Hourly-based improved prediction of present air quality state (PM10, PM2.5, and NO2) with 15 input independent variables (3 hours\' earlier PM, gas and meteorological data of a Korean city affected by 2 days\' earlier PM and gas data of Beijing city in the Yellow Dust route was performed using Machine Learning (ANN-Tanh) and Multivariate Regression techniques. ANN-tanh model of multilayer perception (MLP) structure with a feed-forward input data and backpropagation training process for error calculation with 15 nodes in a single hidden layer was adopted to calculate present urban air quality. Root mean square error (RMSE) and the coefficient of determination (R2; Pearson R) evaluated the performance ability of the model between the predicted and measured values before, during, and after the Yellow Sand event at the Korean coastal city. Multivariate regression technique was also used to predict the urban air quality with the same input data. The prediction accuracies of the two models were compared before, during, and after the Yellow Dust period and regardless of the event, Pearson R correlation coefficients using the ANN-tanh model (multivariate regression model) were quite high as 0.935 (0.961), 0.943 (0.948) and 0. 947 (0.920) for PM10, 0.942 (0.909), 0.969 (0.977) and 0.938 (0.947) for PM2.5, and 0.925 (0.860), 0.853 (0.875) and 0.886 (0.903) for NO2, respectively. Their prediction ability overall was quite excellent, showing a slightly higher R in the ANN-Tanh. Their hourly distributions of predicted and measured values of 3 outputs were drawn to compare their model performance.
Sales surprise (SS) is an important determinant of the inventory turnover (ITO) of firms. This paper aims to examine the effects of SS on ITO using two different sales forecasts. The data was obtained from the Albertina database for the period from 2015 to 2019, for two sectors of the Czech economy: manufacturing and construction. The Czech firms\' data was used to estimate sales forecasts by four different methods, and the two most accurate were chosen to calculate sales surprise (SS): sales surprise linear forecast (SSLF) and sales surprise random walk (SSRW). After estimating four different models by employing the fixed-effect panel model, the results show that SSRW is positively correlated with ITO. Outcomes of the current research will be beneficial to managers, policymakers, and directors of the firms to understand and estimate the relationship between SS and ITO.
Time-dependent deformations of rock are important factors for the design and construction of openings in the soft rock masses with time-dependent strength and deformation properties. Therefore, determining of the openings closure is an indispensable task in geotechnical engineering, especially for design and construction of underground coal mine. However, in some types of rock masses, the tunnel closure may increase for months or years after the excavation owing to rheological behaviour of the surrounding rock masses, which greatly influences the selection of the initial openings support system, the excavation layout, and the determination of its load-carrying capacity. In this paper, we present a method to predict quickly the openings closure in time-dependent rock mass using non-equidistance grey Verhulst model (NGVM). The proposed method is validated fairly via a test for new excavated openings of -600m level in a bituminous coal mine, Democratic People�s Republic of Korea.
Remote exchange of light energy-information must comply with Shannon\'s rules, which propose verifiable limits on the transfer rate. We predict a measurable Shannon decrease in the power-information transfer in a vacuum laboratory or when testing the same transmitter-receiver system in outer space.
Information and communication technology have caused a drastic change in the manner patients are treated in this digital world. Wireless Body Area Network(WBAN) is a type of wireless sensor network consisting of medical sensor devices placed inside and outside the body of a human being. These sensors are used to collect and transfer health-related information from the human body to caretakers. WBAN can be used in the e-health care system. The sensitive data collected by wearable sensor nodes during storage and transmission suffer from security and privacy-related issues. The proposed scheme provides a framework to establish secure end-to-end data transmission in the e-health monitoring system. Further, the mechanism followed in the proposed scheme provides an access control mechanism to prevent the direct access of the patient health-related data by a third party. The results show that the proposed scheme guarantees strong security while transferring health-related data within and outside WBAN and during storage in the database servers.
This article devotes to explain the theories of ethics, the types of ethics, the approaches of normative ethics, and to realize that how research ethics originate and develop, and how do judge them, how do embrace the ethics in research, and what are the problems to maintain research culture in institutions all these issues are discussed in the present article.
Hypersoft set (an extension of soft set) is a new mathematical tool to tackle the inadequacy of soft set for\nattribute-valued sets. In this study, concept of bijective hypersoft set is proposed and some of its set theoretic operations like restricted-AND and relaxed-AND, are characterized. Moreover, new operations of bijective hypersoft set such as dependency, decision system, significance of decision system, reduced decision system and decision rules in decision\nsystem, are discussed with illustrated examples. A decision making algorithm and application are discussed with the\nsupport of these proposed operations.
Soft set theory is inadequate for disjoint attribute-valued sets whereas hypersoft set theory (an extension of soft set theory) addresses such insufficiency efficiently. This study aims to develop the theory of bipolar fuzzy hypersoft expert sets (BFHESs) which is an amalgam of two structures i.e. bipolar fuzzy sets and hypersoft expert sets. Some essential elementary properties (i.e. subset, not set and equal set), important results (i.e. commutative, associative, distributive and D’ Morgan Laws) and aggregation operations (i.e. complement, union intersection AND, and OR ) are investigated with appropriate illustrated examples. A decision making algorithm and application is discussed for the best selection of a certain product.