Miracle-dz/UniFAR-FTUs huggingface dataset:
A modiﬁcation of probabilistic hesitant fuzzy sets and its application to multiple criteria decision making

Probabilistic hesitant fuzzy set (PHFS) is a fruitful concept that adds to hesitant fuzzy set (HFS) the term of probability which is able to retain more information than the usual HFS. Here, we demonstrate that the existing deﬁnitions of PHFS are not still reasonable, and therefore, we ﬁrst improve the PHFS deﬁnition. By endowing the set and algebraic operations with a new re-deﬁnition of PHFS, we propose a class of T-norm-based and S-norm-based operations for PHFSs together with a number of aggregation operators. Eventually, on the basis of the new operators, the eﬀectiveness and practicality of re-deﬁned PHFS will be tested using three multiple criteria decision making (MCDM) problems concerning the automotive industry safety evaluation, the evaluation of Chines hospitals and the evaluation of alternatives in an investment company.
Estimating of Wheat Farmers' Willingness to Pay for Groundwater in the City of Ramhormuz

The purpose of this study was estimating of wheat farmers' willingness to pay for groundwater in the city of Ramhormuz. The study is applied research that conducted in the survey method. The population of the study was all Ramhormuz wheat farmers residing in this city (N = 2000), which among sample 320 persons were assessed using Cochran formula and samples selected with some classes. The main tool for collecting data was questionnaire. The results showed that 85% of farmers are willing to pay some amount for using groundwater. Some variables such as area of land under cultivation, the number of components, land ownership, farmers' incomes, getting or not getting a loan, rice cultivation and access to the river, have impact on willing to pay. Education and awareness have a positive impact on likely to pay for using groundwater. The results showed that variable of offering price and number of family members have significant negative effects; and variables of income, education and acreage, getting or not getting loans, land ownership, land area, access to surface water, have a positive and significant effect on people's willingness to pay. Average farmers' willingness to pay for protecting groundwater in Ramhormuz city was estimated in 1160 rials.
University of São Paulo “Luiz de Queiroz” College of Agriculture Identification of genes associated with intramuscular fat deposition and composition in Nellore breed

Identification of genes associated with intramuscular fat deposition and composition in Nellore breed The amount and composition of intramuscular fat (IMF) influence the sensory characteristics, nutritional value of beef and human health. The amount of fatty acid and its composition in beef varies by breed, nutrition, sex, age or carcass finishing level. The fat deposition and composition are determined by many genes that participate directly or indirectly in adipogenesis and lipid metabolism. The selection of animals with fat amount and composition suitable for the consumer is complex due to high cost of measurement, the moderate heritability and polygenic traits (many genes are involved with these traits). In the last decade with a great advance in bovine genomics resulted in the complete genome sequencing and the development of high-density chips of SNPs. This scientific advance jointly with technological improvement allowed the identification of genes responsible for important quantitative traits in cattle. This study aimed to identify and characterize genes associated with the deposition and composition of intramuscular fat in Nellore. A genomewide association study (genomewide association studies, GWAS) was performed to identify genomic regions associated with traits of interest and positional candidate genes. A total RNA sequencing (RNA-Seq) analysis was applied to transcriptome study of Longissimus dorsi muscle. Three hundred and eighty six Nellore steers were used for the evaluation of lipid content and fatty acid profile of LD, and genotyping with high-density chip SNP (SNP800 Illumina BeadChip). A subset of 14 animals, seven animals for each extremes of genomic estimated values (GEBV) were used to RNA-Seq analysis. Twenty-five genomic regions (1 MB window) were associated with the deposition and composition of intramuscular fat, which explained ≥ 1 % of the genetic variance. These regions were identified on chromosomes 2, 3, 6, 7, 8, 9, 10, 11, 12, 17, 26 and 27, many of these have not previously been found in other breeds and in these regions important genes were identified. Genomic regions and genes identified and presented here should be contribute to a better understanding of the genetic control of deposition and fat composition in beef cattle, and can be applied in breeding programs for animals that produce a quality and healthy beef to human consumers.
UvA-DARE ( Digital Academic Repository ) Design of the Balance @ Work project : systematic development , evaluation and implementation of an occupational health guideline aimed at the prevention of weight gain among employees

Background: Occupational health professionals may play an important role in preventive health promotion activities for employees. However, due to a lack of knowledge and evidenceand practice based methods and strategies, interventions are hardly being implemented by occupational physicians to date. The aim of the Balance@Work project is to develop, evaluate, and implement an occupational health guideline aimed at the prevention of weight gain among employees. Methods: Following the guideline development protocol of the Netherlands Society of Occupational Medicine and the Intervention Mapping protocol, the guideline was developed based on literature, interviews with relevant stakeholders, and consensus among an expert group. The guideline consists of an individual and an environmental component. The individual component includes recommendations for occupational physicians on how to promote physical activity and healthy dietary behavior based on principles of motivational interviewing. The environmental component contains an obesogenic environment assessment tool. The guideline is evaluated in a randomised controlled trial among 20 occupational physicians. Occupational physicians in the intervention group apply the guideline to eligible workers during 6 months. Occupational physicians in the control group provide care as usual. Measurements take place at baseline and 6, 12, and 18 months thereafter. Primary outcome measures include waist circumference, daily physical activity and dietary behavior. Secondary outcome measures include sedentary behavior, determinants of behavior change, body weight and body mass index, cardiovascular disease risk profile, and quality of life. Additionally, productivity, absenteeism, and cost-effectiveness are assessed. Discussion: Improving workers' daily physical activity and dietary behavior may prevent weight gain and subsequently improve workers' health, increase productivity, and reduce absenteeism. After an effectand process evaluation the guideline will be adjusted and, after authorisation, published. Together with several implementation aids, the published guideline will be disseminated broadly by the Netherlands Society of Occupational Medicine. Trial Registration: ISRCTN73545254/NTR1190 Published: 14 December 2009 BMC Public Health 2009, 9:461 doi:10.1186/1471-2458-9-461 Received: 20 November 2009 Accepted: 14 December 2009 This article is available from: http://www.biomedcentral. com/1471-2458/9/461 © 2009 Verweij et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Risk Factor Prediction of Heart Disease using Machine Learning Approaches

Heart disease is a widespread condition that impacts a significant number of individuals and continues to be the leading cause of death globally. It encompasses a range of conditions that affect the heart. There are numerous risk factors associated with heart disease, making it crucial to develop accurate, reliable, and practical methods for early diagnosis and effective management. Artificial intelligence techniques and machine learning algorithms have become widely used in healthcare and offer a means to process vast amounts of medical data. Researchers employ those techniques to analyze complex medical data, assisting healthcare professionals in predicting the likelihood of heart disease. In this research paper, regression techniques are investigated to predict risk factors of heart disease by focusing on different attributes related to this disease. For this aim we pretrained some machine learning approaches such as ElasticNet, K-Nearest Neighbor, XG Boost, Support Vector Regression and Random Forest. The study utilizes an existing dataset from the American Centers for Disease Control and Prevention that contains 319795 lignes and 18 attributes. The objective of this research paper is to estimate risk factors encountred on developing heart disease in patients in order to delay this disease as much as possible.
Stability of Carbonates in the System MgO-CO2-H2O

Published free-energy data on carbonates in the system $$MgO-CO_{2}-H_{2}O$$ are largely either suspect or unavailable. This paper is a review and revaluation of information bearing on the stabilities of the carbonates nesquehonite, lansfordite, hydromagnesite, artinite, and magnesite. Whenever possible, several independent methods have been used to establish free-energy values. Free energies of formation from the elements at 25° C. and 1 atm. total pressure have been calculated from data on solubility, synthesis, thermal decomposition, heats of solution, and heats of combustion, as well as from geological occurrences. Selected free-energy values for magnesite and hydromagnesite differ substantially from published values. Stability diagrams based on preferred free-energy data are presented in summary, and appear to be essentially consistent with the low-temperature laboratory and geologic behavior of phases in this system.
On the determination of marine magnetotelluric receiver orientation using land magnetic observatory data

Marine magnetotelluric measurements using “free‐fall’’ instruments without effective compasses suffer from the problem of unknown orientation of the receivers at the seafloor. While past works indicate that marine magnetotelluric orientation of the instruments can be estimated by reference to land deployments of known orientation using the transfer tensor method, there is limited published information on how this is implemented in practice. We document this method and propose a set of new time‐ and frequency‐domain approaches to solve this orientation problem of the seafloor receivers. We test these methodologies in onshore and offshore magnetotelluric data whose orientations are well known and apply these techniques to marine magnetotelluric data with unknown orientation. For the controlled tests, both time‐ and frequency‐domain approaches produce overall comparable results. To investigate the effects of the subsurface structure distribution on the orientation process, a dimensionality analysis of a controlled dataset is carried out. In subsequent analysis using the available disoriented marine magnetotelluric data from offshore Brazil and from the Vassouras magnetic observatory on the mainland for remote referencing, frequency‐domain methods yield approximate orientation angles among themselves with low standard deviation each. Time‐domain results are consistent for most cases but differ from frequency‐domain results for some situations.
Visual Psychophysics and Physiological Optics Multichannel Functional Testing in Normal Subjects , Glaucoma Suspects , and Glaucoma Patients

METHODS. This multicenter study had a prospective and crosssectional design. A total of 186 eyes were included: 42 with glaucoma, 14 glaucoma suspects due to optic nerve characteristics, 25 ocular hypertensives, and 105 normal eyes. All patients performed standard visual fields (Humphrey 24-2) and ATD with eight stimuli configurations: four achromatic (A), two red-green (T), and two blue-yellow (D). To derive main outcome measures, mean sensitivity, mean defect (MD), and pattern standard deviation (PSD) were calculated and compared among groups and types of stimuli with the KruskalWallis test. The percentage of cases outside normal limits (ONL) was calculated.
Fortifications of the Rapallo border and their role as co-creators of space / Rapallo sienos įtvirtinimai ir jų vaidmuo kuriant erdvę

In the period between the two world wars, the latent tensions between the European nations resulted in the construction of defensive positions on the borderlines of major European countries. Under the same conditions, the Italian Kingdom built an extensive fortification line, i.e. the Alpine Wall. The Kingdom of Serbs, Croats, and Slovenes (SHS) replied to it with the Rupnik Line. On the base of the Treaty of Rapallo, a 289-km long demarcation line was built with an exceptional defence infrastructure, which interfered with vast natural areas of exceptional quality that are protected by various nature conservation regimes today. The research presented in this paper is focused on a specific land expropriation. The goal is to understand the historical perspectives and assess the current significance of this fortification system with its impact on the natural environment. The system of spatial assessment of the architectural, urban, regional and spatial planning aspects and landscape interpretation has been developed with the use of spatial planning mapping and other methods. It was tested on the case study of the Žiri Municipality to establish a possible systemic base on which the entirety of the Rapallo border with its defence infrastructures could be mapped and their contemporary role assessed. Santrauka Tarpukario laikotarpiu užslėpta įtampa tarp Europos tautų lėmė gynybinių įtvirtinimų statybas tarp didžiųjų Europos salių teritorijų. Tokiomis aplinkybėmis Italijos karalystė statė ilgą įtvirtinimų liniją, pavadintą Alpių siena. Jugoslavijos karalystė į tai atsakė Rupniko linijos statyba. Ji atsirado remiantis Rappallo sutartimi. Tai 289 km ilgio demarkacinė linija, nužymėta isskirtine gynybine struktūra, įsiterpusia į didžiulius natūralius ypatingos vertės plotus, siuo metu saugomus įvairiais gamtos apsaugos reglamentais. Straipsnyje pateikiamas specifinio gamtos nusavinimo tyrimas. Pristacius istorine raidą, siekiama nustatyti gynybinių įtvirtinimų dabartine verte ir jų poveikį gamtinei aplinkai. Aplinkos vertinimas architektūriniu, urbanistiniu, regioniniu ir erdvės planavimo aspektais ir krastovaizdžio interpretavimas yra atliekamas taikant erdvės planavimo analizės žemėlapiuose ir kitus metodus. Tyrimas patikrintas Žiri savivaldybės atveju, sukūrus siūlomą sistemine baze, kurioje galima nužymėti Rappalo sieną su jos įtvirtinimais ir įvertinti jos siuolaikinį vaidmenį. Reiksminiai žodžiai: Rappalo siena, Rappalo sutartis, Alpių siena, Rupniko linija, gynybiniai įtvirtinimai ir krastovaizdis, paveldo interpretavimas, kartografijos metodas, Žiri savivaldybė, kultūros paveldo potencialas.
Bulletin of Electrical Engineering and Informatics

Received Oct 25, 2020 Revised Jun 14, 2021 Accepted Dec 16, 2021 The internet of things (IoT) and wireless sensor networks (WSNs) are two promising technologies for supporting new services and applications. However, because IoT and WSN entail interaction and transaction of device topology discovery protocols, WSN facing some challenges. Topology discovery (TD) is important for WSN in IoT since sensor nodes (SNs) are the main points of this network. Networking these SN (IoT-devices) has a few difficulties such as energy and bandwidth consumption, and data storage issues resulting from frequent interactions and transactions between devices. Because these challenges cannot be resolved by one solution, we focused on reducing the energy consumption associated with a WSN. We proposed software-defined networking (SDN) model to tackle energy-efficiency issue in a WSN setup by integrating SDN and WSN, which gave rise to a more robust system, software-defined wireless sensor network (SDWSN). In this direction, we proposed an improved fuzzy-logic-based strategy, fuzzy topology discovery protocol (FTDP). It used IT-SDN (an SDN-based WSN framework) to increase the network’s lifepan due to its low energy consumption. The link layer discovery protocol (LLDP) to build the SDN controller’s topology. The system’s performance was presented and compared, demonstrating that with an effective SDWSN discovery policy, energy efficiency is achievable.
Bulletin of Electrical Engineering and Informatics

Received Apr 27, 2022 Revised Jul 31, 2022 Accepted Sep 21, 2022 Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the regionbased convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.
Bulletin of Electrical Engineering and Informatics

Received Jul 7, 2022 Revised Aug 28, 2022 Accepted Sep 13, 2022 Color blindness is an inherited genetic ocular disorder. Acquired or congenital color blindness can become a major cause of problems in everyday life tasks. Contributing to solving the trouble of color blindness is important. Because the problem of color blindness leads to the nondiscrimination of data seen by the infected person. Data visualization can contribute to solving the hassle of color blindness. This paper proposed a new technique using data visualization ideas that may contribute to easy data recognition. Our focus has been on monochromacy color blindness. The results are based on the famous Ishihara data sets measuring color blindness. The efficiency of the results was mathematically proven using equations and images via a website to simulate color blindness.
Resistance of the diffusive boundary layer to salt release from saline sediments to freshwater

The diffusive boundary layer (DBL) is the zone for matter exchange between surface water and aquatic sediments. To elucidate the influence of DBL on salt release from saline sediments to freshwater,two experiments with or without wind blowing were conducted. According to the experiments,a 3.5 cm DBL is formed above the smoothed sediments at a steady wind field and this thickness is greater than other studies. The observed flux of salt through the DBL is 6% larger than the calculated value from Fick’s first law. The results indicate that molecular diffusion is the dominant mechanism for salt transport through the DBL. The presence of DBL suppresses the hydrodynamic enhancement for matter exchange between sediments and overlying water. Therefore,salts in the sediments of a polder reservoir may influence the water quality chronically.
Bulletin of Electrical Engineering and Informatics

Received Aug 3, 2022 Revised Dec 16, 2022 Accepted Jan 16, 2023 This work introduces an efficient load scheduling method for handling the day-to-day power supply needs. At peak load times, due to its instabilitythe power generation system fails and as a measure, the load shedding process is followed. The presented method overcomes this problem by scheduling the load based on necessity. For this load scheduling is handled with an artificial neural network (ANN). For the training purpose the backpropagation (BP) algorithm is used. The whole load essential is the input of the neural network (NN). The power generation of all resources and power losses at the instant of transmission is the NN output. The optimum scheduling of different power sources is important when considering all the available sources. Load scheduling shares the feasibility of entire load and losses. It is well-known as optimal scheduling if the constraints such as availability of power, load requirement, cost and power losses are considered. Training the system using a large number of parameters would be a difficult task. So, finest number of communally independent inputs is selected. The presented method aims to lower the power generation expenditure and formulate the power available on demand without alteration. The network is designed using MATLAB.
Bulletin of Electrical Engineering and Informatics

Received Aug 22, 2021 Revised Sep 4, 2022 Accepted Oct 23, 2022 In this research paper a method for the robust speed control of Indirect Field orient controlled induction motor (IM) is proposed. The quantitative feedback theory (QFT) in implemented to design the controller in order to achieve the desired performance for the closed loop system in the presence of uncertainties and parameter variations. In this research work the QFT based controller is designed for the simplified model of IM. The worst case of uncertainties and all possible parameter variations are taken into consideration. The IM with the controller developed is simulated using the MATLAB/Simulink and the results are analyzed and compared with the conventional proportional integral derivative (PID) controller. The time domain and frequency domain analysis of both controllers were conducted and compared. A study on the nature of electromagnetic torque and control signal is also included to justify the effectiveness of proposed controller. The simulation results verify the superior performance of the proposed robust control method compared to PID controller.
Bulletin of Electrical Engineering and Informatics

Received Jun 19, 2022 Revised Jul 25, 2022 Accepted Sep 30, 2022 This study addressed a system design to detect the presence of military personnel (combatants or non-combatants) and civilians in real-time using the convolutional neural network (CNN) and a new loss function called electrostatic loss. The basis of the proposed electrostatic loss is the triplet loss algorithm. Triplet loss’ input is a triplet image consisting of an anchor image (xa), a positive image (xp), and a negative image (xn). In triplet loss, xn will be moved away from xa but not far from both xa and xp. It is possible to create clusters where the intra-class distance becomes large and does not determine the magnitude and direction of xn displacement. As a result, the convergence condition is more difficult to achieve. Meanwhile, in electrostatic loss, some of these problems are solved by approaching the electrostatic force on charged particles as described in Coulomb's law. With the inception ResNet-v2 128dimensional vectors network within electrostatic loss, the system was able to produce accuracy values of 0.994681, mean average precision (mAP) of 0.994385, R-precision of 0.992908, adjusted mutual information (AMI) of 0.964917, and normalized mutual information (NMI) of 0.965031.