Values Of Attributes Research Articles

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, for the miss-valued attributes, we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes. However, in order to ensure the model optimality, we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers. The preprocessing step led to 161 out of 279 attributes (features). Thereafter, a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms. In short, our study comprises three main blocks: (1) sensing data and preprocessing; (2) feature queuing, selection, and extraction; and (3) the predictive model. Our proposed method improves classification performance in terms of accuracy, F1 measure, recall, and precision when compared to state-of-the-art techniques. It achieves 98.5% accuracy for binary class mode and 98.9% accuracy for categorized class mode.

Exploring Preference Heterogeneity and Willingness to Pay for Tourist Facility Attributes in Kenyir Lake, Malaysia

Natural environments with great recreational value can potentially support tourism, conserve natural areas and prevent environmental damage. However, visiting recreational destinations depends on several motivations related to visitors’ preferences. Therefore, this study aims to identify the heterogeneity of Kenyir Lake, Malaysia, visitors’ preferences and their willingness to pay (WTP) for tourist facility attributes. The choice experiment method has been widely used for economic valuation, and the latent class model can capture the heterogeneity of preferences by characterising respondents in various groups of preferences. The outcome of this study revealed heterogeneous preferences in two classes. The first class represented 91.3% and the second class represented 8.7% of respondents. Moreover, this study’s respondents showed positive WTP values for most attributes. However, the most preferred attribute was superior toilet service, with a WTP estimation of RM7.63 in Class 1. While in Class 2, the most preferred attribute was new jetty construction with a WTP estimation of RM1.99. The findings of this study can assist policymakers in implementing more effective policies as it has provided detailed information about visitors’ preferences for tourist facility attributes in Malaysia.

Stochastic decision tree acceptability analysis with uncertain state probability

In a fast-changing environment, state in the future is difficult to predict. Traditional approaches are unable to support decision-makers to find out optimal alternative effectively when the probability of future’s environmental state is unknown or uncertain. In this study, we propose a stochastic decision tree acceptability analysis (SDTAA), which aims to manage this decision-making problem effectively. In SDTAA, state probability space with random distribution is utilized to capture unknown or uncertain state probabilities and stochastic values or ordinal values are used to model uncertain attributes values. Then, by computing rank acceptability, holistic expected value and value variance of each alternative, SDTAA can help decision makers find the optimal alternative effectively when state probability is uncertain, unknown or missing. In addition, Monte Carlo simulation based algorithms are proposed to calculate the rank acceptability, holistic expected value and value variance. A numerical example is presented to illustrate the SDTAA method.

Effect of integration of inorganic and organic sources of fertilizers on the yield and economics of rice cultivation var. CR1009

The complimentary role of organics as supplements to chemical fertilizers is important for keeping the soil healthy in order to harness the potential yield in rice. Therefore, a field experiment was carried out during the cropping year of 2020-2021 to ascertain the impact of inorganic and organic source fertilizers integration on rice production's yield and its economics in Cauvery deltaic region of Tamil Nadu. The experiment was laid out in randomized block design with three replications. The experiment comprised of eight treatments viz., T1 - RDF + 12.5 t FYM ( Farmer's practice), T2 - Vermicompost @ 5 t/ha + RDF, T3 - Coirpith compost @ 5 t/ha + RDF, T4 - Pressmud compost @ 5 t/ha + RDF, T5 - Poultry manure compost @ 5 t/ha + RDF, T6 - Goat manure compost @ 5 t/ha + RDF, T7 - Bone sludge compost @ 5 t/ha + RDF and T8 - Sewage sludge compost @ 5 t/ha + RDF was applied to see the effect of integration of both organic and inorganic sources of fertilizers on the yield and economics of rice (CR1009) cultivation. The present study's findings showed that the integrative application of vermicompost @ 5t ha-1 along with 100 per cent recommended dose of fertilizers (T2) excelled all treatments and gave significantly increased grain yield of 5792 kg ha-¹ with a value of 37 per cent over control and benefit-cost ratio of 3.45. The integrated application of Vermicompost @ 5tha-1 along with 100 per cent recommended dose of NPK registered the highest values in yield attributes, yield and economic returns in rice.

Effect of Nutrient Management Options on Production and Profitability of French Bean (Phaseolus vulgaris L.) in Acid Soil of Arunachal Pradesh

The present study was designed for the standardization of soil nutrient management in increasing the production and profitability of French bean (Phaseolus vulgaris L.) in acid soils of Arunachal Pradesh. A field experiment was carried out in randomized block design with three replications during the rabi season (October–January 2018–2021). The treatment consisted of two French bean cultivars (selection-9 and changlabi local) and 10 nutrient levels [control, vermicompost (Vc), vermicompost+lime (Vc+lime), 50% RDF (Recommended dose of fertilizer), 50% RDF+lime, 50% RDF+Vc, 75% RDF, 75% RDF+lime, 75% RDF+Vc, 100% RDF]. The doses of vermicompost, lime and RDF were 2.5 t ha-1, 400 kg ha-1 and 50-50-50 NPK kg ha-1. The results observed among the soil nutrient management, treatment 75% RDF+Vc recorded minimum values of 50% days to flowering (41.7 DOS) and maximum values of pod length (14.8 cm), the number of pod plant-1 (7.17) and yield (4,654 kg ha-1). This treatment also recorded maximum gross and production efficiency. The B:C ratio of 75% RDF+Vc and 75% RDF+lime treatment was recorded above 1.50, irrespective of the cultivar. Between the cultivar, selection-9 obtained higher values of yield attributes, yield, gross return, production efficiency and B:C ratio than changlabi local. Thus, based on French bean crop yield and yield attributes, 75% RDF along with Vc @ 2.5 t ha-1 was superior, irrespective of cultivar and between the cultivars, selection-9 was superior.

Path of modern natural sciences: from discovery of realities to study of their attributes

We analyze the development of the natural sciences according to the scheme: “… reality – attributes – reality – attributes”. Any material reality is interpreted as a carrier of attributes manifested in a certain range of its experimental study and at a certain theoretical level of data understanding. More sophisticated experiments and refined theories lead to a more detailed and correct vision of realities and their attributes. The latter initiates new experiments and creation of new theories. These cognitive processes lead to the discovery of new material realities with their additional or refined attributes compared to those previously known. The objectivity and relative truth of scientific statements about the realities under study and their attributes are based on a qualitative or quantitative correspondence between theoretically calculated values of attributes and their experimentally measured values. We emphasize that the isolation and adequate historical and philosophical analysis of those cycles requires professional knowledge of the analyzed complex scientific material and cannot be implemented within the framework of oversimplified ideas about science and the role, which theories play in it. Examples of such ideas are the understanding of theories solely in terms of their refutation and confirmation or even the replacement of theories with fuzzy sociological concepts such as a paradigm and sociocultural determination of the scientific results. Those views often become the source of idealistic, irrational, and postmodernist interpretations of science and its history.

Effect of Herbicide Combinations on Growth and Yield of Kharif Maize (Zea mays L.)

The present investigation entitled “Effect of Herbicide Combinations on Growth and Yield of Kharif Maize (Zea mays L.)” was carried out during kharif 2019 at College Farm, College of Agriculture, Badnapur, Dist.-Jalna. Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, Maharashtra (India). Randomized Block design was used for study comprised of ten treatments. Among treatments, weed free treatment attributed more number of leaves, maximum plant height, dry matter production and leaf area per plant followed by treatment of topramezone 33.6 % SC @ 25.2 g a.i.ha-1 + atrazine 50 % WP @ 250 g a.i. ha-1. Poor growth attributes were recorded in weedy check. Significantly maximum values of yield attributes were recorded in treatment of topramezone 33.6 % SC @ 25.2 g a.i.ha-1 + atrazine 50 % WP@ 250(g)a.i.ha-1 followed by treatment of tembotrione 42 % SC @ 105 g a.i. ha-1+ atrazine 50% WP @ 250 g a,i. ha-1. Minimum values of yield attributes were attributed in weedy check. Treatment of topramezone + atrazine @ 25.2 + 250 g a.i ha-1 as PoE (T6) found effective in limiting weed growth and recorded lower weed index, weed dry matter with higher weed control efficiency followed by tembotrione +atrazine @105 + 250 (g) a.i.ha-1as PoE (T7) at all growth stages of crop. The benefit cost ratio was highest (2.94) with application of topramezone + atrazine @25.2 + 250 g a.i ha-1 followed by tembotrione + atrazine @ 105 + 250 g a.i. ha-1 as PoE (2.81) whereas weedy check recorded significantly lower B:C ratio(1.50) over other treatments.

Assessment and uncertainty quantification of onshore geological CO2 storage capacity in China

We provide a probabilistic assessment of CO2 storage capacity in major sedimentary basins in China. Our approach embeds constraints associated with the increase of reservoir pore pressure due to injection of CO2 in the presence of resident brine. Pressure build-up must be limited to avoid fault reactivation, caprock failure, and possible leakage, resulting in more conservative estimates of CO2 storage capacity as compared to volumetric estimates.We rely on a numerical Monte Carlo framework considering uncertainty in the values of reservoir size and major geological formation attributes (i.e., absolute permeability, porosity, and reservoir compressibility). Our work shows that 10 major basins can potentially store, on average, 1350 Gt of CO2 during the next 30 years (lower and upper quartiles being 1100 and 1700 Gt of CO2, respectively). This far exceeds the likely amount (up to 175 Gt of CO2) required to be stored by 2050. Our analysis also suggests that 6 basins (located close to the largest emission areas) can store about 93 Gt (on average) of CO2 during the next 30 years. Underground carbon storage in China, coupled with other possible solutions, could meet the aims of the Announced Pledges Scenario (International Energy Agency) to mitigate global warming by 2060.We also perform a global sensitivity analysis to determine how our predictions of storage capacity may be affected by uncertainties in the simulation model input parameters. Moment-based global sensitivity metrics suggest that geological formation attributes are major sources of uncertainty, significantly affecting model outputs and the associated uncertainty.

Incremental concept-cognitive learning approach for concept classification oriented to weighted fuzzy concepts

Fuzzy formal concept analysis (FFCA) is generally used to describe the processes of concept-cognitive learning (CCL). However, for fuzzy formal contexts, each attribute has the same weight (i.e, the same degree of importance) before constructing fuzzy concepts, which limits mining interesting knowledge and affects its application promotion. On the other hand, this model is hard to resist the influence of noise hidden in data, which results in poor classification learning. Moreover, the existing incremental CCL algorithms still face some challenges that the previously acquired knowledge is not fully utilized to improve the classification accuracies for dynamic data. To address these issues, we introduce different weights into fuzzy formal contexts and propose a novel incremental CCL mechanism in dynamic environment. Firstly, weight values of attributes from different decisions based on fuzzy entropy are established to measure the significant degree of attributes. Then, to comprehensively explicate the hierarchical relationships of fuzzy concepts, we construct the weighted fuzzy concept lattice and the weighted fuzzy concept space. Secondly, we design an algorithm to update the weighted fuzzy concepts for facilitating concept classification. To overcome the individual cognitive limitation, we put forward the progressive weighted fuzzy concept to remove repeated information. Furthermore, the classification prediction label and dynamic updating mechanism after adding objects are systematically discussed. Finally, we perform an experimental evaluation on ten data sets which explicate the feasibility and efficiency of our proposed approach.

Response of Weed Management on Growth, Yield and Yield Attributes of Kharif maize (Zea mays L.)

The present investigation entitled “Weed management in kharif maize (Zeamays L)” was carried out during kharif 2019 at College Farm, College of Agriculture, Badnapur, Dist.- Jalna. Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhnai Maharashtra (India). Randomized Block design used for study which comprises ten treatments. Among treatments, weed free treatment attributed more number of leaves, maximum plant height, dry matter production and leaf area per plant followed by treatment of topramezone 33.6% SC @ 25.2g a.i.ha-1 + atrazine 50% WP @ 250g a.i.ha-1. Poor growth attributes were recorded in weedy check. Significantly maximum values of yield attributes were recorded in treatment of topramezone 33.6 % SC @ 25.2g a.i.ha-1 + atrazine 50% WP @ 250 (g) a.i. ha-1 followed by treatment of tembotrione 42% SC @ 105g a.i.ha-1 + atrazine 50% WP @ 250 g a,i. ha-1. Minimum values of yield attributes were attributed in weedy check. Treatment of topramezone + atrazine @ 25.2 + 250g a.i ha-1 as PoE (T6) found effective in limiting weed growth and recorded lower weed index, weed dry matter with higher weed control efficiency followed by tembotrione + atrazine @ 105 + 250(g) a.i.ha-1 as PoE (T7) at all growth stages of crop. Lower uptake of nitrogen, phosphorous and potassium by weeds were recorded in treatment of topramezone + atrazine @ 25.2+ 250g a.i.ha-1 as PoE followed by tembotrione + atrazine @ 105 +250g a.i. ha-1 as PoE. The benefit cost ratio was highest (2.94) with application of topramezone + atrazine @ 25.2 + 250g a.i ha-1 followed by tembotrione + atrazine @ 105 + 250g a.i. ha-1 as PoE (2.81) whereas weedy check recorded significantly lower B:C ratio (1.50) over other treatments.

Appraisal of Buckwheat (Fagopyrum esculentum L.) Genotypes for Growth, Yield, Yield Attributes, Seed and Seedling Characteristics under Prayagraj Agro-Climatic Conditions

Buckwheat is referred as poor man’s food because it has good fibre, carbohydrates and essential amino acids. So, it must be made as an integral crop in agriculture production system. For this purpose, production is to be improved through development and breeding of new varieties. So, an experiment was conducted by using 26 buckwheat genotypes to evaluate them under Prayagraj agro-climatic conditions during 2021-22 to study growth, yield and yield attributes. The field experiment was conducted in randomized block design (RBD) with three replications and lab experiments were conducted in complete randomized design (CRD) with four replications. During the field experiment all genotypes showed wide variations among them for both growth and yield attributing traits. Among twenty-five genotypes, IC 108508 produced highest yield plot-1 (405g) followed by IC 26755 (315g), IC 37275 (299.3g) and IC 107575 (277.3g) higher yield than check PRB 1 (208.3 g). These genotypes also showed high to moderate values for growth and yield attributes. Therefore, these genotypes can be suggested for cultivation under Prayagraj agro-climatic conditions. Under laboratory study, it was observed that IC 42412 performed better for germination percent (97%), seedling length (27.9cm), fresh weight (1.05g), dry weight (0.303 g), vigour index I (2703), vigour index II (29.39), viability percent (99%) and electrical conductivity (0.22dsm-1). The highest protein content (14.1g%) was recorded in IC 24300 genotype. Therefore, IC 42412 can be considered as physiologically potential genotype. Through correlation and path analysis it was clear that number of seeds plant-1, inflorescence length, economic yield, number of inflorescence plant-1 and days to maturity may be considered for selection in crop improvement. Through this study it was concluded that IC 108508, IC 26755, IC 37275, IC 107575 and IC 42412 can be used for further breeding programs.

A Comparison of Statistical Dependency and Functional Dependency between Attributes Based on Data

Chi-squared test is a standard statistical test to ascertain independence between categorical variables. So, it is recommended to do the test for the attributes in the datasets, and remove any redundant attributes before we supply the datasets to machine learning algorithms. But, if we have many attributes that are common in real-world datasets, it is not easy to choose two attributes to do the independence test. On the other hand, several automated algorithms to find functional dependencies based on data have been suggested. Because functional dependencies show many-to-one relationships between values of attributes, we could conjecture that there might be statistical dependence in the found functional dependencies. For us to overcome the problem of choosing appropriate attributes for statistical dependency tests, we may use some algorithms for automated functional dependency finding. We want to confirm that the found functional dependencies can show statistical dependence between attributes in real-world datasets. Experiments were performed for three different real-world datasets using SPSS to confirm the statistical dependence of functional dependencies that are found by an open-source tool called FDtool, where we can use FDtool for automated functional dependency discovery. The experiments confirmed that there exists statistical dependence in the found functional dependencies and showed improvements in decision trees after removing dependent attributes.

Hamming Similarity Programming Model for Multi-Attribute Decision-Making Objects with Attribute Values as Interval Numbers and Its Application

With regard to the interval number-based uncertain multi-attribute decision making problem, in which the attribute weights are unknown and there is no preference on decision-making alternative objects, this paper presents a new decision-making approach. In this method, Hamming distance firstly is used to define the Hamming similarity degree of normative interval numbers, and the Hamming similarity degree of decision-making alternative objects, and then the Hamming similarity superiority relation theory to the comparison of interval numbers is proposed and some relevant results are obtained. Thus, by drawing on the idea of deviations maximization, an interval number-based decision-making object Hamming similarity programming model (IN-DMOHSPM) is established to calculate and solve the weight vector of attributes. Next, all of the selected alternative objects set is screened and sorted by using the overall Hamming similarity degree of each decision-making object compared with the ideal optimal object, and a new algorithm of Hamming similarity programming model for interval number-based multiple attribute decision-making objects is presented. Finally, the feasibility and utility of this model used in this paper are demonstrated by a numerical example.

A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence.

Valuing Urban Bus Attributes: An Experience in Kolkata

The article presents the marginal willingness-to-pay (WTP) estimates for various qualitative and quantitative attributes of travel with reference to the bus transportation system in Kolkata City, India. A stated choice experiment is designed to capture the responses for estimating marginal WTP values for various attributes. WTP values are estimated separately for commuting and noncommuting trips. The effects of model specification and socioeconomic parameters on WTP values are also studied. Estimates from standard multinomial logit (MNL) and different random parameter logit (RPL) models indicate that WTP values vary with model specification. In the process of developing RPL models, successful application of sparsely used constrained triangular distribution is also demonstrated.

What do jobseekers want? Comparing methods to estimate reservation wages and the value of job attributes

Understanding jobseeker preferences – including their reservation wages and how much they value different non-wage amenities – is difficult because they are not directly observable. We test four different methods for estimating these preference parameters using an experiment in a job-matching center. We find large and important differences between the methods. Using a follow up survey for validation, and comparing the consistency of estimates with prior literature, we find that Discrete Choice Experiments perform best. We show how these methods can improve our understanding of labor market frictions and help policymakers and employers develop targeted policies and compensation bundles to address inequities in the labor market.

The Influence of Shopee Pay Later on the Impulsive Buying of E-commerce Shopee Users: A Study from Indonesia Utilitarian and Hedonic Attributes in Online Purchase Intentions

The purpose of this study is to show the influence of Shopee Pay later on impulsive buying through utilitarian attributes, hedonic attributes, affective attitude, and cognitive attitude variables. The researcher disseminates an online questionnaire via Google form to 232 respondents to conduct this study. The analysis of this study uses the method of Structural Equation Modelling (SEM) using the Smart PLS (Partial Least Square) software. The result of this study is that the utilitarian attributes, hedonic attributes, affective attitude, and cognitive attitude have a positive and significant relation to the impulsive buying variable. So, there are the higher value of the utilitarian attributes and hedonic attributes, the higher level of affective attitude and cognitive attitude, and then, there are the higher value of affective attitude and cognitive attitude, the greater level of consumer impulsiveness in shopping using the Shopee Pay later feature.

Well-being evocative places: validating the conceptual model of an evocative place based on the inter-rater reliability test

ABSTRACT An evocative place is defined as a place that evokes emotions, memories and images. Well-being evocative places are places that enable people to recharge and feel at peace. Data about well-being evocative places and their descriptions was collected in a series of mapping experiments in Hamburg (Germany), Ames (Iowa), and Vitória (Brazil). The Conceptual Model of an Evocative Place (CMEP) was designed based on the collected data in these three cities. It conceptualizes descriptions of evocative places as expressed in words by the participants. The model stresses the value of tangible and intangible attributes of evocative places described by their characteristics, experience, senses and values. The main goal of this article is to present the CMEP and the methodology for its validation accomplished with the help of a new set of data collected in the cities of Vienna (Austria) and Grinnell (Iowa). The inter-rater reliability (IRR) test provided a framework for its validation. Two raters used newly collected data and the categories proposed in the CMEP. The content analysis and the calculated IRR served as the basis for the changes and improvements of the CMEP. The article concludes with a discussion of the main results and future research directions.

The value of CMR high-risk attributes in predicting ventricular remodeling in ST-segment-elevation myocardial infarction patients with mildly reduced or preserved ejection fraction

Objective: To evaluate the predictive value of a multiparametric cardiac magnetic resonance (CMR) approach for ventricular remodeling in ST-segment-elevation myocardial infarction (STEMI) patients with mildly reduced or preserved left ventricular ejection fraction (LVEF). Methods: This study is a prospective cohort study. STEMI patients with acute LVEF>40% after primary percutaneous coronary intervention (PCI) in Beijing Anzhen Hospital from October 2019 to September 2021 were enrolled. All patients received acute (3-7 days) and follow-up (3 months) CMR post-PCI. According to absence or presence of ventricular remodeling, patients were divided into ventricular remodeling group and non-ventricular remodeling group. Basic clinical characteristics and CMR indicators were analyzed and compared between the two groups. Logistic regression and receiver operating characteristic (ROC) curves were used to explore the predictive performance of CMR high-risk attributes for ventricular remodeling in STEMI patients with mildly reduced or preserved LVEF. The predictive value of combining multiple high-risk characteristics of CMR for ventricular remodeling was analyzed and compared with the traditional clinical risk factor model. Results: A total of 123 STEMI patients were enrolled (aged (57.1±11.1) years, 102 (82.9%) males). There were 97 cases (78.9%) patients in the non-ventricular remodeling group and 26 cases (21.1%) in the ventricular remodeling group. After adjustment for clinical risk factors, stroke volume<51.6 ml, global circumferential strain>-13.7%, infarct size>39.2%, microvascular obstruction>0.5%, and myocardial salvage index<43.9 were independently associated with ventricular remodeling in STEMI patients with mildly reduced or preserved LVEF. The incidence of ventricular remodeling increased with the increasing number of CMR high-risk attributes (P<0.01). The number of CMR high-risk attributes ≥3 was an independent predictor of adverse remodeling (adjusted OR=5.95, 95 CI%: 2.25-15.72, P<0.01) in STEMI patients with mildly reduced or preserved LVEF. Furthermore, the number of CMR high-risk attributes had incremental predictive value over baseline clinical risk factors (area under curve: 0.843 vs. 0.696, P<0.01). Conclusions: In STEMI patients with mild reduced or preserved LVEF, 5 CMR characteristics are associated with ventricular remodeling. The combination of ≥3 CMR high-risk characteristics is an independent predictor of ventricular remodeling, which has incremental predictive value beyond traditional risk factors in this patient cohort.

Identification and Assessment of Human Manipulation in the Work Environment

This article aims to provide an overview of knowledge related to the complexity of manipulation in the work environment and focus on the specification of the issue. The attributes of manipulation in the work environment were analyzed based on the degree of perception of social intelligence factors, the level of manipulation, and the tendency to distrust others. The aim of the research was to examine statistically significant associations between the number of years of service and the occurrence of manipulation in work. The research was carried out via MPS and MESI methodologies. The research sample (N = 183) comprised working students and employees from the private and public spheres from various work areas in Slovakia. In conclusion, no statistically significant associations existed between the number of worked years and manipulation at the workplace. Statistically, significant gender differences were documented in men within the attributes of manipulation. Higher average values ​​for attributes of empathy, social irritability, and mistrust were found in women.