped clusters, and reduce category proliferation. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: pre-processing using visual assessment of cluster tendency (VAT) or post-processing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter also works for online learning. Experimental results in online mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of density-based spatial clustering of applications with noise (DBSCAN), single linkage hierarchical agglomerative clustering (SL-HAC), and k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm's simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications.

Data on learners emotional states, mental responses and fuzzy learning flows during interaction with learning environment

The emotional state of the learner is an important factor that must be taken into consideration during evaluating learning process and managing learning flows in computer based learning environments. This factor has a significant impact on the process of interaction between the learner and the learning environment. Enriching this type of interaction make the learning flow more dynamic based on emotional and mental responses of the learners. This approach can manage various learning flows based on learner's capabilities which lead to enhance the learning process outcome. This article provides data on learners' emotional states during their interaction with learning environment and other data that describe their learning activities and learning flows. The learning activities data is a combination of data that represents summary of learners' emotional states and data that represents the mental responses per learning session. All of emotional states data and mental responses data are used to provide the next learning level for each learner using fuzzy rules. The datasets are hosted in the Mendeley Dataset Repository (Megahed, 2019).

Fuzzy based algorithms to predict MicroRNA regulated protein interaction pathways and ranking estimation in Arabidopsis thaliana

In living organisms, the MicroRNAs act as an important role by controlling regulatory mechanisms, and likely manipulating the output of numerous protein-coding genes. Several computational databases, algorithms and tools have been developed to discover the miRNA target genes. But, the existing methods obtain poorer results in identification of miRNA target gene. Hence in this research work, integrated prediction scores is used to identify the microRNA target interactions and hybrid fuzzy algorithms are used to make final predictions. The proposed algorithms such as Fuzzy, Fuzzy + Support Vector Machine (SVM) and Fuzzy + SVM + Random Forest (RF) are used to conduct prediction by majority voting and it is compared with the existing techniques such as SVM, RF and Neural Network (NN) to demonstrate the performance of the proposed algorithms. Furthermore, the ranking features are estimated using the Arabidopsis thaliana microRNA sequence. From the experimental results, it is inferred that the proposed Fuzzy + SVM + RF algorithm performs superior than the existing ones.

The data on the effective qualifications of teachers in medical sciences: An application of combined fuzzy AHP and fuzzy TOPSIS methods

In this data article, hybrid fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were used to prioritize the effective qualifications of teachers of medical courses at university from the viewpoint of students of allied medicine school in Tehran University of Medical Sciences in 2013–2014. To obtain data, 200 students of allied medicine school of Tehran University of Medical Sciences were selected using random sampling method, and surveyed according to Cochran׳s formula. Data collection tools were two research-based questionnaires divided to technical, professional and individual parts. Content validity was approved by the experts. Reliability was confirmed by calculating the Cronbach׳s alpha (α=0, 85) in order to measure the degree of internal cohesion.

An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multi-objective fuzzy-based genetic algorithm

The present work studies the effect of butanol in thumba (Citrullus colocynthis) biodiesel in an IDI CI engine at varying percentages of 5 and 10% in 15 and 10% thumba biodiesel respectively with 80% diesel in each blend. Another blend was introduced with 80% diesel and 20% biodiesel without any additive. The experiment was conducted in a single cylinder four-stroke IDI CI engine at 1500 rpm varying from 25% to full-load (100%) conditions. The results showed diesel with less bio diesel and higher butanol in percentage gives good performance and emission compared to diesel at higher loads. Blend containing 10% bio diesel, 10% butanol, and 80% diesel (D80B10Bu10) showed higher cylinder pressure, heat release rate, BThE, and less NO_x. Biodiesels gave less UHC, CO emissions. In this work, multi-objective fuzzy-based genetic algorithm was introduced for the best fit result. Four parameters were used for optimization (BSFC, BThE, CO, NO_x). The result from genetic algorithm was taken for validation and the optimized result was found adequate after validation.

Characterization of Dynamic IDP Complexes by NMR Spectroscopy

NMR spectroscopy has proven to be a key method for studying intrinsically disordered proteins (IDPs). Nonetheless, traditional NMR methods developed for solving structures of ordered protein complexes are insufficient for the full characterization of dynamic IDP complexes, where the energy landscape is broader and more rugged. Furthermore, due to their high sensitivity to environmental changes, NMR studies of IDP complexes must be conducted with extra care and the observed NMR parameters thoroughly evaluated to enable disentanglement of binding events from ensemble distribution changes. In this chapter, written for the non-NMR expert, we start out by outlining sample preparation for IDP complexes, guide through the recording and evaluation of diagnostic ¹H,¹⁵N-HSQC spectra, and delineate more sophisticated NMR strategies to follow for the particular type of complex. The most relevant experiments are then described in terms of aims, needs, pitfalls, analysis, and expected outcomes, with references to recent examples.

A risk-based approach for developing standards for irrigation with reclaimed water

A generalised quantitative risk assessment (QRA) is developed to assess the potential harm to human health resulting from irrigation with reclaimed water. The QRA is conducted as a backward calculation starting from a pre-defined acceptable risk level at the receptor point (defined as an annual infection risk of 10^-4 for pathogens and by reference doses (RfD) for chemical hazards) and results in an estimate of the corresponding acceptable concentration levels of the given hazards in the effluent. In this way the QRA is designed to inform the level of water treatment required to achieve an acceptable risk level and help establish reclaimed water quality standards. The QRA considers the exposure of human receptors to microbial and chemical hazards in the effluent through various exposure pathways and routes depending on the specific irrigation scenario. By considering multiple pathways and routes, a number of key aspects relevant to estimating human exposure to recycled water can be accounted for, including irrigation and crop handling practices (e.g., non-edible vs edible, spray vs. drip, withholding time) and volumes consumed (directly vs indirectly). The QRA relies on a large number of inputs, many of which were found to be highly uncertain. A possibilistic approach, based on fuzzy set theory, was used to propagate the uncertain input values through the QRA model to estimate the possible range of hazard concentrations that are deemed acceptable/safe for reclaimed water irrigation. Two scenarios were considered: amenity irrigation and irrigation of ready-to-eat food crops, and calculations were carried out for six example hazards (norovirus, Cryptosporidium, cadmium, lead, PCB118 and naphthalene) and using UK-specific input values. The human health risks associated with using reclaimed water for amenity irrigation were overall deemed low, i.e. the calculated acceptable concentration levels for most of the selected hazards were generally far greater than levels typically measured in effluent from wastewater treatment plants; however the predicted acceptable concentration levels for norovirus and Cryptosporidium suggested that disinfection by UV may be required before use. It was found that stricter concentration standards were required for hazards that are more strongly bound to soil and/or are more toxic/infectious. It was also found that measures that reduce the amount of effluent directly ingested by the receptor would significantly reduce the risks (by up to 2 orders of magnitude for the two pathogens). The results for the food crop irrigation scenario showed that stricter concentration standards are required to ensure the effluent is safe to use. For pathogens, the dominant exposure route was found to be ingestion of effluent captured on the surface of the crops indicating that risks could be significantly reduced by restricting irrigation to the non-edible parts of the crop. The results also showed that the exposure to some organic compounds and heavy metals through plant uptake and attached soil particles could be high and possibly pose unacceptable risk to human health. For both scenarios, we show that the predicted acceptable concentration levels are associated with large uncertainty and discuss the implications this has for defining quality standards and how the uncertainty can be reduced.

The use of multi-criteria decision making models in evaluating anesthesia method options in circumcision surgery

Background

Determining the most suitable anesthesia method for circumcision surgery plays a fundamental role in pediatric surgery. This study is aimed to present pediatric surgeons’ perspective on the relative importance of the criteria for selecting anesthesia method for circumcision surgery by utilizing the multi-criteria decision making methods.

Methods

Fuzzy set theory offers a useful tool for transforming linguistic terms into numerical assessments. Since the evaluation of anesthesia methods requires linguistic terms, we utilize the fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both mathematical decision-making methods are originated from individual judgements for qualitative factors utilizing the pair-wise comparison matrix. Our model uses four main criteria, eight sub-criteria as well as three alternatives. To assess the relative priorities, an online questionnaire was completed by three experts, pediatric surgeons, who had experience with circumcision surgery.

Results

Discussion of the results with the experts indicates that time-related factors are the most important criteria, followed by psychology, convenience and duration. Moreover, general anesthesia with penile block for circumcision surgery is the preferred choice of anesthesia compared to general anesthesia without penile block, which has a greater priority compared to local anesthesia under the discussed main-criteria and sub-criteria.

Conclusions

The results presented in this study highlight the need to integrate surgeons’ criteria into the decision making process for selecting anesthesia methods. This is the first study in which multi-criteria decision making tools, specifically fuzzy AHP and fuzzy TOPSIS, are used to evaluate anesthesia methods for a pediatric surgical procedure.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0409-5) contains supplementary material, which is available to authorized users.

Health, safety and environmental unit performance assessment model under uncertainty (case study: steel industry)

Performance assessment is a critical objective of management systems. As a result of the non-deterministic and qualitative nature of performance indicators, assessments are likely to be influenced by evaluators' personal judgments. Furthermore, in developing countries, performance assessments by the Health, Safety and Environment (HSE) department are based solely on the number of accidents. A questionnaire is used to conduct the study in one of the largest steel production companies in Iran. With respect to health, safety, and environment, the results revealed that control of disease, fire hazards, and air pollution are of paramount importance, with coefficients of 0.057, 0.062, and 0.054, respectively. Furthermore, health and environment indicators were found to be the most common causes of poor performance. Finally, it was shown that HSE management systems can affect the majority of performance safety indicators in the short run, whereas health and environment indicators require longer periods of time. The objective of this study is to present an HSE-MS unit performance assessment model in steel industries. Moreover, we seek to answer the following question: what are the factors that affect HSE unit system in the steel industry? Also, for each factor, the extent of impact on the performance of the HSE management system in the organization is determined.

Automatic Colorectal Polyp Detection in Colonoscopy Video Frames

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

Estimating the DNA strand breakage using a fuzzy inference system and agarose gel electrophoresis, a case study with toothed carp Aphanius sophiae exposed to cypermethrin

The DNA breakage has been widely used in ecotoxicological studies to investigate effects of pesticides in fishes. The present study used a fuzzy inference system to quantify the breakage of DNA double strand in Aphanius sophiae exposed to the cypermethrin. The specimens were adapted to different temperatures and salinity for 14 days and then exposed to cypermethrin. DNA of each specimens were extracted, electrophoresed and photographed. A fuzzy system with three input variables and 27 rules were defined. The pixel value curve of DNA on each gel lane was obtained using ImageJ. The DNA breakage was quantified using the pixel value curve and fuzzy system. The defuzzified values were analyzed using a three-way analysis of variance. Cypermethrin had significant effects on DNA breakage. Fuzzy inference systems can be used as a tool to quantify the breakage of double strand DNA. DNA double strand of the gill of A. sophiae is sensitive enough to be used to detect cypermethrin in surface waters in concentrations much lower than those reported in previous studies.

Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method.

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals

This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

Unity power factor converter based on a fuzzy controller and predictive input current

This paper proposes analysis and control of a single-phase power factor corrector (PFC). The proposed control is capable of achieving a unity power factor for each DC link voltage or load fluctuation. The method under study is composed of two intelligent approaches, a fuzzy logic controller to ensure an output voltage at a suitable value and predictive current control. The fuzzy controller is used with minimum rules to attain a low cost. The method is verified and discussed through simulation on the MATLAB/Simulink platform. It presents high dynamic performance under various parameter changes. Moreover, in order to examine and evaluate the method in real-time, a test bench is built using dSPACE 1104. The implantation of the proposed method is very easy and flexible and allows for operation under parameter variations. Additionally, the obtained results are very significant.

A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes

We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.

Efficient management design for swimming exercise treatment

Exercise-mediated physical treatment has attracted much recent interest. In particular, swimming is a representative exercise treatment method recommended for patients experiencing muscular and cardiovascular diseases. The present study sought to design a swimming-based exercise treatment management system. A survey questionnaire was completed by participants to assess the prevalence of muscular and cardiovascular diseases among adult males and females participating in swimming programs at sport centers in metropolitan regions of country. Using the Fuzzy Analytic Hierarchy Process (AHP) technique, weighted values of indices were determined, to maximize participant clarity. A patient management system model was devised using information technology. The favorable results are evidence of the validity of this approach. Additionally, the swimming-based exercise management system can be supplemented together with analyses of weighted values considering connectivity between established indices.