Values Of Attributes Research Articles

Regional differences in water beetle communities networks settling in dystrophic lakes in northern Poland

The relationships between the species that form the networks in small dystrophic lakes remain poorly recognised. To investigate and better understand the functioning of beetle communities in different ecosystems, we created three network models that we subjected to graph network analysis. This approach displays correlation–based networks of connections (edges) between objects (nodes) by evaluating the features of the whole network and the attributes of nodes and edges in the context of their roles, expressed by centrality metrics. We used this method to determine the importance of specific species in the networks and the interspecific relationships. Our analyses are based on faunal material collected from 25 dystrophic lakes in three regions of northern Poland. We found a total of 104 species representing different ecological elements and functional trophic groups. We have shown that the network of relationships between the biomass of species differs considerably in the three study regions. The Kashubian Lakeland had the highest cohesion and density, while the network in the Suwalki Lakeland was the thinnest and most heterogeneous, which might be related to the fractal structure and the degree of development of the studied lakes. Small–bodied predators that congregated in different clusters with species with similar ecological preferences dominated all networks. We found the highest correlations in the Masurian Lakeland, where we obtained the highest centralisation of the network. Small tyrphophiles typically occupied the central places in the network, while the periphery of the network consisted of clusters with different habitat preferences, including large predators. The species that were most important for network cohesion and density were mainly tyrphophilous species, such as Anacaena lutescens, Hygrotus decoratus, Enochrus melanocephalus and Hydroporus neglectus. The values of attributes determining the role of species in community networks were influenced by both biotic and environmental factors.

Effect of Storage Period on Acid Value and Sensory Attributes of Puffed Wheat Grains ‘Badyrak Vanilla’ and ‘Badyrak with Whey’

Abstract Puffed grains made from whole wheat, corn or rice grains are ready-to-eat breakfast cereals that do not require additional preparation. Two recipes for puffed grain coatings were developed in the previous studies. Sunflower oil was used in the coating, which may affect storage stability. Due to lipid hydrolysis, free fatty acids may be released, causing rancid off-flavour. The aim of this study was to determine the changes in the acid value of lipids and sensory attributes of sweet-coated puffed wheat grains ‘Badyrak vanilla’ (BV) and ‘Badyrak with whey’ (MC) depending on temperature during their eight-month storage. The products were packaged in metallised polypropylene pouches, 30 g each. The acid value was studied monthly at different storage temperatures (20 ± 2 °С and 4 ± 2 °С). In puffed wheat grain products, 65% of fatty acids are polyunsaturated, 25% monounsaturated, and 10% saturated. The high proportion of unsaturated fatty acids over saturated makes oil more susceptible to oxidation. Literature studies and sensory evaluation of puffed wheat grains allowed setting a limiting acid value at 4.0 mg KOH g−1 lipids. The increase in acid value was storage temperature dependent. Thus, the acid value exceeded the established limit in the fifth month for MC and the sixth month for BV when stored at 20 ± 2 °С. At the same time, noticeable changes in puffed wheat grain flavour were observed. The temperature coefficient (Q10) for MC was 2.0, whereas for BV it was 1.5, indicating that the acid value increase in MC was more affected by temperature than that in BV.

The lexical constructional model meets syntax: guidelines of the formalized lexical-constructional model (FL_CxG )

This paper offers the basic guidelines of a formalized version of the Lexical Constructional Model (LCM; Ruiz de Mendoza & Mairal Usón, 2008, 2011; Ruiz de Mendoza & Galera, 2014), the Formalized Lexical-Constructional Grammar (FL_CxG), which will pave the way for future computational developments, such as parsers or lexical databases. The FL_CxG deploys (i) the typologically oriented syntactic apparatus of Role and Reference Grammar (Van Valin, 2005; Van Valin & LaPolla, 1997), (ii) the catalogue of constructional units arranged in a 4-layer typology, as proposed by the LCM, and (iii) some insights for semantic representations from the Generative Lexicon Theory (Pustejovsky, 1995; Pustejovsky & Batiukova, 2019), and Minimal English (Goddard, 2018). All the components of the FL_CxG (lexical units and construct(ion)s) are formally encoded as Typed Feature Structures in the format of Attribute Value Matrixes. These units are to be understood as constraints operating in the unification processes which underlie the generation/decoding of a given fragment of language.

20-year trends in multimorbidity by race/ethnicity among hospitalized patient populations in the United States

BackgroundThe challenges presented by multimorbidity continue to rise in the United States. Little is known about how the relative contribution of individual chronic conditions to multimorbidity has changed over time, and how this varies by race/ethnicity. The objective of this study was to describe trends in multimorbidity by race/ethnicity, as well as to determine the differential contribution of individual chronic conditions to multimorbidity in hospitalized populations over a 20-year period within the United States.MethodsThis is a serial cross-sectional study using the Nationwide Inpatient Sample (NIS) from 1993 to 2012. We identified all hospitalized patients aged ≥ 18 years old with available data on race/ethnicity. Multimorbidity was defined as the presence of 3 or more conditions based on the Elixhauser comorbidity index. The relative change in the proportion of hospitalized patients with multimorbidity, overall and by race/ethnicity (Black, White, Hispanic, Asian/Pacific Islander, Native American) were tabulated and presented graphically. Population attributable fractions were estimated from modified Poisson regression models adjusted for sex, age, and insurance type. These fractions were used to describe the relative contribution of individual chronic conditions to multimorbidity over time and across racial/ethnic groups.ResultsThere were 123,613,970 hospitalizations captured within the NIS between 1993 and 2012. The prevalence of multimorbidity increased in all race/ethnic groups over the 20-year period, most notably among White, Black, and Native American populations (+ 29.4%, + 29.7%, and + 32.0%, respectively). In both 1993 and 2012, Black hospitalized patients had a higher prevalence of multimorbidity (25.1% and 54.8%, respectively) compared to all other race/ethnic groups. Native American populations exhibited the largest overall increase in multimorbidity (+ 32.0%). Furthermore, the contribution of metabolic diseases to multimorbidity increased, particularly among Hispanic patients who had the highest population attributable fraction values for diabetes without complications (15.0%), diabetes with complications (5.1%), and obesity (5.8%).ConclusionsFrom 1993 to 2012, the secular increases in the prevalence of multimorbidity as well as changes in the differential contribution of individual chronic conditions has varied substantially by race/ethnicity. These findings further elucidate the racial/ethnic gaps prevalent in multimorbidity within the United States.Prior presentationsPreliminary finding of this study were presented at the Society of General Internal Medicine (SGIM) Annual Conference, Washington, DC, April 21, 2017.

Economic analysis of whole-county PV projects in China considering environmental benefits

Distributed photovoltaic generation is an important measure to address climate change and boost rural revitalization. In the context of new energy grid parity, driving rooftop distributed photovoltaics to participate in the green power trading market is an inevitable necessity for energy and market development. Thus, based on the cost-benefit theory, this paper considers both the green power trading market and the carbon trading market in an integrated manner and develops an economic efficiency model that takes taxation into account. The net present value (NPV), dynamic payback period (DPP) and internal rate of return (IRR) were chosen for the economic feasibility analysis, comparing green power trading with whole power carbon trading, and sensitivity analysis. An analysis of a whole-county rooftop DPVG pilot in Northeast China as an example shows that the whole-county rooftop DPVG project is economically viable. After participating in green power trading, the NPV reaches $5956.083 thousand, IRR is 13.629 % and DPP is shortened from 21.517 years to 14.248 years, fully reflecting the value of the environmental attributes of green power. In addition, sensitivity analysis shows that the green power trading price and the self-consumption contract tariff are non-negligible factors, and it is proposed that the green power trading price awaits enhancement. Finally, relevant policy recommendations to promote the development of whole-county DPVG are put forward. The findings of the study provide new directions for the scaled development of new energy sources, as well as effective references for investment decision-makers.

A hybrid estimation procedure for modeling shallow foundation’s settlement: RBF-optimized neural network

The complexity of the cohesive soil structure necessitates settlement modeling beneath shallow foundations. The goal of this research is to use recently discovered machine learning techniques called the hybridized radial basis function neural network (RBFNN) with sine cosine algorithm (SCA) and firefly algorithm (FFA) to detect settlement (Sm) of shallow foundations. The purpose of using optimization methods was to find the optimal value for the primary attributes of the model under investigation. With R2 values of at least 0.9422 for the learning series and 0.9271 for the assessment series, both the produced SCA - RBFNN and FFA - RBFNN correctly replicated the Sm, which indicates a considerable degree of efficacy and even a reasonable match between reported and modeled Sm. In comparison to FFA - RBFNN and ANFIS - PSO, the SCA - RBFNN is believed to be the more correct method, with the values of R2, RMSE and MAE was 0.9422, 7.2255 mm and 5.1257 mm, which is superior than ANFIS - PSO and FFA - RBFNN. The SCA - RBFNN could surpass FFA one by 25% for the learning component and 14.2% for the test data, according to the values of PI index. Ultimately, it is apparent that the RBFNN combined with SCA could score higher than the FFA and even the ANFIS - PSO, which is the proposed system in the Sm forecasting model, after assessing the reliability and considering the assumptions.

Granular computing approach for the ordinal semantic weighted multiscale values for the attributes in formal concept analysis algorithm

In literature granular computing and formal concept analysis algorithm use only single-value attributes to knowledge discovery for the data of spatio-temporal aspects. However, most of the datasets like forest fires and tornado storms involve multiscale values for attributes. The limitation of single-value attributes of the existing approaches indicates only the data related to event occurrence which may be missing the elicitation of important knowledge related to severity of event occurrence. Motivated by these limitations, this research article proposes a novel and generalized method which uses ordinal semantic weighted multiscale values for attributes in formal concept analysis with granular computing measures especially when spatio-temporal attributes are not given. The originality of proposed methodology is using ordinal semantic weighted multiscale values for attributes that give complete information of event occurrences. Moreover, the use of ordinal semantic weighted multiscale values improves the results of granular computing measures. The significance of proposed approach is well explained by experimental evaluation performed on publicly available datasets on storm occurring in different States of America.

A discrete choice experiment to examine the factors influencing consumers' willingness to purchase health apps.

The benefits of health apps can only be realized when consumers purchase them for use. Thus, it is important to understand what factors influence consumers' willingness to purchase health apps. Therefore, this study aimed to examine the influence of health app attributes and sociodemographic characteristics on consumers' willingness to purchase health apps, and how the value of the health app attributes varies for individuals with different sociodemographic characteristics. A questionnaire-based discrete choice experiment (DCE) was conducted with a random sample of 561 adults. A standard logit regression was applied to assess the influence of health app attributes and sociodemographic characteristics on consumers' willingness to purchase health apps, and marginal willingness to pay (MWTP) was calculated for each factor using regression coefficients. Interaction effects were also examined to determine how the value of health app attributes varies by sociodemographic characteristics. Usefulness, ease of use, security and privacy, and attitudes of healthcare professionals toward consumers' use of health apps were the attributes of health apps that positively influenced consumers' willingness to purchase them. Conversely, smartphone storage consumption, mobile Internet data consumption, and app price negatively influenced consumers' willingness to purchase the apps. For sociodemographic characteristics, being male, having a household size greater than three, having a monthly household income of HK$30,000 or more, having a lower education level (below diploma), having previously used health apps, and having previously purchased health apps were associated with a higher willingness to purchase health apps. Health app attributes that influenced consumers' willingness to purchase the apps and populations that were less willing to purchase health apps were identified. Efforts should be made to improve health app attributes and enhance the promotion of health apps among these underserved populations.

Performance of tall wheat under different sowing times and methods using various nutrient sources in western Haryana

To study the effect of different sowing times, methods and nutrient sources on the growth and productivity of tall wheat, a field experiment was conducted in a split-plot design with three replications. The analysis of data revealed that tall wheat crop sown in the last week of October on FIRBS recorded significantly higher plant height, dry matter accumulation (at different growth stages), yield attributes, grain yield (2599 kg ha-1) and biological yield (7212 kg ha-1) as compared to conventionally sown wheat crop in 1st and 2nd week of November. Among the different nutrient sources, the recommended doses of chemical fertilizers provided significantly higher values of growth parameters, yield attributes, grain yield (2658 kg ha-1) and biological yield (7426 kg ha-1) compared to organic fertilizers/ biofertilizers. Maximum temperature showed a significant and positive correlation with yield and yield attributes. In light of the results to maximize the productivity of tall wheat, it may be sown in the last week of October on FIRBS supplied with recommended doses of fertilizers on the basis of soil test.

Risk and UCON-based access control model for healthcare big data

The rapid development of healthcare big data has brought certain convenience to medical research and health management, but privacy protection of healthcare big data is an issue that must be considered in the process of data application. Access control is one of the methods for privacy protection, but traditional access control models cannot adapt to the dynamic, continuous, and real-time characteristics of healthcare big data scenarios. In this paper, we propose an access control model based on risk quantification and usage control (RQ-UCON). The model adds a risk quantification module to the traditional UCON model to achieve privacy protection of medical data. This module classifies risks into direct and indirect risks and quantifies them based on the physician's visit history. The model stores the quantified risk values as subject attributes. The RQ-UCON model uses an improved Exponentially Weighted Moving Average (EWMA) and penalty factors to predict risk value and to update the risk values of the subject attributes in real-time. The RQ-UCON model uses agglomerative hierarchical clustering to cluster the risk values of physicians within the department, resulting in risk intervals for each physician's operational behavior. Each risk interval is stored as a condition in the RQ-UCON model. Finally, according to the model whether the subject attributes meet the model conditions to determine whether the subject has the corresponding access rights, and according to the risk interval to grant the subject the corresponding access rights. Through the final experiment, it can be seen that the access control model proposed in this paper has a certain control on the excessive access behavior of doctors and has a certain limitation on the privacy leakage of healthcare big data.

The Effect of Workforce Diversity Management on Organizational Performance: The Case of Four Universities in Ethiopia

The objective of this study was to explore the degree of association between workforce diversity dimensions and academic performance of four universities in Ethiopia. The diversity management attributes assessed were diversity Climate, values and organizational justice; and identity, schemas and communication with adaptation to the contexts of the higher education institutions in Ethiopia. The Full account of these diversity management dimensions is presented in detail in the methodology section of this paper. The sample universities were selected purposively, and stratified and systematic sampling techniques were further used to identify respondents. The number of respondents that took part in the study was 386. Quantitative and qualitative data were collected to test the various study hypotheses. Descriptive statistics and simple correlation analyses were used to analyze the data. The demographic features of the staff working in the four case study universities was found to be diverse with regard to gender, marital status, age and religion. The agreement level of the staff with regard to the prevalence of diversity management dimension in their university was found to above the ideal average. The mean values of the Likert scale response ranged from 3.72 (with standard deviation of 0.98) for ‘organizational justice’ to 4.12 (with standard deviation of 0.74) for diversity management dimension designated as ‘values.’ Results from Pearson correlation analysis revealed that there are statistically significant positive correlations between the dimensions of workforce diversity and organizational performance. This implies that organizational performance of higher education institutions can be significantly and positively influenced by extant diversity. The practical implication of this is that attention needs to be given to the fair distribution of resources to the teaching staff working in these universities in the future. The freedom to express one’s own identity in the university workforce landscape was also observed to be limited in the universities studied and this has to be also improved. A democratic work environment is critical for the productivity of the staff to be enhanced and an effort has to be towards the goal of creating such an environment. The relationship between diversity management attributes (diversity climate, organizational justice, identity, values, schemas and communication) and overall academic performance was also determined, and the result indicated that there is statistically significant (P<0.00) positive correlations between workforce diversity and academic performance.

Multiple optimization of sensory attributes applied to malt drink formulation

Health-friendly soft drinks are gaining popularity because of their ingredients, such as enzymatically hydrolyzed proteins, easily digestible carbohydrates, and high nutritional value. Consequently, the demand for barley has also increased as it is the primary raw material for these beverages. The present study aimed to develop a new health-friendly soft drink made from malt (MHFSD) that takes into account multiple sensory attributes and purchase intention simultaneously using a desirability function-based approach. To predict the impact of light and dark malt extract concentrations (LME and DME) and added sugar (AS) on MHFSD formulations, a central composite rotational design (CCRD) was used. Optimum formulations for MHFSD attributes and purchase intention were determined using this design. The desirability function-based approach was implemented to examine optimization possibilities. For the scenario with the highest degree of optimization, the input parameters were 5.40% LME, 14.11% DME, and 10.80% AS. The optimized response values for sensory attributes were good, with scores of 8.5 for color, 6.0 for carbonation, 7.3 for flavor and overall impression, and 3.3 for purchase intention. The results indicated good acceptance and purchase intention for the optimized MHFSD. Unlike commonly used approaches in the literature, the desirability function-based approach enabled the consideration of multiple responses simultaneously, which aided decision-making about MHFSD formulation.

PATIENT PREFERENCES IN PHARMACEUTICAL AND DEVICE-BASED TREATMENTS FOR UNCONTROLLED HYPERTENSION

Objective: Radiofrequency renal denervation (RDN) safely reduces high blood pressure (BP) in hypertensive (HTN) patients. Patient preference regarding such a treatment over antihypertensive (AH) drugs for uncontrolled HTN is poorly understood. Design and method: A recent discrete choice experiment (DCE) study in patients with uncontrolled HTN identified the reduction in systolic BP as the most important driver of patient preference between interventional treatments (e.g. RDN) or drugs. Using a predictive model consisting of the preference weights for safety and efficacy attributes from the DCE study, we calculated patients’ treatment preferences in 3 typical clinical scenarios in HTN management. Each scenario presented a choice to either initiate/increase AH drugs, or to have interventional treatment such as RDN: (1) AH drug naïve: initiate AH drugs vs. interventional treatment (2) mild-moderate HTN, 1-3 AH drugs: increasing AH drugs vs. intervention with no drug changes, (3) severe HTN (systolic BP >150mmHg, >3 AH drugs): increasing AH drugs vs. intervention with no drug changes. In each scenario, the average predicted probability of choosing one treatment over the other was calculated based on a benefit-risk profile from the sum of preference-weighted treatment attributes (type of treatment, changes in AH drugs, office systolic BP reducing efficacy, effect duration, risk of drug side effects, interventional treatment adverse events). Values of the treatment attributes were derived from the literature, expert opinion, and published RDN studies (SPYRAL HTN OFF-MED, ON-MED, Global SYMPLICITY Registry). Results: In (1), 17% preferred interventional treatment without drugs vs 83% preferred initiation of drugs. In (2), 24% preferred interventional treatment without increasing drugs vs 76% preferred increasing drugs. In (3), 45% preferred interventional treatment without increasing drugs vs 55% preferred increasing drugs. Preference differences were driven by the magnitude of office systolic BP reductions with each treatment. Risks of treatment-related adverse events were less important to treatment choices. Conclusions: Preference for RDN ranged from one-fifth to one-half of patients depending on clinical scenario. The increase mirrored the extent of prior treatment with AH drugs and HTN severity on drugs. Patient preference was driven predominantly by efficacy and minimally by safety of treatment.

HYDRAULIC RESISTANCE AND SPRAY REMOVAL IN STABILIZED THREE-PHASE FOAM LAYER

Industrial implementation of the stabilization method of the gas-liquid layer can significantly expand the field of use of foaming apparatus and opens up new opportunities for intensifying technological processes with the simultaneous creation of low-waste technologies. The article establishes the basic parameters influencing the hydrodynamics of foam apparatus, considers the basic constructions and operating modes of foam apparatus. The connection of hydrodynamic parameters is revealed. The hydrodynamic laws of the foam layer are considered. The indicated factors affecting the process of mass transfer, both in the gas and in the liquid phases. The conducted analysis of a number of studies showed that the perspective direction of intensification of the mass transfer process is the development of apparatuses with a three-phase fluidized bed of an irrigated nozzle of complex forms with mesh materials. A new design of the stabilizer with a large free volume and a spherical movable nozzle was developed. The advantage of the proposed design is the transition to a structured foam mode of operation at relatively low gas speeds, as well as a developed phase contact surface. After experimental studies of the hydrodynamic characteristics of the combined contact element, experimental data on hydrodynamic resistance and experimental indicators of spray attribution for a contact stage with combined contact elements were obtained. As a result of research, it was found that when using foam layer stabilizers, the spray ratio at the contact stage is reduced, which leads to more stable operation of the device. Empirical equation for determining the value of the spray attribution is given. It is indicated that the mechanism of transition of the nozzle to the mode of advanced fluidization will be significantly different from conventional ballistic nozzles. The study of mass transfer in an absorber with a fluidized bed of an irrigated nozzle is complicated by the fact that the contact surface between phases can vary considerably depending on the hydrodynamic conditions, in particular, on the speed of gas and irrigation density.

Roasting optimization of peaberry coffee beans from cherry fruits

The aim of this study was to determine the best interaction between roasting time and temperature of peaberry beans from cherry fruits combined with three different degrees of roast in order to archive the best results for sensorial attributes as well as verify changes in physical parameters. The roasting curves showed similarity of temperature profiles, indicating proper conduction of the roasting process. According to the different ways of roasting, significant differences at 5% probability level were observed for the bulk density, moisture content, swelling while shade and weight loss was the same. The optimum roasting condition for peaberry beans was high temperature all the time (T1) in dark color, which obtained higher values for sensorial attributes.

Linking learning goal orientation to learning from error: the mediating role of motivation to learn and metacognition

PurposeErrors are increasingly recognized as beneficial to the learning process and are more frequently integrated into training curriculums. Despite this growing interest, the work carried out so far offers little evidence highlighting the psychological qualities implicit in learning from error. By focussing on the role of specific trainee’s attributes [i.e. learning goal orientation (LGO) motivation to learn and metacognition], this study aims to better understand the reasons why some trainees benefit more (than others) from being confronted with errors during training.Design/methodology/approachA total of 142 trainees took part in this study by participating in a training on interviewing techniques that also exposed them to various committable errors, and by completing questionnaires at two different times (i.e. before and after training).FindingsResults of bootstrap regression analysis highlights three main findings: LGO is positively linked to learning from errors; a significant portion of the link between LGO and learning from error is explained by motivation to learn and metacognition; and these effects are presented in the form of a double-mediated model which suggests two different explanatory pathways (i.e. motivational and cognitive).Originality/valueTo the best of the authors’ knowledge, this study is among the first to offer insight on the psychological attributes influencing learning from errors and to bring forward the role of two underlying mechanism that are linked to this specific type of learning. It also invites researchers and practitioners to reflect on the best ways to make use of errors in training and promote the value of personal attributes on trainees’ learning experience.

Performance of Pinoxaden Herbicide against Complex Weed Flora in Wheat (Triticum aestivum L.)

A field experiment was conducted during rabi season (2020-21) at AICRP on Wheat, College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur (MP), India, to study the effect of pinoxaden on weeds and the yield of wheat. The field experiment was laid out in a randomized block design with seven treatments and replicated thrice. Treatments included applying different doses of pinoxaden at 40, 45, and 90 g a.i. ha-1, clodinafop propargyl at 90 g a.i. ha-1, sulfosulfuron at 25 g a.i. ha-1 as post-emergence along with hand weeding at 30 DAS and weedy check. The experimental field was dominated by Phalaris minor (15.6%) among monocot weeds, while Medicago denticulata (30.82%), Cichorium intybus (29.94%), Chenopodium album (15.32%), and Anagallis arvensis (8.30%) among the dicot weeds throughout the crop growing period. Among the different herbicidal treatments, pinoxaden at 90 g a.i. ha-1 effectively controlled the monocot and dicot weeds and recorded higher weed control efficiency and the lowest weed index. However, the highest value of growth parameters, yield attributes, and grain yield was recorded with the application of pinoxaden at 45 g a.i. ha-1 among all the herbicidal treatments.

Gaussian processes for missing value imputation

A missing value indicates that a particular attribute of an instance of a learning problem is not recorded. They are very common in many real-life datasets. In spite of this, however, most machine learning methods cannot handle missing values. Thus, they should be imputed before training. Gaussian Processes (GPs) are non-parametric models with accurate uncertainty estimates that combined with sparse approximations and stochastic variational inference scale to large data sets. Sparse GPs (SGPs) can be used to get a predictive distribution for missing values. We present a hierarchical composition of sparse GPs that is used to predict the missing values at each dimension using the observed values from the other dimensions. Importantly, we consider that the input attributes to each sparse GP used for prediction may also have missing values. The missing values in those input attributes are replaced by the predictions of the previous sparse GPs in the hierarchy. We call our approach missing GP (MGP). MGP can impute all observed missing values. It outputs a predictive distribution for each missing value that is then used in the imputation of other missing values. We evaluate MGP on one private clinical data set and on four UCI datasets with a different percentage of missing values. Furthermore, we compare the performance of MGP with other state-of-the-art methods for imputing missing values, including variants based on sparse GPs and deep GPs. Our results show that the performance of MGP is significantly better.

On the conformity of scales of multidimensional normalization: An application for the problems of decision making

The main goal of this paper is to harmonize the scales of normalized values of various attributes for multi-criteria decision-making models (MCDM). A class of models is considered in which the ranking of alternatives is performed based on the performance indicators of alternatives obtained by aggregating private attributes. The displacement of the domains of the normalized values of various attributes relative to each other and the local priorities of the alternatives are the main factors that change the rating when using various normalization methods. Three different linear transformations are proposed, which make it possible to bring the scales of normalized values of various attributes into conformity. The first transformation, the Reverse Sorting (ReS) algorithm, inverts the direction of optimization without displacing the areas of normalized values. The second transformation ‒ IZ-method ‒ allows researchers to align the boundaries of the domains of normalized values of various attributes in each range. The third transformation ‒ MS-method ‒ converts Z-scores into a sub-domain of the interval [0, 1] with the same mean values and the same variance values for all attributes. All transformations preserve the dispositions of the natural values of the attributes of the alternatives and ensure the equality of the contributions of various criteria to the performance indicator of the alternatives. The ReS-algorithm is universal for all normalization methods when converting cost attributes to benefit attributes. IZ and MS transformations expand the range of normalization methods when using nonlinear functions aggregation of attributes.

Pixel Value Graphical Password Scheme: Analysis on Time Complexity performance of Clustering Algorithm for Passpix Segmentation

Passpix is a key element in pixel value access control, containing a pixel value extracted from a digital image that users input to authenticate their username. However, it is unclear whether cloud storage settings apply compression to prevent deficiencies that would alter the file's 8-bit attribution and pixel value, causing user authentication failure. This study aims to determine the fastest clustering algorithm for faulty Passpix similarity classification, using a dataset of 1,000 objects. The source code for the K-Means, ISODATA, and K-Harmonic Mean scripts was loaded into a clustering experiment prototype compiled as Clustering.exe. The results demonstrate that the number of clusters affects the time taken to complete the clustering process, with the 20-cluster setting taking longer than the 10-cluster setting. The K-Harmonic Mean algorithm was the fastest, while K-Means performed moderately and ISODATA was the slowest of the three clustering algorithms. The results also indicate that the number of iterations did not affect the time taken to complete the clustering process. These findings provide a basis for future studies to increase the number of clusters for better accuracy.